Predictors and mediators of pressure/tension in university students’ distance learning during the Covid-19 pandemic: A self-determination theory perspective

ABSTRACT

Due to the global restrictions to decrease the risk of infection in classrooms, the transition from face-to-face education to distance learning was a necessity during the Covid-19 pandemic. Grounded in Self-Determination Theory, the present research sought to explore how the pandemic affects university students during distance learning. Specifically, the study examined the predictors of pressure/tension and attempted to identify the unique and mediator roles of correlates of pressure/tension of university students. This cross-sectional study was conducted with 432 university students from different departments of different universities in Turkey. The online survey was administered between the last week of October and the second week of December 2020. Our findings revealed that there is a positive association between pressure/tension and Covid-specific worry. Also, there is a negative association between learning climate and pressure/tension and between perceived competence and pressure/tension. Further, learning climate mediated the link between Covid-specific worry and pressure/tension. The data of the present study depends on students’ academic (learning climate) and also non-academic (Covid worry) experiences during the pandemic. Methodological limitations concerning the research design are discussed.

KEYWORDS:

• Covid-19
• distance learning
• self-determination theory
• pressure/tension
• Covid-specific worry
• learning climate

According to the World Health Organization (WHO) weekly report of December 2020, the total number of new cases of Covid-19 was over 4 million, and the number of recent deaths was 72,000 globally (World Health Organization, 2021). As these numbers imply, the pandemic had been continuing to disrupt daily life, along with its massive impact on socialising, educational, cultural, and economic aspects (Dwivedi et al., 2020). To control the spread of the virus, governments implemented quarantine practices and restrictions such as prolonged periods of lockdowns intending to cease human contact outside (Bourouiba, 2020). These restrictions caused a new normal reality, which necessitated a fundamental change in the way people communicate and function (Griffin & Denholm, 2020). One of the most affected sectors by the pandemic is education, as it has undergone a substantial digital transition (Dwivedi et al., 2020). Briefly, the current study aimed to explore how the global pandemic has been associated with distance learning from the self-determination theory perspective.

As the pandemic forced the global closure of university campuses, institutions switched to distance learning by transforming the existing educational processes to develop more productive learning environments (Toquero, 2020). Internet technologies have been used as a medium for distance learning for years, as some institutions already have distance learning programs or online courses (Lang & Zhao, 2000). However, the global pandemic highlighted the importance of distance learning and forced institutions to transform all face-to-face teaching. This compulsory situation may have influenced students’ perception of the learning climate differently. The pressure/tension of the students may also be affected by this enforced transition to distance learning. Therefore, it is necessary to explore the impacts of the Covid-19 pandemic on distance learning.

Covid-specific worry and distance learning challenges of university students

The closure of university campuses and the transition to distance learning have created profound changes in university students’ social and family lives. Following this, some psychological problems, such as anxiety (e.g. Saddik et al., 2020; Wang & Zhao, 2020), stress (e.g. Peixoto et al., 2021), fear (e.g. Fawaz et al., 2021), and depression (Asanov et al., 2021) have been observed in students across the globe. Besides, students have to face the concerns that the pandemic has triggered, such as personal health and health of loved ones (e.g. Saddik et al., 2020; Wang et al., 2020), financial concerns (Sundarasen et al., 2020), concerns over food supply (Baloran, 2020), lifestyle concerns (Wang et al., 2020), and an unstable situation (Sundarasen et al., 2020) during the precipitous transition period. One can argue that since the student population is young and has fewer responsibilities as compared to the adult population (Brooks et al., 2020), some of the aforementioned concerns may not be relevant to them. However, most of the students had to live with their family members due to the closure of the campuses. Being in a family home may make these concerns relevant for them.

According to UNESCO’s report of April 2020, approximately 1.6 million students from 188 countries at all levels of education had been affected by the global pandemic due to rapid and radical changes in the learning style (Dietrich et al., 2020; UNESCO, 2020). According to the results of a study that analysed the strategies of 20 universities regarding distance learning during Covid-19, while some countries made the transition successfully, others had some problems in terms of available resources and cohort of students. In addition, access for students to online content from remote and restricted locations was one of the main challenges faced in this period (Crawford et al., 2020). Findings of recent research conducted with undergraduate and postgraduate students provided evidence that distance learning is not a desirable option for students who are deprived of high-speed, affordable, and reliable internet services and struggle with technology (Asanov et al., 2021). Unsurprisingly, the sudden transition to distance learning may increase the pressure/tension on the students.

