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  • Published: 24 February 2024

Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level

  • Monika Teuber 1 ,
  • Daniel Leyhr 1 , 2 &
  • Gorden Sudeck 1 , 3  

BMC Public Health volume  24 , Article number:  598 ( 2024 ) Cite this article

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Physical activity has been proven to be beneficial for physical and psychological health as well as for academic achievement. However, especially university students are insufficiently physically active because of difficulties in time management regarding study, work, and social demands. As they are at a crucial life stage, it is of interest how physical activity affects university students' stress load and recovery as well as their academic performance.

Student´s behavior during home studying in times of COVID-19 was examined longitudinally on a daily basis during a ten-day study period ( N  = 57, aged M  = 23.5 years, SD  = 2.8, studying between the 1st to 13th semester ( M  = 5.8, SD  = 4.1)). Two-level regression models were conducted to predict daily variations in stress load, recovery and perceived academic performance depending on leisure-time physical activity and short physical activity breaks during studying periods. Parameters of the individual home studying behavior were also taken into account as covariates.

While physical activity breaks only positively affect stress load (functional stress b = 0.032, p  < 0.01) and perceived academic performance (b = 0.121, p  < 0.001), leisure-time physical activity affects parameters of stress load (functional stress: b = 0.003, p  < 0.001, dysfunctional stress: b = -0.002, p  < 0.01), recovery experience (b = -0.003, p  < 0.001) and perceived academic performance (b = 0.012, p  < 0.001). Home study behavior regarding the number of breaks and longest stretch of time also shows associations with recovery experience and perceived academic performance.


Study results confirm the importance of different physical activities for university students` stress load, recovery experience and perceived academic performance in home studying periods. Universities should promote physical activity to keep their students healthy and capable of performing well in academic study: On the one hand, they can offer opportunities to be physically active in leisure time. On the other hand, they can support physical activity breaks during the learning process and in the immediate location of study.

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Physical activity (PA) takes a particularly key position in health promotion and prevention. It reduces risks for several diseases, overweight, and all-cause mortality [ 1 ] and is beneficial for physical, psychological and social health [ 2 , 3 , 4 , 5 ] as well as for academic achievement [ 6 , 7 ]. However, PA levels decrease from childhood through adolescence and into adulthood [ 8 , 9 , 10 ]. Especially university students are insufficiently physically active according to health-oriented PA guidelines [ 11 ] because of academic workloads as well as difficulties in time management regarding study, work, and social demands [ 12 ]. Due to their independence and increasing self-responsibility, university students are at a crucial life stage. In this essential and still educational stage of the students´ development, it is important to study their PA behavior. Furthermore, PA as health behavior represents one influencing factor which is considered in the analytical framework of the impact of health and health behaviors on educational outcomes which was developed by the authors Suhrcke and de Paz Nieves [ 13 , 14 ]. In light of this, the present study examines how PA affects university students' academic situations.

Along with the promotion of PA, the reduction of sedentary behavior has also become a crucial part of modern health promotion and prevention strategies. Spending too much time sitting increases many health risks, including the risk of obesity [ 15 ], diabetes [ 16 ] and other chronic diseases [ 15 ], damage to muscular balances, bone metabolism and musculoskeletal system [ 17 ] and even early death [ 15 ]. University students are a population that has shown the greatest increase in sedentary behavior over the last two decades [ 18 ]. In Germany, they show the highest percentage of sitting time among all working professional groups [ 19 ]. Long times sitting in classes, self-study learning, and through smartphone use, all of which are connected to the university setting and its associated behaviors, might be the cause of this [ 20 , 21 ]. This goes along with technological advances which allow students to study in the comfort of their own homes without changing locations [ 22 ].

To counter a sedentary lifestyle, PA is crucial. In addition to its physical health advantages, PA is essential for coping with the intellectual and stress-related demands of academic life. PA shows positive associations with stress load and academic performance. It is positively associated with learning and educational success [ 6 ] and even shows stress-regulatory potential [ 23 ]. In contrast, sedentary behavior is associated with lower cognitive performance [ 24 ]. Moreover, theoretical derivations show that too much sitting could have a negative impact on brain health and diminish the positive effects of PA [ 16 ]. Given the theoretical background of the stressor detachment model [ 25 ] and the cybernetic approach to stress management in the workplace [ 26 ], PA can promote recovery experience, it can enhance academic performance, and it is a way to reduce the impact of study-related stressors on strain. Load-related stress response can be bilateral: On the one hand, it can be functional if it is beneficial to help cope with the study demands. On the other hand, it can be dysfunctional if it puts a strain on personal resources and can lead to load-related states of strain [ 27 ]. Thus, both, the promotion of PA and reduction of sedentary behavior are important for stress load, recovery, and performance in student life, which can be of particular importance for students in an academic context.

A simple but (presumably) effective way to integrate PA and reduce sedentary behavior in student life are short PA breaks. Due to the exercises' simplicity and short duration, students can perform them wherever they are — together in a lecture or alone at home. Short PA breaks could prevent an accumulation of negative stressors during the day and can help with prolonged sitting as well as inactivity. Especially in the university setting, evidence of the positive effects of PA breaks exists for self-perceived physical and psychological well-being of the university students [ 28 ]. PA breaks buffer university students’ perceived stress [ 29 ] and show positive impacts on recovery need [ 30 ] and better mood ratings [ 31 , 32 ]. In addition, there is evidence for reduction in tension [ 30 ], overall muscular discomfort [ 33 ], daytime sleepiness or fatigue [ 33 , 34 ] and increase in vigor [ 34 ] and experienced energy [ 30 ]. This is in line with cognitive, affective, behavioral, and biological effects of PA, all categorized as palliative-regenerative coping strategies, which addresses the consequences of stress-generating appraisal processes aiming to alleviate these consequences (palliative) or restore the baseline of the relevant reaction parameter (regenerative) [ 35 , 36 ]. This is achieved by, for example, reducing stress-induced cortisol release or tension through physical activity (reaction reduction) [ 35 ]. Such mechanisms are also in accordance with the previously mentioned stressor detachment model [ 25 ]. Lastly, there is a health-strengthening effect that impacts the entire stress-coping-health process, relying on the compensatory effects of PA which is in accordance to the stress-buffering effect of exercise [ 37 ]. Health, in turn, effects educational outcomes [ 13 , 14 ]. Therefore, stress regulating effects are also accompanied with the before mentioned analytical framework of the impact of health and health behaviors on educational outcomes [ 13 , 14 ].

Focusing on the effects of PA, this study is guided by an inquiry into how PA affects university students' stress load and recovery as well as their perceived academic performance. For that reason, the student´s behavior during home studying in times of COVID-19 is examined, a time in which reinforced prolonged sitting, inactivity, and a negative stress load response was at a high [ 38 , 39 , 40 , 41 , 42 ]. Looking separately on the relation of PA with different parameters based on the mentioned evidence, we assume that PA has a positive impact on stress load, recovery, and perceived academic performance-related parameters. Furthermore, a side effect of the home study behavior on the mentioned parameters is assumed regarding the accumulation of negative stressors during home studying. These associations are presented in Fig.  1 and summarized in the following hypotheses:

figure 1

Overview of the assumed effects and investigated hypotheses of physical activity (PA) behavior on variables of stress load and recovery and perceived academic performance-related parameters

Hypothesis 1 (path 1): Given that stress load always occurs as a duality—beneficial if it is functional for coping, or exhausting if it puts a strain on personal resources [ 27 ] – we consider two variables for stress load: functional stress and dysfunctional stress. In order to reduce the length of the daily surveys, we focused the measure of recovery only on the most obvious and accessible component of recovery experience, namely psychological detachment. PA (whether performed in leisure-time or during PA breaks) encourages functional stress and reduce dysfunctional stress (1.A) and has a positive effect on recovery experience through psychological detachment (1.B).

Hypothesis 2 (path 2): The academic performance-related parameters attention difficulties and study ability are positively influenced by PA (whether done in leisure-time or during PA breaks). We have chosen to assess attention difficulties for a cognitive parameter because poor control over the stream of occurring stimuli have been associated with impairment in executive functions or academic failure [ 43 , 44 , 45 , 46 ]. Furthermore, we have assessed the study ability to refer to the self-perceived feeling of functionality regarding the demands of students. PA reduces self-reported attention difficulties (2.A) and improves perceived study ability, indicating that a student feels capable of performing well in academic study (2.B).

Hypothesis 3: We assume that a longer time spent on studying at home (so called home studying) could result in higher accumulation of stressors throughout the day which could elicit immediate stress responses, while breaks in general could reduce the influence of work-related stressors on strain and well-being [ 47 , 48 ]. Therefore, the following covariates are considered for secondary effects:

the daily longest stretch of time without a break spent on home studying

the daily number of breaks during home studying

Study setting

The study was carried out during the COVID-19 pandemic containment phase. It took place in the middle of the lecture period between 25th of November and 4th of December 2020. Student life was characterized by home studying and digital learning. A so called “digital semester” was in effect at the University of Tübingen when the study took place. Hence, courses were mainly taught online (e.g., live or via a recorded lecture). Other events and actions at the university were not permitted. As such, the university sports department closed in-person sports activities. For leisure time in general, there were contact restrictions (social distancing), the performance of sports activities in groups was not permitted, and sports facilities were closed.

Thus, the university sports department of the University of Tübingen launched various online sports courses and the student health management introduced an opportunity for a new digital form of PA breaks. This opportunity provided PA breaks via videos with guided physical exercises and health-promoting explanations for a PA break for everyday home studying: the so called “Bewegungssnack digital” [in English “exercise snack digital” (ESD)] [ 49 ]. The ESD videos took 5–7 min and were categorized into three thematic foci: activation, relaxation, and coordination. Exercises were demonstrated by one or two student exercise leaders, accompanied by textual descriptions of the relevant execution features of each exercise.


