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. 2022 Nov 17;26(1):161–189. doi: 10.1007/s11218-022-09739-x

COVID-19’s impact on learning processes in Australian university students

Darrell Eckley 1, Andrew Allen 1,✉, Prudence Millear 1, Karina Tirsvad Rune 1
PMCID: PMC9668712  PMID: 36415584

Abstract

The novel coronavirus SARS-CoV-2 (COVID-19) has accentuated the role and interplay of numerous educational factors, inviting pedagogical research concerning online education. Using self-determination theory’s basic psychological needs and fundamental learning theories, identified educational factors were integrated into three pathways: (1) autonomy, technology acceptance, and self-regulation of learning; (2) relatedness, authentic happiness, and a classroom community; and (3) competency, harmonious passion, and trait conscientiousness. This study extends educational research by elucidating the relationships between psychological need fulfilment, educational factors, and students’ expectations of their future grades during the impact of COVID-19. Australian university students (N = 226, 77% female) completed questionnaires assessing their experience of home isolation, factors of each hypothesised pathway, and their expected grades. Structural equation modelling revealed that higher need fulfilment significantly predicted engagement in educational factors and that educational factors are complexly interrelated, providing resilience, motivation, and the mechanisms that facilitate learning. Most importantly, relatedness between academics and students positively influenced all learning pathways. Reciprocal determinism demonstrated the most substantial association with expected grades, and new insight was gained into the interrelationships of passion, trait conscientiousness, and self-regulation of learning.

Keywords: COVID-19, Education, Psychological needs, Self-determination theory

Introduction

COVID-19’s Impact on Learning Processes in Australian University Students.

Knowledge acquisition, knowledge processing, and internalisation of knowledge occur through processes that are primarily accomplished by students’ interactions with information, academics (i.e., teachers, tutors, lecturers, or scholars), and peers in a social learning environment (Bandura, 1977; Piaget, 1973; Vygotsky, 1978). Typically, changes to this learning environment have occurred incrementally; as technologies develop, academics have embraced an active learning approach facilitated by universities increasing investment in digital infrastructure (Aithal & Aithal, 2019; Dziuban et al., 2018; Tucker, 2012; Williamson, 2020). In contrast to this incremental environmental transition, the COVID-19 pandemic declaration of March 2020 was the catalyst for public health strategies that brought about sudden and unexpected change (Department of Health, 2020; World Health Organisation, 2020). Within days, face-to-face interaction on purposefully designed university campuses transformed into virtual interactive meetings with staff and students from home isolation. As yet, no published studies have assessed the unique impact of this transformation on the processes of learning.

Active learning is a function of distinct and complex relationships between diverse variables (Elen & Clark, 2006), the interaction between the student and external stimuli, the internal processes of knowledge construction, and the motivational forces that drive both (Illeris, 2018). Typically, educational research has attempted to simplify this complexity through a reductionist approach, often examining the unique contribution of a single educational factor (Elen & Clark, 2006). However, reductionism “generally signifies a loss of complexity which hinders an adequate understanding of reality” (Wrigley, 2019, p. 146). Therefore, to gain a more nuanced understanding of COVID-19’s complex impact within the expanding digital domain a holistic model of investigation is suggested, initiated by motivational forces and aligning educational factors in learning pathways according to the principles of classic learning theories.

One critical factor that applies to all learning is a student’s motivation (Thoonen et al., 2011; Chen & Jang, 2010). Self-determination theory (SDT; Deci & Ryan, 2000) posits that a student’s optimal functioning (a self-motivation to achieve growth; Maslow, 1943) requires specific support from their educational environment to fulfil three basic needs: autonomy, relatedness, and competency (Niemiec & Ryan, 2009). Given that learning is broadly defined as “any process that in living organisms leads to permanent capacity change and which is not solely due to biological maturation or ageing” (Illeris, 2007, p. 3), it may be considered analogous to growth. Therefore, it is feasible that the basic psychological needs required to motivate growth would align with the processes of learning that better facilitate growth. Autonomy, defined as an independent and active involvement in learning that is regulated by the self (Niemiec & Ryan, 2009), is consistent with psychological constructivism. Relatedness, a feeling of being genuinely liked, respected, and valued by educators and colleagues (Niemiec & Ryan, 2009), is consistent with social learning principles. Finally, competency, a feeling of efficiency, effectiveness, and self-efficacy in one’s studies (Niemiec & Ryan, 2009), is consistent with the principles of co-construction of knowledge within the zone of proximal development of social constructivism.