It has been demonstrated that students had difficulty adapting to distance learning (Wang et al., 2020). A recent study comparing students’ current and retrospective appraisals regarding engagement in online classes demonstrated that engagement of students decreased in the pandemic period as compared to the pre-pandemic period (Daniels et al., 2021). The challenges of the pandemic also led students to procrastinate in their academic activities, especially those they do not like to perform (Peixoto et al., 2021). Besides, course requirements such as the completion of group projects and lab work also created extra tension and pressure on the students. As evidenced in recent research, a large number of students found participation in group projects challenging because face-to-face meetings were not possible with their classmates. Moreover, according to the students, schools’ efforts to sustain traditional lab work were more time-consuming and frustrating. Assessments may also be problematic for both students and instructors (Dietrich et al., 2020). Yet another study conducted with university students demonstrated that the majority of the participants were concerned about academic performance and about half of them also felt pressured in terms of class workload (Son et al., 2020). As a result, students were likely to perceive the situation in the courses as unstable.

Moreover, students have had to keep up with all these compulsory changes and adaptation processes suddenly and over a short period. To our knowledge, there is no empirical research investigating the role of Covid-specific worry on distance learning. The present study aimed to examine the role of Covid-specific worry on pressure/tension that students have been experiencing. To date, there is no certain pattern between the two. This study contributes in terms of exploring the predictors of pressure/tension and capturing the unique and mediator roles of correlates of pressure/tension. To test these associations, the propositions of self-determination theory (SDT; Deci & Ryan, 1985; Ryan & Deci, 2017) were used in the current study. The theory has been utilised within a wide range of applied fields, and education is one of the most studied ones, so the utilisation of SDT is highly relevant in this context.

Self-determination theory

SDT is a theory of human motivation, and personality development, behavioural self-regulation, and well-being are the primary research topics of the theory (Ryan et al., 1997). The theory focuses on autonomous behaviour and the surrounding social environment which influences this behaviour. According to SDT, social contexts either satisfy or frustrate well-being, vitality, and growth (Deci & Ryan, 2000). The theory has been utilised in various life issues within a wide range of applied fields, such as sport, business, health care, and education. In the present study, the propositions of SDT, learning climate, perceived competence, and reasons for learning were utilised to examine their roles on the pressure/tension of the university students.

Pressure/tension and learning climate

In traditional college education, a one-way transfer of information is observed frequently. Previous research has shown that supporting active student learning improves college-level education (e.g. Black & Deci, 2000; Williams & Deci, 1996). The activities improving active student learning include encouraging group problem solving, peer support, and active participation in course materials. In this way, instructors adopt a student-centred instead of a teacher-centred approach (Black & Deci, 2000). Students feel that their interests, values, and preferences are supported by instructors. A controlling instructor, on the other hand, can thwart the autonomy of the students in a variety of ways, such as using a controlling language, affecting the way students think, and offering extrinsic incentives (Reeve & Jang, 2006). As a result, students desire less challenge and do not perceive the school as engaging due to the controlling practices of the instructors (Ryan & Lynch, 2003).

Education is a sector in which external regulations are routinely implemented with the expectation that such contingencies foster students’ learning (Niemiec & Ryan, 2009). Experiencing a controlling environment compared to a non-controlling environment puts pressure and tension on individuals (Benita et al., 2014; Ryan et al., 1983). The unpredictable and highly controlling progress of the pandemic and the obligatory transition to distance learning may be sources of pressure/tension for the students. This controlling situation may also influence the perception of students regarding the learning climate during online classes. As such, fear and anxiety about the spread of the virus and new governmental rules related to social and educational life may occupy the students’ minds during online classes. Although not directly, experiencing the pressuring and controlling circumstances of the pandemic may make students transmit their worries to the learning environment. Regarding how the pandemic interferes with learning climate, the spillover hypothesis can shed light on the mechanism underlying the relationship between learning climate and Covid-specific worry. According to the spillover hypothesis, affect or behaviour within one domain repeats itself in another, leading to similar or shared responses in the two domains (Edwards & Rothbard, 2000). More specifically, negative affect in one domain is associated with negative affect in another domain (Nelson et al., 2009). For example, stress at work is transferred at home (Guest, 2002). In a similar vein, Covid-specific worry may be transferred to the learning climate, which may be perceived as pressuring by the students.

Besides, the difficulty of adapting to the rapid transition and subsequent uncertainty about the new learning climate and course demands might also result in heavy pressure. As a result, students may perceive the learning climate negatively, which increases their perceived pressure/tension. In this framework, Covid-specific worry may be indirectly associated with the students’ pressure/tension in online courses through the learning climate. Based on this information, it is expected that the learning climate will have a mediating role in the link between Covid-specific worry and pressure/tension during online courses.

