Sage Journals: Discover world-class research

Abstract

In the wake of COVID-19, university students have experienced fundamental changes of their learning and their lives as a whole. The present research identifies psychological characteristics associated with students’ well-being in this situation. We investigated relations of basic psychological need satisfaction (experienced competence, autonomy, and relatedness) with positive emotion and intrinsic learning motivation, considering self-regulated learning as a moderator. Self-reports were collected from 6,071 students in Austria (Study 1) and 1,653 students in Finland (Study 2). Structural equation modeling revealed competence as the strongest predictor for positive emotion. Intrinsic learning motivation was predicted by competence and autonomy in both countries and by relatedness in Finland. Moderation effects of self-regulated learning were inconsistent, but main effects on intrinsic learning motivation were identified. Surprisingly, relatedness exerted only a minor effect on positive emotion. The results inform strategies to promote students’ well-being through distance learning, mitigating the negative effects of the situation.

Keywords

COVID-19 higher education self-determination theory well-being

To contain the spread of the COVID-19 pandemic, many countries instituted temporal closures of higher education institutions in March 2020. According to UNESCO (2020a), by the end of April 2020, schools and higher education institutions were closed in 178 countries, affecting roughly 1.3 billion learners worldwide. As a consequence, students have been facing a fundamentally altered situation not only with respect to their studies but also with their lives as a whole, due to manifold containment measures. Lockdowns, restrictions on movement, disruption of routines, physical distancing, curtailment of social interactions, and deprivation of traditional learning methods have led to increased stress, anxiety, and mental health concerns for learners worldwide (UNESCO, 2020b). On the whole, COVID-19 and its containment measures have created unique challenges for psychological well-being. To counteract negative developmental outcomes, resources must be identified that foster resilience in times of crisis. Therefore, the present research seeks to identify resources that support psychological well-being of students in higher education institutions in this unprecedented situation.

Self-Determination Theory as a Framework for Resilience

Resilience, the capacity to overcome hardships, to flourish in the face of challenges (Ryff & Singer, 2003), and to activate resources, including taking chances to experience feelings of well-being (Ungar, 2005), has consistently been associated with basic psychological need satisfaction (e.g., González et al., 2019; Riggenbach et al., 2019; D. A. Thomas & Woodside, 2011; Trigueros et al., 2019). Accordingly, self-determination theory (SDT; Deci & Ryan, 2000) states that the basic psychological needs for competence, autonomy, and relatedness represent core conditions for personal growth, integration, social development, and psychological well-being. These theoretical assumptions have been consistently proven empirically across different life domains and samples (e.g., Amorose & Anderson-Butcher, 2007; Reinboth & Duda, 2006; Riggenbach et al., 2019; Van den Broeck et al., 2016). Moreover, basic psychological need satisfaction can act as a buffer in times of stress, reducing appraisals of stress and promoting adaptive coping (Vansteenkiste & Ryan, 2013; Weinstein & Ryan, 2011). The need for competence refers to experiencing one’s behavior as effective. For example, students feel competent when they are able to meet the requirements of their studies. The need for autonomy refers to experiencing one’s behavior as volitional and self-endorsed. For instance, students feel autonomous when they willingly devote time and effort to their studies. Finally, the need for relatedness refers to feeling connected with and experiencing mutual support from significant others (Deci & Ryan, 2000, 2008b; Niemiec & Ryan, 2009). In order to enable personal growth, intrinsic motivation and psychological well-being, basic psychological need satisfaction has been increasingly taken up and promoted in the educational context in recent years, with SDT acting as a framework for interventions (e.g., Guay et al., 2008; Lüftenegger et al., 2016; Reeve, 2002; Reeve & Jang, 2006).

Distance Education and Basic Psychological Need Satisfaction in Times of COVID-19

Beyond the economic return to individuals and to society as a whole (e.g., Baum et al., 2010), higher education has the potential to improve quality of life in various ways. The role of higher education institutions in the European Union is not only to impart knowledge but also to develop the whole student by providing opportunities for personal growth and thriving. In order to enable students to become successful, resilient members of society, universities convey a range of transversal skills such as complex and autonomous thinking, creativity, and effective communication (European Commission, 2017). Moreover, universities are social spaces, enabling social interaction, offering the chance to build networks and new friendships, and generating a sense of identity and belonging with regard to the institution (Tonon, 2020). Concluding, universities represent an important developmental context for students to develop and unfold their potentials and to experience sense of belonging. The temporal closures of universities due to COVID-19 therefore represent an unprecedented challenge for students’ quality of life and thriving.

As an emergency response to the pandemic, universities worldwide have switched to distance education, marked by a rapid transition of face-to-face classes to online learning systems (Marinoni et al., 2020; Murphy, 2020). Distance education, or distance learning, is understood as an umbrella term, as its implementation varies greatly from case to case. A general characteristic is, however, the lack of physical presence and the lesser extent of informal discourse and spontaneous interaction. This bears the risk of transactional distance, a communication gap that creates negative emotions, gaps in understanding, and misconceptions (Moore, 1993). To counteract, it is crucial to explicitly address learners’ individual needs, feelings, and difficulties in distance learning environments (e.g., Richardson et al., 2015). Also, in light of numerous studies that report associations between social relatedness and academic success in both traditional and distance learning settings, interaction among learners in any learning setting should be explicitly supported (Giesbers et al., 2014; Heublein et al., 2017; Smith & Naylor, 2001; Tomás-Miquel et al., 2016). Moreover, there is consistent evidence that relatedness contributes to psychological well-being (e.g., Connell et al., 2012; Olsson et al., 2013; Reis et al., 2000; Weich et al., 2011). This further highlights the relevance of maintaining social contacts during the COVID-19 pandemic, whether with fellow students in a distance learning setting or with significant others from out-of-university contexts.

In addition to promoting social relatedness in virtual learning groups, distance education bears the potential to also promote experienced competence and autonomy when providing learners with opportunities to practice and apply what they are learning at their own pace (Paechter & Maier, 2010). In this way, students are challenged according to their abilities, and can test and expand them autonomously. Research has shown that individualized, autonomous learning environments create optimal conditions for learners to experience themselves as competent (Niemiec & Ryan, 2009). Both autonomy and competence are necessary conditions for intrinsic motivation, according to SDT (Ryan & Deci, 2000).

With all the advantages of individualized learning opportunities, it must be taken into account that self-directed and individual learning requires the learner to deal with flexibility. Learners have to structure and organize their learning themselves to a greater extent, and are required to independently integrate their learning into everyday life. Accordingly, academic success in distance education settings has repeatedly been associated with self-regulated learning competences (e.g., Geduld, 2016; Toaldo Avila & Bragagnolo Frison, 2015; Vanslambrouck et al., 2019). It has been shown, that the application of self-regulatory strategies within web-based instructions can improve learners’ self-efficacy and motivation (Chang, 2005). Moreover, in self-directed learning settings, self-regulated learning fosters students’ sense of control, thus increasing positive emotions (Pekrun, 2006). The role of self-regulated learning should therefore not be neglected when it comes to investigating distance education in times of COVID-19.

