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Rethinking student-teacher relationships in higher education: a multidimensional approach

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• Published: 12 April 2021
• Volume 82 , pages 993–1011, ( 2021 )

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• Roland Tormey   ORCID: orcid.org/0000-0003-2502-9451 1  

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Student-teacher relationships play an important role in both teacher and student experiences in higher education and have been found to be linked to learning, classroom management, and to student absenteeism. Although historically conceptualised in terms of immediacy or distance and measured with reference to behaviours, the growing recognition of the role of emotions and of power—as well as the development of a range of multidimensional models of social relationships—all suggest it is time to re-evaluate how student-teacher relationships are understood. This paper develops a theoretical model of student-teacher affective relationships in higher education based on three dimensions: affection/warmth, attachment/safety, and assertion/power. The three-dimensional model was tested using the Classroom Affective Relationships Inventory (CARI) with data from 851 students. The data supported the use of this multidimensional model for student-teacher relationships with both two- and three-dimensional models of relationships being identified as appropriate. The theoretical development of a multidimensional model and the empirical development of an instrument with which to explore these dimensions has important implications for higher education teachers, administrators and researchers.

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Introduction

Student-teacher relationships are an important feature of the higher education learning environment and have long been the subject of considerable interest. Forty years ago, Anderson ( 1979 ) identified that teacher immediacy was predictive of the quality of college students’ course experience, while Boice ( 1996 ), in his landmark study of higher education classroom management, argued that students’ classroom misbehaviour is, in part, a function of the kinds of immediacy that teachers display ( 1996 , p. 464). Student-teacher relationship has also been found to have an impact on absenteeism in higher education settings (Rocca, 2004 ), and Witt et al. ( 2004 ) found a strong correlation between teacher immediacy and affective learning ( r = .49) and a weak correlation with cognitive learning ( r = .17). Over recent years, the focus of research has also shifted towards the role of emotions in higher education student-teacher relationships, with research highlighting a range of student emotional reactions in class, including hope, enjoyment, pride, shame, boredom, hopelessness, anxiety, and anger (Mazer et al., 2014 ; Quinlan, 2016 ). Positive emotions such as hope have been found to be associated with college GPA (Snyder et al., 2002 ), while emotions like enjoyment and pride have been found to be linked to performance on midterm exams (Pekrun et al., 2006 ). Titsworth et al. ( 2010 ) have found that positive emotions were strongly associated with both students’ ratings of their perceived learning, and their use of cognitive processes likely to give rise to learning. It is hardly surprising then that Titsworth et al. ( 2013 ) would characterise emotions as “knowledge-producing resources” and argue that “emotions are viewed as essential resources that both enable and constrain the learning experiences of students and teachers” ( 2013 , p. 192–193). Overall, then, there is good reason for seeing the emotional dimensions of student-teacher relationship as an important aspect of life in higher education.

Yet, while it is clear the student-teacher relationships are important, it is less clear how these relationships should be measured and conceptualised. Three issues arise with pre-existing studies which look at the emotional quality of student-teacher relationships in higher education. First, many of those working from a communication studies perspective (e.g. Alt & Itzkovic, 2019 ; Boice, 1996 ; Witt et al., 2004 ) describe these relationships in terms of teacher behaviours that can be interpreted in terms of immediacy. But there is a growing recognition in the wider literature that it is not the teacher behaviour itself which should be the focus of attention but rather the emotional response to that behaviour by the student. Second, many of these studies are underpinned by a notion of valence or “closeness” which is understood as being measured along a single continuum ranging from positive/close to negative/distant. So, while Titsworth et al. ( 2010 ) use a multi-dimensional scale to assess emotional valence, support, and work, and Marsh’s SEEQ (Marsh, 1982 ) distinguishes between two emotional dimensions of enthusiasm and rapport, the emotional quality of the student-teacher relationship itself is understood as being located along a single continuum ranging from positive/close to negative/distant. The use of a single dimension of valence or immediacy in this way has value, but it also may hide important aspects of teaching relationships. For example, a teacher who surprises students with an unexpected mid-term exam may generate negative emotions in students just as much as a teacher who is perceived as incompetent in their subject area, but these two situations have important differences between them and the simple classification of emotional experiences as being “positive” or “negative” may, therefore, hide a great deal of important information. The third issue which arises with a number of these studies is that they do not consider the interplay between the emotions of student-teacher relationship and the cultural and social organisation of interaction in the university. Indeed, students feelings towards teachers do not simply arise from teacher behaviour but also depend in part on intergroup stereotypes (Cuddy et al., 2007 ) and implicit biases (Nosek et al., 2002 ). As such, teacher and student ethnicity and gender may help to shape the emotional quality of students’ responses to teachers. In addition to mapping the emotional quality of student-teacher relationships in more complex ways, we also may need to situate research work on these relationships in more socially aware ways.

The goal of this paper is to describe an approach to thinking about student-teacher relationships in higher education which (a) moves beyond a focus on teacher behaviour and instead deals with the emotional quality of student-teacher relationships, and (b) which is parsimonious enough to provide useful information to higher education teachers without, at the same time, reducing all emotions to a measure of valence. The paper also aims to locate an analysis of the emotional quality of student-teacher relationships within the context of an understanding of the social and cultural dimensions of such emotional appraisals.

In this paper I will first describe the ways in which student-teacher relationships have been understood and researched. Following on from this I will describe the development and testing of the Classroom Affective Relationships Inventory (CARI). This instrument has the potential to change our understanding of what is happening between teachers and students, to aid researchers aiming to understand the ways in which student-teacher relationships impact on learning, student experience, student absenteeism, and classroom incivility, and to help professors and lecturers better understand how to manage these relationships in their classes. In the final section, the findings from the use of CARI will be discussed in the context of the cultural and organisational structures of higher education institutions.

Conceptualising and measuring student-teacher relationship in higher education

In higher education settings, the question of student-teacher relationships has often been viewed from the point of view of teacher immediacy, defined as “the extent to which the teacher gives off verbal and nonverbal signals of warmth, friendliness, and liking (e.g. forward leans, smiles, purposeful gestures, and eye contact)” (Boice, 1996 , p. 458). As Goodboy and Myers note (Goodboy & Myers, 2009 ), most research on higher education immediacy has focused on measuring it with reference to teacher behaviour, including appropriate touch, eye contact, vocal expressiveness, forward leaning, and straight posture. The “perceived non-verbal immediacy scale”, for example, asks students to rate the frequency with which the teacher demonstrates particular behaviours such as smiling at the class, moving around the class, and having a very relaxed body position (Thomas et al., 1994 ). More recent measures (e.g. Alt & Itzkovic, 2015 ) similarly focus on observable behaviours. This focus on behavioural indicators poses certain difficulties when one seeks to move student-teacher relationship research to an international or cross-cultural context. Non-verbal behaviours like making eye contact or standing close to another person may be interpreted differently by students and faculty members from different cultural settings. Beyond this, the extent to which particular forms of physical distance may be seen as appropriate or inappropriate in faculty-student relationships may also differ from culture to culture (e.g. Hofstede & Hofstede, 2005 ).

Beyond this, there is also a growing recognition that it is probably not the behaviour itself, but the emotional quality of that relationship that should be the focus of attention. It is this emotional appraisal of behaviour that is often at the heart of research which explores student-teacher relationships from a more sociological and qualitative perspective. Hargreaves ( 2001 ) introduced the concept of “emotional geographies” to describe the way in which emotions provide information on closeness of relationships: emotional geographies are defined as “the spatial and experiential patterns of closeness and/or distance in human interactions and relationships, that help to create, configure, and colour the feeling and emotions we experience about ourselves, our world, and each other” ( 2001 , p. 1061). For Hargreaves, emotional distance is problematic in that it serves to “threaten the emotional understanding that is foundational to high standards of teaching and learning” (Hargreaves, 2001 , p. 1075). His data, which is based on teacher interviews, highlights that working with learners was a source of both positive and negative emotions for teachers. Among the factors identified by his respondents as important were a sense of care (Noddings, 1988 ), a sense of warmth, and a sense of empowerment.

While it has been stated that there are very few studies on the emotions of student-teacher relationships in higher education (Walker & Gleaves, 2016 ), in the 1990s, an “emotional revolution” ( 2003 , p. 328) took place in psychology and sociology, and by the turn of the century this gave rise to a literature which started to highlight the importance of emotions to students’ experiences of teaching and learning. Walker et al. ( 2006 ) suggest that changes in the demographics of higher education have made faculty sensitivity and empathy more central to the faculty role. Beard et al. ( 2007 ) identified a range of student emotions expressed, including hope, loss, worry, fear, loneliness, frustration, pride, and excitement. Beard et al. highlighted the centrality of relationships to understanding the link between emotions and learning, while Moore and Kuol ( 2007 ) identified how students’ descriptions of good teaching made reference to love, passion, exhilaration, compassion, empathy and care. As at other educational levels, there is also a recognition that relationships in teaching can be difficult for teachers, with research on university teacher emotions identifying feelings of fear, shame, and powerlessness with increased emotional labour being required of some faculty members. These emotional experiences are also embedded within power and gendered hierarchies within universities (see, for example, Acker, 2012 ). But, if there is a growing awareness of the importance of emotional relationships in teaching in higher education, there is not yet a clear theory of emotional relationships that might help move the conversation from personal preferences and opinions to a solid grounding in theory or evidence (Quinlan, 2016 , p. 105).

Alongside, more qualitative and descriptive work there have also been a number of attempts to develop quantitative tools for measuring the emotional quality of teaching and learning relationships in higher education. Among older tools, Marsh’s ( 1982 ) Student Evaluation of Educational Quality (SEEQ) contains a scale—called teacher rapport—which measures aspects of the emotional quality of class relationships. Teacher rapport is measured on a single dimension and includes items for teacher friendliness, accessibility, and interest in students. Titsworth et al. ( 2010 ), drawing on Emotional Response Theory, developed a more comprehensive three-dimensional Classrooms Emotions Scale (CES) aimed to measure (a) the emotional valence which the student experiences in teaching (positive to negative), (b) the emotional support the student feels, and (c) the emotional work done by the student. While all three scales address aspects of the emotional environment of teaching and learning, only the “valence” scale directly addresses the emotions experienced in the teaching and learning relationship, and this is based on just two items which measure only the “positivity” of the emotional experience (“I would generally describe the emotions towards this class as positive” and “I would generally describe the emotions I feel towards my instructor as positive” [ 2010 , p. 439]). Trigwell ( 2012 ) focused attention on teacher emotions and used measures which combines experienced emotions with behaviours and contexts (examples include. “If something I design hasn’t worked in class I feel annoyed” and “I am embarrassed when my planned learning activities appear to fail” [ 2012 , p. 618]). Using this scale, he identified five dimensions of teachers’ emotional experience: motivation, embarrassment, frustration, anxiety, and pride. However, the mixing of emotional descriptions with contexts and behaviours is somewhat problematic. For example, the two items listed above both emerged on the same scale but it is not clear if the covariance is due to the nature of the emotions experienced (embarrassment and annoyance) or due to the context described (planned learning activities fail). Trigwell’s instrument has also been criticised as not being sufficiently grounded in a coherent theory of emotion (Hagenauer & Volet, 2014 , p. 243). White ( 2013 ) takes a different approach, listing sixteen different emotions and asking students how often they had experienced these emotions in courses that they had taken. Based upon this, White identified three scales; positive emotions (examples include enjoy, happy, engaged), passive negative emotions (e.g. stress, worried, scared), and activating negative emotions (e.g., annoyed, angry, disappointed). This model focuses very clearly on the emotional quality of the students’ experience. The emergent model is, however once more, dominated by a sense of valence (positive and negative emotions) and does not have a well-developed underlying theory of emotions in social relationships.