Psychological constructivism: the autonomy pathway

Psychological constructivism (Piaget, 1973) holds that individuals must actively construct their knowledge through the interaction of prior learning and new information. During COVID-19’s restrictions, students were forced to access new information primarily online, possibly accentuating behaviours related to their readiness to use the required technology (e.g., the technology acceptance model; Davis et al., 1989) and their engagement in cognitive strategies to regulate their learning more independently (e.g., self-regulation of learning; Zimmerman, 1990). Thus, both factors may be valuable in actively constructing knowledge in online environments.

The autonomy pathway comprises the elements of autonomy, technology acceptance, and self-regulation of learning, and their influence on expected grades during and beyond the COVID-19 lockdown (see Fig. 1). Autonomy support is derived from academics’ support of a student’s independent and active involvement in learning through classroom environments that nurture students’ preferences, interests, and internal motives (Dickinson, 1995; Reeve et al., 2004). In education, research has demonstrated autonomy-supportive measures by academics to be associated with self-regulation of learning by secondary school students (Wang et al., 2016), and that self-regulation skills predict academic performance at university (Broadbent & Fuller-Tyszkiewicz, 2018; Xiao et al., 2019). Studies of university students also support the notion that the association between autonomy support and self-regulation of learning may be mediated by technology acceptance (Liaw & Huang, 2013; Nikou & Economides, 2017). However, the potential for technology acceptance to fully mediate the relationship between autonomy support and self-regulation of learning has not been directly examined. COVID-19’s lockdown may have challenged a student’s feelings of autonomy and their ability to access information through new virtual platforms. Thus, the construction of personal knowledge through self-regulation strategies may have been reduced (Pelletier et al., 2002; Rocchi et al., 2013).

Fig. 1.

Fig. 1

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The autonomy pathway

Social learning: the relatedness pathway

In contrast, social learning theories emphasise the importance of social interactions to model learning through observation (Bandura, 1977) and continuous interaction between an active learner and the persons, objects, and symbols in their immediate environment (Bronfenbrenner, 1979; Bronfenbrenner & Ceci, 1994). During the COVID-19 lockdown, social interaction existed primarily and almost solely on virtual platforms, resulting in reduced social interactions and, in turn, diminished psychological wellbeing (Cao et al., 2020; Wang & Zhao, 2020). Therefore, the extent that impediments to social interactions affected active learning may be a function of students’ resilience in emotional stability (e.g., authentic happiness; Seligman, 2004) and sense of belonging and trust (e.g., classroom community; Rovai, 2001).

The relatedness pathway comprises the elements of relatedness, authentic happiness, a classroom community, and their influence on expected grades during and beyond the COVID-19 lockdown (see Fig. 2). Relatedness support is derived from academics’ use of processes that enhance a student’s sense of interpersonal connection, such as video conferencing, group assignments, encouraging questioning, and emotional support (Niemiec & Ryan, 2009). Educational research indicates that the formation of a classroom community in online university students can be predicted by relatedness support (Booker, 2008; Rovai, 2001). Moreover, authentically happy people more readily accept diversity and create inclusive social groups that may contribute to a sense of community (Dunn & Schweitzer, 2005; Morcom & MacCallum, 2012). However, only research outside the educational domain associates happiness with an increased sense of community (i.e., neighbourhood community; Ross et al., 2019). When a classroom community forms, student retention, engagement, and knowledge sharing increase, assisting academic success (Booker, 2016; Yilmaz, 2016), although the direct effect on grades remains controversial (for review see Beachboard et al., 2011; Boydie, 2020). Therefore, in theory, it is feasible that authentic happiness may mediate the relationship between relatedness support and the development of a classroom community (e.g., King, 2015; Ross et al., 2019). COVID-19’s physical restrictions forced students to study from home isolation; thus, an individual’s feeling of relatedness, happiness, and sense of community may have diminished by the lack of in-person classroom contacts (Cao et al., 2020; Santini et al., 2020; Schaefer et al., 2020; Wang & Zhao, 2020).