The Present Research

To support students’ psychological well-being in times of COVID-19, it is necessary to identify resources of well-being in the current, unprecedented situation. In this respect, SDT (Deci & Ryan, 2000) represents a promising framework. Recognizing the educational context as an important setting that provides opportunities for personal growth and thriving, the present research examines to what extent basic psychological need satisfaction acts as a buffer for university students’ psychological well-being during the COVID-19 pandemic. We thus investigate whether basic need satisfaction of competence and autonomy with respect to one’s studies and experienced relatedness with significant others relate to psychological well-being. Following Deci and Ryan (2008a), we understand well-being as consisting of both hedonic and eudaimonic well-being. We therefore include positive emotion and psychological functioning, that is, intrinsic learning motivation, to operationalize students’ psychological well-being. Based on SDT, we expect that all three basic needs, namely, competence, autonomy, and relatedness, predict psychological well-being in terms of positive emotion and psychological functioning (i.e., intrinsic learning motivation; Hypotheses 1a and 1b). However, existing studies report that experiencing competence and autonomy in a distance learning setting is related to self-regulated learning (e.g., Geduld, 2016; Toaldo Avila & Bragagnolo Frison, 2015; Vanslambrouck et al., 2019). Thus, we assume that the relations between experienced competence and positive emotion and between experienced autonomy and positive emotion will be moderated by self-regulated learning (Hypotheses 2a and 2b). Accordingly, we hypothesize that the relations between experienced competence and intrinsic learning motivation and between experienced autonomy and intrinsic learning motivation will be moderated by self-regulated learning (Hypotheses 2c and 2d).

Comprising data from Austria (Study 1) and Finland (Study 2), the present research takes a multistudy approach. To examine whether findings are consistent in both countries, we first collected data in Austria and then conducted a follow-up in Finland.

Method

Participants and Procedure

The overall sample comprised 7,724 university students (28.3% males, 71.0% females, 0.7% diverse) with a mean age of 25.76 years (SD = 7.52, Mdn = 23.00, range = 18–71). Data were collected via online questionnaires in spring 2020. Before being forwarded to the items, participants were informed about the study’s goals; approximate duration of the questionnaire; inclusion criteria for participation, that is, attending university in the respective country; and the complete anonymity of their data. All students participated voluntarily and only those who gave active consent were included in the dataset.

Study 1: Austria

The sample comprised 6,071 university students (30.7% males, 68.9% females, 0.4% diverse) with a mean age of 25.02 years (SD = 6.90, Mdn = 23.00, range = 18–71). The students were from higher education institutions all over Austria. Data were collected from April 7 to April 24. We distributed the link to the online questionnaire by contacting diverse stakeholders such as university rectorates and higher educational networks. Additionally, the Federal Ministry of Education, Science, and Research published the study link and recommended participation on its website.

In Austria, universities stopped providing onsite learning on March 16. Also, as of March 16, the government announced that residents could leave their homes only for work, making necessary purchases, assisting other people, or outdoor exercise alone or in the company of people living in the same household. Beginning on April 6, residents were required to wear face masks in stores and on public transport (Federal Ministry of Health, 2020). During the entire period of data collection, universities ensured continued education by providing distance learning.

Study 2: Finland

The sample comprised 1,653 university students (19.6% males, 78.5% females, 1.9% diverse) with a mean age of 28.49 years (SD = 8.93, Mdn = 25.00, range = 19–69). The students were from the University of Helsinki, Finland. Data were collected from April 29 to June 2, 2020. The link to the online questionnaire was distributed via faculty email lists and the University of Helsinki’s social media channels.

In Finland, universities stopped providing onsite learning on March 18. Universities ensured continued education by providing distance learning. As of May 14, the national government allowed a reopening of higher education institutions. However, it was strongly suggested that the institutions would stay closed the whole semester. The University of Helsinki was closed during the entire period of the data collection.

Measures

Due to the novelty of the COVID-19 situation, we adapted existing scales or developed new items to suitably address the current circumstances. To ensure content validity of the measures, we revised the items in a first step based on expert judgments from members of our research group. In a next step, the questionnaire was piloted with cognitive interview testing. Finally, the original German questionnaire was translated into Finnish using the translation–back-translation method (Brislin, 1986). To ensure the construct validity of the finally implemented measures, we conducted confirmatory factor analyses (CFAs) and analyzed composite reliability (CR; Raykov, 2009). According to common cutoff criteria for reliability, CR scores above .60, .70, .80, and .90 are deemed marginal, acceptable, good, and excellent, respectively (Hair et al., 2010). All items in the questionnaire were rated on a 5-point Likert-type scale, ranging from 1 (strongly agree) to 5 (strongly disagree). Participants were instructed to respond to items with respect to the current situation, that is, learning from home due to the COVID-19 pandemic. In order to simplify the interpretation of results, all analyses were conducted with recoded items so that higher values reflected higher agreement with the statements.

Competence was measured with three items adapted from the Work-related Basic Need Satisfaction Scale (Van den Broeck et al., 2010). We adapted the work-related items to the university context (sample item: “Currently, I am dealing well with the demands of my studies”). The scale’s CR was .79 for Austria and .90 for Finland.

Autonomy was assessed with three newly developed items that addressed the extent to which students felt that they were self-determined in approaching their studies in the current situation (sample item: “Currently, I can perform tasks in the way that best suits me”; CR = .65 and .66 for Austria and Finland, respectively).

Relatedness was measured with three items inspired by the Work-Related Basic Need Satisfaction Scale (Van den Broeck et al., 2010) and the German Basic Psychological Need Satisfaction and Frustration Scale (Heissel et al., 2018). In contrast to competence and autonomy, the items targeting relatedness did not refer solely to the university context but also to significant others in general (sample item: “Currently, I feel connected with the people who are important to me”; CR = .75 and .82 for Austria and Finland, respectively).

Self-regulated learning in terms of goal setting and planning one’s learning process was assessed with three items, slightly adapted from the short version of the Learning Strategies of University Students questionnaire (Klingsieck, 2018; sample item: “In the current home-learning situation, I plan my course of action”; CR = .78 and .76, for Austria and Finland, respectively).

Positive emotion was measured with two items inspired by the Scale of Positive and Negative Experience (Diener et al., 2010; “I feel good,” “I feel confident”) and one item adapted from the optimism subscale of the EPOCH Measure of Adolescent Well-Being (Engagement, Perseverance, Optimism, Connectedness, and Happiness; Kern et al., 2016; “Even if things are difficult right now, I believe that everything will turn out all right”; CR = .85 and .87 for Austria and Finland, respectively).

Intrinsic learning motivation was assessed with three items slightly adapted from the Scales for the Measurement of Motivational Regulation for Learning in University Students (A. E. Thomas et al., 2018; sample item: “Currently, I am really enjoying studying and doing work for university”; CR = .92 and .86 for Austria and Finland, respectively).