Outside of higher education, other instruments have also been developed to look at student-teacher relationships. The Questionnaire on Teacher Interaction (QTI) (Wubbels & Brekelmans, 2005 ) is one such quantitative tool and is based upon the idea that student-teacher interactions can be mapped in terms of two-dimensions: dominance-submission, and opposition-cooperation. Although the QTI has been adapted for higher education settings (e.g. Kendall & Schussler, 2013 ), it is based on describing teacher behaviour and is not a measure of affective student-teacher interaction. A second multidimensional approach to student-teacher relationships sees these relationships as being characterised by closeness, independence/dependency, and conflict, and is measured by the Student-Teacher Relationship Scale (STRS), (O’Connor, 2010 ; Pianta, 2001 ; Rey et al., 2007 ). The applicability of this model with older children has however been questioned (Koomen et al., 2012 ) and its suitability for the nature of relationships that exist in higher education is doubtful: for example, items in the short form of the STRS include “If upset, the child will seek comfort from me”.

The emotional quality of relationships cannot be separated from the social appraisals and implicit beliefs which colour each person’s perceptions of others and so it is useful to also consider multi-dimensional models from other work describing social relationships. For example, research on social perceptions suggests that our perceptions of others can typically be modelled on two dimensions: warmth and status/competence (Fiske et al., 2002 ; Fiske et al., 2007 ). While these perceptions can be construed as being “cognitive” appraisals, to separate these appraisals from emotion is to ignore the ways in which emotions are “multicomponent responses to challenge or opportunities” (Oatley et al., 2006 , p. 29), in which cognitive processes are intertwined with biological and which are experienced in part through patterns of thought-action tendencies. Indeed, Cuddy et al. ( 2007 ) identify that emotions such as admiration, pride, contempt, envy, disgust, and pity locate us with respect to others along these dimensions. Similar multidimensional approaches emerge from work on assessing students’ assessment of instructor’s credibility: McCroskey and Teven ( 1999 ) propose that credibility can be thought of in terms of three dimensions, namely competence, trustworthiness, and goodwill. This model has been applied to higher education settings (Neville Miller et al., 2014 ).

While these range of different ways of conceptualising relationships come from different research traditions (social cognition, attachment theory, clinical psychology, emotional response theory, and communication studies) and, indeed, aim to explain different things (ranging from rapid social appraisals to significant social relationships), a number of common features do seem to emerge that may help to frame a conceptual model for student-teacher relationships in higher education. It is notable, for example, that some dimensions appear across a range of models (for example, warmth/closeness/goodwill). This raises the question as to whether some coherence can be fashioned from these diverse strands.

Three dimensions of student-teacher relationships

A synthesising framework for these diverse strands can be found in the work of Jennifer Jenkins and Keith Oatley (see for example Oatley, 2004 , p. 81) who argue that emotional-social distance between people is generally experienced in terms of whether relationships give us a sense of Affiliation (warmth/affection), Attachment (safety/security), and Assertion (position within a social hierarchy) (Fig. 1 ).

A three-dimensional emotional space (adapted from Oatley et al., 2006 , p. 229). Note: Although the three dimensions are represented in Fig. 1 as orthogonal for ease of visualisation, there is no requirement that the three dimensions are at 90° to each other in practice. In the original, the emotions displayed on the Assertion axis were “Shame” and “Anger”. These have been replaced here with emotions more closely linked to the status dimension of power (Disdain and Awe)

Affiliation/warmth

Also referred to as affection, warmth, liking or love, this dimension can be thought of as the foundation for social living. Warmth and friendliness are at the heart of the idea of “immediacy”, linked to the “cooperation” dimension on the QTI, “closeness” on the STRS, and “goodwill” for McCroskey and Teven ( 1999 ). For Hargreaves ( 2001 ), it is part of what is in the concept of “care”.

Attachment/security

Alongside warmth, a feeling of security and trust in another person is also important in pedagogical relationships—where students feel anxiety or some degree of fear about the teacher they are probably less likely to learn. `For McCroskey and Teven ( 1999 ) “trustworthiness” (attachment) is distinct to “goodwill” (affiliation). In other models, however, attachment and affiliation are collapsed into each other, such as in the dimensions of “cooperation” on the QTI and in the “warmth” dimension for Fiske et al. ( 2007 ). Oatley et al. ( 2006 ) suggest that this may reflect a western cultural bias: while western researchers tend to conflate attachment and affiliation (that is, they tend to work on the basis that people who make us feel liked will also make us feel secure), others identify that affiliation and attachment should be thought of as a separate dimension of social-emotional relationships (Goldberg et al., 1999 ). To take an educational example, it is possible to imagine a professor that students think of as fair and trustworthy (high attachment) but who does not communicate warmth or liking to the same students (low affiliation).

Assertion/power/status

In addition to these two dimensions which emerge from the psychological attachment literature, Oatley et al. identify that emotions also play a role in the kind of social dimension that is often of more interest to sociologists—that of power or status (e.g. Kemper & Collins, 1990 ). The emotions at interest here are emotions such as awe, anger, and shame. In existing models, empowerment is identified as an important aspect of student-teacher relationships for Hargreaves ( 2001 ) and was central to teacher incivility for Alt and Itzkovic ( 2019 ). In the QTI, it is represented in terms of dominance-submission. Fiske et al. ( 2007 ) identify “competence” judgements as representing “status” which (alongside organisational or political position and social class) has been identified as one of the dimensions of power as far back as the sociology of Max Weber—in more contemporary sociological terms “status”can be seen as a recognition of the possession of embodied cultural capital (Bourdieu, 1986 ). Feelings of awe and admiration (and perhaps envy) locate the person experiencing the emotion as being in a lower position with respect to another on a status or embodied cultural capital dimension.

For the first two dimensions, greater closeness might be regarded as pedagogically useful. Hence, we might hypothesise that greater perception of warmth and safety would be valued by students. For the third dimension, however, it seems more likely that students would value status distance rather than status closeness. Therefore, we might hypothesise that on this dimension, greater feelings of awe or respect would be valued by students.

This three-dimensional conceptualisation for student-teacher relationships is potentially a very useful development in the understanding of classroom dynamics in higher education. In comparison to the older behavioural work on immediacy, it has the potential to be more cross-culturally valid (something which is of practical as well as conceptual relevance in a time of growth in international student mobility) and to focus attention on the emotional quality of students’ experience rather than on teacher behaviour, a topic which is increasingly identified as crucial in understanding higher education teaching (Quinlan, 2016 ). The three-dimensional model also has the potential to direct the attention of teachers and researchers to important aspects of relationships (such as status and security) which have either been under-emphasised or subsumed in previous work which emphasised valence and immediacy/distance. However, whether or not this three-dimensional conceptualisation of relationships is useful will depend on whether or not it can be empirically verified and whether or not it can be seen to be linked to some meaningful measure of student experience. Despite the theoretical coherence of the model as outlined thus far, it is possible that it does not match empirical reality—for example, it is possible that a one-, or two-dimensional model may be a better fit for describing these relationships. The next sections of this paper will, then, describe the Class Affective Relationships Inventory (CARI), a survey instrument designed to rapidly assess students’ perceptions of the emotional quality of the relationships with teachers in higher education.

Methodology

The CARI is a 15-item questionnaire based on a 7-point scale covering the three dimensions of student-teacher relationships. The emotional relationships items were based on the following stem question: “To what extent do you associate this course’s professor with the following terms?” Fifteen terms related to the emotional quality of the student-teacher relationships were then presented, 5 which were thought to be positively associated with each of the 3 dimensions. The terms were impressive, admirable, influential, exciting, and inspiring (assertion/status), friendly, warm, compassionate, positive towards students, and caring (affiliation/warmth), trustworthy, well-intentioned, reassuring, reliable, and inspires confidence (attachment/safety). The 7-point scale ranged from “Not at all” to “Very much”. A number of different approaches were piloted before arriving at this formulation, including an approach similar to that of Trigwell ( 2012 ) and one similar to that of White ( 2013 ) (both approaches were described above in the “Conceptualising and Measuring Student-Teacher Relationship in Higher Education” section). Data analysis of these pilots showed that neither approach effectively measured dimensions similar to those presented by Jenkins and Oatley. The approach ultimately chosen to assess the student perceptions of the emotional quality of student-teacher relationships draws on Fiske et al.’s ( 2007 ) work on measuring social appraisals as well as on Jenkins and Oatley’s three-dimensional model. The terms used were identified through two methods. First, the work of Fiske et al. was reviewed to identify terms which they had used. Secondly a dictionary and thesaurus search was completed and a list of terms associated with “awe”, “warmth”, and “feeling assurance” was drawn up. The final list of terms were drawn from this list. The questionnaire was in French, which is the language of instruction in the school and the native tongue of the vast majority of students. The items were originally written in English, and translated into French by a native speaker before being verified through retranslation back into English, again by a native speaker.

Of these three scales, affiliation and attachment are more or less self-explanatory, whereas the assertion scale is the result of some decisions as to how the power/assertion scale should best be represented. As noted above, competence or status judgements reflect the embodied cultural capital (one dimension of power) of the teacher. While Alt and Itzkovic ( 2019 ) conceptualise teacher power in terms of behaviours which are perceived as non-legitimate (shouting, ignoring students, missing classes etc.), at least some of the kinds of emotions which these behaviours illicit are probably already picked up in the “safety” scale (by terms like “reliable”, and “well intentioned”). In keeping with the structure of other dimensions, the status/power dimension was framed positively (using terms intended to capture the feeling of awe), rather than focusing on negative terms such as contempt. This was not straightforward to do, given that negative emotional vocabulary is often richer and more diverse than the language associated with positive emotions. However, piloting of previous iterations of the questionnaire showed that the mixing of positively and negatively valanced terms increased noise and decreased reliability in the instrument. Therefore, all three dimensions were framed in ways that could be perceived as positive by both teachers and students.

In addition to the CARI items, 4 additional questions collected demographic data about the student and 7 questions collected data about the course. A single question asked their overall evaluation of the quality of the course (agreement with the statement “Overall, I found the course to be good”). It is worth noting that student evaluations of teaching, are highly contested, may not be correlated with student learning (Uttl et al., 2017 ), and may also show bias, for example, against female teachers (Boring, 2017 ). The inclusion of a student evaluation of teaching measure here should not be read as an endorsement of the way such evaluations are commonly used in higher education. Nonetheless, it does provide useful information on the way students perceive the quality of their experience in a course.

Participants

An overview of the participants is included in Table 1 . The 36 to 37% of respondents who were female constitutes a slight over-representation of female students (who make up 29% of the Bachelor cohort in the university as a whole).

Only 6 to 7% of respondents referred to courses which were taught by female teachers. This slightly under-represents female teachers within the school as a whole (women made up less than 20% of the teachers in all faculties of the university in question).

Missing data (due to students leaving an item blank) accounted for less than 4% of all responses in each of the 15 emotional variables analysed (as described above under the heading “materials”), an acceptable level of missing-ness. An analysis of the demographic data did not indicate any systematic missing-ness of data as can be seen in Table 1 ). The large sample size allowed a conservative approach to dealing with missing data: any case in which any data on the 15 emotional variables was missing was excluded from the analysis. This left 851 cases for inclusion in the analysis.