Participants were recruited within the framework of an intervention study, which was conducted to investigate whether a digital nudging intervention has a beneficial effect on taking PA breaks during home study periods [ 49 ]. Students at the University of Tübingen which counts 27,532 enrolled students were approached for participation through a variety of digital means: via an email sent to those who registered for ESD course on the homepage of the university sports department and to all students via the university email distribution list; via advertisement on social media of the university sports department (Facebook, Instagram, YouTube, homepage). Five tablets, two smart watches, and one iPad were raffled off to participants who engaged actively during the full study period in an effort to motivate them to stick with it to the end. In any case, participants knew that the study was voluntary and that they would not suffer any personal disadvantages should they opt out. There was a written informed consent prompt together with a prompt for the approval of the data protection regulations immediately within the first questionnaire (T0) presented in a mandatory selection field. Positive ethical approval for the study was given by the first author´s institution´s ethics committee of the faculty of the University of Tübingen.

Participants ( N  = 57) who completed the daily surveys on at least half of the days of the study period, were included in the sample (male = 6, female = 47, diverse = 1, not stated = 3). As not all subjects provided data on all ten study days, the total number of observations was between 468 and 540, depending on the variable under study (see Table  1 ). The average number of observations per subject was around eight. Their age was between 18 and 32 years ( M  = 23.52, SD  = 2.81) and they were studying between the 1st to 13th semester ( M  = 5.76, SD  = 4.11) within the following major courses of study: mathematical-scientific majors (34.0%), social science majors (22.6%), philosophical majors (18.9%), medicine (13.2%), theology (5.7%), economics (3.8%), or law (1.9%). 20.4% of the students had on-site classroom teaching on university campus for at least one day a week despite the mandated digital semester, as there were exceptions for special forms of teaching.

Design and procedures

To examine these hypothesized associations, a longitudinal study design with daily surveys was chosen following the suggestion of the day-level study of Feuerhahn et al. (2014) and also of Sonnentag (2001) measuring recovery potential of (exercise) activities during leisure time [ 50 , 51 ]. Considering that there are also differences between people at the beginning of the study period, initial base-line value variables respective to the outcomes measured before the study period were considered as independent covariates. Therefore, the well-being at baseline serves as a control for stress load (2.A), the psychological detachment at baseline serves as a control for daily psychological detachment (2.B), the perception of study demands serves as a control for self-reported attention difficulties (1.A), and the perceived study ability at baseline serves as a control for daily study ability (2.B).

Subjects were asked to continue with their normal home study routine and additionally perform ESD at any time in their daily routine. Data were collected one to two days before (T0) as well as daily during the ten-day study period (Wednesday to Friday). The daily surveys (t 1 -t 10 ) were sent by email at 7 p.m. every evening. Each day, subjects were asked to answer questions about their home studying behavior, study related requirements, recovery experience from study tasks, attention, and PA, including ESD participation. The surveys were conducted online using the UNIPARK software and were recorded and analyzed anonymously.

Measures and covariates

In total, five outcome variables, two independent variables, and seven covariates were included in different analyses: three variables were used for stress load and recovery parameters, two variables for academic performance-related parameters, two variables for PA behavior, two variables for study behavior, four variables for outcome specific baseline values and one variable for age.

Outcome variables

Stress load & recovery parameters (hypothesis 1).

Stress load was included in the analysis with two variables: functional stress and dysfunctional stress. Followingly, a questionnaire containing a word list of adjectives for the recording of emotions and stress during work (called “Erfassung von Emotionen und Beanspruchung “ in German, also known as EEB [ 52 ]) was used. It is an instrument which were developed and validated in the context of occupational health promotion. The items are based on mental-workload research and the assessment of the stress potential of work organization [ 52 ]. Within the questionnaire, four mental and motivational stress items were combined to form a functional stress scale (energetic, willing to perform, attentive, focused) (α = 0.89) and four negative emotional and physical stress items were combined to form dysfunctional stress scale (nervous, physically tensioned, excited, physically unwell) (α = 0.71). Participants rated the items according to how they felt about home studying in general on the following scale (adjustment from “work” to “home studying”): hardly, somewhat, to some extent, fairly, strongly, very strongly, exceptionally.

Recovery experience was measured via psychological detachment. Therefore, the dimension “detachment” of the Recovery Experience Questionnaire (RECQ [ 53 ]) was adjusted to home studying. The introductory question was "How did you experience your free time (including short breaks between learning) during home studying today?". Students responded to four statements based on the extent to which they agreed or disagreed (not at all true, somewhat true, moderately true, mostly true, completely true). The statements covered subjects such as forgetting about studying, not thinking about studying, detachment from studying, and keeping a distance from student tasks. The four items were combined into a score for psychological detachment (α = 0.94).

Academic performance-related parameters (hypothesis 2)

Attention was assessed via the subscale “difficulty maintaining focused attention performance” of the “Attention and Performance Self-Assessment” (ASPA, AP-F2 [ 54 ]). It contains nine items with statements about disturbing situations regarding concentration (e.g. “Even a small noise from the environment could disturb me while reading.”). Participants had to answer how often such situations happened to them on a given day on the following scale: never, rarely, sometimes, often, always. The nine items were combined into the AP-F2 score (α = 0.87).

The perceived study ability was assessed using the study ability index (SAI [ 55 ]). The study ability index captures the current state of perceived functioning in studying. It is based on the Work Ability Index by Hasselhorn and Freude ([ 56 ]) and consists of an adjusted short scale of three adapted items in the context of studying. Firstly, (a) the perceived academic performance was asked after in comparison to the best study-related academic performance ever achieved (from 0 = completely unable to function to 10 = currently best functioning). Secondly, the other two items were aimed at assessing current study-related performance in relation to (b) study tasks that have to be mastered cognitively and (c) the psychological demands of studying. Both items were answered on a five-point Likert scale (1 = very poor, 2 = rather poor, 3 = moderate, 4 = rather good, 5 = very good). A sum index, the SAI, was formed which can indicate values between 2 and 20, with higher values corresponding to higher assessed functioning in studies (α = 0.86). In a previous study it already showed satisfying reliability (α = 0.72) [ 55 ].

Independent variables

Pa behavior.

Two indicators for PA behavior were included via self-reports: the time spent on ESD and the time spent on leisure-time PA (LTPA). Participants were asked the following overarching question daily: “How much time did you spend on physical activity today and in what context”. For the independent variable time spent on PA breaks, participants could answer the option “I participated in the Bewegungssnack digital” with the amount of time they spent on it (in minutes). To assess the time spent on LTPA besides PA breaks, participants could report their time for four different contexts of PA which comprised two forms: Firstly, structured supervised exercise was reported via time spent on (a) university sports courses and (b) other organized sports activities. Secondly, self-organized PA was indicated via (c) independent PA at home, such as a workout or other physically demanding activity such as cleaning or tidying up, as well as via (d) independent PA outside, like walking, cycling, jogging, a workout or something similar. Referring to the different domains of health enhancing PA [ 57 ], the reported minutes of these four types of PA were summed up to a total LTPA value. The total LTPA value was included in the analysis as a metric variable in minutes.

Covariates (hypothesis 3)

Regarding hypothesis 3 and home study behavior, the longest daily stretch of time without a break spent on home studying (in hours) and the daily number of breaks during home studying was assessed. Therein, participants had to answer the overarching question “How much time did you spend on your home studying today?” and give responses to the items: (1) longest stretch of time for home studying (without a break), and (2) number of short and long breaks you took during home studying.

In principle, efforts were made to control for potential confounders at the individual level (level 2) either by including the baseline measure (T0) of the respective variable or by including variables assessing related trait-like characteristics for respective outcomes. The reason why related trait-like characteristics were used for the outcomes was because brief assessments were used for daily surveys that were not concurrently employed in the baseline assessment. To enable the continued use of controlling for person-specific baseline characteristics in the analysis of daily associations, trait-like characteristics available from the baseline assessment were utilized as the best possible approximation.To sum up, four outcome specific baseline value variables were measured before the study period (at T0). The psychological detachment with the RECQ (α = 0.87) [ 53 ] was assessed at the beginning to monitor daily psychological detachment. Further, the SAI [ 55 ] was assessed at the beginning of the study period to monitor daily study ability. To monitor daily stress load, which in part measures mental stress aspects and negative emotional stress aspects, the well-being was assessed at the beginning using the WHO-Five Well-being Index (WHO-5 [ 58 ]). It is a one-dimensional self-report measure with five items. The index value is the sum of all items, with higher values indicating better well-being. As the well-being and stress load tolerance may linked with each other, this variable was assumed to be a good fit with the daily stress load indicating mental and emotional stress aspects. With respect to student life, daily academic performance-related attention was monitored with an instrument for the perception of study demands and resources (termed “Berliner Anforderungen Ressourcen-Inventar – Studierende” in German, the so-called BARI-S [ 59 ]). It contains eight items which capture overwork in studies, time pressure during studies, and the incompatibility of studies and private life. All together they form the BARI-S demand scale (α = 0.85) which was included in the analysis. As overwork and time pressure may result in attention difficulties (e.g. Elfering et al., 2013), this variable was assumed to have a good fit with academic performance-related attention [ 60 ]. Additionally, age in years at T0 was considered as a sociodemographic factor.