Data Analysis

Data were analyzed using SPSS Version 25.0 and Mplus Version 8.4 (Muthén & Muthén, 2017). We conducted CFAs and structural equation models with latent interactions. The proportion of missing values ranged from 0.3% to 4.5% on the item level. To deal with the missing values, the full information maximum likelihood approach was employed. Statistical significance testing was performed at the .05 level. Due to the large sample size, rather than relying on statistical significance, we focused on the effect sizes of the regression parameters when interpreting the results. We followed Cohen’s (1988) recommendations, according to which standardized values of 0.10, 0.30, and 0.50 reflect small, moderate, and large effects.

First, CFAs using robust maximum likelihood estimation were conducted to analyze the construct validity of the scales. Goodness-of-fit was evaluated using χ² test of model fit, the comparative fit index (CFI), and the root mean square error of approximation (RMSEA). Following Hu and Bentler (1999), CFI > .95 and .90 and RMSEA < .06 and .08 represent excellent and adequate model fit, respectively.

Second, we tested for measurement invariance across countries of data collection. CFAs for the three basic needs, self-regulated learning, and the outcomes were set up to investigate the dimensionality of the scales. We tested for measurement invariance (configural invariance, metric invariance, and scalar invariance) across countries by calculating a set of increasingly constrained CFAs. While configural invariance tests whether the same factor structure is valid for each group, metric invariance indicates that participants in both countries attribute the same meaning to the latent constructs. Finally, if the assumption of scalar invariance holds, the meaning of the levels of the underlying items is equal in both groups (van de Schoot et al., 2012). We followed Chen (2007) when evaluating the measurement invariance assumptions. Accordingly, when the sample size is adequate (N > 300), declines in CFI > .01 and increases in RMSEA > .015 indicate meaningful model fit changes, making the assumptions of measurement invariance not tenable. For all three models, which were measured on 5-point Likert-type scales, the robust maximum likelihood estimator was used for the CFAs.

Third, we set up two models to test the main effects and the latent interactions in both studies. Model 0 tested the main effects of competence, autonomy, relatedness, and self-regulated learning on positive emotion and intrinsic learning motivation. In Model 1, latent interactions between competence and self-regulated learning as well as between autonomy and self-regulated learning were added, as appropriately specified latent-interaction models include both main-effect variables and the product term (Cohen, 1978; Cronbach, 1987). Additionally, following Maslowsky et al. (2015), we compared the relative fit of Model 0 and Model 1 using a log-likelihood ratio test. A significant log-likelihood ratio test indicates that Model 0 represents a significant loss in fit compared to the more complex Model 1 (Satorra, 2000).

Results

Preliminary Analyses

Table 1 provides bivariate latent correlations among all variables as well as descriptive statistics and CRs in both samples.

Table 1

Bivariate Latent Correlations, Descriptive Statistics, and Composite Reliabilities for Study 1 and Study 2

Variable/descriptive statistic	1	2	3	4	5	6
1. Competence	—	.57	.37	.42	.69	.53
2. Autonomy	.84	—	.22	.41	.47	.50
3. Relatedness	.31	.27	—	.15	.35	.34
4. Self-regulated learning	.32	.35	.15	—	.33	.43
5. Positive emotion	.64	.51	.23	.16	—	.46
6. Intrinsic learning motivation	.76	.72	.24	.34	.56	—
Study 1: Austria
No. of items	3	3	3	3	3	3
M	3.27	2.93	3.15	3.29	3.68	2.77
SD	1.00	0.95	0.95	1.02	0.86	1.14
Range	4.00	4.00	4.00	4.00	4.00	4.00
Composite reliability	.79	.65	.75	.78	.85	.92
Study 2: Finland
No. of items	3	3	3	3	3	3
M	3.62	3.37	3.40	3.39	3.53	3.40
SD	1.03	0.82	0.90	0.93	0.85	0.94
Range	4.00	4.00	4.00	4.00	4.00	4.00
Composite reliability	.90	.66	.82	.76	.87	.86

Note. N_Study1 = 6,071, N_Study2 = 1,653. All scales were 5-point Likert-type scales. Correlations for Study 1 (Austria) are below the diagonal and correlations for Study 2 (Finland) are above the diagonal. All correlation coefficients are statistically significant at p < .001.

Due to the high correlation between competence and autonomy in Study 1, we investigated collinearity of the predictors and computed the variance inflation factor (VIF) for the two variables, yielding VIF_comp = 3.94 and VIF_auto = 3.40. Generally, VIFs higher than 5 are considered to indicate potential difficulties in separating out the independent contribution of the variables concerned (James et al., 2013). Other authors suggest more conservative cutoffs, considering VIFs greater than 2.5 indicative of collinearity (Johnston et al., 2018). If the stricter criterion is applied, the effects of competence and autonomy on the outcomes in Study 1 should be interpreted cautiously, as it cannot be ruled out that the respective slope parameters are over- or underestimated.

Confirmatory Factor Analyses and Measurement Invariance Testing

The CFAs revealed excellent fit indices for all scales for both Study 1, χ²(120) = 1963.24, p < .001, RMSEA = .050, CFI = .959, and Study 2, χ²(120) = 515.09, p < .001, RMSEA = .045, CFI = .968. The tests for measurement invariance showed that configural and metric invariance could be established for all variables based on Chen’s (2007) recommendations. As for scalar invariance, considering the declines in CFI greater than .01 in all three models and increases in RMSEA greater than .015 for self-regulated learning and the outcome variables, the scalar invariance assumptions did not hold. Accordingly, the meanings of the levels of the items were not equal in both groups. Therefore, while the same factor structure and the same meanings attributed to the latent constructs can be assumed, factor means should not be compared across the two countries of data collection. Results of the measurement invariance testing are reported in Table 2.

Table 2

Measurement Invariance Testing Across Countries for the Confirmatory Factor Analytic Measurement Models for Basic Psychological Needs, Self-Regulated Learning, Learning Motivation, and Positive Emotion

Model	χ²	df	CFI	ΔCFI	RMSEA	ΔRMSEA	BIC	Model description
Basic psychological needs (competence, autonomy, relatedness)
	813.273*	48	0.962		0.064		196270.416	Configural invariance
	867.890*	54	0.959	−0.003	0.063	−0.001	196275.910	Metric invariance
	1309.725*	60	0.937	−0.022	0.074	0.011	196718.698	Scalar invariance
Self-regulated learning
	0.000	0	1.000		0.000		66283.023	Configural invariance
	32.920*	2	0.993		0.064		66302.676	Metric invariance
	292.518*	4	0.933	−0.06	0.138	0.074	66561.219	Scalar invariance
Outcomes (intrinsic learning motivation, positive emotion)
	209.779*	16	0.991		0.056		109910.784	Configural invariance
	245.671*	20	0.989	−0.002	0.054	−0.002	109909.441	Metric invariance
	561.627*	24	0.975	−0.014	0.076	0.022	110223.000	Scalar invariance

Note. Note that the model for self-regulated learning had only three factor indicators and that the configural invariance model was therefore saturated; χ² = chi-square test of model fit; df = degrees of freedom; CFI = comparative fit index; ΔCFI = change in CFI compared to the weaker measurement invariance model above; RMSEA = root mean square error of approximation; ΔRMSEA = change in RMSEA compared to the weaker measurement invariance model above; BIC = Bayesian information criterion.

p ≤ .001.