Bachelor courses were selected and teachers were asked if they would permit questionnaires to be distributed and collected in class. Classes were selected with a view to ensuring the gender distribution of the student sample matched that of the school (i.e. on a non-random basis). In line with the ethics protocol approved by the institutional Human Research Ethics Committee, the questionnaires ensured that both teachers and students were anonymous at the point of data collection—neither the student nor the course they were referring to when answering the questionnaire were identified in the questionnaire. In order to ensure this anonymity for teachers concerned, students were asked to fill out the questionnaire thinking of a course they were taking other than the one in which they were sitting when completing the questionnaire, without identifying the course referred to. This meant that separate consent was not required from all teachers of the courses referred to by students, which in turn allowed a large student sample to be collected. However, it also meant that relatively little data about the teachers or courses referred to could be collected.

The suitability of the data for principal component analysis was first assessed. The analysis indicated the data is ideally suited for principal component analysis (KMO = .943; Bartlett’s test of sphericity is significant with Chi square = 8762, df = 105, p < 0.001; all values on the diagonal of the anti-image correlation matrix are above .9). The determinant of the R-matrix is .00003, greater than the .00001 cut off to avoid difficulties of multicollinearity. Communalities range from .619 to .794—all comfortably above the recommended cut off for large samples of an average greater than .6 (Field, 2005 , p. 655).

As Table 2 illustrates, the eigenvalues for the first three factors are greater than .7 which meets Joliffe’s criterion for extraction of factors (Field, 2005 , p.644). The next largest eigenvalue is below the .7 cut-off point. This matches the underlying theoretical model of three emotional dimensions. The combination of the numerical and theoretical elements justifies the extraction of three components in this case. Together these three explain 70.637% of the total variance. It is worth noting that although the three component model is justified in this case, a cut-off value of 1 is also often used for eigenvalues (Field, 2005 ). This would mean extraction of two components rather than three. Another criteria often used for determining the number of components to extract is a qualitative analysis of the added value in terms of percentage of variance explained through the addition of each additional component. Again, this suggests that the amount of added value drops quite a lot from two to three factors but does not decline substantially thereafter. This also suggests that a two-dimensional model would be justifiable. In other words, this analysis of the data appears to support either a two- or three-dimensional model for emotional geographies of learning. As Jenkins and Oatley suggest that there is a theoretical reason for seeing the three-dimensional model as more generalisable (that is, applicable in a wider set of cultural contexts), we will continue to explore the three-component solution here; however, this issue is discussed below.

Table 3 presents the rotated component matrix for the extracted factors. Jenkins and Oatley note that in Western cultures, at least two of the components (affiliation/warmth and attachment/safety) may well be correlated with each other. Therefore, it made sense to use an oblique rotation. By and large, the items load onto the expected factor. Friendly, Warm, Caring, Positive towards students, and Compassionate load most strongly onto component 1 (Affiliation/warmth) while Impressive, Admirable, Influential, Exciting and Inspiring load onto component 2 (Assertion/status). Trustworthy, Inspires confidence, Reliable and Well-intentioned load most strongly on component 3 (Attachment/Safety). The item Reassuring was expected to load most strongly onto Attachment/Safety, however, in our data is loaded most strongly onto Affiliation/warmth.

Reliability for these scales is high: Cronbach’s alpha is .903 for the six-item Affiliation/Warmth scale, .881 for the five-item Assertion/Status scale and .860 for the four-item Attachment/Safety scale. All of these are well above the .7 threshold typically used to designate sufficient reliability in a scale of this type.

A final question worth considering at this time is whether or not the scales in question actually predict some aspect of the quality of student experience. To this end, a multiple regression was done to analyse whether the three-dimensional model contributed to our understanding of the factors which influence student evaluations of teaching. The suitability of the data for multiple regression was verified: the tolerance/variance inflation factor (VIF) multicollinearity diagnostic are all within acceptable ranges (tolerance diagnostic for example, range from .81 to .67; Durbin-Watson statistic = 1.823; observation of the regression standardised residuals plot also indicates the data’s suitability for multiple regression).

The regression analysis itself (Table 4 ) shows that the student ratings of the emotional relationships of learning are a very good predictor of their evaluations of courses. In total, the three dimensions explain a remarkably high 59.7% of the variance in the student evaluations of courses (i.e. the sum of the change in R 2 at each step is .597) suggesting the three dimensions of emotional geographies are a very good predictor of student ratings of teaching. Each dimension adds significantly to the model, with the assertion/status dimension contributing most.

The question at the heart of this paper is how the emotional quality of student-teacher relationships as perceived by students should be theorised and measured.

Firstly, it was suggested that these relationships should perhaps be framed in terms of their emotional quality rather than in terms of teacher behaviour itself. Framing the relationships in terms of emotions is more in keeping with the way research on relationship has developed in the last two decades and is likely to be less culturally and contextually specific than using descriptions of behaviour. The data here suggests that it is appropriate to conceptualise student-teacher relationships in terms of student perceptions of their emotional quality: the low rate of missing data, for example, suggests that students did not have difficulty with rating the extent to which they associate a course’s professor with terms like compassionate, impressive, or trustworthy. This was despite the fact that most students were in large classes (over 200 students) and despite the fact that they were studying in disciplines which are typically not strongly associated with emotion (natural sciences and engineering).

Secondly, it was suggested that rather than thinking of teacher-student relationships only in terms of valence, we should think of them in a multidimensional space. Two-dimensional (e.g. Fiske et al., 2007 ) or three-dimensional (Oatley et al., 2006 ) models were both suggested in the literature. The results of the principal component analysis supports either a two- or three-dimensional model. The two-component model essentially collapses the Affiliation/Warmth and Attachment/Safety dimensions onto each other with Assertion/Status remaining as an independent dimension. This is a justifiable interpretation of the data. However, Jenkins and Oatley note that while such a two-dimensional model fits with occidental cultures, it may be less applicable outside western cultural contexts. While the three-component model is favoured here, the two-dimensional model is also a good fit for the data (which is actually what one might expect given that the study took place in an occidental cultural setting). In fact, both the two- and three-dimensional models will satisfy the requirements of measurability and multi-dimensionality.

It was noted at the outset that teacher-student relationship has been found to be important in terms of some dimensions of learning, student learning behaviours, student incivility, student attendance and dropout, and overall student experience. The data reported here confirms that student’s emotional responses are a good predictor of students’ perceptions of the quality of their experience. Indeed, the three scales together explain almost 60% of the variance in the student course rating. This suggests that if we are interested in improving the students’ perceptions of the quality of their experience, we will pay attention to the development of warmth, trust, and admiration in classes.

Implications for higher education teachers

The data presented here reconfirms what Quinlan ( 2016 ) and others have been saying: the emotional relationships in classes are an important dimension of the higher education experience. Care work in universities is often challenging for faculty members (Walker et al., 2006 ) but the evidence here suggests that the emotional quality of relationships explains a large part of student satisfaction with courses. Quinlan includes a number of recommendations for higher education teachers to improve relationships including learning names, seeking student feedback during term, being clear and consistent, being enthusiastic, and being accessible to students outside class ( 2016 , p. 105).

Thinking about relationships in a multidimensional way is also useful for teachers. Personality differences mean that not every faculty member feels as if they can be warm or friendly towards students. The multidimensional model suggests that these teachers should perhaps focus their attention on eliciting trust (the safety dimension), and on eliciting admiration (the status dimension). Indeed, for those who dismiss student evaluations of courses as being nothing more than a “beauty contest” or a “likability test”, it is notable that most of the explained variance in student evaluation of courses came from the status and safety dimensions. As such it may be that warmth has been somewhat overemphasised in the literature thus far.

The multidimensional nature of relationships also suggests that some of the folk wisdom about teaching in universities is likely to be wrong. It is not unusual to hear novice teachers being told by other teachers that they should not smile too much for the first few weeks in order to make sure students respect them. This idea focuses solely on the immediacy of the relationship and proceeds from the hypothesises that “positive” relationships are not conducive to respect: the evidence presented here suggests that these two dimensions (perceptions of warmth and perceptions of competence) should be regarded as two different things. Smiling and seeming competent are, it seems, reasonably independent: it is possible to do either, both, or neither.

Implications for university administration

It was noted at the outset that measures of student-teacher relationship need to be understood in the wider context of an analysis of the social and cultural organisation of university life. Viewed from this perspective, while it makes sense for individual teachers to think of approaching student-teacher relationships with a view to “maximising” the student’s sense of the teacher’s warmth, trust and intellectual competence, it may well be a mistake for universities to approach this question in the same way. This is not to suggest that we should ignore the students’ perception of the student-teacher relationship: it has, for example, been argued (e.g. Dalton & Crosby, 2013 ) that students are treated like second-class citizens in universities and that central to improving their position in our communities is to instil an ethic of care. At the same time, the relationship between the multidimensional approach to relationships and intergroup stereotypes (Cuddy et al., 2007 ) and implicit biases (Nosek et al., 2002 ) should draw our attention to the way ethnicity and gender can help to shape students’ emotional responses to teachers. Recognising the potential for there to be discriminatory patterns in students’ perceptions of relationships does require the ability to step back from the rich detail of qualitative descriptions of interaction to be able to see how patterns play out across larger groups of people. This can be achieved through the use of quantitative tools like CARI, which can allow us to see social patterns and inequalities that might not otherwise be seen. However, quantitative tools also carry a risk if institutions simply seek to “maximise” the student’s feeling of the teacher’s warmth, trust, and intellectual competence without taking into account the way in which such feelings are mediated by social categories such as gender or ethnicity. This would be a major error.

This issue is all the more crucial, given that the role of emotions in higher education is already a space in which power dynamics are clearly evident. It has already been identified that “care” can be readily gendered in academia, that many women academics experience different patterns of socialisation and progression compared to male colleagues and that the allocation of roles and duties means that they typically end up devoting more time to “service” and “care” work than do men (see Acker & Feuerverger, 1996 ; Ducharme & Ducharme, 1996 ; Murray, 2006 ). Where this “care” work is assigned a low status it becomes an exploitative form of emotional labour. Mariskind, ( 2014 , p.318) has argued that, “care” has often been narrowly associated with warmth and with supporting student well-being through care-giving, and this has been part of a broader process of gendering and individualising responsibility for care which in turn entails professional costs when that care work is not acknowledged or supported by institutions. Mariskind argues that in order for care work to be adequately valued, care in higher education needs to be re-imagined outside of this narrow view to include “qualities traditionally seen as masculine and feminine”, including both a focus on student well-being as a precursor to academic success, but also on academic success as a precursor to well-being: “staff, and students would benefit from an understanding of care as a life-sustaining web of relations …throughout higher education institutions that support individual and collective well-being and enhance teaching and learning”. In de-centering the “warmth” dimension of relationships and drawing attention to feelings of trust and admiration, the multi-dimensional model can contribute to this re-thinking of relationships in ways which are less tied to exploitative discourses. This is not to suggest warmth should be seen as unimportant—rather that warmth, trust, and admiration are seen together as elements of a re-imagined and less exploitative conceptualisation of care work.

Implications for student-teacher relationships research

Quinlan has recently noted that while there is a growing awareness of the importance of emotional relationships in teaching in higher education, there is not yet a clear theory of emotional relationships that might help move the conversation from personal preferences and opinions to a solid grounding in theory or evidence (Quinlan, 2016 , p. 105). The multidimensional model of affective student-teacher relationship provides a theoretical basis on which these relationships can be conceptualised, and the CARI provides a tool for collecting evidence that can deepen our understanding of the role of student perceptions of the emotional quality of relationships in teacher and student experience. The three-dimensional model of student-teacher relationships can help both quantitative and qualitative researchers see more and understand better by broadening the focus of the discussion to include all three emotional dimensions of social relationships. A better understanding of how warmth, safety, and awe are differentiated and linked can enable researchers to develop a richer understanding of what is at play in classroom relationships. It should also ensure that notions of power and status are intrinsically linked to discussions of care. CARI has been developed in order to be suitable for use in a range of different cultural contexts, and to reflect important dimensions of social life—including power relations, warmth, and trust. It should, therefore, be a valuable tool for researchers who wish to explore how cultural and social factors influence student-teacher emotional relationships.