Pressure/tension and perceived competence

The other factor which might affect the pressure/tension of the students is perceived competence. Literature has demonstrated that the competence or efficacy beliefs of the students foster their interest and enjoyment of an activity (Bandura, 1994). Not surprisingly, perceived competence was found to be positively associated with pressure/tension in past research, and it was suggested that participants with high perceived competence might feel more relaxed (Li et al., 2005). Although the pandemic has constituted an external stressor in the environment, students with a sense of confidence in their competence may feel this pressure/tension to a lower degree during distance learning. Thus, a high level of perceived competence may decrease the pressure/tension. Based on this, it was expected that perceived competence will predict pressure/tension negatively.

Pressure/tension and reasons for learning

Reasons for learning concern the ‘whys’ of individuals while learning in particular settings, such as a university. SDT claims that motivated actions differ depending on whether they are autonomous or controlled. Autonomous behaviours are initiated and regulated through volition and performed for their inherent value to the individuals (Deci & Ryan, 1985). As Guay et al. (2008) suggested, autonomous motivation is associated with positive outcomes, such as better learning performance, the experience of positive emotions, and higher grades at school. In contrast, controlled behaviours have external reasons or internal pressures to perform (Deci & Ryan, 1987). For instance, striving to obtain high grades in a particular course for ego involvement can be considered a controlled behaviour originating from internal pressure.

As self-determined or autonomous behaviours provide individuals with a sense of choice about their behaviours, they are marked by a lack of pressure and tension (Deci & Ryan, 1987). Therefore, if the reasons for learning for a particular course are autonomous, it is likely that the students will experience less pressure and tension in that course. The pressure and tension that originated from the global pandemic and obligatory distance learning constitute external pressures for the students, as already mentioned. Similar to perceived competence, autonomous reasons for learning may decrease the negative effects of Covid-specific worry and enforced distance learning. Thus, depending on the preceding information, the following hypothesis was formulated: Autonomous reasons for learning will be negatively predictive of pressure/tension.

The present study

As noted earlier, students had to adapt quickly to distance learning tools and new course demands in a highly controlling environment. Mainly, the present study aimed to identify the predictors of students’ pressure/tension during distance learning. This study pursued two objectives. First, the relative contributions of the Covid-specific worry, learning climate, perceived competence, and reasons for learning were examined in predicting the pressure/tension of the university students. The second objective was to test the mediating role of the learning climate on the association between Covid-specific worry and pressure/tension. Three hypotheses were formulated:

H1:

The learning climate will mediate the link between Covid-specific worry and pressure/tension during online courses

H2:

Perceived competence will predict pressure/tension negatively.

H3:

Autonomous reasons for learning will predict pressure/tension negatively.

Method

Sample and procedure

Our survey was completed during a period when universities were closed and a nationwide shutdown (with exceptions for certain types of jobs) was in effect. The study was reviewed and approved by the Middle East Technical University’s Research Ethics Committee. Participants were provided with information about the study, and informed consent was obtained from the participants who agreed to participate. The study was conducted with 432 university students. More than half of them were female (79%), and the average age was 21.28 (SD = 2.69). The majority of the participants were undergraduate students (96%), mainly from engineering and social sciences faculties. At the time of data collection, all universities were in distance learning mode. All participants completed the questionnaire for extra course credits.

Between October and December 2020, an online survey was launched to assess the pressure/tension of the students. An anonymised survey package prepared in Qualtrics was distributed via several channels to three different universities, including the SONA system,¹ announcements in psychology courses, and social media. Since the Learning Climate Questionnaire includes questions about the course instructor, and other scales include questions about the course activities, the timing of the survey was critical. Students do not have sufficient information about the instructor and the course demands at the beginning of the semester. Besides, concerns about grades can influence the students’ answers to the questionnaires at the end of the semester. Therefore, the survey was administered between the last week of October and the second week of December 2020, from the middle of the semester to the final exam period.