Main Effects and Latent Interactions

To analyze the main effects of the three basic needs and self-regulated learning on positive emotion and intrinsic learning motivation, we conducted two structural equation models (Model 0₁ and Model 0₂) for the two studies. Model estimation for the main effect models revealed that competence positively predicted both outcomes in both studies. Autonomy positively predicted intrinsic learning motivation in both studies, and positive emotion in Finland. Albeit the small effect size, autonomy was identified as a negative predictor for positive emotion in Austria. In this regard, however, it must be noted that competence and autonomy were highly correlated in the Austrian sample. Therefore, the parameter estimates for the unique effects of competence and autonomy on the outcomes might not be reliable. Relatedness positively predicted positive emotion in both studies, and intrinsic learning motivation in Finland only, with small effect sizes, respectively. Self-regulated learning negatively predicted positive emotion with a small effect in Austria, and positively predicted intrinsic learning motivation in both studies (see Table 3 for Austria and Table 4 for Finland). According to the path coefficients, competence was the relatively more important predictor for both outcomes in Austria, and for positive emotion in Finland. In Finland, autonomy exerted the greatest effect on intrinsic learning motivation. However, besides comparing the coefficients on the descriptive level, we tested the differences of the regression slopes for each outcome variable for statistical significance using the MPlus Model Constraint command. In Austria, all regression coefficients, except for those between autonomy and positive emotion and between self-regulated learning and positive emotion, were statistically significantly different from each other. In Finland, all coefficients for positive emotion differed significantly, except for the regression coefficients of autonomy and relatedness. Regarding the effects on intrinsic learning motivation in Finland, there were no statistically significant differences between the regression coefficients of competence and autonomy, of competence and self-regulated-learning, and of autonomy and self-regulated learning.

Table 3

Path Coefficients of the Main Effect Model (Model 0) and the Latent-Interaction Model (Model 1) for Study 1

Outcome and predictor	Model 0₁			Model 1₁
Outcome and predictor	Est. (SE)	Std. Est.	p	Est. (SE)	Std. Est.	p
Positive emotion
Competence	0.57 (0.03)	0.71	<.001	0.58 (0.03)	0.72	<.001
Autonomy	−0.06 (0.03)	−0.08	.047	−0.08 (0.03)	−0.10	.021
Relatedness	0.03 (0.01)	0.04	.011	0.03 (0.01)	0.04	.009
SRL	−0.04 (0.02)	−0.05	.004	−0.04 (0.02)	−0.04	.012
Competence × SRL				−0.06 (0.05)	−0.06	.213
Autonomy × SRL				0.11 (0.05)	0.11	.038
R²	.41			.42
Intrinsic learning motivation
Competence	0.61 (0.04)	0.51	<.001	0.62 (0.04)	0.52	<.001
Autonomy	0.32 (0.04)	0.27	<.001	0.30 (0.04)	0.25	<.001
Relatedness	0.00 (0.01)	0.00	0.980	0.00 (0.01)	0.00	.924
SRL	0.11 (0.02)	0.08	<.001	0.12 (0.02)	0.09	<.001
Competence × SRL				0.07 (0.06)	0.05	.214
Autonomy × SRL				0.02 (0.06)	0.01	.777
R²	.61			.61
Goodness of fit
AIC	290942.372			290910.182
BIC	291405.428			291400.082

Note. SRL = self-regulated learning; Est. = unstandardized parameter estimate; Std. Est. = standardized estimate; AIC = Akaike information criterion; BIC = Bayesian information criterion.

Table 4

Path Coefficients of the Main Effect Model (Model 0) and the Latent-Interaction Model (Model 1) for Study 2

Outcome and predictor	Model 0₂			Model 1₂
Outcome and predictor	Est. (SE)	Std. Est.	p	Est. (SE)	Std. Est.	p
Positive emotion
Competence	0.45 (0.03)	0.59	<.001	0.47 (0.03)	0.60	<.001
Autonomy	0.10 (0.03)	0.11	.003	0.09 (0.03)	0.10	.005
Relatedness	0.07 (0.02)	0.10	<.001	0.07 (0.02)	0.10	<.001
SRL	0.02 (0.03)	0.03	.409	0.02 (0.03)	0.02	.434
Competence × SRL				0.08 (0.04)	0.08	.047
Autonomy × SRL				−0.06 (0.05)	−0.05	.240
R²	.50			.51
Intrinsic learning motivation
Competence	0.23 (0.04)	0.25	<.001	0.21 (0.04)	0.23	<.001
Autonomy	0.27 (0.04)	0.24	<.001	0.27 (0.04)	0.24	<.001
Relatedness	0.14 (0.02)	0.16	<.001	0.14 (0.02)	0.16	<.001
SRL	0.24 (0.04)	0.21	<.001	0.25 (0.04)	0.21	<.001
Competence × SRL				−0.06 (0.05)	−0.05	.266
Autonomy × SRL				−0.02 (0.06)	0.01	.753
R²	.39			.39
Goodness of fit
AIC	73915.633			73907.526
BIC	74288.947			74302.481

Note. SRL = self-regulated learning; Est. = unstandardized parameter estimate; Std. Est. = standardized estimate; AIC = Akaike information criterion; BIC = Bayesian information criterion.

To investigate moderation effects of self-regulated learning on the outcomes, latent interactions (competence × self-regulated learning and autonomy × self-regulated learning) were added to Model 0 for both studies, resulting in Model 1₁ and Model 1₂. In Austria, a statistically significant positive latent interaction emerged between autonomy and self-regulated learning for positive emotion, b* = 0.11, SE = 0.05, p = .038 (see Model 1₁ in Table 3). Therefore, the effect of autonomy varied with the level of self-regulated learning for positive emotion. In other words, the effect of autonomy on positive emotion increased, as the moderator increased, and vice versa. The relative fit of Model 1₁ versus Model 0₁ was determined via a log-likelihood ratio test, yielding a significant log-likelihood difference of D(4) = 472.778, p < .001. This result indicates that the null model represents a significant loss in fit relative to Model 1₁. Model 1₁ should therefore be kept for Study 1 (see Figure 1).

Figure 1.

Structural equation model predicting positive emotion and intrinsic learning motivation (Study 1: Model 1₁).

In Finland, a statistically significant positive latent interaction emerged between competence and self-regulated learning for positive emotion, b* = 0.08, SE = 0.04, p = .047 (see Model 1₂ in Table 4). Therefore, the effect of competence on positive emotion increased, as the level of self-regulated learning increased, and vice versa. The log-likelihood ratio test was significant with D(4) = 16.108, p < .005, indicating a better relative fit for Model 1₂. Model 1₂ is therefore kept for Study 2 (see Figure 2).