It is perhaps also worth thinking about how CARI could be used to shed new light on the work of academics. It could be used, for example, to explore whether there is evidence of systematic bias in students’ emotional responses to faculty based upon their gender or ethnicity (something which was not possible in this study since teachers were not identifiable). It could be applied in a range of cultural setting to see if, for example, warmth or status were regarded as more or less important to students in different settings. Perhaps it could also be redeveloped to explore the ways in which faculty see their relationships with university leadership—an Academic Affective Relationship Inventory. Indeed, such a tool may well shed interesting light on warmth, trust, and perceived competence in higher education.

Limitations

As with any study, it is important to note that this study has a number of limitations. This research took place in one institution in one cultural context. One peculiarity of this context is the comparatively low percentage of female teachers and students. The use of the instrument in more settings—including more gender-balanced settings—will certainly enhance our understanding of the multidimensional nature of student-teacher relationships in higher education. Most of the students who participated in this study were first-year students; all were studying natural sciences or engineering, and many were in large classes. The strict anonymity requirement also meant that relatively little data was collected about the students and teachers. It would be interesting to know in the future if year of study, class size, discipline, or student or teacher background impacts on the students’ perceptions of the emotional quality of relationships in teaching.

In this study classes were selected to ensure a gender balance in student respondents and so respondents were not selected randomly. This limits the ability to draw inferences from the data (even if it should not actually make any difference to the exploration of the multidimensional nature of emotional relationships). Since the goal was to have a wide variety of classes represented in the study while at the same time respecting the need to retain the anonymity of the teachers at the point of data collection, the design did not allow for the collection of much data about the teachers or courses in question. Nor did it allow for courses or teachers to be randomly sampled, or for the collection of meaningful learning data and so it is not possible in this study to assess if the different dimensions of emotional relationships are associated with learning.

The literature on emotional dimensions of student-teacher relationship in higher education makes clear that such relationships can play a significant role in both student and teacher experience. Existing attempts to make sense of these relationships generally characterised them in behavioural terms and in terms of closeness (immediacy) or distance. The emotional revolution in psychology and sociology over the last few decades has also reached studies of higher education and the role of emotions in characterising these relationships has become clearer. So too has their multidimensional nature. The emotional quality of relationships between students and teachers in higher education is probably best characterised in three dimensions: teachers and student can feel warmth towards each other, they can feel trust for each other, and they can feel admiration—perhaps even awe—for each other’s competence. These three dimensions are likely to be related but can also be seen and measured as separate; a teacher who may not communicate warmth and friendliness to students can still communicate that they are consistent and worthy of admiration. On the other hand, students may well feel some degree of fear with respect to a teacher who is perceived to be unreliable or inconsistent even if that teacher is also friendly and approachable. This three -dimensional model allows for greater clarity and nuance in how teachers, university administrators, and researchers think about the kinds of relationships we want students and teachers to have.

Hargreaves’ ( 2001 ) work on emotional relationships proved an inspiring concept and metaphor for educational researchers—that of “emotional geographies”. The multidimensional approach to affective relationships extends this notion of “emotional space” into three-dimensions. The Classroom Affective Relationships Inventory provides a compass to help further explore and describe the landscape of higher education’s emotional geographies.

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Tormey, R. Rethinking student-teacher relationships in higher education: a multidimensional approach. High Educ 82 , 993–1011 (2021). https://doi.org/10.1007/s10734-021-00711-w

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ORIGINAL RESEARCH article

Influence of teacher-student relationships and special educational needs on student engagement and disengagement: a correlational study.

• 1 Departamento de Psicología, Facultad de Ciencias Sociales, Universidad de Concepción, Concepción, Chile
• 2 Departamento de Infancia y Educación Básica, Facultad de Educación, Universidad Católica de Temuco, Temuco, Chile
• 3 Centro de Investigación en Educación y Desarrollo, Facultad de Educación, Universidad Católica de la Santísima Concepción, Concepción, Chile

Contemporary educational research has found that student engagement and disengagement have a relevant influence on learning outcomes. However, research on the influence of teacher–student relationships in the engagement of students with special educational needs (SEN) is scarce. The purpose of this study is to analyze the impact of teacher–student relationships, peer support at school, family support for learning, opportunities to participate at school, and SEN on engagement and disengagement of students using a sample of secondary students with SEN and typical development (TD). Through a non-experimental, correlational, and cross-sectional design, we evaluated 1,020 high school students (340 with SEN and 680 with TD) in the 9th grade (13–19 years old, M = 14.8; SD = 0.89). Teacher–student relationships, peer support at school, and family support for learning were assessed via subscales from the Student Engagement Inventory (SEI), opportunities to participate at school were measured with a subscale of the School Participation Questionnaire (SP), whereas engagement and disengagement were measured using the Multidimensional Scale of School Engagement (MSSE). Results show significant statistical differences between SEN and TD students in both student engagement and disengagement indicators. Engagement of SEN students is higher in the cognitive, emotional, and social dimensions than that of TD students. However, they also have higher disengagement in the cognitive and behavioral dimensions. Furthermore, SEN students rate their relationships with teachers more highly and perceive more opportunities for school participation than their peers. Further analyses show that teacher–student relationships are positively associated with all dimensions of student engagement and inversely with behavioral and cognitive disengagement. Although correlational, the findings suggest teacher–student relationships and school participation opportunities could be important variables for diminishing disengagement and its negative consequences for both SEN and TD students, while improving student engagement. We discuss these results considering possible implications for educational policies, practices, and research.

Introduction

Student engagement is the quality of involvement of students with school activities ( Skinner and Pitzer, 2012 ) including their participation in learning activities and interactions with teachers and peers. As a theoretical construct, student engagement is a multidimensional concept that involves distinctive and interrelated dimensions, such as student behaviors, emotions, and cognitive beliefs about school and learning ( Fredricks et al., 2004 ). Behavioral engagement involves attendance and participation in academic and extracurricular activities. Emotional engagement involves positive and negative reactions to school, teachers, and peers ( Finn, 1989 ; Voelkl, 1997 ), and cognitive engagement refers to the effort invested in learning ( Fredricks et al., 2004 ). Recently, social engagement has been added as a dimension and refers to the quality of social interactions of students in the context of classroom tasks and the broader school context ( Linnenbrink-Garcia et al., 2011 ; Rimm-Kaufman et al., 2015 ; Wang et al., 2016 ).

There is a vast literature on student engagement and its relationship with academic achievement ( Fredricks et al., 2004 ; Chang et al., 2016 ): higher attendance rates, lower dropout rates, and fewer antisocial behaviors among pre-school, primary, and secondary students ( Fredricks et al., 2004 ; Wigfield et al., 2006 ; Wang and Holcombe, 2010 ; Shin and Ryan, 2012 ).

Student engagement is understood as part of a broader motivational process with the learning context feeding back the conceptualization of individuals of themselves ( Fredricks et al., 2019 ). As the self-system model states ( Connell and Wellborn, 1991 ), individual and contextual factors influence student engagement based on how the school context helps to satisfy three relevant needs for the individual: relatedness, autonomy, and competence. The need for relatedness refers to the way in which the individual feels safe, connected, and valued by others. Autonomy is related to the need to experience agency over own behavior of an individual, both in its initiation and regulation and in the maintenance of the activity. Competence is related to the degree to which the individual knows how to obtain certain positive results and avoid negative ones. When psychological needs are met, engagement occurs, which manifests in emotion, cognition, and behavior. However, when these psychological needs are not satisfied, disaffection with the school will arise ( Connell and Wellborn, 1991 ).

School disengagement relates to maladaptive behaviors and attitudes toward schools and learning, and it reflects the ways in which students begin to withdraw and become disaffected with school ( Skinner et al., 2008 ). It has been associated with negative outcomes, including low achievement, disruptive and risky behaviors, and psychological problems ( Morrison et al., 2002 ; Wang and Fredricks, 2014 ). Disengagement is a multidimensional construct that involves the behavioral, emotional, cognitive ( Skinner et al., 2008 ; Wang et al., 2017 ), and social dimensions ( Wang et al., 2017 ). Wang et al. (2017) specify that behavioral disengagement includes getting in trouble at school, not paying attention in class or goofing off, and finding ways to be late for school or getting out of classes. Cognitive disengagement involves giving up quickly and speeding through homework rather than trying to understand or benefit from it. Emotional disengagement is feeling worried, overwhelmed, and frustrated in school. Finally, social disengagement implies a student feels invisible at school and does not consider interaction with others an important aspect of his school life.

Initially, researchers treated engagement and disengagement as opposite poles of the same continuum. However, this approach disregards the fact that disengagement is more than the absence of engagement, but the presence of maladaptive processes ( Skinner et al., 2009 ). Engagement and disengagement are not fixed states, and student levels of both constructs vary over time ( Jang et al., 2016 ; Burns et al., 2019 ). In the secondary school years, engagement tends to decrease ( Burns et al., 2018 ; Engels et al., 2021 ) and disengagement increases ( Burns et al., 2019 ; Engels et al., 2021 ). Hence, although engagement and disengagement are related constructs, measuring them separately can potentially provide more nuanced information regarding the phenomena, as disengagement captures aspects that engagement cannot ( Jang et al., 2016 ; Bergdahl et al., 2020 ).

Unfortunately, most studies on the engagement and disengagement of students have focused on students with typical development (TD) ( O'Donnell and Reschly, 2020 ). Consequently, little is known about the engagement or disengagement of students with special educational needs (SEN), especially those enrolled at mainstream schools ( Schindler, 2018 ). This is, however, starting to change because of the importance of engagement and disengagement in academic achievement ( Moreira et al., 2015 ). Studying the student engagement of SEN students is important since these students face significant challenges in school, and there is building evidence on the academic, social, and psychological consequences of their school struggles ( Douglas et al., 2012 ; Cortiella and Horowitz, 2014 ; Moreira et al., 2015 ). However, as Moreira et al. (2015) reported, studies providing this evidence are not conclusive and present mixed results. Some found lower levels of engagement for SEN students compared with their TD peers, whereas others showed no differences in engagement between the two groups.

Comparisons of engagement between SEN and TD students in the context of inclusive settings have also yielded inconclusive results. Employing an eco-behavioral observation tool with adolescents in inclusive classrooms, Wallace et al. (2002) found no differences in academic and behavioral engagement. Both groups showed high levels of academic engagement and low levels of inappropriate behaviors. Furthermore, using large-scale survey data ( N = 10,000) of 5–9th-grade pupils, Schindler (2018) obtained lower scores in all dimensions of engagement for SEN students (motivation and effort, belonging/well-being at school, participation in learning activities, and participation in social activities). The raw difference was larger for motivation and effort: SEN students scored a.7 SD lower than TD students. According to Schindler (2018) , the differences in engagement between SEN and TD students in her research cannot be explained by differences in backgrounds of students or at the school level. Yang et al. (2020) , in a research project with 118 secondary school students with special needs integrated into mainstream schools, reported intermediate levels of student engagement ( M = 3.10; SD = 0.85) on the five-point Likert School Engagement Scale of Fredricks et al. (2005) .