Statistical analysis

Since the study design provided ten measurement points for various people, the hierarchical structure of the nested data called for two-level analyses. Pre-analyses of Random-Intercept-Only models for each of the outcome variables (hypothesis 1 to 3) revealed an Intra-Class-Correlation ( ICC ) of at least 0.10 (range 0.26 – 0.64) and confirmed the necessity to perform multilevel analyses [ 61 ]. Specifically, the day-level variables belong to Level 1 (ESD time, LTPA time, longest stretch of time without a break spent on home studying, daily number of breaks during home studying). To analyze day-specific effects within the person, these variables were centered on the person mean (cw = centered within) [ 50 , 62 , 63 , 64 ]. This means that the analyses’ findings are based on a person’s deviations from their average values. The variables assessed at T0 belong to Level 2, which describe the person level (psychological detachment baseline, SAI baseline, well-being, study demands scale, age). These covariates on person level were centered around the grand mean [ 50 ] indicating that the analyses’ findings are based how far an individual deviates from the sample's mean values. As a result, the models’ intercept reflects the outcome value of an average student in the sample at his/her daily average behavior in PA and home study when all parameters are zero. For descriptive statistics SPSS (IBM) and for inferential statistics R (version 4.1.2) were used. The hierarchical models were calculated using the package lme4 with the lmer-function in R in the following steps [ 65 ]. The Null Model was analyzed for all models first, with the corresponding intercept as the only predictor. Afterwards, all variables were entered. The regression coefficient estimates (”b”) were considered for statistical significance for the models and the respective BIC was provided.

In total, five regression models with ‘PA break time’ and ‘LTPA time’ as independent variables were computed due to the five measured outcomes of the present study. Three models belonged to hypothesis 1 and two models to hypothesis 2.

Hypothesis 1: To test hypothesis 1.A two outcome variables were chosen for two separate models: ‘functional stress’ and ‘dysfunctional stress’. Besides the PA behavior variables, the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’, and the ‘well-being’ at the beginning of the study as corresponding baseline variable to the output variable were also included as independent variables in both models. The outcome variable ‘psychological detachment’ was utilized in conjunction with the aforementioned independent variables to test hypotheses 1.B, with one exception: psychological detachment at the start of the study was chosen as the corresponding baseline variable.

Hypothesis 2: To investigate hypothesis 2.A the outcome variable ‘attention difficulties’ was selected. Hypothesis 2.B was tested with the outcome variables ‘study ability’. Both models included both PA behavior variables as well as the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’ and one corresponding baseline variable each: the ‘study demand scale’ at the start of the study for ‘attention difficulties’ and the ‘SAI’ at the beginning of the study for the daily ‘study ability’.

Hypothesis 3: In addition to both PA behavior variables, age and one baseline variable that matched the outcome variable, the covariates ‘daily longest stretch of time spent on home studying’ and ‘daily number of breaks during home studying’ were included in the models for all five outcome variables.

Handling missing data

The dataset had up to 18% missing values (most exhibit the variables ‘daily longest stretch of time without a break spent on home studying’ with 17.89% followed by ‘daily number of breaks during homes studying’ with 16.67%, and ‘functional / dysfunctional stress’ with 12.45%). Therefore, a sensitivity analysis was performed using the multiple imputation mice-package in the statistical program R [ 66 ], the package howManyImputation based on Von Hippel (2020, [ 67 ]), and the additional broom package [ 68 ]. The results of the models remained the same, with one exception for the Attention Difficulties Model: The daily longest stretch of time without a break spent on home studying showed a significant association (Table  1 in supplement). Due to this almost perfect consistency of results between analyses based on the dataset with missing data and those with imputed data alongside the lack of information provided by the packages for imputed datasets, we decided to stick with the main analysis including the missing data. Thus, in the following the results of the main analysis without imputations are presented.

Table 1 shows the descriptive statistics of the variables used in the analysis. An overview of the analysed models is presented in Table  2 .

Effects on stress load and recovery (hypothesis 1)

Hypothesis 1.A: The Model Functional Stress explained 13% of the variance by fixed factors (marginal R 2  = 0.13), and 52% by both fixed and random factors (conditional R 2  = 0.52). The time spent on ESD as well as the time spent on PA in leisure showed a positive significant influence on functional stress (b = 0.032, p  < 0.01). The same applied to LTPA (b = 0.003, p  < 0.001). The Model Dysfunctional Stress (marginal R 2  = 0.027, conditional R 2  = 0.647) showed only one significant result. The dysfunctional stress was only significantly negatively influenced by the time spent on LTPA (b = 0.002, p  < 0.01).

Hypothesis 1.B: With the Model Detachment, fixed factors contributed 18% of the explained variance and fixed and random factors 46% of the explained variance for psychological detachment. Only the amount of time spent on LTPA revealed a positive impact on psychological detachment (b = 0.003, p  < 0.001).

Effects on academic performance-related parameters (hypothesis 2)

Hypothesis 2.A: The Model Attention Difficulties showed 13% of the variance explained by fixed factors, and 51% explained by both fixed and random factors. It showed a significant negative association only for the time spent on LTPA (b = 0.003, p  < 0.001).

Hypothesis 2.B: The Model SAI showed 18% of the variance explained by fixed factors, and 39% explained by both fixed and random factors. There were significant positive associations for time spent on ESD (b = 0.121, p  < 0.001) and time spent on LTPA (b = 0.012, p  < 0.001). The same applied to LTPA (b = 0.012, p  < 0.001).

Effects of home study behavior (hypothesis 3)

Regarding the independent covariates for the outcome variables functional and dysfunctional stress, there were no significant results for the number of breaks during homes studying or the longest stretch of time without a break spent on home studying. Considering the outcome variable ‘psychological detachment’, there were significant results with negative impact for both study behavior variables: breaks during home studying (b = 0.058, p  < 0.01) and daily longest stretch of time without a break (b = 0.120, p  < 0.01). Evaluating the outcome variables ‘attention difficulties’, there were no significant results for the number of breaks during home studying or the longest stretch of time without a break spent on home studying. Testing the independent study behavior variables for the SAI, it increased with increasing number in daily breaks during homes studying relative to the person´s mean (b = 0.183, p  < 0.05). No significant effect was found for the longest stretch of time without a break spent on home studying ( p  = 0.07).

The baseline covariates of the models showed expected associations and thus confirmed their inclusion. The baseline variables well-being showed a significant impact on functional stress (b = 0.089, p  < 0.001), psychological detachment showed a positive effect on the daily output variables psychological detachment (b = 0.471, p  < 0.001), study demand scale showed a positive association on difficulties in attention (b = 0.240, p  < 0.01), and baseline SAI had a positive effect on the daily SAI (b = 0.335, p  < 0.001).

The present study theorized that PA breaks and LTPA positively influence the academic situation of university students. Therefore, impact on stress load (‘functional stress’ and ‘dysfunctional stress’) and ‘psychological detachment’ as well as academic performance-related parameters ‘self-reported attention difficulties’ and ‘perceived study ability’ was taken into account. The first and second hypotheses assumed that both PA breaks and LTPA are positively associated with the aforementioned parameters and were confirmed for LTPA for all parameters and for PA breaks for functional stress and perceived study ability. The third hypothesis assumed that home study behavior regarding the daily number of breaks during home studying and longest stretch of time without a break spent on home studying has side effects. Detected negative effects for both covariates on psychological detachment and positive effects for the daily number of breaks on perceived study ability were partly unexpected in their direction. These results emphasize the key position of PA in the context of modern health promotion especially for students in an academic context.

Regarding hypothesis 1 and the detected positive associations for stress load and recovery parameters with PA, the results are in accordance with the stress-regulatory potential of PA from the state of research [ 23 ]. For hypothesis 1.A, there is a positive influence of PA breaks and LTPA on functional stress and a negative influence of LTPA on dysfunctional stress. Given the bilateral role of stress load, the results indicate that PA breaks and LTPA are beneficial for coping with study demands, and may help to promote feelings of joy, pride, and learning progress [ 27 ]. This is in line with previous evidence that PA breaks in lectures can buffer university students’ perceived stress [ 29 ], lead to better mood ratings [ 29 , 31 ], and increase in motivation [ 28 , 69 ], vigor [ 34 ], energy [ 30 ], and self-perceived physical and psychological well-being [ 28 ]. Looking at dysfunctional stress, the result point that LTPA counteract load-related states of strain such as inner tension, irritability and nervous restlessness or feelings of boredom [ 27 ]. In contrast, short PA breaks during the day could not have enough impact in countering dysfunctional stress at the end of the day regarding the accumulation of negative stressors during home studying which might have occurred after the participant took PA breaks. Other studies have been able to show a reduction in tension [ 30 ] and general muscular discomfort [ 33 ] after PA breaks. However, this was measured as an immediate effect of PA breaks and not with general evening surveys. Blasche and colleagues [ 34 ] measured effects immediately and 20 min after different kind of breaks and found that PA breaks led to an additional short‐ and medium‐term increase in vigor while the relaxation break lead to an additional medium‐term decrease in fatigue compared to an unstructured open break. This is consistent with the results of the present study that an effect of PA breaks is only observed for functional stress and not for dysfunctional stress. Furthermore, there is evidence that long sitting during lectures leads to increased fatigue and lower concentration [ 31 , 70 ], which could be counteracted by PA breaks. For both types of stress loads, functional and dysfunctional stress, there is an influence of students´ well-being in this study. This shows that the stress load is affected by the way students have mentally felt over the last two weeks. The relevance of monitoring this seems important especially in the time of COVID-19 as, for example, 65.3% of the students of a cross-sectional online survey at an Australian university reported low to very low well-being during that time [ 71 ]. However, since PA and well-being can support functional stress load, they should be of the highest priority—not only as regards the pandemic, but also in general.