Figure 2.

Structural equation model predicting positive emotion and intrinsic learning motivation (Study 2: Model 1₂).

Discussion

The present research aimed at identifying resources that relate to university students’ psychological well-being in the challenging period of the COVID-19 pandemic. Following SDT (Deci & Ryan, 2000), we examined to what extent basic psychological need satisfaction related to university students’ psychological well-being when involuntarily learning from home during the COVID-19 pandemic. Additionally, we considered the role of self-regulated learning as a moderator. To examine whether evidence is consistent across countries, we took a multistudy approach and collected data in Austria (Study 1) and Finland (Study 2).

Based on SDT, we expected that all three basic needs, that is, experienced competence, autonomy, and relatedness, predicted psychological well-being in terms of positive emotion and intrinsic learning motivation. In contrast to a broad body of research clearly pointing to the assumed associations (e.g., Reinboth & Duda, 2006; Riggenbach et al., 2019; Van den Broeck et al., 2016), our results only revealed competence to predict positive emotion with a large effect in Austria, and a moderate effect in Finland. In Austria, relatedness was a further positive predictor and autonomy was a negative predictor of positive emotion. In Finland, both autonomy and relatedness were further positive predictors of positive emotion. However, the minor to small effect sizes of autonomy and relatedness in both studies do not allow to conclude on the practical relevance of the identified coefficients. Moreover, due to potential collinearity of competence and autonomy in the Austrian sample, it must be assumed that the unique effect of autonomy on positive emotion was underestimated. This is also underlined by the high bivariate correlation between autonomy and positive emotion in Austria (Hypothesis 1a). Regarding associations of the basic needs and intrinsic learning motivation, competence and autonomy were positive predictors with a large (competence) and a moderate (autonomy) effect on the outcome in Austria. However, the possible collinearity of these two predictors must also be considered here. Accordingly, the unique contribution of competence and autonomy might have been incorrectly estimated for the Austrian sample. In Finland, all three basic needs positively predicted intrinsic learning motivation with small to moderate effect sizes (Hypothesis 1b).

The assumed moderation effects of self-regulated learning on the relationship between autonomy and competence and the outcomes were inconsistent. While we found a statistically significant effect for the interaction between self-regulated learning and autonomy on positive emotion in Austria, we identified a statistically significant effect for the interaction between self-regulated learning and competence on positive emotion in Finland (Hypotheses 2a and 2b). For the moderation effect of self-regulated learning on the relationships between competence and intrinsic learning motivation, and between autonomy and intrinsic learning motivation, we found no significant effects (Hypotheses 2c and 2d). Although the interactions were not consistently identified, it should be noted that the main effects of self-regulated learning on intrinsic learning motivation were significant with small to moderate effect sizes in both studies. This speaks in favor of the relevance of self-regulated learning for enabling intrinsic learning motivation.

On the whole, the results of the analyses were broadly consistent across both Study 1 and Study 2, providing convergent evidence across countries. This applies in particular to the identified high relevance of experienced competence for positive emotion and the relevance of autonomy and self-regulated learning for intrinsic learning motivation. Both studies further indicated an only minor relevance of relatedness for positive emotion. This unexpected finding could be due to the fact that in the time of the pandemic, social contacts play a different role than under usual circumstances, and that the directive to reduce face-to-face contacts could have resulted in social contacts having a different connotation overall: to withdraw and refrain from social contact in order to be protected from the virus and feel safe. In this respect, the term cocooning had a revival, referring to staying inside one’s private home, shielded from perceived danger, instead of going out (Merriam-Webster, 2020; Oxford English Dictionary, 2020). With respect to the COVID-19 pandemic, cocooning has been referred to as self-isolation of the elderly and risk groups (Duque et al., 2020) but has also been positively connotated as a lifestyle trend among young people that is about peace, protection, coziness, and control (see Popcorn, 1992).

Implications for Higher Education in Times of COVID-19

Both studies identified a high relevance of experienced competence for positive emotion and the relevance of autonomy and self-regulated learning for intrinsic learning motivation. In addition, there are indications that, to a certain extent, relatedness has a positive influence on intrinsic learning motivation. All three basic needs as well as self-regulated learning can be specifically promoted in the university context through distance learning.

Based on the identified high relevance of experienced competence, distance education in times of COVID-19 is required to explicitly enable students to experience successes. This can be achieved through individualized and at the same time autonomy-supportive learning opportunities, challenging students based on their individual strengths and weaknesses. Experiencing success can be further promoted by setting intermediate goals. In addition, there should be enough room for individual feedback (see, e.g., Lawn et al., 2017; Oliveira et al., 2018; Paechter & Maier, 2010). To foster self-regulated learning, which proved to be a positive predictor for intrinsic learning motivation, universities should instruct students to structure and plan their learning consciously. The necessity to explicitly convey strategies for self-regulated learning is underlined by studies, according to which students know about them in theory but in many cases do not use self-regulated learning strategies in everyday life and perceive them as tedious and unnecessary (e.g., Foerst et al., 2017). Promoting the explicit use of self-regulated learning strategies is a relevant short-term objective in the current distance learning situation, but it also bears the potential to equip students for lifelong learning in general (Lüftenegger et al., 2012).

Finally, when it comes to promoting relatedness and identification with the university in the current situation, digital learning platforms can be used to enable online group work at a physical distance. To foster the feeling of learning together as a group, synchronous learning units (e.g., video group calls) could be used to reflect on learning processes, successes, as well as struggles and to promote cohesion within the group. For further strategies to promote the development of an online community, see, for example Rovai (2007).

Limitations and Future Directions

We consider the high explanatory power of our models, with proportions of explained variance ranging from 39% to 61%, and the large sample size as substantial strengths of our research. Moreover, we followed a multistudy approach and tested our assumptions in samples from two different countries. The results are further supported by the identified configural and metric measurement invariance, indicating that the same factor structure and the same meanings attributed to the latent constructs can be assumed across both countries of data collection.

Despite these noteworthy strengths, the present research is limited in some respects. First, the results rely on self-reports. While this is the usual practice for most of the examined constructs, there are concerns about the validity of self-report tools for assessing self-regulated learning (e.g., Winne et al., 2002). Second, data were collected online. This led to a self-selection of our sample and as a consequence to an overrepresentation of females. In addition, the sample sizes of Study 1 and Study 2 differ greatly, leading to inequivalent statistical power to detect main and moderating effects. A further limitation relates to the cross-sectional design of our research, limiting the possibility for causal inferences. Finally, due to the novelty of the COVID-19 situation, some of our measures were newly developed for this study. Because of the urge to quickly start collecting data to generate information about the sudden situation, it was not possible to carry out a comprehensive validation study of the instruments. Nevertheless, we can account for the validity of our instruments in several ways, including cognitive interview testing, CFAs, CR, and measurement invariance testing.