The inconclusive results on the student engagement of SEN students can be attributed to conceptual and methodological reasons. First, different studies conceptualize student engagement in different ways (unidimensional/multidimensional), the definition of engagement dimensions differ (e.g., including social or academic dimensions besides cognitive/behavioral/emotional-affective or measuring only one of them) ( Moreira et al., 2015 ; O'Donnell and Reschly, 2020 ), and variation in terms of whether engagement is measured on a single continuum (low or high) or there is a separate measurement of engagement and disengagement ( O'Donnell and Reschly, 2020 ). Douglas et al. (2012) state that most studies on the engagement of SEN students use either behavioral (e.g., attendance, dropouts, and participation in school activities) or cognitive indicators of engagement (e.g., achievement in specific subjects, such as math or literacy), and disregard the emotional and social aspects thereof. These elements highlight the need for more research in this field considering all dimensions involved in student engagement.

Age could also be an important variable when studying these concepts. For example, Janosz et al. (2008) found different types of engagement trajectories for 12–16-year-old students. One of these pathways (2% of the overall sample) contained around one-third of the SEN students (the most common for those students). It characterized a decreasing pattern of engagement. That is, these adolescents reported very high levels of school engagement at age 12, which rapidly decreased to the lowest levels in the study by age 16. Although not all students in the “decreasing pattern of engagement” trajectory had SEN, researchers should keep this finding in mind when comparing engagement of SEN and TD students because the results could be age dependent.

Regarding the variables involved in student engagement, Fredricks et al. (2004) describe three main groups: school-level factors (e.g., school size and opportunities for participating), classroom context (e.g., teacher–student relationships, peer acceptance, and classroom structure), and individual needs (e.g., relatedness, autonomy, and competence). Among these factors, the quality of teacher–student relationships has been identified as a key element in engagement and disengagement, including cognitive, behavioral, and emotional components for TD students (e.g., Roorda et al., 2011 , 2017 ; Quin, 2017 ). Research showed that positive teacher–student relationships in high school contribute to adaptive behaviors and improve intentions to graduate ( Burns et al., 2019 ; Burns, 2020 ). Furthermore, the perception of students of high levels of emotional and instructional support from teachers has been positively associated with emotional and behavioral engagement ( Skinner et al., 2008 ; Havik and Westergård, 2020 ). Martin and Collie (2019) found that positive relationships of high school students with their teachers predict greater school engagement, and importantly, engagement is higher as the number of positive relationships outnumbered negative ones.

The association between engagement and teacher–student relationships has been studied through several paradigms: From the self-system model perspective , the quality of interacting with teachers provides information to adolescents that they are competent to succeed at school, related to others in these settings, and are autonomous learners (e.g., Roorda et al., 2011 ; Wang and Eccles, 2013 ; Krane et al., 2016 ). Attachment theory states that teachers who create warm, safe, and supportive relationships with their students can serve as important non-parental attachment figures and role models ( Bergin and Bergin, 2009 ). Thus, students could use teachers as a safe base from which to explore the environment and engage in learning activities knowing they have support even in stressful situations ( Verschueren and Koomen, 2012 ). Affective teacher–student relationships have been found to contribute to the engagement and academic outcomes of students ( Engels et al., 2021 ). Relational/rhetorical goal theory explains that each student and teacher brings to the classroom their own expectations and experiences, and to have a successful learning process, instructors must meet the goals of students for being in the class: rhetorical or relational. Rhetorical goals focus on learning or task outcomes, and relational goals include perceived supportiveness, caring, and connectedness with others ( Mottet et al., 2006 ). This theory explains that although rhetorical and relational goals could be considered independent, they are interrelated phenomena, as failing to achieve one goal could lead to failing to achieve the other goal. Recent studies provide evidence for this theory ( Kaufmann and Frisby, 2017 ; Frisby et al., 2020 ). Finally, the working alliance theory conceptualizes teacher–student relationships as a collaborative working alliance. In this frame, the concept of working alliance in psychotherapy is applied to the classroom setting, emphasizing that the emotional bond between teacher and student and their collaboration in achieving the goals and tasks of their work together influence achievement ( Toste et al., 2015 ). Noble et al. (2020) found that the ratings of the working alliance of students predicted their reports of risk of dropout mediated by school engagement.

Despite differences regarding the mechanisms for the effect of teacher–student relationships on engagement and achievement in the above-mentioned theories, important and consistent research findings stress the importance of teacher–student relationships in the experiences of high school students ( Roorda et al., 2011 , 2017 ; Quin, 2017 ).

However, again, the focus of most research about teacher–student relationships has been on students with TD, with less and inconclusive evidence about the effect of these relationships in SEN students (see Roorda et al., 2011 ). Thus, specific research in this regard is needed ( Sabol and Pianta, 2012 ; Ewe, 2019 ), especially in inclusive settings ( Pennington and Courtade, 2015 ) and considering their emotional, social, and/or learning difficulties ( Murray and Greenberg, 2001 ; Murray and Pianta, 2007 ).

The research conducted on this topic indicates that SEN students have poorer teacher–student relationships than their typical developed peers ( Murray and Greenberg, 2001 ; Al-Yagon and Mikulincer, 2004 ; Freire et al., 2020 ), and according to Henricsson and Rydell (2004) , these relationships tend to be stable over time in elementary school for SEN students. In addition, most research on the teacher–student relationships of students with SEN is limited to the upper years of primary schools (for an exception, see Freire et al., 2020 ); thus, studying these relationships as the high school level is even more important.

This study analyzes the impact of teacher–student relationships and SEN on engagement and disengagement of students in a sample of SEN and TD secondary students in mainstream schools. Trying to fill the gaps in the literature on the engagement of SEN students, we used three widely agreed dimensions of engagement in this study: cognitive, emotional, and behavioral ( Fredricks et al., 2004 ), with the addition of social engagement ( Wang et al., 2017 ). Finally, we measure engagement and disengagement as separate continua.

Design and Participants

This study used a non-experimental, correlational, and cross-sectional design to evaluate student engagement among adolescents with SEN and their TD peers. The inclusion criteria for the SEN group were (a) being enrolled in the 9th grade, (b) being in the inclusion program at a mainstream school, and (c) having a SEN diagnosis. For the TD group, the criteria were (a) being enrolled in the 9th grade, and (b) not having being diagnosed with SEN. The exclusion participation criterion for both groups was having autism ( n = 16). Schools provided information regarding diagnoses to verify compliance with the inclusion criteria.

Participants were 9th-grade students recruited from 38 public mainstream schools from the Biobio Region in Chile. All schools were in urban areas and all enrolled SEN students as mandated by Chilean legislation. There were 340 students with SEN (306 with learning disabilities, 90% of the SEN group; 21 with attention deficit disorder, 6%; six with motor disability, 2%; four with a mild hearing impairment, 1%; and three with a mild visual impairment, 1%). Furthermore, 640 TD students participated in the study. The overall group included 575 female students (56%) and 445 male students (44%), with the gender breakdown being similar between groups [ χ ( 1 ) 2 = 2.040; p = 0.153]. Note there was a slight age difference [ t (946) = 3.146; p = 0.002]. The mean age in the SEN group was 15.01 years ( SD = 0.94) and 14.82 ( SD = 0.86) for the TD group, that is, SEN students were on average 3 months older than TD students. Regarding economic status, 82.5% of the TD and 87.7% of the SEN sample had a family income below 690 USD, which corresponds to a low socioeconomic status.

Instruments and Variables

(a) Special educational needs: The inclusion program for students with SEN to attend mainstream schools in Chile—called the school integration program—requires that students have a medical and psychological evaluation to identify their special need(s) prior to enrolment. The relevant Decree 170 (2009) states that SEN students enrolled in public mainstream schools to receive academic support from a special needs teacher along with attending regular classes. This is done in both the classroom and in a special resource room, allowing for more individualized assistance.

(b) The engagement measures of teacher–student relationships, peer support at school, and family support for learning were assessed with the subscales teacher – student relationship (nine items: “My teachers are there for me when I need them”), peer support at school (six items: “Other students at school care about me”), and family support for learning (four items: “My family/guardian(s) want me to keep trying when things are tough at school”) of the Student Engagement Inventory (SEI; Appleton et al., 2006 ). Although this instrument is called “student engagement,” the nature of its items better captures factors that influence engagement than indicators of student engagement per se ( Veiga et al., 2014 ). Each item was answered on a four-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = agree, and 4 = strongly agree) as in the original instrument ( Appleton et al., 2006 ). The omission of the midpoint in a Likert scale to measure attitude is debated. However, we decided not to change the number of options because that could alter the psychometric properties of the instrument. Furthermore, omitting a neutral option could in some circumstances be beneficial in terms of forcing the respondent to choose an answer in areas with high social desirability pressures ( Chyung et al., 2017 ), which could be the case in this study.

Reliability indices in the Chilean validation process were between ω = 0.76 and ω = 0.88 for all scales. The reliability indices in the present sample were ω = 0.875, ω = 0.785, and ω = 0.700 for teacher–student relationships, peer support at school, and family support for learning subscales, respectively. In the validation sample, the SEI showed a good fit for the proposed six-factor model ( Appleton et al., 2006 ), and its factorial invariance has been demonstrated in various countries ( Virtanen et al., 2017 ) including Chile ( Espinoza et al., 2018 ).

(c) The perception of school participation was measured with the subscale positive perception of school participation (six items: “At my school, all students have the chance to participate”) from the School Participation Scale developed by John-Akinola and Nic-Gabhainn (2014) . This subscale measures if students perceive that school participation is real or symbolic in their educational institution. Each item is answered on a five-point Likert scale (1 = strongly disagree; 5 = strongly agree). This study used the Spanish version, which has been validated in a sample of 1,428 students in secondary schools in central-southern Chile ( M = 15.59; SD = 1.52) ( Pérez-Salas et al., 2019 ). Reliability in the Chilean validation process was ω = 0.877 for this subscale ( Pérez-Salas et al., 2019 ), and in the present sample, it was α = 0.857 (ω = 0.868).

(d) Engagement and disengagement were measured with the Multidimensional Scale of School Engagement (MSSE; Wang et al., 2017 ). It consists of 37 items that assess engagement and disengagement on a five-point Likert scale. The engagement factor contains 19 items: (a) behavioral engagement (four items: “I ask questions when I don't understand”), (b) cognitive engagement (five items: “I look over my schoolwork and make sure it is done well”), (c) emotional engagement (five items: “I am happy at school”), and (d) social engagement (five items: “I enjoy working with peers at school”). The disengagement factor contains 18 items: (a) behavioral disengagement (eight items: “I don't follow school rules”), (b) cognitive disengagement (two items: “Finishing my homework fast is more important to me than doing it well”), (c) emotional disengagement (four items: “I feel overwhelmed by my schoolwork”), and (d) social disengagement (four items: “I don't care about the people at my school”). This instrument was validated by the Pérez-Salas (2021) among Chilean students. The reliability indices in the present sample for the engagement factor were α = 0.902 (ω = 0.902) and α = 0.869 (ω = 0.869) for the disengagement factor. These indices were similar to those found in the validation process in Chile ( Pérez-Salas, 2021 ).

This study is part of ongoing longitudinal research on engagement trajectories of high school students. The data for this particular study is from the first wave of data collection, and the experiment was conducted during the second semester of the school year (August/December 2018). The ethical committee of the Universidad de Concepción of the First Author approved this research, and both the school boards of each city and the school gave their authorization. After this, eligible participants were determined according to the study inclusion criteria for both samples (students with SEN or students who were TD).