Looking at hypothesis 1.B; while there is a positive influence of LTPA on experienced psychological detachment, no significant influence for PA breaks was detected. The fact that only LTPA has a positive effect can be explained by the voluntary character of the activity [ 50 ]. The voluntary character ensures that stressors no longer affect the student and, thus, recovery as detachment can take place. Home studying is not present in leisure times, and thus detachment from study is easier. The PA break videos, on the other hand, were shot in a university setting, which would have made it more difficult to detach from study. In order to further understand how PA breaks affect recovery and whether there is a distinction between PA breaks and LTPA, future research should also consider other types of recovery (e.g. relaxation, mastery, and control). Additionally, different types of PA breaks, such as group PA breaks taken on-site versus video-based PA breaks, should be taken into account.

Considering the confirmed positive associations for academic performance-related parameters of hypothesis 2, the results are in accordance with the evidence of positive associations between PA and learning and educational success [ 6 ], as well as between PA breaks and better cognitive functioning [ 28 ]. Looking at the self-reported attention difficulties of hypothesis 2.A, only LTPA can counteract it. PA breaks showed no effects, contrary to the results of a study of Löffler and collegues (2011, [ 31 ]), in which acute effects of PA breaks could be found for higher attention and cognitive performance. Furthermore, the perception of study demands before the study periods has a positive impact on difficulties in attention. That means that overload in studies, time pressure during studies, and incompatibility of studies and private life leads to higher difficulties with attention in home studying. In these conditions, PA breaks might have been seen as interfering, resulting in the expected beneficial effects of exercise on attention and task-related participation behavior [ 72 , 73 ] therefore remaining undetected. With respect to the COVID-19 pandemic, accompanying education changes, and an increase in student´s worries [ 74 , 75 ], the perception of study demands could be affected. This suggests that especially in times of constraint and changes, it is important to promote PA in order to counteract attention difficulties. This also applies to post-pandemic phase.

Regarding the perceived academic performance of hypothesis 2.B, both PA breaks and LTPA have a positive effect on perceived study ability. This result confirms the positive short-term effects on cognition tasks [ 76 ]. It is also in line with the positive function of PA breaks in interrupting sedentary behavior and therefore counteracting the negative association between sitting behavior and lower cognitive performance [ 24 ]. Additionally, this result also fits with the previously mentioned positive relationship between LTPA and functional stress and between PA breaks and functional stress.

According to hypothesis 3, in relation to the mentioned stress load and recovery parameters, there are negative effects of the daily number of breaks during home studying and the longest stretch of time without a break spent on home studying on psychological detachment. As stressors result in negative activation, which impede psychological detachment from study during non-studying time [ 25 ], it was expected and confirmed that the longest stretch of time without a break spent on home studying has a negative effect on detachment. Initially unexpected, the number of breaks has a negative influence on psychological detachment, as breaks could prevent the accumulation of strain reactions. However, if the breaks had no recovery effect through successful detachment, the number might not have any influence on recovery via detachment. This is indicated by the PA breaks, which had no impact on psychological detachment. Since there are other ways to recover from stress besides psychological detachment, such as relaxation, mastery, and control [ 53 ], PA breaks must have had an additional impact in relation to the positive results for functional stress.

In relation to the mentioned academic performance-related parameters, only the number of breaks has a positive influence on the perceived study ability. This indicates that not only PA breaks but also breaks in general lead to better perceived functionality in studying. Paulus and colleagues (2021) found out that an increase in cognitive skills is not only attributed to PA breaks and standing breaks, but also to open breaks with no special instructions [ 28 ]. Either way, they found better improvement in self-perceived physical and psychological well-being of the university students with PA breaks than with open breaks. This is also reflected in the present study with the aforementioned positive effects of PA breaks on functional stress, which does not apply to the number of breaks.

Overall, it must be considered that the there is a more complex network of associations between the examined parameters. The hypothesized separate relation of PA with different parameters do not consider associations between parameters of stress load / recovery and academic performance although there might be a interdependency. Furthermore, moderation aspects were not examined. For example, PA could be a moderator which buffer negative effects of stress on the study ability [ 55 ]. Moreover, perceived study ability might moderate stress levels and academic performance. Further studies should try to approach and understand the different relationships between the parameters in its complexity.


Certain limitations must be taken into account. Regarding the imbalanced design toward more female students in the sample (47 female versus 6 male), possible sampling bias cannot be excluded. Gender research on students' emotional states during COVID-19, when this study took place, or students´ acceptance of PA breaks is diverse and only partially supplied with inconsistent findings. For example, during the COVID-19 pandemic, some studies reported that female students were associated with lower well-being [ 71 ] or worse mental health trajectories [ 75 , 77 ]. Another study with a large sample of students from 62 countries reported that male students were more strongly affected by the pandemic because they were significantly less satisfied with their academic life [ 74 ]. However, Keating and colleges (2020) discovered that, despite the COVID-19 pandemic, females rated some aspects of PA breaks during lectures more positively than male students did. However, this was also based on a female slanted sample [ 78 ]. Further studies are needed to get more insights into gender bias.

Furthermore, the small sample size combined with up to 16% missing values comprises a significant short-coming. There were a lot of possibilities which could cause such missing data, like refused, forgotten or missed participation, technical problems, or deviation of the personal code for the questionnaire between survey times. Although the effects could be excluded by sensitive analysis due to missing data, the sample is still small. To generalize the findings, future replication studies are needed.

Additionally, PA breaks were only captured through participation in the ESD, the specially instructed PA break via video. Effects of other short PA breaks were not include in the study. However, participants were called to participate in ESD whenever possible, so the likelihood that they did take part in PA breaks in addition to the ESD could be ignored.

With respect to the baseline variables, it must be considered that two variables (stress load, attention difficulties) were adjusted not with their identical variable in T0, but with other conceptually associated variables (well-being index, BARI-S). Indeed, contrary to the assumption the well-being index does only show an association with functional stress, indicating that it does not control dysfunctional stress. Although the other three assumed associations were confirmed there might be a discrepancy between the daily measured variables and the variables measured in T0. Further studies should either proof the association between these used variables or measure the same variables in T0 for control the daily value of these variables.

Moreover, the measuring instruments comprised the self-assessed perception of the students and thus do not provide an objective information. This must be considered, especially for measuring cognitive and academic-performance-related measures. Here, existing objective tests, such as multiple choice exams after a video-taped lecture [ 72 ] might have also been used. Nevertheless, such methods were mostly used in a lab setting and do not reflect reality. Due to economic reasons and the natural learning environment, such procedures were not applied in this study. However, the circumstances of COVID-19 pandemic allowed a kind of lab setting in real life, as there were a lot of restrictions in daily life which limited the influence of other covariates. The study design provides a real natural home studying environment, producing results that are applicable to the healthy way that students learn in the real world. As this study took place under the conditions of COVID-19, new transformations in studying were also taken into account, as home studying and digital learning are increasingly part of everyday study.

However, the restrictions during the COVID-19 pandemic could result in a greater extent of leisure time per se. As the available leisure time in general was not measured on daily level, it is not possible to distinguish if the examined effects on the outcomes are purely attributable to PA. It is possible that being more physical active is the result of having a greater extent of leisure time and not that PA but the leisure time itself effected the examined outcomes. To address this issue in future studies, it is necessary to measure the proportion of PA in relation to the leisure time available.

Furthermore, due to the retrospective nature of the daily assessments of the variables, there may be overstated associations which must be taken into account. Anyway, the daily level of the study design provides advantages regarding the ability to observe changes in an individual's characteristics over the period of the study. This design made it possible to find out the necessity to analyze the hierarchical structure of the intraindividual data nested within the interindividual data. The performed multilevel analyses made it possible to reflect the outcome of an average student in the sample at his/her daily average behavior in PA and home study.

Conclusion and practical implications

The current findings confirm the importance of PA for university students` stress load, recovery experience, and academic performance-related parameters in home studying. Briefly summarized, it can be concluded that PA breaks positively affect stress load and perceived study ability. LTPA has a positive impact on stress load, recovery experience, and academic performance-related parameters regarding attention difficulties and perceived study ability. Following these results, universities should promote PA in both fashions in order to keep their students healthy and functioning: On the one hand, they should offer opportunities to be physically active in leisure time. This includes time, environment, and structural aspects. The university sport department, which offers sport courses and provides sport facilities on university campuses for students´ leisure time, is one good example. On the other hand, they should support PA breaks during the learning process and in the immediate location of study. This includes, for example, providing instructor videos for PA breaks to use while home studying, and furthermore having instructors to lead in-person PA breaks in on-site learning settings like universities´ libraries or even lectures and seminars. This not only promotes PA, but also reduces sedentary behavior and thereby reduces many other health risks. Further research should focus not only on the effect of PA behavior but also of sedentary behavior as well as the amount of leisure time per se. They should also try to implement objective measures for example on academic performance parameters and investigate different effect directions and possible moderation effects to get a deeper understanding of the complex network of associations in which PA plays a crucial role.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.


Attention and Performance Self-Assessment

"Berliner Anforderungen Ressourcen-Inventar – Studierende" (instrument for the perception of study demands and resources)

Centered within

Grand centered

“Erfassung von Emotionen und Beanspruchung “ (questionnaire containing a word list of adjectives for the recording of emotions and stress during work)

Exercise snack digital (special physical activity break offer)


Leisure time physical activity

  • Physical activity

Recovery Experience Questionnaire

Study ability index

World Health Organization-Five Well-being index

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We would like to thank Juliane Moll, research associate of the Student Health Management of University of Tübingen, for the support in the coordination and realization study. We would like to express our thanks also to Ingrid Arzberger, Head of University Sports at the University of Tübingen, for providing the resources and co-applying for the funding. We acknowledge support by Open Access Publishing Fund of University of Tübingen.