Considering these limitations, we recommend that follow-up studies incorporate further informants (e.g., teacher ratings, observations) and other methods of data collection (e.g., experience sampling, in-depth qualitative methods) to obtain a more comprehensive picture. This especially relates to the role of self-regulated learning and to the further investigation of the surprisingly low association between relatedness and positive emotion. Future research should also consider different approaches of sample recruitment to ensure a better representation of the population. Particularly, longitudinal studies should be carried out to further substantiate the evidence for the large effects found. With regard to the delivery of distance education, it should be considered to evaluate concrete design options such as modality, pacing, instructional practices, role of assessments, and feedback (see Means et al., 2014) and to investigate the extent to which they are suitable to enable experience of competence and to support self-regulated learning.

Conclusion

The present research highlights the relevance of perceived competence, autonomy and self-regulated learning for university students’ well-being in times of unplanned and involuntary remote studying. The results also indicate a potential relevance of relatedness for intrinsic learning motivation. The requirement for higher education institutions is to explicitly promote these identified dimensions. To take advantage of the potential, distance learning should be designed in a way that maximizes the strengths and constrains the weaknesses of distance education.

Footnotes

Acknowledgements

This work was funded by the Vienna Science and Technology Fund (WWTF) and the MEGA Bildungsstiftung through project COV20-025 and Academy of Finland 1308351.

ORCID iDs

Julia Holzer

Marko Lüftenegger

Elisabeth Pelikan

Authors

JULIA HOLZER is a university assistant at the Department for Psychology of Development and Education at the University of Vienna. Her research focusses on well-being in the school context, its impact on achievement, educational motivation, and goal orientation of teachers and pupils.

MARKO LÜFTENEGGER holds a position as assistant professor of developmental and educational psychology at the Faculty of Psychology and the Centre of Teacher Education at the University of Vienna. His research focusses on motivation and emotions in education, giftedness, classroom environments that facilitate students’ (positive) development, and the evaluation of educational programs.

SELMA KORLAT is a university assistant at the Department for Psychology of Development and Education at the University of Vienna. Her research focusses on stereotypes and their effects on performance, academic success, and motivation.

ELISABETH PELIKAN is a university assistant at the Department for Psychology of Development and Education at the University of Vienna. Her research focusses on self-regulated learning in educational contexts.

KATARIINA SALMELA-ARO is a professor of educational psychology at the Department of Education at the University of Helsinki. Her main research interests are motivation, critical life transitions, productive development, well-being, and related interventions.

CHRISTIANE SPIEL is a professor of Bildung-Psychology and Evaluation at the Faculty of Psychology, University of Vienna. Her research interests include future education, self-regulated learning, learning motivation, gender stereotypes in education, (cyber) bullying, and intervention, evaluation, and implementation research.

BARBARA SCHOBER is a professor of psychological research on education and transfer and the dean of the faculty of Psychology at the University of Vienna. Her main research focus is on competences for lifelong learning, learning motivation, self-regulation, teacher training, development, evaluation and implementation of intervention programs in educational contexts, and gender in education.

References

Amorose

A. J.

Anderson-Butcher

(2007). Autonomy-supportive coaching and self-determined motivation in high school and college athletes: A test of self-determination theory. Psychology of Sport and Exercise, 8(5), 654–670. https://doi.org/10.1016/j.psychsport.2006.11.003

Baum

Payea

(2010). Education pays 2010. The benefits of higher education for individuals and society. The College Board Advocacy & Policy Center.

Brislin

R. W.

(1986). The wording and translation of research instruments. In Lonner

W. J.

Berry

J. W.

(Eds.), Field methods in cross-cultural psychology (pp. 137–164). Sage.

Chang

M.-M.

(2005). Applying self-regulated learning strategies in a web-based instruction: An investigation of motivation perception. Computer Assisted Language Learning, 18(3), 217–230. https://doi.org/10.1080/09588220500178939

Chen

F. F.

(2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14(3), 464–504. https://doi.org/10.1080/10705510701301834

Cohen

(1978). Partialed products are interactions; partialed powers are curve components. Psychological Bulletin, 85(4), 858–866. https://doi.org/10.1037/0033-2909.85.4.858

Cohen

(1988). Statistical power analysis for the behavioral sciences. Erlbaum.

Connell

Brazier

O’Cathain

Lloyd-Jones

Paisley

(2012). Quality of life of people with mental health problems: a synthesis of qualitative research. Health and Quality of Life Outcomes, 10(1), 138. https://doi.org/10.1186/1477-7525-10-138

Cronbach

L. J.

(1987). Statistical tests for moderator variables: Flaws in analyses recently proposed. Psychological Bulletin, 102(3), 414–417. https://doi.org/10.1037/0033-2909.102.3.414

10.

Deci

E. L.

Ryan

R. M.

(2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01

11.

Deci

E. L.

Ryan

R. M.

(2008a). Hedonia, eudaimonia, and well-being: an introduction. Journal of Happiness Studies, 9(1), 1–11. https://doi.org/10.1007/s10902-006-9018-1

12.

Deci

E. L.

Ryan

R. M.

(2008b). Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology/Psychologie Canadienne, 49(3), 182–185. https://doi.org/10.1037/a0012801

13.

Diener

Wirtz

Tov

Kim-Prieto

Choi

Oishi

Biswas-Diener

(2010). New well-being measures: short scales to assess flourishing and positive and negative feelings. Social Indicators Research, 97(2), 143–156. https://doi.org/10.1007/s11205-009-9493-y

14.

Duque

Morton

D. P.

Singh

Pasco

Meyers

L. A.

(2020). Timing social distancing to avert unmanageable COVID-19 hospital surges. Proceedings of the National Academy of Sciences of the United States of America, 117(33), 19873–19878. https://doi.org/10.1073/pnas.2009033117

15.

European Commission. (2017). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the regions on a renewed EU agenda for higher education. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52017DC0247

16.

Federal Ministry of Health. (2020). Current information about the Coronavirus (SARS-CoV-2/COVID-19). https://www.sozialministerium.at/en/Coronavirus.html

17.

Foerst

N. M.

Klug

Jöstl

Spiel

Schober

(2017). Knowledge vs. action: Discrepancies in university students’ knowledge about and self-reported use of self-regulated learning strategies. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.01288

18.

Geduld

(2016). Exploring differences between self-regulated learning strategies of high and low achievers in open distance learning. Africa Education Review, 13(1), 164–181. https://doi.org/10.1080/18146627.2016.1182739

19.

Giesbers

Rienties

Tempelaar

Gijselaers

(2014). A dynamic analysis of the interplay between asynchronous and synchronous communication in online learning: The impact of motivation. Journal of Computer Assisted Learning, 30(1), 30–50. https://doi.org/10.1111/jcal.12020

20.

González

Castillo

Balaguer

. (2019). Análisis del papel de la resiliencia y de las necesidades psicológicas básicas como antecedentes de las experiencias de diversión y aburrimiento en el deporte femenino [Exploring the role of resilience and basic psychological needs as antecedents of enjoyment and boredom in female sports]. Revista de Psicodidáctica, 24(2), 131–137. https://doi.org/10.1016/j.psicod.2019.01.002

21.