Fig. 2.

Fig. 2

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The relatedness pathway

Social constructivism: the competency pathway

Social constructivism (Vygotsky, 1978) posits that individuals learn when knowledge is co-constructed with support from others in a zone of proximal development. “What the child is able to do in collaboration today, he will be able to do independently tomorrow” (Vygotsky, 1987, p. 211). During the lockdown, the academic’s crucial role in the co-construction of knowledge may have been impeded by the online environment, necessitating more student independence in learning. Therefore, how readily students adapted to this independence may be a function of factors associated with persistence (e.g., harmonious passion; Vallerand et al., 2003) and conscientiousness (e.g., trait conscientiousness; Costa & McCrae, 1992).

The competency pathway comprises the elements of competency, harmonious passion, trait conscientiousness, and their influence on expected grades during and beyond the COVID-19 lockdown (see Fig. 3). Competency support in an online environment is derived from academics’ support of a challenging but achievable experience through classroom environments that provide optimal challenges with positive feedback (Niemiec & Ryan, 2009). Studies have demonstrated that competency support directly predicts university students’ academic success (Nahyun & Hana, 2017; Sulea et al., 2015; Talsma et al., 2018; Trautwein et al., 2009) through the process of reciprocal determinism (a reciprocal interaction between self-efficacy beliefs and the learning environment; Bandura, 1983).

Fig. 3.

Fig. 3

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The competency pathway

Competency support has demonstrated the ability to increase the motivational force of harmonious passion in high school students (Ruiz-Alfonso & León, 2019). Harmonious passion leads to a motivational state that underlies the strength of the behaviour in the direction of achievement resulting in a healthy and manageable engagement in learning (Vallerand et al., 2003). Interestingly, while trait conscientiousness is often considered the key non-intellective trait predictor of academic success in adolescents (Dumfart & Neubauer, 2016), the literature suggests it comprises both heritable stable trait facets (Krueger & Johnson, 2008) and facets such as perseverance, that may be amenable to change (Duckworth et al., 2007). In their review of trait conscientiousness, Roberts et al. (2014) suggest future research should examine the relationship between trait conscientiousness and motivational forces such as SDT’s basic needs or an individual’s “interests and values” (p. 1325). As such, there is a need to investigate the potential for passion for mediating the relationship between competency-supportive measures and trait conscientiousness. The COVID-19 lockdown resulted in academics having to make many time-consuming changes to match assessments with new virtual platforms; thus, their ability to provide a typical level of competency support may have been reduced (McNiff & Aicher, 2017; Pelletier et al., 2002).

The reviewed literature supported all individual connections on each pathway, except that of harmonious passion and trait conscientiousness. Additionally, despite a theoretical basis, no empirical evidence demonstrated the theorised mediation effects of technology acceptance, authentic happiness, or harmonious passion. Moreover, although theorised and reviewed as distinct, the pathways are likely to be interrelated. For example, an effective zone of proximal development requires academics to have the sensitivity to calibrate learning tasks to students’ current capabilities (Sivan, 1986). Academics would likely develop this sensitivity from interpersonal connectedness. Therefore, a holistic investigation of learning processes would benefit from not only an examination of these individual pathways but also an examination of an integrated model originating from relatedness.

The rationale for the present study

By accentuating the role that educational factors play in online learning, COVID-19 represents a unique research opportunity. Although a reductionist perspective taken by researchers has identified a diverse range of educational factors in the continually expanding digital domain, there is a paucity of research that acknowledges and examines the interrelated nature of these factors. SDT explains the role of basic needs of autonomy, relatedness, and competency in driving the intrinsic motivation that results in optimal functioning. Being self-motivated for growth in this way may be analogous to a desire to learn; thus, core needs and learning theories may overlap. Indeed, it is acknowledged that the boundaries between theories of learning are blurred and overlap (Brown, 2006; Jacobson et al., 2019), yet through the propensity for a reductionist approach, educational research continues to suffer a loss of understanding in complexity, openness, and values (Elen & Clark, 2006; Wrigley, 2019). According to one of the most influential psychologists in the field of learning, research can proceed from the study of a datum that varies in a significant fashion, and there appears to be no a priori reason why a complete description of higher mental processes cannot be reached without theory (Skinner, 1950). Therefore, this study aims to develop a final model that interrelates all data to explore this holistic gap in the existing literature.