Measures

The learning climate questionnaire (LCQ)

The LCQ (Williams & Deci, 1996) measures the degree of autonomy support provided by the instructor in a specific learning environment, including a particular class. Participants rated the items on a 1 (strongly disagree) to 7 (strongly agree) scale. Some example items from the measure: ‘I feel understood by my instructor’, ‘I feel that my instructor accepts me’. Four experts translated the original scale, one of whom specialised in translation and interpretation. After the translation process was completed, the researchers decided on the latest version of the scale for adaptation. Before the main data collection, 20 students completed the translated scale to check the language compatibility, comprehensibility, and ambiguity of the items. All of them had reported that the whole scale was clear for them. In the last step, the main study was conducted for reliability and validity. According to the exploratory factor analysis (EFA), the Turkish version of the scale has a single factor, as in the original scale. This factor accounted for nearly 64% of the variance with an eigenvalue of 9.56. The alpha coefficient of the translated scale was .96 in the current study. Confirmatory factor analysis (CFA) also verified the single-factor structure.

Perceived competence for learning scale (PCS)

PCS is a 4–item questionnaire with specific items measuring the relevant behaviour. It is considered one of the most face-valid scales of SDT. The PCS measures participants’ competence feelings regarding a particular college course. Responses are rated on a 1 (not at all true) to 7 (very true) scale. Some example items from the scale: ‘I feel confident in my ability to learn this material’; ‘I am able to achieve my goals in this course’. The scale was also translated and adapted into Turkish with the same procedures used for the previous questionnaire. EFA showed that the Turkish version of the scale has a single factor, as in the original scale. This factor accounted for nearly 76% of the variance with an eigenvalue of 3.10. The alpha coefficient of the translated scale was .90 in the present study. CFA also verified the single-factor structure.

Covid-specific worry (feelings of uncertainty and threat)

This scale was adapted from Chen et al. (2015) to measure Covid-specific worry in a large-scale SDT-based study. It is a 10-item measure with five subfactors, and responses are rated on a 1 (not at all true) to 7 (very true) scale considering the last week. Some example items from the scale: ‘I was concerned about my health’ (personal health); ‘I had the feeling that my daily routines were threatened’ (unstable situation). The scale was also translated and adapted into Turkish with the same procedures used for the previous questionnaires. EFA showed that the Turkish version of the scale has three factors, unlike the original scale. The alpha coefficient of the translated scale was .86 in the present study. CFA did not verify either single or three-factor structure. Since the original five-factor model showed a better fit than the single and three-factor structure, it was preferred in the current study.

Learning self-regulation questionnaire (SRQ-L)

SRQ-L (Black & Deci, 2000) measures people’s reasons for learning in a particular setting, such as a university. It is a 12-item measure with two subscales: autonomous regulation and controlled regulation. Responses are rated on a 1 (not at all true) to 7 (very true) scale. Two subscales can be used separately, or a Relative Autonomy Index can be calculated by subtracting the controlled subscale from the autonomous subscale. The same procedures used for the previous measures were applied for the translation and adaption of this scale into Turkish. In the present study, only the autonomous regulation subscale was used as the controlled subscale was not validated (see the results section for more details about the CFA results). Briefly, EFA showed that the autonomous regulation subscale emerged as a single factor as in the original scale and accounted for 52% of the variance with an eigenvalue of 2.64. The alpha coefficient of the translated autonomous subscale scale was .77 in the present study.

Intrinsic motivation inventory (IMI)

IMI includes seven subscales with 45 items in total. Responses are coded on a 1 (not at all true) to 7 (very true) scale. In general, researchers choose and use relevant subscales in their studies. Interest/enjoyment, perceived competence, pressure/tension, and perceived choice are some of the subscales. The items of the IMI have often been modified slightly to fit them in a particular context. Some example items from the scale: ‘I enjoyed doing this activity very much’ (interest/enjoyment); ‘I put a lot of effort into this’ (effort/importance), and ‘I did not feel nervous at all while doing this’ (pressure/tension). The pressure/tension sub-scale, which has five items, was used to measure the pressure/tension of the students in online classes. The pressure/tension subscale contains two reverse items, and higher scores indicate high pressure/tension. This subscale was also translated and adapted into Turkish with the same procedures used for the previous questionnaires. EFA showed that the Turkish version of the scale has a single factor as the original scale. This factor accounted for nearly 36% of the variance with an eigenvalue of 3.63. The alpha coefficient of the translated scale was .90 in the present study. CFA also verified the single-factor structure.

Data analysis

First, exploratory and confirmatory factor analyses were performed to test the validity of the measures. Further, we ran a series of hierarchical regression analyses to determine the predictors of the pressure on the students. Last, we used Process Macro Model 4 (Hayes, 2017) to test the mediating role of learning climate between Covid worry and pressure.