An invitation to participate in the study was sent to the parents of eligible participants. After explaining the rights and the purpose of the study of students and obtaining active informed consent from the parents and student informed assent, trained psychologists gave the instruments to TD students for self-administration, and individually assessed SEN students using a reading aloud application format. We decided to use different methods because difficulties in applying self-administration questionnaires in SEN students have been identified ( Finlay and Lyons, 2001 ; Goegan et al., 2018 ), suggesting that accommodations should be made ( Goegan et al., 2018 ). However, to ensure there was not a skew from the application format, we conducted a quasi-experimental study with another sample that showed that the application format (self-administered vs. read aloud) had no effect, confirming similar reliability indexes for both samples 1 .

The evaluations were conducted in schools of participants and lasted approximately 45 min. Participants received a movie ticket for their collaboration.

Data Analysis

The percentage of missing data was evaluated by item and participants, and then missing values were replaced with the Expectation-Maximization imputation method to enable analysis with all cases.

As the SEN participants had different conditions (learning, sensorial, and motor disabilities), we analyzed if there were differences in their engagement and disengagement before conducting the main analysis. Furthermore, before the analysis, we tested compliance with the assumptions of the parametric technique: normal distribution with asymmetry and kurtosis, and the homogeneity of variances with a Box's M test and Levene's test. Heteroscedasticity corrections were made when needed. Finally, to evaluate possible differences between groups (SEN vs. TD), we performed a multivariate analysis of variance with engagement and disengagement dimensions. We employed SPSS, version 25 ( IBM, 2017 ) for all the analyses.

The total missing values per item in the sample were <1% across cases. We had full data for 80.8% of the participants (81 items) and only four individuals (0.4% of the sample) had omitted 6–18 items (7–22%) in their protocols. As mentioned, missing data were replaced with the Expectation-Maximization imputation method to enable the analysis with all available data ( N = 1,020). A multivariate ANOVA did not indicate differences between participants with different SEN conditions when it came to student engagement [ F (16, 1340) = 0.909; p = 0.558; η p 2 = 0.011] or disengagement [ F (16, 1340) = 0.645; p = 0.849; η p 2 = 0.008]. Thus, we decided to treat all SEN participants as one group. Asymmetry and kurtosis values were lower than I2I in all dependent variables in both samples, supporting compliance of the assumption of the normal distribution of the variables.

Table 1 shows the mean, SD, t- tests, and effect sizes for teacher–student relationships, peer support at school, family support for learning, and perception of school participation for students with SEN and TD. Results indicate good levels of teacher–student relationships, peer support at school, and family support for learning, and very positive perceptions of school participation in both TD and SEN students. Mean comparisons revealed that SEN students report having better teacher–student relationships and an even more positive perception of school participation than do TD students. No group differences were found in peer support at school or in family support for learning.

Table 1 . Mean, SD, t -tests, and effect sizes for teacher–student relationship, peer support at school, family support for learning and perception of school participation in students with SEN and TD.

The multivariate ANOVA showed a significant statistical difference between SEN and TD students for the student engagement indicators (behavioral, cognitive, emotional, and social) [ F (4, 1015) = 12.484; p < 0.001; η p 2 = 0.047], although both had good levels ( Table 1 ). The intersubjects effect test showed that cognitive, emotional, and social engagement were higher in SEN students than TD students ( p < 0.01) ( Table 1 ). This means that students with SEN reported working harder at school, having more fun at school, and enjoying spending time with their peers at school more than those with TD.

Regarding the student disengagement dimension, a significant statistical difference was found between SEN and TD students in the indicators (behavioral, cognitive, emotional, and social) [ F (4, 1015) = 10.173; p < 0.001; η p 2 = 0.039]. In general, SEN and TD students had low levels of behavioral, cognitive, and emotional disengagement, but both groups reported some degree of social disengagement. The intersubjects effect test showed that cognitive and behavioral disengagement were higher in SEN students than TD students ( p < 0.01) ( Table 1 ). This means that students with SEN reported more maladaptive behaviors at school and more disaffection with learning than their peers with TD ( p < 0.01). No differences were found between samples in emotional disengagement or social disengagement.

Next, using the stepwise method, linear regressions were analyzed to predict the scores in each engagement and disengagement dimension for teacher–student relationships, peer support at school, family support for learning, and perception of school participation.

For behavioral engagement, the regression model included perceptions of school participation, peer support at school, teacher–student relationships, and group (SEN vs. TD) was statistically significant [ R adj 2 = 0.232, F (4, 1015) = 77.991, p < 0.001]. Of the predictive variables, the most important was the positive perception of school participation, followed by peer support at school, teacher–student relationships, and group (TD). That is, the better the perceptions of (a) school participation opportunities, (b) peer support, and (c) teacher–student relationships, along with (d) being TD, the higher the scores for behavioral engagement, in that order ( Table 2 ).

Table 2 . Linear regression models for behavioral, cognitive, emotional, and social engagement dimensions.

For cognitive engagement, the regression model included teacher–student relationships, family support for learning, and perception of school participation [ R adj 2 = 0.190, F (3, 1016) = 80.656; p < 0.001]. Among the predictive variables, the most important was again positive perception of school participation, followed by teacher–student relationships and family support for learning ( Table 2 ). This model implies that the better is (a) perception of school participation opportunities, (b) teacher–student relationships, and (c) family support for learning, the higher are the scores for cognitive engagement.

For emotional engagement, the regression model was statistically significant and included perception of school participation, teacher–student relationships, and peer support at school [ R adj 2 = 0.477, F (3, 1016) = 311.276, p < 0.001]. Among the predictive variables, the most important was positive perception of school participation, followed by teacher–student relationships and peer support at school ( Table 2 ). This model implies that the better is (a) perception of school participation opportunities, (b) teacher–student relationships, and (c) peer support at school, the higher are the scores for emotional engagement.

For social engagement, the regression model that included perception of school participation, peer support at school, and teacher–student relationships was statistically significant [ R adj 2 = 0.415, F (3, 1016) = 242.155; p < 0.001]. Among the predictive variables, the most important was again positive perception of school participation, followed by peer support at school and teacher–student relationships ( Table 2 ). This model implies that the better is (a) perception of school participation opportunities, (b) peer support at school, and (c) teacher–student relationships, the higher are the scores for social engagement.

For behavioral disengagement, the regression model that included all predictive variables was statistically significant [ R adj 2 = 076, F (5, 1014) = 17.728; p < 0.001]. Among the predictive variables, the most important was group (SEN = 1), followed by teacher–student relationships (–), peer support at school (+), perception of school participation (–), and family support for learning (–) ( Table 3 ). This model means that (a) having SEN, (b) having poorer teacher–student relationships, (c) higher peer support at school, (d) perception of scarce opportunities to participate at school, and (e) lower support from families for learning leads to higher scores for behavioral disengagement.

Table 3 . Linear regression models for behavioral, cognitive, emotional, and social disengagement dimensions.

For cognitive disengagement, the regression model including the positive perception of school participation, having SEN, and family support for learning was statistically significant [ R adj 2 = 0.065, F (3, 1016) = 26.67, p < 0.001]. Among the predictive variables, the most important was the perception of school participation (–), followed by group (SEN = 1) and family support for learning (–) ( Table 3 ). This model implies that (a) having SEN and (b) a poorer perception of both school participation opportunities, and (c) family support for learning will lead to lower scores for cognitive disengagement.

For emotional disengagement, the regression model including the positive perception of school participation, teacher–student relationships, and the group was statistically significant [ R adj 2 = 0.103, F (3, 1016) = 39.943, p < 0.001]. Among the predictive variables, the most important was the perception of school participation (–), followed by teacher–student relationships (–) and group (SEN = 1) ( Table 3 ). This model suggests that (a) having SEN and (b) a poorer perception of school participation opportunities, and (c) a negative perception of teacher–student relationships will lead to lower scores for emotional disengagement.

Finally, for social disengagement, the regression model including peer support at school and perception of school participation was statistically significant [ R adj 2 = 0.159, F (2, 1017) = 97.48, p < 0.001]. Among the predictive variables, the most important was peer support at school (–), followed by the perception of school participation (–) ( Table 3 ). This model means that (a) the poorer the perception of peer support at school and (b) a negative perception of school participation opportunities leads to lower scores for social disengagement.

Few studies have measured the engagement and disengagement of students with SEN, and even fewer have examined the impact of factors such as teacher–student relationships on their engagement and disengagement in school. This cross-sectional study extended prior research investigating student engagement in a sample of SEN and TD students measuring this construct in a multidimensional manner (cognitive, behavioral, emotional, and social), while considering engagement and disengagement as separate but related phenomena.

Inconsistent with previous research, we found engagement of SEN students was higher than that of TD students for the cognitive, emotional, and social indicators. We also found no differences between both groups for the behavioral indicator. Much of the literature in this field suggests that SEN students could be conceptualized as at risk for low engagement due to their struggles at school ( Douglas et al., 2012 ; Cortiella and Horowitz, 2014 ; Moreira et al., 2015 ). In addition, previous research reported lower levels of engagement in this population than in TD students ( Lovelace et al., 2014 ; Schindler, 2018 ).

Scant research has directly examined the construct of cognitive engagement for students with SEN ( O'Donnell and Reschly, 2020 ). However, O'Donnell and Reschly (2020) highlight that academic difficulties experienced by students with SEN may reflect a lack of self-regulation strategies and thus could impact engagement. Our results contradict this, showing that SEN students present higher scores in cognitive engagement. According to the conceptualization of cognitive engagement in the MSSE used in this study, this finding means that the SEN students in our sample reported “higher metacognitive strategies (…) to productively coordinate their energy and behavior in school” ( Wang et al., 2017 , p. 12). This contradictory finding is explainable because multiple studies have shown that students with SEN can successfully learn metacognitive skills [for more detail, see the meta-analysis of de Boer et al. (2018) and Donker et al. (2014) ]; thus, the work of special needs teachers with SEN students at schools could be reflecting the positive results thereof in their higher scores for cognitive engagement in our study.

Our findings also contradict previous research reporting that emotional engagement in students with SEN is lower than that in TD students. This could be attributed to the different conceptualization of emotional engagement in various studies. In the MSSE, Wang et al. (2017 , p. 3) state that emotional engagement represents “the external manifestations of students' feelings regarding school” (having fun at school, being happy at school, being proud of their school, and being interested in what they are learning at school) and do not include facilitators of engagement (contextual predictors). Our results also show higher social engagement scores for SEN students than for the TD group, reflecting the very good quality of this involvement of adolescents in social interactions (enjoy working with peers at school, enjoy spending time with peers at school, and openness to working with peers and making friends at school). These results are encouraging for SEN education, since the importance of positive emotions for development and well-being has been emphasized by positive psychology ( Norrish and Vella-Brodrick, 2009 ; McKeering et al., 2021 ).

Aligned with the self-system model theory ( Connell and Wellborn, 1991 ) and our hypothesis, our findings show that close relationships with teachers positively contribute to all dimensions of student engagement in our sample, an effect consistently reported in research in this field with TD students ( Roorda et al., 2011 , 2017 ; Quin, 2017 ). We also found that the higher was the perception of opportunities to participate at school, the higher were all indicators of engagement (cognitive, emotional, behavioral, and social), reflecting the relevance of school-level and classroom-level variables in student engagement ( Fredricks et al., 2004 ).

However, the better teacher–student relationships and more opportunities to participate at school reported by SEN students compared to their TD peers were unexpected findings of our study. Previous research mostly reported poorer relationships between SEN students and their teachers ( Murray and Greenberg, 2001 ; Al-Yagon and Mikulincer, 2004 ; Murray et al., 2006 ; Freire et al., 2020 ) and fewer opportunities to participate at school than TD students ( Coster et al., 2013 ). Our results show the opposite, as Schwab and Rossmann (2020) similarly showed in a recent study that found SEN students rated their teacher–student relationships more positively than TD students.