First, it was hypothesised that students who reported higher levels of basic psychological needs fulfilment would also report more engagement in educational factors that lead to academic success. Specifically, students with greater autonomy will more readily accept technology leading to increased self-regulation of learning and course grades (H1a). COVID-19’s interference would reduce a student’s grades by undermining their feeling of autonomy, and by creating barriers to technology acceptance leading to reduced self-regulation and course grades (H1b). Students who feel stronger relationships with academics and peers will be more authentically happy, leading to an increased sense of classroom community and course grades (H2a). COVID-19’s interference would reduce a student’s grades by undermining their feeling of relatedness diminishing authentic happiness, reducing their sense of classroom community and course grades (H2b). A student who perceives more competency support from academics will be more passionate about the course and better engage trait conscientiousness, both increased competency support and conscientiousness will independently increase course grades (H3a). COVID-19’s interference would reduce a student’s grades by undermining an academics’ capacity to provide competency support, reducing a student’s harmonious passion, their engagement of trait conscientiousness, and course grades (H3b). Finally, it was hypothesised that students’ academic success would be predicted by the integration of the discrete linear pathways. Specifically, the integration of the three pathways would provide a better-fitting model with more interrelated effects than the linear pathways alone (H4).

Participants

Ethical approval for the study was granted by the University of the Sunshine Coast Human Ethics Committee (approval number S201243). The sample size was estimated through the statistical power analysis a priori in G*Power 3.1 (Faul et al., 2007). The analysis indicated a minimum requirement of 172 participants for the detection of a small effect size (f = 0.15).

Participation in the study was limited to students who were 18 years or older, recruited through a snowball approach using social media, an undergraduate student research pool, and in-class invitations by undergraduate teaching staff via the Zoom video conference platform. The sample comprised students (N = 226, 77% female) from the University of the Sunshine Coast, Queensland, Australia, and may be considered representative given the reported prevalence of females in Australian universities, ranges between 46–72% (Australian Bureau of Statistics, 2021). Ages ranged from 18 to 70 years (M = 29, SD = 11.25). Most students reported their method of study as full-time (84%), were from the home institution’s main campus (81%), and in their first year of study (54%). Additional demographic information is presented in Table 1.

Table 1.

Summary of Demographic Variables of Previous Online Experience, Physical Study Space, Study Distractions, and Internet Problems

Variable Frequency Percent
Previous online experience Very experienced 25 11.1
Somewhat experienced 43 19.0
Experienced 44 19.5
Little experience 72 31.9
No experience 42 18.6
Physical study space Dedicated study space 57 25.2
Dual-use study space 93 41.2
Shared study space 29 12.8
Study where I can 46 20.4
Must go elsewhere 1 .4
Study distractions No distractions 28 12.4
Low level distractions 71 31.4
Medium level distractions 48 21.2
High level of distractions 75 33.2
Must go elsewhere 4 1.8
Internet problems No problems 85 37.6
Low level of problems 79 35.0
Medium level of problems 50 22.1
High level of problems 11 4.9
Must go elsewhere 1 .4
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Design

The study used a descriptive cross-sectional survey design. The predictor variables were autonomy, relatedness, competency, technology acceptance, authentic happiness, harmonious passion, self-regulation of learning, classroom community, trait conscientiousness, and COVID-19 study interference. The outcome variable was self-report of the expected course grade. Structural equation modelling was used to assess possible model fit. Parametric data was created by calculating the mean scores and averaging responses across items for all scales (Carifio & Perla, 2008; Norman, 2010).

Measures

All scales demonstrated acceptable psychometric properties for use with university student populations. Cronbach’s alphas from the present study are presented in Table 2.

Table 2.