Results

Confirmatory factor analyses

Several CFAs were performed to test the construct validity of the scales with AMOS Version 21 (Arbuckle, 2012). First, to test the fit of a one-factor model of the Learning Climate Questionnaire to the current data, CFA with maximum likelihood estimation was performed. Results showed that the χ² statistic was significant, χ² (90) = 366.48, p < .001 (see Table 1 for details). To increase the model fit, the covariances between the errors of Item 1 and Item 2 and between Item 9 and Item 10 were added, respectively. We let two pairs of errors covariate since all items in pairs share a conceptual similarity. In the final model, one-factor structure of the scale fits the data well (χ² (88) = 303.39, p < .001, GFI = .90, RMSEA = .08, and CFI = .96).

Table 1. Fit indices of the CFA of the learning climate questionnaire.

Model	χ^2	df	GFI	RMSEA	CFI
Null	366.47	90	.89	.09	.94
Model 1	332.84	89	.89	.08	.95
Model 2	303.39	88	.90	.08	.96

Second, Perceived Competence for Learning Scale was examined to ascertain the one-factor structure with CFA. Results showed that the χ² statistic was significant, χ² (2) = 51.32, p < .001 (see Table 2 for details). To increase the model fit, the covariance between the errors of Item 3 and Item 4 was added since the items in this pair are similar. In the final model, one-factor structure of the scale fits the data well (χ² (1) = 1.84, p = .18, GFI = .99, RMSEA = .05, and CFI = .99) (see Table 2).

Table 2. Fit indices of the CFA of the perceived competence for the learning scale.

Model	χ^2	df	GFI	RMSEA	CFI
Null	102.62	2	.88	.35	.91
Model 1	1.84	1	.99	.05	.99

Third, the Feeling of Uncertainty and Threat Scale were submitted to factor analysis to determine the five-factor structure. Results showed that the χ² statistic was significant, χ² (25) = 149.97, p < .001 (see Table 2 for details). To increase the model fit, modification indices were examined, but there were no suitable modifications to be made between the errors of the same factor, so a one-factor solution was tried. However, the five-factor model showed a better fit than the one-factor model (χ² (35) = 689.74, p < .001, so the five-factor model was retained (see Table 3).

Table 3. Fit indices of the CFA of the feelings of uncertainty and threat.

Model	χ^2	df	GFI	RMSEA	CFI
Null	149.97	25	.92	.11	.93
Model 1	689.74	35	.77	.21	.65

The Learning for Self-Regulation Questionnaire was also submitted to CFA to determine the fit of the two-factor structure of the model to the current data. Results showed that the χ² statistic was significant, χ² (53) = 443.05, p < .001 (see Table 4 for details). This version of the model did not fit the data well, and the modifications were not theoretically meaningful. Thus, to increase model fit, the controlled regulation subscale was deleted, and the one-factor structure with autonomous regulation showed a better fit to data, χ² (5) = 66.86, p < .001. To further increase the model fit, two modifications were done by adding covariances between the errors of Item 1 and Item 8 and between Item 9 and Item 10, respectively. We let two pairs of errors covariate since the items in pairs are similar. In the final model, one-factor structure of the scale fits the data well (χ² (3) = 9.91, p < .01, GFI = .99, RMSEA = .07, and CFI = .99).

Table 4. Fit indices of the CFA of the learning self-regulation questionnaire (autonomous regulation).

Model	χ^2	df	GFI	RMSEA	CFI
Null	443.05	53	.85	.13	.69
Model 1	66.86	5	.94	.17	.90
Model 2	27.72	4	.97	.12	.96
Model 3	9.91	3	.99	.07	.99

Lastly, the pressure/tension subscale of the Intrinsic Motivation Inventory was submitted to CFA to determine the fit of the single-factor structure of the model to the current data. Results showed that the χ² statistic was significant, χ² (5) = 63.37, p < .001 (see Table 5 for details). According to modification indices, the covariances between the errors of Item 6 and Item 8 were added since these are similar items. This modification increased the model further and the one-factor structure of the scale fits the data well, χ² (4) = 10.44, p < .01, GFI = .99, RMSEA = .06, and CFI = .99 (see Table 5).

Table 5. Fit indices of the CFA of the pressure/tension.

Model	χ^2	df	GFI	RMSEA	CFI
Null	63.37	5	.94	.17	.95
Model 1	10.44	4	.99	.06	.99

Overall, CFAs showed that the psychometric properties of the scales in terms of reliability and validity are adequate except for the Feelings of Uncertainty and Threat scale, which has a lower RMSEA value (.11) than the excepted value (.08) (Byrne, 2010; Wuensch, 2008). Since this model fitted better than the one-factor structure model, and there was no theoretically meaningful modification, the five-factor model was retained and used in the subsequent analyses.