Open Access funding enabled and organized by Projekt DEAL. This research regarding the conduction of the study was funded by the Techniker Krankenkasse, health insurance fund.

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Monika Teuber, Daniel Leyhr & Gorden Sudeck

Methods Center, Faculty of Economics and Social Sciences, University of Tübingen, Tübingen, Germany

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Interfaculty Research Institute for Sports and Physical Activity, University of Tübingen, Tübingen, Germany

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M.T. and G.S. designed the study. M.T. coordinated and carried out participant recruitment and data collection. M.T. analyzed the data and M.T. and D.L. interpreted the data. M.T. drafted the initial version of the manuscript and prepared the figure and all tables. All authors contributed to reviewing and editing the manuscript and have read and agreed to the final version of the manuscript.

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Correspondence to Monika Teuber .

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Teuber, M., Leyhr, D. & Sudeck, G. Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level. BMC Public Health 24 , 598 (2024). https://doi.org/10.1186/s12889-024-18082-z

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The Effect of University Students’ Emotional Intelligence, Learning Motivation and Self-Efficacy on Their Academic Achievement—Online English Courses

Yuan-cheng chang.

1 Department of Education Management, Chinese International College, Dhurakij Pundit University, Bangkok, Thailand

Yu-Ting Tsai

2 Department of International Business, Chinese International College, Dhurakij Pundit University, Bangkok, Thailand

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

The COVID-19 pandemic has had a significant impact on education worldwide. The disease first hit China and numerous Chinese cities then started to conduct online courses. Therefore, this study aims to explore the effect of the Shanghai students’ emotional intelligence, learning motivation, and self-efficacy on their academic achievement when they participated in online English classes during the latter phase of the pandemic in China. Furthermore, the research also examines whether the students’ emotional intelligence can influence their academic achievement through the mediation effect of their learning motivation and self-efficacy. Social Cognitive Theory (SCT) and the social cognitive Expectancy-Value Model were employed to build the research framework, and the method of structural equation modeling (SEM) was utilized to conduct the model verification. Ten universities in Shanghai, China were selected for sampling. In total, 450 students were surveyed of which 404 questionnaires were valid. The results show that the students’ emotional intelligence did not directly affect their academic achievement. Nevertheless, the students’ emotional intelligence had a positive effect on their learning motivation and self-efficacy. In addition, mediation analysis showed that the relation between emotional intelligence and academic achievement was sequentially mediated by learning motivation and self-efficacy.


The COVID-19 pandemic has had a significant impact on education. There have been several schools closed in 180 countries or regions since the end of April 2020 and 85% of students could not go to school ( World Bank, 2020a , b ). The COVID-19 pandemic has been a typically adaptive and revolutionary challenge for educators, who needed to take countermeasures rapidly. Thus, numerous schools worldwide have managed to continue to teach online with their resources during the pandemic ( Reimers et al., 2020 ).

There are several factors influencing students’ online academic achievement. A body of recent studies have shown that emotional intelligence (EI) ( Berenson et al., 2008 ), learning motivation ( Nonis and Fenner, 2012 ), and self-efficacy ( Cussó-Calabuig et al., 2018 ; Yokoyama, 2019 ) have an effect on academic achievement.

Mortiboys (2012) points out that there have been various scholars interested in the effect of EI on education and there has been a dramatic increase in the number of studies on that ( Perera, 2016 ). Mayer et al. (2008) suggested that EI refers to how people manage, comprehend, and use their relevant emotional traits and cognitive ability when they get along with others. EI also means that individuals’ social intelligence enables them to recognize and differentiate their own and others’ emotions in order to make appropriate decisions and take responsive actions ( Alhebaishi, 2019 ). In terms of language learning in EI, emotional characteristics and cognitive ability are beneficial to reading comprehension ( Motallebzadeh, 2009 ; Abdolrezapour and Tavakoli, 2012 ), introspection ( Afshar and Rahimi, 2016 ; Chang, 2021 ), speaking ( Asadollahfam et al., 2012 ), listening comprehension ( Serraj, 2013 ), and writing performance ( Pishghadam, 2009 ; Shao et al., 2013 ). Moreover, high EI has a positive impact on language development ( Rostampour and Niroomand, 2013 ; Kourakou, 2018 ) and language learning strategies ( Aghasafari, 2006 ).

Dubey (2012) found that students’ EI was positively correlated with their learning motivation. Henter (2014) also proposed that EI, motivation, and linguistic performance correlated positively. According to Schunk and Meece (2005) , motivation is a deep mental phenomenon, normally defined as the strength of dominating individuals’ behavior, and drives them to be engaged in goal-directed behavior ( Jenkins and Demaray, 2015 ). Furthermore, Bain et al. (2010) pointed out that students’ motivation was connected to the effectiveness of their learning. Students’ learning could also be maintained through the stimulation of motivation. Tella (2007) reported that it was difficult to reach satisfactory learning outcomes if there was a lack of learning motivation. Ivanova et al. (2019) noted in their research of second language learning that students’ learning motivation influenced their grades of foreign languages. As a result, learning motivation was essential since it was closely related to academic achievement and performance ( Titrek et al., 2018 ; Duchatelet and Donche, 2019 ).

Self-efficacy plays a vital role in learning processes and learning outcomes ( Zhang and Ardasheva, 2019 ). It allows learners to be more involved in their learning processes regarding their motivation, cognition and behavior ( Anam and Stracke, 2016 ). One of the components of social cognition is self-efficacy; Bandura defined self-efficacy as one’s belief in his or her ability to achieve assignments ( Bandura, 2001 ). The major element of personal efficacy in mankind’s accomplishments, attitude, and performance is belief, which is an important component in Social Cognitive Theory (SCT) ( Kirk et al., 2008 ). In addition, Morali (2019) suggested that reading self-efficacy and attitude has a crucial predictive effect on EFL (English as a foreign language) reading comprehension achievement ( Rachmajanti and Musthofiyah, 2017 ).

Bandura (1997) connected the function of efficacy and the concept of EI in his research and considered that the control of self-awareness and emotions might be linked with higher levels of self-efficacy. Gundlach et al. (2003) also indicated that EI could influence self-efficacy through emotions and the process of causal reasoning, which impacted important work outcomes. Moreover, students’ self-efficacy had the mediation effect between EI and academic performance ( Udayar et al., 2020 ). Therefore, students’ emotional intelligence and the ability to manage their emotions affect both their learning motivation and belief in their ability and performance. Furthermore, students’ EI is helpful for enhancing their learning results owing to the belief in their own ability ( Udayar et al., 2020 ).

As mentioned above, students’ EI exercises an influence on their learning motivation, self-efficacy, and academic achievement. Additionally, students’ learning motivation and self-efficacy impact their academic achievement. Under the effect of the COVID-19 pandemic, most schools have been utilizing online teaching ( Reimers et al., 2020 ). However, online teaching is distinct from traditional methods. Teachers, students as well as classmates can not discuss face-to-face, which may lead to different learning outcomes, as students’ emotional cognition, the control of their emotions, and the way they express themselves online may be dissimilar from those offline. Consequently, the major purpose of this study is to explore the relationship among university students’ EI, learning motivation, self-efficacy, and English academic achievement when they take online English courses. The research is based on SCT and the social cognitive Expectancy-Value Model (E-VM) of achievement motivation.

In this paper, a model is built to discuss the relationship among university students’ EI, learning motivation, self-efficacy, and English academic achievement. Moreover, in order to verify the model, structural equation modeling (SEM) is applied to it. The aim of this research is threefold:

  • 1. to explore the effect of university students’ emotional intelligence on their learning motivation, self-efficacy, and academic achievement when they take online courses.
  • 2. to explore the mediation effect of university students’ self-efficacy between their learning motivation and academic achievement when they take online courses.
  • 3. to explore whether university students’ emotional intelligence has an indirect effect on their academic achievement through their learning motivation and self-efficacy when they take online courses.

Emotional Intelligence and Academic Achievement

The concept of EI was proposed by Salovey and Mayer earliest ( Salovey and Mayer, 1990 ; Mayer and Salovey, 1993 ; Bar-On, 1997 ). According to their research, EI was defined as individuals’ ability to monitor and discriminate their own and others’ feelings and emotions, which could guide their thoughts and behavior. Furthermore, EI is a set of cognitive abilities and emotional competencies, which are connected ( Ciarrochi et al., 2001 ). It also refers to the ability that lets people differentiate, express, control, and utilize their emotions through self-adaptive approaches ( Nordin, 2012 ; Shafiq and Rana, 2016 ). Humans need to sense their own and others’ feelings to enable themselves to adapt to social behavior ( Salovey and Mayer, 1990 ; Mayer and Salovey, 1993 ). Emotion perception includes how people alter their own emotions and modify them towards others, and what emotional content they utilize when resolving problems ( Salovey and Mayer, 1990 ; Mayer and Salovey, 1993 ). EI is a tendency where individuals are likely to distinguish, evaluate and cope with their own and others emotional states in order to achieve particular goals ( Fox and Spector, 2000 ; Choudary, 2010 ). Mayer et al. (2000) considered that EI was a zeitgeist, which comprised a group of personality traits and a set of abilities that processed related emotional information. The term zeitgeist also implied the combination of individuals’ emotions and rationality in human history ( Mayer et al., 2000 ).