Means, Standard Deviations, Zero-Order Correlations for all Scales, Age, Biological, Sex, Grades, and Cronbach’s Alpha

Variable M (SD) 1 2 3 4 5 6 7 8 9 10 11 122 13
Autonomy 23.72 (5.81) (.78)
Relatedness 24.18 (6.39) .61** (.84)
Competency 23.37 (8.69) .75** .62** (.93)
Technology Acceptance 71.19 (21.50) .72** .50** .78** (.94)
Authentic Happiness 26.21 (4.60) .18** .28** .27** .20** (.72)
Harmonious Passion 29.11 6.65) .42** .40" .51** .53** .43** (.87)
Self-Regulation of Learning 70.19 (12.92) .45** .44** .61** .52** .42** .68** (.79)
Classroom Community 55.65 (13.92) .69** .78** .73** .65** .30** .42** .51** (.92)
Trait Conscientiousness 35.43 (6.78) .12 .13* .16* .16* .36** .49** .44** .11 (.86)
CO\TD-19's Interference 10.33 (2.74) −.43** −.33** −.55** −.52** −.15* −.34** −.43** −.39** −.08
Age 29 (11.52) .07 .07 .21** .17* .15* .21** .25** .14* .19** −.17*
Biological Sex NA (NA) .04 .02 −.07 .02 .11 .09 .01 .05 .02 −.22** .05
Grades COVID-19 5.91* (2.02) .32** .27** .55** .41** .24** .44** .53** .33** .41** −.37** .22** −.01
Grades Degree 7.22b (1.52) .08 .10 .25** .10 .09 .25** .36** .09 .34** −.26** .15* −1.0 .64
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N = 226. NA = Not Applicable

a5.91 = a grade of 70%

b7.22 = a grade of 76%. Biological Sex, Male = 1, Female = 2. Cronbach’s Alphas from the present study are presented in brackets. *p < .05; **p < .01; ***p < .001.

Autonomy, relatedness, and competency

Students’ perception of basic need fulfilment was measured using the 24-item Basic Needs and Frustration Scale (adult version; Chen et al., 2015). The items were rated on a 5-point Likert scale, ranging between (1) not at all true and (5) completely true. Composite scores were created by combining the need satisfaction and reversed need frustration items of each separate need. Possible scores ranged from 8 to 40. Scores ≥ 24 indicate an overall positive perception of needs satisfaction. Sample (autonomy scale) items included ‘I feel a sense of choice and freedom in the online learning I undertake’. The scale is widely used as a measure of satisfaction or frustration of an individual's basic psychological needs and has demonstrated reliability and validity with internal consistency calculated for each subscale ranging from α = 0.71 to 0.89 (Chen et al., 2015).

Technology acceptance

Students’ behavioural intention to use technology was measured using the 17-item Technology Acceptance Model (Davis et al., 1989) modified to fit the specific context of online learning for university students (see Park, 2009). Items were scored on a 7-point Likert scale from (1) strongly disagree to (7) strongly agree. Possible scores ranged from 17 to 119, with higher scores indicating higher acceptance of technology use. Sample item: ‘I find online learning systems easy to use’. The scale is widely used as a measure of a student’s capacity to use technology and has demonstrated reliability and validity with internal consistency for each subscale ranging from α = 0.76 to 0.94 (Park, 2009).

Authentic happiness

Authentic happiness was measured using the 7-item authentic happiness scale (Sanli et al., 2019). Items were scored on a 5-point Likert scale from (1) not like me to (5) very like me. Possible scores ranged from 7 to 35, with higher scores indicating a higher level of authentic happiness. Sample Item: ‘I am aware of the meaning of life’. The scale was developed to examine the concept of authentic happiness (Seligman, 2004) and is a reliable and valid scale to ascertain the authentic happiness levels of university students with an internal consistency of α = 0.84 (Sanli et al., 2019).

Harmonious passion

Harmonious passion was measured using the eight-item Passion Scale (Sigmundsson et al., 2020). Items were scored on a 5-point Likert scale from (1) not like me to (5) very like me. Possible scores ranged from 8 to 40 with higher scores indicating a higher level of harmonious passion. Sample Item: ‘I have passion enough to become very good in the content of the online course’. The scale was developed to examine the concept of engagement in valued activities and is a reliable and valid scale to ascertain the passion levels of university students with an internal consistency of α = 0.86 (Sigmundsson et al., 2020).