Hypothesis testing

Descriptive statistics and correlations between the major variables are displayed in Table 6. Pressure/Tension was significantly and negatively correlated with all variables, except for Covid-specific worry, gender, and age. The correlation between pressure/tension and Covid-specific worry was significant and positive. Since age did not correlate with any variable significantly, it was not used in further analyses.

Table 6. Correlations between study variables and descriptive statistics (Gender: 1 = female, 2 = male).

Variables	1	2	3	4	5	6	7
(1) Gender	-
(2) Age	.05	-
(3) Learning climate	−.14**	.04	-
(4) Perceived competence	.08	−.05	.27**	-
(5) Covid-specific Worry	−.01	.02	−.24**	−.02	-
(6) A. Reasons for learning	−.10*	.02	.51**	.25**	−.01	-
(7) Pressure/Tension	.01	−.02	−.58**	−.41**	.24**	−.31**	-
M	1.22	21.28	4.81	5.17	4.15	5.28	4.01
SD	.43	2.69	1.34	1.20	1.35	1.56	1.55
α			.96	.90	.88	.77	.90

Note. *p < .01, **p < .001, A = Autonomous.

To test the hypothesis stating that the learning climate will have a mediating effect on the link between Covid-specific worry and pressure/tension (H1), Process Macro Model 4 (Hayes, 2017) for SPSS, which employs the bootstrapping method to calculate the indirect effects, was used to test the mediating role of learning climate. Before the analysis, all variables were standardised to obtain β coefficients. Gender and perceived competence were also controlled. The significance of the model was tested with bootstrapped 95% confidence intervals (CI) for the standardised indirect effects (ab) constructed with 5,000 resamples and a percentile distribution.

Results showed that Covid-specific worry negatively predicted learning climate (β = −.24, SE = .05, p < .001, 95% CI [−.33, −.15]) and positively predicted pressure/tension (β = .27, SE = .05, p < .001, 95% CI [.17, .36]). Learning climate also had a direct negative effect on pressure/tension (β = −.54, SE = .05, p < .001, 95% CI [−.63, −.44]), after controlling for Covid-specific worry. Further mediation analysis based on the bootstrapping method showed that the hypothesis stating that the effect of Covid-specific worry on pressure/tension is mediated by learning climate (H1) was supported (β = .11, SE = .02, 95% CI [.07, .16]) (see Figure 1).

Figure 1. Results of the mediation model of Covid-worry, learning climate, and pressure/tension of the students controlling for gender and perceived competence (all coefficients were standardised).

To examine the unique roles of variables on the pressure/tension, regression analyses were performed (see Table 7). In all analyses, the effect of gender was controlled. Covid-specific worry (β = .12, SE = .04, p < .01, 95% CI = [.05, .22]), learning climate (β = −.49, SE = .05, p < .01, 95% CI = [−.66, −.45]), and perceived competence (β = −.30, SE = .05, p < .01, 95% CI = [−.48, −.28]) were significant predictors of pressure/tension. Autonomous reasons for learning, on the other hand, did not predict pressure/tension, contrary to expectations (β = .03, SE = .06, p = .81, 95% CI = [−.07, .16]). Results showed that the hypothesis stating that perceived competence will predict pressure/tension negatively (H2) was supported. The other hypothesis stating that autonomous reasons for learning will be positively predictive of pressure/tension (H3) was not supported.

Table 7. Summary of hierarchical regression analysis in predicting pressure/tension.

Predictors	B	β	SE	95% CI
Step 1
Constant	4.05		.23	[3.61, 4.50]
Gender	−.01	−.01	.18	[−.35, .35]
Step 2
Constant	8.04		.43	[7.20, 8.88]
Gender	−.12	−.03	.14	[−.39, .15]
Covid worry	.14	.12	.04	[.05, .22]
Learning	.04	.03	.06	[−.07, .16]
Climate	−.55	−.49	.05	[−.66, −.45]
Competence	−.38	−.30	.05	[−.48, −.28]
R^2					.43
F					77.14

Note. Learning: Autonomous Reasons for Learning.

Discussion

The current study sought to explore the predictors of university students’ pressure/tension while taking online courses during the Covid-19 pandemic. The obtained findings were mostly in line with the expectations. As hypothesised, learning climate mediated the link between Covid-specific worry and pressure/tension. Regarding the unique effects, there is a significant positive association between pressure/tension and Covid-specific worry. Also, there is a significant negative association between learning climate and pressure/tension and between perceived competence and pressure/tension. Moreover, as the results of CFAs showed, the scales that had been adapted for the current study were also satisfactory in terms of psychometric properties.