O'Donnell and Reschly (2020) state that the inconsistence in school connectedness or teacher–student relationships in the literature on SEN students could be attributable to the availability of resource rooms and close relationships with special education teachers in each context. Similarly, Schwab and Rossmann (2020) explain their results by arguing that in the Austrian school system, SEN students are often supported by two teachers in regular classrooms, one of whom is a special needs teacher who spends much time with the students, providing opportunities to develop a closer relationship with them. We think the same hypothesis could explain our positive results for teacher–student relationships and the better perception of participation of the SEN students in our sample, as such students attending public mainstream schools in Chile receive academic support by special needs teachers in regular classrooms and additional support in small groups in a special resource room. This reflects the increased time special needs teachers spend with these students and that these teachers may be more sensitive to their needs.

The positive effect of special needs teachers for Chilean students is also supported by a qualitative study that we conducted with a sample of adolescents with learning disabilities 2 . Based on the perceptions of students, that study concluded that special needs teachers are crucial for their engagement, as their pedagogical practices are oriented to satisfy the needs of students for competence and relatedness, aspects that have been shown as key in adjusting to school ( Connell and Wellborn, 1991 ).

Exposure to more positive relationships with special needs teachers could also explain the better teacher–student relationships reported by SEN students and their higher emotional and social engagement scores. This is aligned with the study of Martin and Collie (2019) that predicted greater engagement of high school students as to when the number of positive relationships outnumbered negative relationships with their teachers.

Finally, we found significant statistical differences between SEN and TD students for some disengagement indicators. On the one hand, engagement of SEN students was higher in the cognitive, emotional, and social dimensions; however, on the other hand, they also had higher scores for cognitive and behavioral disengagement. These results emphasize that engagement and disengagement are two distinctive phenomena ( Skinner et al., 2009 ). Thus, although students with SEN report working harder at school, enjoying being at school and studying, and have positive interactions with others at school, they also perceive higher “disaffection” with learning ( Skinner et al., 2008 ) than their TD peers. This should alert educators, as it could lead SEN students to gradually withdraw from the social environment in response to negative experiences ( Finn, 1989 ).

A possible explanation for this apparent contra-intuitive result for SEN students (high cognitive and behavioral disengagement alongside high cognitive and emotional engagement) might be because according to a meta-analysis, the relationship between academic achievement and engagement is not always conclusive ( Lei et al., 2018 ). Therefore, although extensive empirical research on the relationship between academic achievement and engagement exists, some scholars have found non-significant associations between these variables ( Lei et al., 2018 ). Possibly, this is because students who achieve good grades better master the abilities needed for easier learning than low achievement students, and so apply less effort and strategies when studying ( Lei et al., 2018 ). We think this hypothesis could be applicable to our results, meaning the better cognitive engagement of SEN students may reflect their extra educational effort compared to their classmates. Furthermore, despite that they seem to enjoy being at school, being with peers, and learning, they may be starting to experience a higher level of cognitive and behavioral disengagement, perhaps because they feel some frustration when learning.

Implications for Policies and Educational Practice

The current study provides evidence of the need for continuing research on students with SEN to unpack the conditions that provide support or hinder their participation and achievement in schools. Overall, this research suggests that teachers have a relevant influence in all dimensions of engagement of students and on emotional and behavioral disengagement for TD and SEN students. At the same time, the positive relationship between teachers and students was inversely associated with the disengagement of students. These findings are particularly relevant for students with SEN who often experience more struggles in school and higher dropout rates.

These results have implications for policy and practice. We hope this study will inform policymakers and authorities when drafting policies regarding students with SEN, especially when it comes to the relevance of teacher–student relationships in the achievement and well-being of students. In addition, this study highlights the relevance of including students with SEN in research. Authorities must consider this when evaluating topics impacting the trajectories of students.

Regarding implications for practice, it would benefit school systems to structure student interactions in ways to strengthen opportunities to provide academic and emotional support. School districts and administrators have an important role in providing professional development to improve the abilities of teachers to create strong teacher–student relationships. In the case of inclusive education, students with SEN have the additional support of special education teachers, which could impact their perceptions of teacher–student relationships, as the additional support could provide further opportunities to enhance these relationships. Schools should also make efforts to ensure that both TD and SEN students feel like there are plenty of opportunities to engage in school participation, since that was also a key factor.

Limitations and Future Research

Despite the strengths of this study, some limitations must be considered when interpreting its results. First, this is a correlational and cross-sectional study; thus, no cause–effect conclusions should be derived from our results. Second, all our measures rely on self-reporting of students. It would have been informative to have impressions of teachers on teacher–student relationships and more direct measures of school participation opportunities to disentangle in terms of whether the level of opportunities, belief that there are many opportunities or a combination of both have an impact.

Future quantitative work should examine practices of teachers to help determine what creates good teacher–student relationships, and what other impacts teachers may have on engagement and disengagement dimensions of students (cognitive, behavioral, emotional, and social). Furthermore, qualitative work (e.g., interviews with teachers, and TD and SEN students) should be considered to provide detailed insight into how such relationships are created and if specific factors have a greater influence on the performance and well-being of students. In this regard, mixed research methods could be a productive approach to collect comprehensive data to better understand the experiences of students, particularly those who face barriers to participating in schools.

Despite our limitations, this study adds to a fairly limited field of research. It includes a relatively large sample of students with SEN studying in mainstream settings, whom it compared with their TD peers. Simply focusing on the engagement and disengagement of students with SEN is a contribution to this field considering the lack of information on both constructs for this more vulnerable population. It also suggests clear future paths for additional research and potential school-level improvements.

Finally, we hope this article draws attention to the challenges faced by SEN students and the relevance of teacher–student relationships in contributing to both engagement and disengagement depending on the quality of these relationships. These findings suggest clear future paths for additional research and potential school-level interventions to strengthen student engagement and avoid the negative consequences of disengagement.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Comité de Ética, Bioética y Bioseguridad de la Vicerrectoría de Investigación y Desarrollo de la Universidad de Concepción, Concepción, Chile. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author Contributions

All authors contributed to this manuscript by doing secondary research, conceptualizing the methodology, and drafting and revising the manuscript.

This work received funding from the National Research and Development Agency of the Chilean Government [ANID/CONICYT, Proyecto FONDECYT Regular 1181265].

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to extend our thanks to DAEM Talcahuano, DAEM Los Ángeles, DEM LotaDAEM Antuco, DAEM Curanilahue, and SLEM Andalién Sur, and all the schools that participated in this project. Special thanks are extended to the integration program coordinators of each school.

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Keywords: student engagement, student disengagement, special educational needs, teacher-student relationships, school participation

Citation: Pérez-Salas CP, Parra V, Sáez-Delgado F and Olivares H (2021) Influence of Teacher-Student Relationships and Special Educational Needs on Student Engagement and Disengagement: A Correlational study. Front. Psychol. 12:708157. doi: 10.3389/fpsyg.2021.708157

Received: 11 May 2021; Accepted: 11 June 2021; Published: 14 July 2021.

Reviewed by:

Copyright © 2021 Pérez-Salas, Parra, Sáez-Delgado and Olivares. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Claudia P. Pérez-Salas, cperezs@udec.cl

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Our mission is to close the gaps in opportunity and achievement that disproportionately impact students who are the most underserved, with a particular focus on Black and Latino/a students and students from low-income backgrounds.

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EdTrust in Texas advocates for an equitable education for historically-underserved students across the state. We believe in centering the voices of Texas students and families as we work alongside them for the better future they deserve.

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The Importance of Strong Relationships Between Teachers & Students

Anxiety, stress, and in some cases, trauma are prevalent as we live through the COVID-19 pandemic. Students are facing…

A Strategy to Solve Unfinished Learning

Anxiety, stress, and in some cases, trauma are prevalent as we live through the COVID-19 pandemic. Students are facing food and housing insecurities, isolation caused by school and business closures, uncertainty due to parents losing jobs, and the fear of catching the coronavirus or grief of losing family members to it. Educators are facing their own personal stresses, in addition to being concerned about teaching academic content and about the well-being of their students, which can ultimately wear on their well-being.

But even with all of these stressors, teachers and students are trying to remain connected to schools and each other. Strong relationships with teachers and school staff can dramatically enhance students’ level of motivation and therefore promote learning. Students who have access to more strong relationships are more academically engaged, have stronger social skills, and experience more positive behavior. Unfortunately, too many students do not have this experience. A survey of 25,400 sixth to 12th graders in a large diverse district, found that less than a third of middle schoolers had a strong relationship with their teachers, and that number dropped to 16% by the time students reached 12th grade. Students from low-income backgrounds report even fewer strong relationships with their teachers.

When schools closed their doors in March 2020, these connections went away for many. But building trusting relationships will be critical to addressing the months of stress and missed classroom instruction, or unfinished learning, that has followed. Estimates show that as many as 3 million students are offline, hard to find, or have left school altogether as a result of school closures. In some places, data shows as many as 1 in 5 students did not participate in virtual learning in the spring. Building and maintaining strong “developmental relationships” that reconnect students with adults in school buildings will matter more now and in coming months than in previous school years. Without these trusting relationships and connections, educators cannot catch students up.

Strong relationships between adults and students must include: expressing care, challenging growth, providing support, sharing power, and expanding possibilities (see related chart for explanations). Importantly, these relationship-building actions must be done with an equity lens, one that supports positive racial, cultural, and ethnic identity development. The country’s attempt to reckon with 400 years of anti-blackness in response to recent acts of racial violence and injustice is highlighting the long-standing systemic inequities affecting students of color. And the pandemic is exacerbating them.

Creating strong relationships between students and those charged with educating them therefore will require adults to acknowledge the long-standing harms caused by racism in schools. Bias and discrimination, both implicit and explicit, can easily lead to harmful in-school practices that erase students’ cultural identities. Relationship building, however, must be done intentionally with the needs of students of color in mind and with a strength-based lens that recognizes and values the rich cultural and linguistic assets they bring to the classroom.

In this brief, we highlight the important practices of fostering strong relationships between students and adults, as well as how to build these relationships in ways that encourage and support students to engage in tasks that move them beyond their current understanding and skills.

What Do We Know About What Works?

District and school leaders considering emphasizing relationships as a strategy to help students catch up and stay connected with school will have to make intentional and important decisions about structuring time for teachers and staff, investing in activities, training on building developmental relationships, and about how to most effectively group students.

As school leaders consider what type of strategy could work best with their staff and students to build strong developmental relationship, they will have to make challenging decisions based on their specific circumstances or contexts. These decisions will come with tradeoffs. In this brief, we draw on research on strengthening student-teacher relationships, school-based mentoring, school-based after-school programming, and school-based case management to provide insight on those tradeoffs. The following chart shows how implementing different elements of building strong developmental relationships impacts the effectiveness of those relationships.

How Effective are Strong Relationships?

We looked at the research to help leaders navigate complicated decisions. The chart below shows how implementing various features of strong relationships impact effectiveness.

Critical Questions for Leaders

Who benefits most from strong relationships?

Students from all backgrounds and ages benefit from strong relationships.

Below are critical questions to ask, based on available research, as schools and districts are building plans to completed unfinished learning.

Why are strong relationships important?

Strong relationships provide a foundation for student engagement, belonging, and, ultimately, learning. The more high-quality relationships students have with their teachers, the better their engagement in school.

How can schools strengthen relationships among students and staff?

The most important thing schools can do to foster these relationships is to have a culture that explicitly values adults nurturing relationships with students and providing teachers and school staff with the time, space, and occasions to interact repeatedly with individual students, especially those that seem less engaged.