Self-regulation of learning

Students use of learning strategies was measured by a 16-item adapted version (see Johnson & Cooke, 2016) of the Motivated Strategies for Learning Questionnaire (Pintrich, 1991). Items were scored on a 7-point Likert scale from (1) not at all true of me to (5) very true of me. Possible scores ranged from 16 to 112 with higher scores indicating a higher level of self-regulation of learning. Sample Item: ‘When reading for the online courses, I make up questions to help focus my reading’. The MSLQ has been widely used to assess the self-regulation of learning in students (Credé & Phillips, 2011) and has demonstrated reliability and validity with internal consistency for each subscale ranging from α = 0.59 to 0.91 (Johnson & Cooke, 2016).

Classroom community

Classroom community was measured by the 20-item classroom community scale (Rovai, 2002). Items were scored on a 5-point Likert scale from (1) strongly disagree to (5) strongly agree. Possible scores ranged from 20 to 100 with higher scores indicating a greater sense of community. Sample item: ‘I do not feel a spirit of community in the online course’. The scale was developed to examine the concept of community in a learning environment and is a reliable and valid scale to ascertain the classroom community levels of university students with internal consistency for the subscale of connectedness, α = 0.92 and learning α = 0.87 (Rovai, 2002).

Conscientiousness

Trait conscientiousness was measured by the 10-item subscale of conscientiousness from the NEO-PI-R (Costa & McCrae, 2008). Items were scored on a 5-point Likert scale from (1) strongly disagree to (5) strongly agree. Possible scores ranged from 10 to 50, with higher scores indicating a higher level of trait conscientiousness. Sample item: ‘Make plans and stick to them’. The NEO-PI-R scale is widely used as a measure of personality traits and has demonstrated reliability and validity with internal consistency for the subscale of conscientiousness, α = 0.91 (Costa & McCrae, 2008).

COVID-19 study interference

The possible challenges resulting from pandemic restrictions were constructed by combining four questions that were designed to meet the requirement of unidimensionality (Sijtsma, 2009) in assessing the impact of COVID-19. They included previous online experience, physical study space, study distractions, and internet problems that a student experienced during pandemic restrictions. The individual questions were scored on a 5-point Likert scale. For example, (1) I can study without distractions (5) There are too many distractions to study at home. Possible scores ranged from 4 to 20, with higher scores indicating a higher level of interference. Sample item: ‘Select the statement that best represents the current amount of distractions that impact your study activities where you live during the COVID-19 pandemic?’.

Students’ expectations of their future grades

Grades were predicted via self-efficacy of performance (Zimmerman & Bandura, 1994). The items were ‘Select the highest grade that you feel you are most certain you could attain overall at the graduation of your degree?’ and ‘Select the highest grade that you feel you are most certain you could attain in your remaining online courses during the COVID-19 pandemic’. Expectancies were indicated via grade outcome percentages (50% or lower to 85% or higher). The results were converted into a scale resulting in a score reflecting the grade percentage level an individual feels capable of achieving for their degree overall and for courses completed during pandemic restrictions. A student’s expectation of their future grades was found to be the most precise of 50 typical predictors of grades in a meta-analysis of educational research (Richardson et al., 2012).

Procedure

The survey was available to be completed in the final three weeks of the 12-week semester that ran from February 2020 to June 2020. The home institution ceased face-to-face education on the 23rd of March after approximately 3 weeks of study. Thus, students had completed a minimum of 6 weeks of online study. Participants responded to advertisements that included a link to the online questionnaire via Qualtrics (https://www.qualtrics.com/au/). Participants were informed that the aim of the study was to investigate university students’ experience of the move to online curriculum delivery in response to enforceable physical distancing during the COVID-19 pandemic. Prior to commencement, informed consent was actively obtained via tick-box. Participants were subsequently provided with the questionnaires which took an average of 25 min to complete.