Mediating role of learning climate on the link between Covid-specific worry and pressure/tension

Since there is little known about the mechanism between the effects of Covid-specific worry and pressure/tension for students during online courses, the association between these two was one of the main interests of the present study and investigated through a mediator. As expected, learning climate mediated the relationship between Covid-specific worry and pressure/tension. More specifically, the findings of the current study suggested that Covid-specific worry predicts the learning climate negatively, which in turn increases pressure/tension felt by students (H1). The spillover hypothesis was confirmed by this finding. Our results suggest that students transmitted Covid-specific worry to the learning environment and perceived the learning climate as pressuring. More specifically, students experiencing pressure from the pandemic and obligatory transition to distance learning may continue to feel strain in online courses. The reflections of the Covid-19 pandemic on higher education were presented with this finding. As highlighted by Daniels et al. (2021), the challenges imposed by the pandemic changed the learning environment and influenced the students’ outcomes profoundly.

The current study also suggested that learning environments can be sensitive to external stressors outside of the classroom. These external stressors can influence the learning environments both directly and indirectly, as the spillover hypothesis suggested. Several studies demonstrated that the Covid-19 pandemic had increased the psychological distress of university students due to new challenges (e.g. Crawford et al., 2020; Daniels et al., 2021; Sundarasen et al., 2020). The present study not only supported and replicated these findings but also demonstrated the indirect effect of the Covid-19 pandemic on the pressure/tension of the students through the distance learning climate. The way the Covid-19 pandemic is associated with the perceived pressure of the students in online courses was clarified to some degree. To the best of our knowledge, this is the first study testing these relationships.

Predictors of pressure/tension

Consistent with the theoretical prediction, participants who perceived the learning climate as controlling were more likely to feel high pressure/tension in the present study. This finding is in line with recent research demonstrating that autonomy support enhances positive learning outcomes (Chiu, 2021) and psychological needs should be supported to decrease tension in distance learning (Han, 2022). Furthermore, previous studies showed that instructors have also experienced a controlling climate due to this obligatory transition and unstable situation (e.g. Dietrich et al., 2020). Although speculative, they may transmit their concerns to the learning environments, which in turn, have created distress. As a result, instructors may behave in a controlling manner to ensure the students meet course requirements. As shown, instructors who set up controlling learning climates expect students to adopt certain behaviours and communication styles. Specifically, deadline statements and controlling questions pressure students to obey the instructors’ perspective (Reeve & Jang, 2006). Moreover, recent research depending on students’ reports showed that distance learning had restricted students’ talking, asking questions about course-related topics, and natural interaction occurring during face-to-face courses between students and instructors (Alawamleh et al., 2020; Dietrich et al., 2020; Fawaz et al., 2021). Not surprisingly, the aforementioned factors are likely to cause students to perceive the distance learning climate as controlling and feel pressured during online courses.

Perceived competence predicted pressure/tension negatively, as expected (H2). Specifically, students who perceive themselves as competent in a particular class were less likely to feel pressure/tension. Past research showed that students feel competent when they meet schoolwork demands (Niemiec & Ryan, 2009). Recent research also showed that supporting competence need, one of the needs among basic psychological needs (Ryan & Deci, 2017), is associated with positive learning outcomes (Shah et al., 2021). The current study suggests that perceived competence should be increased by designing optimally challenging tasks to buffer the distressing effects of the pandemic. However, autonomous reasons for learning did not predict the pressure of the students (H3). It seems that the perceived learning climate and competence have more prominent roles on the perceived pressure. This finding suggests that students may not have enough space to select a particular way of learning during online courses because of the restrictive circumstances.

As expected, Covid-specific worry positively predicted pressure/tension of students during online courses, which indirectly supports a recent study exploring Covid-19 stressors among university students (Fawaz et al., 2021). In this study, fear of becoming infected and risking family health were some of the stressors observed in the students during the quarantine period, which was also a source of Covid-specific worry in the present study. Although not directly, experiencing such a fear of health may prevent students from feeling relaxed and calm during online courses. Furthermore, another study showed that distance learning and uncertainty about academic performance were among the main stressors for university students (Sundarasen et al., 2020). As the current study suggested, when all these stressors are combined, students are likely to feel pressured during online courses.