Guay

Ratelle

C. F.

Chanal

(2008). Optimal learning in optimal contexts: The role of self-determination in education. Canadian Psychology/Psychologie Canadienne, 49(3), 233–240. https://doi.org/10.1037/a0012758

22.

Hair

J. F.

Black

W. C.

Babin

B. J.

Anderson

R. E.

(2010). Multivariate data analysis. Pearson College Division.

23.

Heissel

Pietrek

Flunger

Fydrich

Rapp

M. A.

Heinzel

Vansteenkiste

(2018). The validation of the German Basic Psychological Need Satisfaction and Frustration Scale in the context of mental health. European Journal of Health Psychology, 25(4), 119–132. https://doi.org/10.1027/2512-8442/a000017

24.

Heublein

Ebert

Hutzsch

Isleib

König

Richter

Woisch

. (2017). Motive und Ursachen des Studienabbruchs an baden-württembergischen Hochschulen und beruflicher Verbleib der Studienabbrecherinnen und Studienabbrecher [Motives and causes for quitting studies at higher education institutions in Baden-Wuerttemberg, and the occupational destination of the dropouts].

25.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

26.

James

Witten

Hastie

Tibshirani

(2013). An introduction to statistical learning (Vol. 103). Springer. https://doi.org/10.1007/978-1-4614-7138-7

27.

Johnston

Jones

Manley

(2018). Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour. Quality & Quantity, 52(4), 1957–1976. https://doi.org/10.1007/s11135-017-0584-6

28.

Kern

M. L.

Benson

Steinberg

E. A.

Steinberg

(2016). The EPOCH Measure of Adolescent Well-Being. Psycho-logical Assessment, 28(5), 586–597. https://doi.org/10.1037/pas0000201

29.

Klingsieck

K. B

. (2018). Kurz und knapp–die Kurzskala des Fragebogens “Lernstrategien im Studium” (LIST) [Short and sweet–the short version of the questionnaire “Learning Strategies of University Students” (LIST)]. Zeitschrift Für Pädagogische Psychologie, 32(4), 249–259. https://doi.org/10.1024/1010-0652/a000230

30.

Lawn

Zhi

Morello

(2017). An integrative review of e-learning in the delivery of self-management support training for health professionals. BMC Medical Education, 17(1), 183. https://doi.org/10.1186/s12909-017-1022-0

31.

Lüftenegger

Finsterwald

Klug

Bergsmann

van de Schoot

Schober

Wagner

(2016). Fostering pupils’ lifelong learning competencies in the classroom: Evaluation of a training programme using a multivariate multilevel growth curve approach. European Journal of Developmental Psychology, 13(6), 719–736. https://doi.org/10.1080/17405629.2015.1077113

32.

Lüftenegger

Schober

van de Schoot

Wagner

Finsterwald

Spiel

(2012). Lifelong learning as a goal: Do autonomy and self-regulation in school result in well prepared pupils? Learning and Instruction, 22(1), 27–36. https://doi.org/10.1016/j.learninstruc.2011.06.001

33.

Marinoni

an’t Land

Jensen

(2020). The impact of COVID-19 on higher education around the world. IAU Global Survey report. https://www.iau-aiu.net/IMG/pdf/iau_covid19_and_he_survey_report_final_may_2020.pdf

34.

Maslowsky

Jager

Hemken

(2015). Estimating and interpreting latent variable interactions. International Journal of Behavioral Development, 39(1), 87–96. https://doi.org/10.1177/0165025414552301

35.

Means

Bakia

Murphy

(2014). Learning online. What research tells us about whether, when and how. Routledge.

36.

Merriam-Webster. (2020). Cocooning. In Merriam-Webster dictionary. https://www.merriam-webster.com/dictionary/cocooning

37.

Moore

M. G.

(1993). Theory of transactional distance. In Keegan

(Ed.), Theoretical principles of distance education (pp. 22–38). Routledge.

38.

Murphy

M. P. A.

(2020). COVID-19 and emergency eLearning: Consequences of the securitization of higher education for post-pandemic pedagogy. Contemporary Security Policy, 41(3), 492–505. https://doi.org/10.1080/13523260.2020.1761749

39.

Muthén

L. K.

Muthén

B. O.

(2017). Mplus user’s guide (8th ed.). Muthén & Muthén.

40.

Niemiec

C. P.

Ryan

R. M.

(2009). Autonomy, competence, and relatedness in the classroom. Theory and Research in Education, 7(2), 133–144. https://doi.org/10.1177/1477878509104318

41.

Oliveira

M. M. S. de

Penedo

A. S. T.

Pereira

V. S.

(2018). Distance education: Advantages and disadvantages of the point of view of education and society. Dialogia, 29, 139–152. https://doi.org/10.5585/dialogia.N29.7661

42.

Olsson

C. A.

McGee

Nada-Raja

Williams

S. M.

(2013). A 32-year longitudinal study of child and adolescent pathways to well-being in adulthood. Journal of Happiness Studies, 14(3), 1069–1083. https://doi.org/10.1007/s10902-012-9369-8

43.

Oxford English Dictionary. (2020). Cocoon. https://www.lexico.com/en/definition/cocoon

44.

Paechter

Maier

(2010). Online or face-to-face? Students’ experiences and preferences in e-learning. The Internet and Higher Education, 13(4), 292–297. https://doi.org/10.1016/j.iheduc.2010.09.004

45.

Pekrun

(2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9

46.

Popcorn

(1992). The Popcorn report : Faith Popcorn on the future of your company, your world, your life. Harper Business.

47.

Raykov

(2009). Evaluation of scale reliability for unidimensional measures using latent variable modeling. Measurement and Evaluation in Counseling and Development, 42(3), 223–232. https://doi.org/10.1177/0748175609344096

48.

Reeve

(2002). Self-determination theory applied to educational settings. In Deci

E. L.

Ryan

R. M.

(Eds.), Handbook of self-determination research (pp. 183–203). University of Rochester Press.

49.

Reeve

Jang

(2006). What teachers say and do to support students’ autonomy during a learning activity. Journal of Educational Psychology, 98(1), 209–218. https://doi.org/10.1037/0022-0663.98.1.209

50.

Reinboth

Duda

J. L.

(2006). Perceived motivational climate, need satisfaction and indices of well-being in team sports: A longitudinal perspective. Psychology of Sport and Exercise, 7(3), 269–286. https://doi.org/10.1016/j.psychsport.2005.06.002

51.

Reis

H. T.

Sheldon

K. M.

Gable

S. L.

Roscoe

Ryan

R. M.

(2000). Daily well-being: The role of autonomy, competence, and relatedness. Personality and Social Psychology Bulletin, 26(4), 419–435. https://doi.org/10.1177/0146167200266002

52.