The cognitive structure of EI consisted of the following four parts: “emotional self-assessment,” “self-expression assessment,” “identification of others’ emotions for emotional self-regulation,” and “the use of emotions to facilitate performance” ( Mohammad et al., 2009 ). Emotions make people’s cognitive processes adjustable and let them have rational thinking ( Brackett et al., 2011 ) and EI allows individuals to have the ability to appreciate and discriminate emotions ( Prati et al., 2003 ). In other words, EI empowers individuals to know how to merge their rationality and emotions ( Mayer et al., 2000 ). Hence, EI refers to one’s acceptance of emotions and his or her use of those in order to make appropriate decisions in life and interpersonal relationships ( Karimi et al., 2014 ; Vidyarthi et al., 2014 ). It also refers to the understanding of ourselves and others, the self-control of immediate requirements, peoples’ empathy, and the positive exercise of emotions ( Karimi et al., 2014 ; Vidyarthi et al., 2014 ). Furthermore, Goleman et al. (2013) proposed that EI encompasses individuals’ ability to manage their emotions effectively and their capacity to master their emotions and impulses when they feel like a failure, depressed, and disappointed. They also stated that EI is people’s competence in constraining their feelings in interpersonal relationships and encouraging or guiding others when they get on with each other.

In order to create effective learning opportunities in the educational environment, students not only need to gain knowledge at school, but also to cultivate social and emotional abilities ( Amirian and Behshad, 2016 ). Numerous studies have noted that EI is pertinent to success in several fields including effective teaching ( Ghanizadeh and Moafian, 2009 ), students’ learning ( Brackett and Mayer, 2003 ), and academic achievement ( Márquez et al., 2006 ; Fallahzadeh, 2011 ). In addition, EI, academic achievement and other emotional and cognitive characteristics, which were helpful for learning, were proven positively correlated through empirical research. In the research of Shamradloo (2004) , EI could predict one’s academic achievement twice as much as cognitive intelligence. As a consequence, the study of students’ emotional intelligence is beneficial for facilitating their academic achievement. The first research hypothesis is as follows:

  • H 1 : Emotional intelligence has a positive effect on academic achievement.

Emotional Intelligence and Self-Efficacy

Self-efficacy was a crucial individual variable from Bandura’ SCT ( Bandura, 1986 ), which emphasized the significance of social experience and the necessity of observational learning in the process of developing character ( Mahler et al., 2018 ). Bandura (1997) also defined self-efficacy as individuals’ belief in their own competence in arranging and carrying out operations to create the expected accomplishments and outcomes. In Qureshi’ investigation, the interaction of cognition (personal factor), behavioral element and environmental component determined one’s behavior ( Qureshi, 2015 ). To put it in another way, individuals’ decisions in certain situations depended on their own observation. The observation of others’ behavior in one’s memory would influence his or her cognitive process and social behavior in future events. Bandura (1994) suggested that individuals with high self-efficacy had various positive traits that are comprised of having confidence in one’s ability to handle arduous tasks and then continuing to work on them. Other characteristics include setting challenging objectives and then proceeding with them, putting more effort into assignments and then reviving positive self-efficacy after experiencing failure and encountering obstacles ( Bandura, 1994 ). Self-efficacy enables us to control our thoughts, feelings, and behaviors; it is also concerned with people’s belief in their competence ( Baron et al., 2016 ; Halper and Vancouver, 2016 ). Self-efficacy involves individuals’ perspective on what they can and cannot do ( Bandura, 1997 ; Kirk et al., 2008 ). The belief in self-efficacy, which was a key element in SCT, played a vital role in mankind’s accomplishments, attitudes, and performance ( Bandura, 1997 ; Kirk et al., 2008 ). On the contrary, people with low self-belief or low self-efficacy might suppose that things were more strenuous than reality, which contributed to the increase in pressure as well as depression, and tunnel vision in problem-solving ( Pajares and Schunk, 2001 ).

With respect to the relation between EI and self-efficacy, Salovey and Mayer (1990) showed that the concept of EI was individuals’ ability to deal with their emotions. They also defined EI as the competence in monitoring and distinguishing emotions, which were applied to leadership mindset and behavior. Moreover, managing this kind of self-awareness was essential to the adjustment of emotions ( Bandura, 1997 ). Self-awareness was tied closely with self-efficacy, since self-efficacy gave prominence to self-awareness and self-regulation ( Bandura, 1997 ). This element affects the development of self-efficacy.

Bandura (1997) observed that when people recognized thoughts, feelings and behavior to explain organizational reality through their self-awareness, self-regulation and self-control, their EI and self-efficacy would be internalized ( Bandura, 1997 ).

The emphasis on self-awareness, self-regulation and self-control was the major component causing the development and realization of self-efficacy in SCT, which was similar to the area of research that was focused on in the study of EI ( Gundlach et al., 2003 ). From this point of view, some researchers have considered that the studies on self-efficacy and EI are interrelated. The main reason for that is EI can assist individuals to produce the causal attributions that damage their belief in self-efficacy the least, through altering their possible emotions ( Gundlach et al., 2003 ). Furthemore, Emmer and Hickman (1991) suggested that researchers could explore the relationship between emotions and the belief in efficacy in academic settings.

In groundbreaking study Bandura’s (1997) , the effect of efficacy and the framework of EI were linked. He considered that the control of self-awareness and emotions might result in higher degrees of self-efficacy.

There have been several studies showing that EI and self-efficacy are closely connected and positively correlated ( Kirk et al., 2008 ; Rastegar and Memarpour, 2009 ; Hamdy et al., 2014 ; Gurbuz et al., 2016 ). It may be difficult for people with low EI and self-efficacy to complete their daily tasks in order ( Rostami et al., 2010 ). Furthermore, serious anxiety contributes to the decrease in performance, which then reduces self-efficacy. As a result, individuals with high EI can manage their emotions and actively handle problems.

Emotional intelligence influences one’s ability to control his or her self-efficacy through causal reasoning and it also impacts essential work results ( Gundlach et al., 2003 ). Chan (2007) and Mikolajczak and Luminet (2007) also found that people who appeared to have high EI had higher self-efficacy. Nonetheless, more investigation needs to be conducted to explore which elements of EI play a more significant role in demonstrating the changes in self-efficacy ( Shipley et al., 2010 ). In SCT, the ability to control emotions and self-efficacy are related ( Bandura, 1997 ; Gundlach et al., 2003 ), and emotional intelligence affects self-efficacy ( Mikolajczak and Luminet, 2007 ; Hamdy et al., 2014 ; Gurbuz et al., 2016 ). As has been discussed, the second research hypothesis is as follows:

  • H 2 : Students’ emotional intelligence has a positive effect on their self-efficacy

The Relationship Among Emotional Intelligence, Learning Motivation, Self - Efficacy, and Academic Achievement

Motivation is the ability in which individuals encourage themselves and others to conduct a certain behavior or a series of behaviors; it also enables people to achieve great accomplishments ( Rahim and Psenicka, 2002 ). Keller (1987) introduced the ARCS model (ARCS stands for attention, relevance, confidence, and satisfaction) to seek a more constructive approach to comprehend what greatly influences motivation and search for a systematic method to recognize and resolve problems concerning learning motivation. Doménech-Betoret et al. (2017) considered that one of the most reliable approaches to linking variables such as learning motivation, self-efficacy and academic achievement was employing the social cognitive E-VM ( Eccles, 1983 ; Wigfield and Eccles, 1992 , Wigfield and Eccles, 2000 ). This model encompasses a variety of components and connections that are divided into three blocks or categories of variables, and these are “social world”, “cognitive processes” and “motivational beliefs” in sequence. All of the blocks of variables can be directly or indirectly utilized as a predictive index of students’ willpower, options and achievement behavior. This model brought up a hypothesis based on motivational beliefs. First, people’s expectations of success and subjective task values are directly associated with accomplishments, options of assignments and determination. Second, “expectancies and task values” are affected by people’s objectives and “self-schemata.”

Moreover, self-efficacy and individuals’ beliefs in their own ability can be viewed as a significant part of self-schemata. Elliot (1999) defined achievement motivation as the route of competence-based affect, cognition, and behavior which stimulated the course of accomplishment leading students to failure or success. The crucial evidence, provided by past research on verified structural models based on the expectancy value theory, approves of the fact that the variables of motivational expectancy value play an essential role in students’ self-beliefs (such as self-efficacy, self-concept, and self-esteem) and academic achievement ( Doménech-Betoret et al., 2014 , 2017 ). It also emphasizes the significance of the variables of motivational expectancy value in terms of their prediction of students’ academic achievement.

  • H 3 : Self-efficacy has the mediation effect between learning motivation and academic achievement

Therefore, SCT and the social cognitive EV-M can be utilized to explain the relationship among EI, learning motivation, self-efficacy and academic achievement. Dubey (2012) found a positive correlation between EI and learning motivation; moreover, students with high, medium and low levels of motivation had a significant difference in EI. Additionally, Henter (2014) reported that EI could enhance motivation and linguistic performance, and it had a positive impact on self-efficacy ( Ngui and Lay, 2020 ). Individuals with high EI could also accommodate themselves to different types of lifestyles, make use of effective coping skills when encountering problems and have self-efficacy ( Shipley et al., 2010 ). Gharetepeh et al. (2015) showed that EI correlated positively with self-efficacy and could be used to forecast academic achievement, and self-efficacy was a major factor in successful performance ( Baron et al., 2016 ). Usher and Pajares (2008) also pointed out that self-efficacy could predict student academic achievement in every academic area. Students’ self-efficacy, sense of responsibility for their projects and GPAs of their final exams were positively correlated ( Zimmerman and Kitsantas, 2005 ; Yazici et al., 2011 ). Doménech-Betoret et al. (2017) also notes that there have been a considerable body of studies showing that the belief in self-efficacy directly influences academic achievement. Consequently, students’ ability to control their emotions affects the creation of their learning motivation, which also impacts self-efficacy and eventually influences academic achievement.