Which adult relationships are most impactful?

All in-school adults should strive for strong relationships with students. When students have strong relationships with their teachers, in-class motivation increases the most. In these instances, students are motivated by teachers’ high expectations as well as their own.

How should schools group students to foster relationships between adults and students?

Smaller groups are most effective for fostering relationships. One-on-one interactions allow for the greatest opportunity for individualized attention and support, but some adults and students benefit from a larger group setting.

What tasks will foster strong relationships in individual or group settings?

Activities are most effective when they are based on students’ interests or goals.

What training do adults need to build strong relationships?

Schools should provide all the adults in the school building with training on the elements of developmental relationships, time, and strategies to build developmental relationships. Schools should also provide individual feedback based on observations of adult interactions with students. This training will ensure that relationships are stronger and more effective in accelerating academic learning. students of color.

Using Federal & COVID-19 Relief Funds to Address Long-Standing Inequities

Holding Students Back – An Inequitable and Ineffective Response to Unfinished Learning

Back in March 2020, widespread rapid school closures in response to the onslaught of the COVID-19 pandemic presented unique…

Expanded Learning Time

As the nation continues to battle the COVID-19 pandemic and at-home learning continues, there will be a need to…

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Study shows how students and teachers are using AI for college essays, letters of recommendation

by Nathalie Graham on August 21, 2024 at 7:18 am August 21, 2024 at 7:50 am

A new study from Seattle-based education research organization foundry10 found that 30% of students and teachers surveyed are using AI to help with the college essay and letters of recommendation process.

But while generative AI may help level the playing field by providing students who normally wouldn’t have access to college tutors or advisors, the ethical implications of using these tools are still unclear , at all levels of education.

“This is a huge question in education right now,” said Jennifer Rubin, a senior researcher at foundry10. “What does ethical use of generative AI look like?”

In the study, Rubin found a range in the way students utilized AI. Of the 30% who used generative AI for help on their essays, 50% used it for brainstorming ideas, 48% used it for spelling and grammar checks, 47% had it create an essay outline, 32% used it to generate a first draft, and 20% used it to create a final draft. 

“There’s a range of activities and they really vary in regards to ethics as to what use can look like as opposed to questionable use,” Rubin said. “Generating a first essay and final essay draft is the ethically-murky area.”

Brainstorming, spell checking, and outline formation are viewed as more ethical. That’s on par with how a college admissions coach or tutor would help a student in the application process, Rubin said. Yet, it isn’t seen as a valid tool.

“There’s a bias against students who are using ChatGPT to help assist in the college essay writing process,” Rubin said. 

That bias is held by students and teachers alike. In this same study, foundry10 polled teachers as well. Around 31% have used generative AI to craft letters of recommendations for students applying to colleges. They viewed their own use of AI as ethical — a timesaver for an increasingly demanding job— but viewed students using AI as unethical. 

The difference, Rubin believes, is that when students utilize AI, they are viewed as taking a shortcut and not building necessary skills. 

Part of the study included an experiment where people read the same paragraph from a college essay. While the paragraphs stayed the same, the information about how the student wrote the essay changed. One scenario explained the student received assistance from ChatGPT while writing the essay, another stated the student received help from a tutor. The control was a scenario where the student received no help. Participants answered questions about the student who wrote the paragraph.

Across the board, people rated the student who used ChatGPT as less competent, agentic, and likeable. On the other hand, students using a college admissions coach received an average competency rating. 

“The generative AI approach was rated as less ethical and less beneficial,” Rubin said. “Surprisingly, participants rated this as more accessible than an admissions coach.”

College admissions tutoring and coaching has been on the rise for the better part of a decade. These for-hire people lay bare the application process and can often steer students in the right direction with their personal essays. They are sounding boards and guides which only those with means can utilize. 

Rubin said she was a first-generation college student. The landscape was much different when she applied back in 2002, but it was still daunting for her. “At the time, I didn’t have resources to explore what the admissions cycle looked like and what made a good college essay,” she said. “I can see these [AI] tools as helping students who might not have access to those resources.”

Generative AI, which can be accessed for free via several available online tools, can do some of the work that a college coach could. For instance, Khan Academy last year launched an AI chatbot, Khanmigo, specifically designed to help students think through their college essays. 

Rubin clarified that she didn’t believe generative AI would fix all the problems in college admissions. But, it can give students more agency over the process by helping them easily research what colleges match their interests, potential majors, and giving them a tool to help with their essays. 

“Generative AI can potentially fill a gap,” Rubin said.

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Academic Referencing: How to Cite a Research Paper

Learning how to conduct accurate, discipline-specific academic research can feel daunting at first. But, with a solid understanding of the reasoning behind why we use academic citations coupled with knowledge of the basics, you’ll learn how to cite sources with accuracy and confidence.

When it comes to academic research, citing sources correctly is arguably as important as the research itself. "Your instructors are expecting your work to adhere to these professional standards," said Amanda Girard , research support manager of Shapiro Library at Southern New Hampshire University (SNHU).

With Shapiro Library for the past three years, Girard manages the library’s research support services, which includes SNHU’s 24/7 library chat and email support. She holds an undergraduate degree in professional writing and a graduate degree in library and information science. She said that accurate citations show that you have done your research on a topic and are knowledgeable about current ideas from those actively working in the field.

In other words, when you cite sources according to the academic style of your discipline, you’re giving credit where credit is due.

Why Cite Sources?

Citing sources properly ensures you’re following high academic and professional standards for integrity and ethics.

“When you cite a source, you can ethically use others’ research. If you are not adequately citing the information you claim in your work, it would be considered plagiarism ,” said Shannon Geary '16 , peer tutor at SNHU.

Geary has an undergraduate degree in communication  from SNHU and has served on the academic support team for close to 2 years. Her job includes helping students learn how to conduct research  and write academically.

“In academic writing, it is crucial to state where you are receiving your information from,” she said. “Citing your sources ensures that you are following academic integrity standards.”

According to Geary and Girard, several key reasons for citing sources are:

• Access. Citing sources points readers to original sources. If anyone wants to read more on your topic, they can use your citations as a roadmap to access the original sources.
• Attribution. Crediting the original authors, researchers and experts  shows that you’re knowledgeable about current ideas from those actively working in the field and adhering to high ethical standards, said Girard.
• Clarity. “By citing your sources correctly, your reader can follow along with your research,” Girard said.
• Consistency. Adhering to a citation style provides a framework for presenting ideas within similar academic fields. “Consistent formatting makes accessing, understanding and evaluating an author's findings easier for others in related fields of study,” Geary said.
• Credibility. Proper citation not only builds a writer's authority but also ensures the reliability of the work, according to Geary.

Ultimately, citing sources is a formalized way for you to share ideas as part of a bigger conversation among others in your field. It’s a way to build off of and reference one another’s ideas, Girard said.

How Do You Cite an Academic Research Paper?

Any time you use an original quote or paraphrase someone else’s ideas, you need to cite that material, according to Geary.

“The only time we do not need to cite is when presenting an original thought or general knowledge,” she said.

While the specific format for citing sources can vary based on the style used, several key elements are always included, according to Girard. Those are:

• Title of source
• Type of source, such as a journal, book, website or periodical

By giving credit to the authors, researchers and experts you cite, you’re building credibility. You’re showing that your argument is built on solid research.

“Proper citation not only builds a writer's authority but also ensures the reliability of the work,” Geary said. “Properly formatted citations are a roadmap for instructors and other readers to verify the information we present in our work.”

Common Citation Styles in Academic Research

Certain disciplines adhere to specific citation standards because different disciplines prioritize certain information and research styles . The most common citation styles used in academic research, according to Geary, are:

• American Psychological Association, known as APA . This style is standard in the social sciences such as psychology, education and communication. “In these fields, research happens rapidly, which makes it exceptionally important to use current research,” Geary said.
• Modern Language Association, known as MLA . This style is typically used in literature and humanities because of the emphasis on literature analysis. “When citing in MLA, there is an emphasis on the author and page number, allowing the audience to locate the original text that is being analyzed easily,” Geary said.
• Chicago Manual of Style, known as Chicago . This style is typically used in history, business and sometimes humanities. “(Chicago) offers flexibility because of the use of footnotes, which can be seen as less distracting than an in-text citation,” Geary said.

The benefit of using the same format as other researchers within a discipline is that the framework of presenting ideas allows you to “speak the same language,” according to Girard.

APA Citation for College: A Brief Overview

Are you writing a paper that needs to use APA citation, but don’t know what that means? No worries. You’ve come to the right place.

How to Use MLA Formatting: A Brief Overview

Are you writing a paper for which you need to know how to use MLA formatting, but don’t know what that means? No worries. You’ve come to the right place.

How to Ensure Proper Citations

Keeping track of your research as you go is one of the best ways to ensure you’re citing appropriately and correctly based on the style that your academic discipline uses.

“Through careful citation, authors ensure their audience can distinguish between borrowed material and original thoughts, safeguarding their academic reputation and following academic honesty policies,” Geary said.

Some tips that she and Girard shared to ensure you’re citing sources correctly include:

• Keep track of sources as you work. Writers should keep track of their sources every time an idea is not theirs, according to Geary. “You don’t want to find the perfect research study and misplace its source information, meaning you’d have to omit it from your paper,” she said.
• Practice. Even experienced writers need to check their citations before submitting their work. “Citing requires us to pay close attention to detail, so always start your citation process early and go slow to ensure you don’t make mistakes,” said Geary. In time, citing sources properly becomes faster and easier.
• Use an Online Tool . Geary recommends the Shapiro Library citation guide . You can find sample papers, examples of how to cite in the different academic styles and up-to-date citation requirements, along with information and examples for APA, MLA and Chicago style citations.
• Work with a Tutor. A tutor can offer support along with tips to help you learn the process of academic research. Students at SNHU can connect with free peer tutoring through the Academic Support tab in their online courses, though many colleges and universities offer peer tutoring.

Find Your Program

How to cite a reference in academic writing.

A citation consists of two pieces: an in-text citation that is typically short and a longer list of references or works cited (depending on the style used) at the end of the paper.

“In-text citations immediately acknowledge the use of external source information and its exact location,” Geary said. While each style uses a slightly different format for in-text citations that reference the research, you may expect to need the page number, author’s name and possibly date of publication in parentheses at the end of a sentence or passage, according to Geary.

A longer entry listing the complete details of the resource you referenced should also be included on the references or works cited page at the end of the paper. The full citation is provided with complete details of the source, such as author, title, publication date and more, Geary said.

The two-part aspect of citations is because of readability. “You can imagine how putting the full citation would break up the flow of a paper,” Girard said. “So, a shortened version is used (in the text).”

“For example, if an in-text citation reads (Jones, 2024), the reader immediately knows that the ideas presented are coming from Jones’s work, and they can explore the comprehensive citation on the final page,” she said.

The in-text citation and full citation together provide a transparent trail of the author's process of engaging with research.

“Their combined use also facilitates further research by following a standardized style (APA, MLA, Chicago), guaranteeing that other scholars can easily connect and build upon their work in the future,” Geary said.

Developing and demonstrating your research skills, enhancing your work’s credibility and engaging ethically with the intellectual contributions of others are at the core of the citation process no matter which style you use.

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A former higher education administrator, Dr. Marie Morganelli is a career educator and writer. She has taught and tutored composition, literature, and writing at all levels from middle school through graduate school. With two graduate degrees in English language and literature, her focus — whether teaching or writing — is in helping to raise the voices of others through the power of storytelling. Connect with her on LinkedIn .

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