Contributions

Understanding the associations among Covid-specific worry, learning climate, and perceived competence is critical if minimum pressure/tension is to be designed for students. By integrating the SDT perspective, the findings of this study provide some insight into how these factors are associated with the pressure/tension of the students, especially during these types of obligatory transitions and circumstances, which create a considerable amount of stress. The findings of the current study show that the global pandemic and the enforced transition to distance learning created a considerable amount of uncertainty and pressure for the students in terms of the progress of educational activities.

The data of the present study relies on students’ academic (learning climate) and non-academic (Covid-specific worry) experiences during the Covid-19 pandemic. Although it can be argued that every student may have a unique experience during this period, the findings suggest the pandemic created a common experience: perceiving the learning climate negatively and feeling pressure/tension. The present study provides an insight into the experiences of the students.

Furthermore, the findings of this study support the development of screening strategies for students experiencing distress due to the challenges of the global pandemic. Effective intervention strategies can be developed to decrease the negative psychological influences of the pandemic and increase the psychological resilience of university students in the next step. Particularly, the current study also shows that instructors influence the learning climate profoundly. Intervention programmes specifically targeting instructors can be helpful in that respect. Corroborating the previous research (Han, 2022), the present research indicates that teacher influence is crucial in supporting positive learning outcomes in distance learning. Thus, instructors can be trained to effectively design an autonomous learning environment and support perceived competence with optimally challenging tasks to reduce the students’ distress. Lastly, the findings of the present study also suggest that authorities should support students’ self-determined learning goals and address their psychological needs to decrease their pressure in times of crisis.

Limitations and suggestions for future research

This study has some limitations that should be mentioned. Firstly, it has a correlational design, and because of the time constraints and uncertain progress of the situation, we collected data within a certain period. However, the level of pressure/tension of the students has likely changed from the beginning of the pandemic, as already suggested (Sundarasen et al., 2020). For instance, it was much higher at the beginning and decreased over time. To capture these changes depending on the pandemic’s progress, employing longitudinal designs can be more beneficial to lend further confidence in making causal inferences. Another limitation is that we did not collect data directly addressing the distance learning challenges that students had been experiencing during the pandemic. However, the unstable situation, a subscale of the Covid-specific worry (Feelings of Uncertainty and Threat Scale), may correspond to experiencing the evolving crisis in higher education to some degree. Moreover, although the survey instructions stated that students should think about a specific course during the entire survey, we cannot be sure whether they considered the same course until the end of the survey. Lastly, we collected data only from students, but instructors were also experiencing difficulties in terms of usage and adaptation of the distance learning tools, as previous research has shown (e.g. Alawamleh et al., 2020; Dietrich et al., 2020). Future studies can examine the mutual influence between the instructors and students in these kinds of obligatory transitions for a better understanding.

Conclusion

The Covid-19 pandemic has influenced many populations, including university students across the globe. It was indicated that there is a need in the education sector for urgent and comprehensive policies to recognise and manage the psychological influences of the pandemic (Sundarasen et al., 2020) since its influence can be long-lasting (Chiu et al., 2021). Specifically, the results of the current study demonstrated that the pandemic is associated with the pressure/tension of the students through its role on the learning climate. Moreover, perceived competence predicted pressure/tension negatively, indicating that high perceived competence can play a buffering role. These findings can be applied at the government and university levels to establish effective learning environments in times of unplanned and urgent situations to foster the perception of competence and learning environments. All in all, there are a number of parties (e.g. government, teachers) who are responsible for developing effective pedagogical strategies in distance learning. As recent research has suggested (Shah et al., 2021), a transitional period is necessary for the adaptation of students to a new learning environment. In this way, the students’ distress can be reduced, learning experiences can be enhanced, and psychological resilience can be built.

Compliance with ethical standards

All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

The authors did not receive support from any organisation for the submitted work.

Notes on contributors

Elif Manuoğlu

Elif Manuoğlu (Ph.D. in Social Psychology, Middle East Technical University, Turkey) is currently a Research Fellow in the Department of Psychology (Work and Organizational Psychology) at Palacky University Olomouc, Czech Republic. She studies psychological well-being from the Self-Determination Theory perspective. Specifically, she focuses on student motivation and well-being with a focus on autonomous learning climates and need satisfaction and frustration.

Elis Güngör

Elis Güngör (Ph.D. in Social Psychology, Middle East Technical University, Turkey) works as an instructor in the Department of Psychology at Atılım University. Her current areas of interest include the maintenance of close relationships, the psychology of the self, and scientific values.

Notes

1. A participant recruitment and study management platform.