  • H 4 : Emotional intelligence has a positive effect on learning motivation.
  • H 5 : Learning motivation and self-efficacy have the mediation effect between emotional intelligence and academic achievement.

Materials and Methods


There have been a considerable number of universities in China utilizing online teaching due to the COVID-19 outbreak. Shanghai is one of the first-tier cities in China and is better equipped with educational facilities. Thus, the participants in this study were university students in Shanghai, China, majoring in Business Management. One hundred and fifty students were selected from three universities for pre-testing. Ten universities running online English courses were selected through purposive sampling, with one class drawn from each of the universities, and 45 students drawn from each class. The questionnaires were distributed by the students’ teachers and they filled them out online. In total, 450 students were surveyed and 432 questionnaires were retrieved. With invalid questionnaires excluded, a total of 404 valid questionnaires were captured. 149 of the respondents were male and 255 were female.


The students’ academic achievement was measured by their scores ranging from zero to 100 of an English final examination. The average score of the participants was 80.978. The maximum was 100, and the minimum was 24. The standard deviation was 11.819.

The ARCS Model’s four constructs (Attention, Relevance, Confidence, and Satisfaction) proposed by Keller (1987) were employed to design the survey questions for the Chinese students’ learning motivation, which includes 10 questions with scaled responses, for example “The course’s teaching style motivates me to actively learn.”, “This course is very interesting.”, “I think the content of this course is worth learning.”

In terms of the reliability analysis of the pre-testing scale, the Cronbach’s alpha was 0.931, which showed good reliability. Moreover, confirmatory factor analysis (CFA) was conducted to test the returned questionnaires. The factor loading for all questions in the survey recorded between 0.648 and 0.837. The construct reliability (CR) value of the scale was 0.932, exceeding the evaluative criteria of 0.60. The average variance extracted (AVE) value of the scale was 0.579, exceeding the evaluative criteria of 0.50 ( Fornell and Larcker, 1981 ). This indicates that the scale had a high level of construct validity and discrimination. As for the scale’s goodness of fit test, the results were as follows: SRMR = 0.048, χ 2 / df = 6.099, GFI = 0.899, AGFI = 0.841, PGFI = 0.572, NFI = 0.920, IFI = 0.932, CFI = 0.932, PNFI = 0.716, RMSEA = 0.112, which shows that the scale had a satisfactory goodness of fit.

The self-efficacy scale, comprising of 10 questions, proposed by Scholz et al. (2002) , was adopted for estimating self-efficacy. The research subjects were Chinese students; therefore, the questionnaire was translated into Mandarin by a translator. In order to verify the accuracy of the translation, the Mandarin version of the survey was then translated back into English by another translator. The reliability analysis shows that the Cronbach’s alpha was 0.891. In terms of CFA, the factor loadings of all questions recorded between 0.595 and 0.813, with a CR of 0.892 and an AVE of 0.457, which indicates that the reliability and credibility of the scale were still acceptable. The results were as follows: SRMR = 0.048, χ 2 / df = 4.797, GFI = 0.923, AGFI = 0.879, PGFI = 0.588, NFI = 0.906, IFI = 0.924, CFI = 0.924, PNFI = 0.705, RMSEA = 0.097, which shows that the scale had a satisfactory goodness of fit.

The Wong and Law Emotional Intelligence Scale consists of four dimensions including self-emotion appraisal (SEA), others’ emotional appraisal (OEA), use of emotion (UOE), and regulation of emotion (ROE) ( Wong and Law, 2002 ). This was the scale employed to design the survey questions. Each of the above mentioned aspects comprised of four questions and (16 questions in total). The questionnaire was also translated into Mandarin by a translator and translated back to verify accuracy. In terms of the reliability analysis of the scale, the Cronbach’s alpha was 0.929. In terms of CFA, the factor loadings of SEA recorded between 0.626 and 0.878, with a CR of 0.860 and an AVE of 0.610. The factor loadings of OEA recorded between 0.796 and 0.856, with a CR of 0.899 and an AVE of 0.691. The factor loadings of UOE recorded between 0.626 and 0.818, with a CR of 0.841 and an AVE of 0.573. The factor loadings of ROE recorded between 0.821 and 0.858, with a CR of 0.906 and an AVE of 0.707. The results were as follows: SRMR = 0.048, χ 2 / df = 3.046, GFI = 0.922, AGFI = 0.892, PGFI = 0.665, NFI = 0.937, IFI = 0.956, CFI = 0.956, PNFI = 0.781, RMSEA = 0.071.

In terms of research results, the data were tested first for serious common method variance (CMV), then for differential validity and correlation analysis, and finally for overall path model analysis.

Common Method Variance

This study used Harman’s single-factor test to examine the CMV ( Aulakh and Geneturk, 2000 ). The first part consisting of five factors extracted with the exploratory factor analysis (EFA) account for 43.051% of the total variance, which is less than 50%, indicating that the common method variance was not of great concern ( Aulakh and Geneturk, 2000 ; Podsakoff et al., 2003 ).

Next, the confirmatory factor analysis (CFA) was adopted to compare the single-factor and multi-factor models. The single-factor model constitutes a one-factor structure for all dimensions, whereas the multi-factor model has a fully correlated structure for the theoretical CFA. The single-factor and multi-factor models were compared to observe if any significant difference existed in their overall levels of goodness-of-fit, degrees of freedom, and chi-square values. A significant difference would indicate that the multi-factor model achieved a higher level of goodness-of-fit than the single-factor model, and that the single-factor structure was not present; therefore, the CMV was not serious ( Mossholder et al., 1998 ; Iverson and Maguire, 2000 ). As can be seen in Table 1 , the multi-factor model performed better than the single-factor model in all indicators for the overall level of goodness-of-fit (χ 2 /DF, GFI, AGFI, NFI, CFI, SRMR), and the comparison of the degrees of freedom and chi-squared values between the two models displayed significant differences (Δχ 2 = 2,441.377, ΔDF = 15, p = 0.000). On this basis, this study does not have serious common method variance.

Difference between single-factor model and multi-factor model.

Discriminant Validity and Relevant Analysis

Discriminant validity was assessed according to the Fornell-Lacker criterion ( Fornell and Cha, 1994 ). According to this criterion, if the square root of the AVE of each latent variable is greater than the correlation coefficients between that latent variable and other latent variables in the measurement model, then the model satisfies the discriminant validity criterion ( Hair et al., 2006 ).

The discriminant validity was assessed using Fornell and Larcker (1981) by comparing the square root of each AVE in the diagonal with the correlation coefficients (off-diagonal) for each construct in the relevant rows and columns. For the self-efficacy—EI construct and the self-efficacy—learning motivations construct, there are little disputes. However, the difference is too small, each with 0.053 and 0.028, respectively, and can be ignored ( Rahim and Magner, 1995 ; Hamid et al., 2017 ). Overall, discriminant validity can be accepted for this measurement model.

Table 2 shows that the mean values of self-efficacy, EI, learning motivation and academic achievement were 3.631, 3.604, 3.571, and 80.968, respectively. The mean values of self-efficacy, EI and learning motivations were between 3.5 and 4. The correlations of the variables all reached significance ( p < 0.001). These correlations led to further verification of the overall model in this study.

The AVE and correlation coefficients of all variables ( N = 404).

***p < 0.001.

a Square root of AVE (average variance extracted).

Path Analysis of the Overall Model

Firstly, a goodness of fit test of the overall model was performed. Secondly, the path analysis of the overall model related to EI, learning motivation, self-efficacy and academic achievement of the university students in Shanghai was implemented. As for the scale’s goodness of fit test, the three aspects suggested by Hair et al. (2006) were taken as a reference, namely “measures of absolute fit,” “incremental fit measures,” and “parsimonious fit measures.” The results were as follows. In terms of measures of absolute fit: χ 2 = 1,509.224, df = 621, χ 2 /df = 2.430, which was close to the requirement of χ2/df < 3. RMSEA was 0.060, which was acceptable as it was lower than 0.08. The results reveal that GFI was 0.826 and AGFI was 0.803, which met the criteria of 0.80 ( Doll et al., 1994 ). SRMR was 0.0747, which met the criteria of less than 0.08 ( Hu and Bentler, 1999 ). As for incremental fit measures, the CFI was 0.909, IFI was 0.910 and NNFI was 0.856, which met or was close to the criteria of 0.09. For parsimonious fit measures, the PNFI, PGFI, and PCFI were 0.798, 0.730, and 0.848, respectively, exceeding the criteria of 0.50 ( Ullman, 2001 ). This indicates the overall model exhibited goodness of fit.

As shown in Figure 1 and Table 3 , the path coefficients of the students’ EI related to their learning motivation and self-efficacy were 0.664 ( p < 0.05) and 0.328 ( p < 0.05), respectively, which indicates that the students’ EI had a significant positive effect on their learning motivation and self-efficacy.

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SEM path analysis. SEA, self-emotion appraisal; OEA, others’ emotional appraisal; UOE, use of emotion; ROE, regulation of emotion. *** p < 0.001.

Bootstrap SEM analysis of total, direct, and indirect effects.