Richardson

J. C.

Koehler

A. A.

Besser

E. D.

Caskurlu

Lim

Mueller

C. M.

(2015). Conceptualizing and investigating instructor presence in online learning environments. International Review of Research in Open and Distributed Learning, 16(3). https://doi.org/10.19173/irrodl.v16i3.2123

53.

Riggenbach

Goubert

Van Petegem

Amouroux

(2019). Topical review: Basic psychological needs in adolescents with chronic pain—A self-determination perspective. Pain Research & Management, 2019, 8629581. https://doi.org/10.1155/2019/8629581

54.

Rovai

A. P.

(2007). Facilitating online discussions effectively. The Internet and Higher Education, 10(1), 77–88. https://doi.org/10.1016/j.iheduc.2006.10.001

55.

Ryan

R. M.

Deci

E. L.

(2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68

56.

Ryff

Singer

(2003). Flourishing under fire: Resilience as a prototype of challenged thriving. In Keyes

C. L. M.

Haidt

(Eds.), Flourishing: Positive psychology and the life well-lived (pp. 15–36). American Psychological Association. https://doi.org/10.1037/10594-001

57.

Satorra

(2000). Scaled and adjusted restricted tests in multi-sample analysis of moment structures. In Heijmans

R. D. H.

Pollock

D. S. G.

Satorra

(Eds.), Advanced studies in theoretical and applied econometrics: Vol. 36. Innovations in multivariate statistical analysis (pp. 233–247). Springer. https://doi.org/10.1007/978-1-4615-4603-0_17

58.

Smith

J. P.

Naylor

R. A.

(2001). Dropping out of university: A statistical analysis of the probability of withdrawal for UK university students. Journal of the Royal Statistical Society: Series A (Statistics in Society), 164(2), 389–405. https://doi.org/10.1111/1467-985X.00209

59.

Thomas

A. E.

Müller

F. H.

Bieg

. (2018). Entwicklung und Validierung der Skalen zur motivationalen Regulation beim Lernen im Studium (SMR-LS) [Development and validation of scales for the measurement of motivational regulation for learning in university students (SMR-LS)]. Diagnostica, 64(3), 145–155. https://doi.org/10.1026/0012-1924/a000201

60.

Thomas

D. A.

Woodside

(2011). Resilience in adult children of divorce: A multiple case study. Marriage & Family Review, 47(4), 213–234. https://doi.org/10.1080/01494929.2011.586300

61.

Toaldo Avila

Bragagnolo Frison

L. M.

(2015). Autorregulação da aprendizagem e a formação de professoras do campo na modalidade de ensino a distância [Learning self-regulated and the field teacher training in the education distance mode]. RIED. Revista Iberoamericana de Educación a Distancia, 19(1). https://doi.org/10.5944/ried.19.1.14487

62.

Tomás-Miquel

J.-V.

Expósito-Langa

Nicolau-Juliá

(2016). The influence of relationship networks on academic performance in higher education: A comparative study between students of a creative and a non-creative discipline. Higher Education, 71(3), 307–322. https://doi.org/10.1007/s10734-015-9904-8

63.

Tonon

G. H.

(2020). Student’s quality of life at the university: A qualitative study. Applied Research in Quality of Life. https://doi.org/10.1007/s11482-020-09827-0

64.

Trigueros

Aguilar-Parra

J. M.

Cangas-Díaz

A. J.

Fernández-Batanero

J. M.

Mañas

M. A.

Arias

V. B.

López-Liria

(2019). The influence of the trainer on the motivation and resilience of sportspeople: A study from the perspective of self-determination theory. PLOS ONE, 14(8), e0221461. https://doi.org/10.1371/journal.pone.0221461

65.

UNESCO. (2020a). COVID-19 impact on education. https://en.unesco.org/covid19/educationresponse

66.

UNESCO. (2020b). Nurturing the social and emotional wellbeing of children and young people during crises. UNESCO COVID-19 Education Response. https://unesdoc.unesco.org/ark:/48223/pf0000373271

67.

Ungar

(2005). Resilience among children in child welfare, corrections, mental health and educational settings: Recommendations for service. Child and Youth Care Forum, 34(6), 445–464. https://doi.org/10.1007/s10566-005-7756-6

68.

Van den Broeck

Ferris

D. L.

Chang

C.-H.

Rosen

C. C.

(2016). A review of self-determination theory’s basic psychological needs at work. Journal of Management, 42(5), 1195–1229. https://doi.org/10.1177/0149206316632058

69.

Van den Broeck

Vansteenkiste

Witte

Soenens

Lens

(2010). Capturing autonomy, competence, and relatedness at work: Construction and initial validation of the Work-Related Basic Need Satisfaction scale. Journal of Occupational and Organizational Psychology, 83(4), 981–1002. https://doi.org/10.1348/096317909X481382

70.

van de Schoot

Lugtig

Hox

(2012). A checklist for testing measurement invariance. European Journal of Developmental Psychology, 9(4), 486–492. https://doi.org/10.1080/17405629.2012.686740

71.

Vanslambrouck

Zhu

Pynoo

Thomas

Lombaerts

Tondeur

(2019). An in-depth analysis of adult students in blended environments: Do they regulate their learning in an “old school” way? Computers & Education, 128, 75–87. https://doi.org/10.1016/j.compedu.2018.09.008

72.

Vansteenkiste

Ryan

R. M.

(2013). On psychological growth and vulnerability: Basic psychological need satisfaction and need frustration as a unifying principle. Journal of Psychotherapy Integration, 23(3), 263–280. https://doi.org/10.1037/a0032359

73.

Weich

Brugha

King

McManus

Bebbington

Jenkins

Cooper

McBride

Stewart-Brown

(2011). Mental well-being and mental illness: findings from the Adult Psychiatric Morbidity Survey for England 2007. British Journal of Psychiatry, 199(1), 23–28. https://doi.org/10.1192/bjp.bp.111.091496

74.

Weinstein

Ryan

R. M.

(2011). A self-determination theory approach to understanding stress incursion and responses. Stress & Health, 27(1), 4–17. https://doi.org/10.1002/smi.1368

75.

Winne

P. H.

Jamieson-Noel

D. L.

Muis

(2002). Methodological issues and advances in researching tactics, strategies, and self-regulated learning. In Pintrich

P. R.

Maehr

M. L.

(Eds.), Advances in motivation and achievement: New directions in measured and methods (12th ed., pp. 121–155). JAI.

Higher Education in Times of COVID-19: University Students’ Basic Need Satisfaction,Self-Regulated Learning,and Well-Being

Abstract

Keywords

Self-Determination Theory as a Framework for Resilience

Distance Education and Basic Psychological Need Satisfaction in Times of COVID-19

The Present Research

Method

Participants and Procedure

Study 1: Austria

Study 2: Finland

Measures

Data Analysis

Results

Preliminary Analyses

Confirmatory Factor Analyses and Measurement Invariance Testing

Main Effects and Latent Interactions

Discussion

Implications for Higher Education in Times of COVID-19

Limitations and Future Directions

Conclusion

Footnotes

Acknowledgements

ORCID iDs

Authors

References