However, the path coefficient of the students’ EI related to their academic achievement was -0.006 ( p > 0.05) ( Table 3 and Figure 1 ), which shows that the students’ EI did not have a positive effect on their academic achievement. This demonstrates that the higher the student’s EI, the higher their learning motivation ( Dubey, 2012 ; Henter, 2014 ) and self-efficacy ( Hamdy et al., 2014 ; Gharetepeh et al., 2015 ; Gurbuz et al., 2016 ). However, the levels of the students’ EI did not have an effect on their academic achievement, which does not correspond with various research studies and is worth noting ( Shamradloo, 2004 ; Márquez et al., 2006 ). Therefore, H 2 and H 4 are valid but H 1 is invalid.

Moreover, the mediation model was tested by using the bootstrapping method proposed by Shrout and Bolger (2002) . This model was used to test the accuracy of the estimated value of the mediation effect. The procedure involves resampling which results in the mean value and the 95% confidence interval of the mediation effect ( Preacher and Hayes, 2008 ). If the 95% confidence interval of the mediation effect does not include 0, it indicates that the mediation effect reaches the significance level of p < 0.05 ( Shrout and Bolger, 2002 ).

The indirect effect of the students’ learning motivation on their EI and self-efficacy was 0.368 (0.664 × 0.553), while the confidence interval [0.281, 0.474] did not include 0 and reached a significant effect ( p < 0.05), which indicates that learning motivation carried a mediation effect. In other words, the students’ self-efficacy could be increased by their EI through their learning motivation. Furthermore, the indirect effect of the students’ self-efficacy on their learning motivation and academic achievement was 0.154 (0.553 × 0.278), while the confidence interval [0.075, 0.241] did not include 0, which shows that self-efficacy carried a mediation effect. In other words, the students’ academic achievement could be improved by their learning motivation through their self-efficacy. Therefore, H 4 is valid.

The total indirect effect of learning motivation and self-efficacy between EI and academic achievement was 0.193 (0.664 × 0.553 × 0.278 + 0.328 × 0.278), while the confidence interval [0.093, 0.303] did not include 0, and the path coefficients were positive, as shown in Table 3 and Figure 1 . This shows that the students’ EI had an indirect effect on their learning achievement through self-efficacy. Furthermore, the students’ academic achievement could be enhanced by their EI through the process of their learning motivation and self-efficacy. Therefore, H 5 is valid.

However, the direct effect of EI on academic achievement was -0.006, while the confidence interval [-0.149, 0.131] included 0, and the total effect was 0.187, while the confidence interval [0.093, 0.287] did not include 0. This indicates that the students’ learning motivation and self-efficacy had a total mediation effect between their EI and academic achievement ( Table 3 and Figure 1 ). As a consequence, through the model verification, the EI of the students in Shanghai, who participated in online English courses, could improve their academic achievement through self-efficacy. Additionally, we found that the relation between emotional intelligence and academic achievement was sequentially mediated by learning motivation and self-efficacy.

The results indicated that the correlation between the EI of the university students in Shanghai and their academic achievement did not reach a significant effect in terms of statistics, which is different from this study’s hypothesis. Humphrey-Murto et al. (2014) also suggest that it appears EI cannot reliably forecast students’ future academic performance, and Zahed-Babelan and Moenikia (2010) found that EI in interpersonal relationships has a negative influence on student’s academic performance when engaged in distance learning. Independent learning is the major element of distance learning, as teachers and students are apart from one another. Consequently, students must be highly engaged in their studies ( Zahed-Babelan and Moenikia, 2010 ). Students who successfully accomplish their studies barely require their teachers’ supervision or encouragement ( Gros and López, 2016 ). In this research study, the students’ EI was measured by self-reporting tools, and their academic achievement was assessed by their scores of the final examination. Nonetheless, several researchers used abilities tests to assess EI and utilized GPA to measure academic achievement ( Márquez et al., 2006 ; Berenson et al., 2008 ). Moreover, there may be other variables involved in academic achievement such as learning motivation ( Ruchi, 2012 ; Henter, 2014 ) and self-efficacy ( Doménech-Betoret et al., 2017 ; Udayar et al., 2020 ), which have been proven to be greatly connected with EI.

In this research, the students’ EI had a positive effect on their learning motivation, which was consistent with Dubey’s (2012) and Henter’s (2014) work. Additionally, the students’ EI positively affected their self-efficacy, which was compatible with a substantial body of research ( Gharetepeh et al., 2015 ; Gurbuz et al., 2016 ; Ngui and Lay, 2020 ). These aforementioned studies were involved with physical classes. However, this investigation was based on online lessons. The results suggests that students’ EI assists in improving their learning motivation and self-efficacy. In other words, students with higher EI tend to have higher learning motivation and self-efficacy.

Mediation analysis indicated that the relation between emotional intelligence and academic achievement was sequentially mediated by learning motivation and self-efficacy. This study is based on Social Cognitive Theory and the social cognitive EV-M. In SCT, EI influences one’s self-efficacy and work outcomes ( Bandura, 1997 ; Gundlach et al., 2003 ). The social cognitive EV-M combines learning motivation, self-efficacy and academic achievement ( Doménech-Betoret et al., 2017 ). When the students were participating in the online courses, their self-efficacy had the mediation effect between their EI and academic achievement, which corresponds with the authors’ research ( Udayar et al., 2020 ) and SCT ( Bandura, 1997 ; Gundlach et al., 2003 ). Furthermore, the relation between emotional intelligence and academic achievement was sequentially mediated by learning motivation and self-efficacy. This shows despite the fact that students can not interact with their classmates and teachers face to face while involved in online English classes, they can still experience others’ emotions in the process of learning and produce their own emotions based on their understanding of the course, which in turn leads to appropriate reactions ( Choudary, 2010 ; Alhebaishi, 2019 ) and stimulates learning motivation ( Dubey, 2012 ). Additionally, students with high EI can obtain a higher degree of belief in self-efficacy by managing their own emotions ( Bandura, 1997 ; Gundlach et al., 2003 ). When students are motivated to learn, they become energized and engaged with their English courses ( Schunk and Meece, 2005 ; Jenkins and Demaray, 2015 ) and therefore their results improve ( Doménech-Betoret et al., 2017 ; Udayar et al., 2020 ).

Due to the impact of the COVID-19 pandemic, there have been a substantial number of schools running online courses. In this study, the EI of Chinese students, who took part in the online English lessons, did not influence their academic achievement.

Students’ EI does not directly affect their academic achievement; however, it directly and positively impacts their learning motivation and self-efficacy. Students, who have higher EI, tend to have higher learning motivation and can feel others’s emotions during online courses, which affects their self-efficacy and indirectly influences their academic achievement. As a consequence, it is still critical for them to properly manage and develop their EI. Schools, which implement online teaching, also need to pay attention to enhancing the development of students’ EI by arranging appropriate online lessons.

Teachers should attach importance to, and advance, students’ learning motivation and self-efficacy when utilizing online courses, since their EI can improve their English academic achievement through their learning motivation and self-efficacy. Thus, learning motivation and self-efficacy play a key role between EI and academic achievement. Researchers could include the concepts of learning motivation and self-efficacy when carrying out future studies on EI and academic achievement. There are still numerous schools conducting online teaching in the world due to the influence of the COVID-19 pandemic. Although the research subjects were specific Chinese students in Shanghai, the conclusion and recommendations of this study can still be a reference to other schools running online courses. These findings are beneficial for the exploration of the complex relation between emotional intelligence and academic achievement.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by Ethical Committee of Dhurakij Pundit University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

Y-CC: responsible for the conceptualization, investigation, methodology, and writing analyzing data for this manuscript. Y-TT: responsible for the suggesting revision to the concept and writing style of the manuscript. Both authors have read and agreed to the published version of the manuscript.

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.

Publisher’s Note

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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This article is part of the research topic.

Education for the Future: Learning and Teaching for Sustainable Development in Education

Blending Pedagogy: Equipping Student Teachers to Foster Transversal Competencies in Future-oriented Education Provisionally Accepted

  • 1 Department of Education, Faculty of Educational Sciences, University of Helsinki, Finland

The final, formatted version of the article will be published soon.

Blended teaching and learning, combining online and face-to-face instruction, and shared reflection are gaining in popularity worldwide and present evolving challenges in the field of teacher training and education. There is also a growing need to focus on transversal competencies such as critical thinking and collaboration. This study is positioned at the intersection of blended education and transversal competencies in the context of a blended ECEC teacher-training program (1000+) at the University of Helsinki. Blended education is a novel approach to training teachers, and there is a desire to explore how such an approach supports the acquisition of transversal competencies and whether the associated methods offer something essential for the development of teacher training. The aim is to explore what transversal competencies this teacher-training program supports for future teachers, and how students reflect on their learning experiences. The data consist of documents from teacher-education curricula and essays from the students on the 1000+ program. They were content-analyzed from a scoping perspective. Students' experiences of studying enhanced the achievement of generic goals in teacher education, such as to develop critical and reflective thinking, interaction competence, collaboration skills, and independent and collective expertise. We highlight the importance of teacher development in preparing for education in the future during the teacher training. Emphasizing professional development, we challenge the conventional teaching paradigm by introducing a holistic approach.

Keywords: blended teacher training, Transversal competencies, future of education, Teacher Education, early childhood education

Received: 19 Jan 2024; Accepted: 15 May 2024.

Copyright: © 2024 Niemi, Kangas and Köngäs. 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) or licensor 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: Dr. Laura H. Niemi, Department of Education, Faculty of Educational Sciences, University of Helsinki, Helsinki, 00014, Uusimaa, Finland

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