Statistical analyses

The model design of the three pathways was based on the reviewed literature, with analysis following suggestions by Kline (2011) to use parcels, “a total score across a set of homogeneous items each with a Likert-type scale. Parcels are generally treated as continuous variables” (p. 179). Results were considered significant at p < 0.05. Statistical Package for the Social Sciences (SPSS Version 26.0; IBM Corp, 2017) program was used for all statistical analyses; structural equation modelling was conducted using IBM; Amos 26.0. in SPSS. Model fit was regarded as acceptable if: the Normed Fit Index (NFI) ≥ 0.90 (Byrne, 1994) or 0.95 (Schumacker & Lomax, 2004); the Tucker Lewis index (TFI) ≥ 0.90 (Hoyle, 1995); the Comparative Fit Index (CFI) ≥ 0.93 (Byrne, 1994); RMSEA ≤ 0.08 (Browne & Crudeck, 1993) and ideally ≤ 0.05 (Steiger, 1990); the relative chi-square (χ2/df) is ≤ 2 (Kline, 2011; Tabachnick et al., 2007). AIC is a fit measure relative to the value of the saturated model; good fit occurs when the AIC is less than the saturated model (Burnham & Anderson, 1998).

Results

Preliminary analysis

Mean scores, standard deviations, and zero-order correlations between study variables are presented in Table 2. Students who perceived greater psychological need fulfilment of autonomy, relatedness, and competency reported significantly higher levels of technology acceptance, authentic happiness, harmonious passion, self-regulation of learning, classroom community, trait conscientiousness and grades. The four COVID-19 interference questions shared small positive correlations (r = .10 to .22) and were all negatively correlated with the outcome measure of expected academic grades (r = − .17 to − .22).

Assumptions

Of the original 328 survey responses, 99 cases were removed due to missing data. An analysis of standardised values of all variables revealed one univariate outlier fell outside the cut off (Maximum, Z > 3.29, Minimum, Z < − 3.29) which was removed. Multivariate outliers were assessed via Mahalanobis distance and interpreted via a χ2 distribution, with degrees of freedom equivalent to the number of independent variables in the regression (Tabachnick et al., 2007). Two multivariate outliers were removed due to a violation of the critical χ2 value (α = 0.001) of the structural models. Cook’s distance was below 0.85 for all cases (Cook & Weisberg, 1982). Considering the final sample (N = 226) in the context of the central limit theory (Wilcox, 2010), the assumptions of normality for means-testing, and sample size for structural equation modelling were met (Field, 2018; Kline, 2011).

Main analysis

COVID-19’s impact on grades

The incorporation of a self-report strategy allowed the overall impact of COVID-19 on a student’s grades to be estimated via a paired sample t-test. The result from the questions ‘estimate what you believe your overall degree grade will be when you finish all courses’ (M = 7.22, SD = 1.52) and ‘estimate what you expect to receive as a grade in the online courses during the COVID 19 pandemic’ (M = 5.91, SD = 2.02) demonstrate that the presence of pandemic restrictions resulted in a statistically significant expected reduction, on average, of 6% of a student’s grade, t(225) = 12.50, p < .001, d = 0.73.

Group differences

A series of one-way between-groups ANOVA revealed no significant differences between the groups of method of study, campus, or year of study on the variables of COVID-19 interference or grades. However, differences were apparent based on age (i.e., comparing students under and over 25 years) and gender. First, older students (46%) reported less COVID-19 interference (M = 9.93, SD = 2.88) than younger students (M = 10.67, SD = 2.58), t(224) = 2.04, p = .042, g = 0.30. Second, older students expected higher grades (M = 6.39, SD = 1.98) than younger students (M = 5.51, SD = 1.98), t(224) = - 3.32, p = .001, g = 0.44. Third, women (M = 10.67, SD = 2.69) reported significantly higher COVID19 interference than men (M = 9.23, SD = 2.65), t(224) = − 3.39, p = .001, g = 0.54. Specifically, for women (M = 2.97, SD = 1.07) study distraction caused by family or others in the home was significantly higher compared to men (M = 2.25, SD = 0.97), t(224) = − 4.367, p < .001, g = 0.69.

Structural equation modelling

Autonomy

The first model to be tested was the hypothesised autonomy pathway, as shown in Fig. 1, which was supported by all hypothesised connections being significant as shown in Fig. 4. The fit of the model was excellent, χ2/df = 1.587 (χ2 = 15.87, 10 df), p = 0.104; CFI = 0.986, NFI = 0.965, TLI = 0.971, RMSEA = 0.051 (95% CI = 0.000–0.096), AIC = 51.87 (AIC saturated 56.00).

Fig. 4.

Fig. 4

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The Autonomy Pathway Model. Note Beta coefficients greater than β =  ± .3 are presented in bold lines and negative beta coefficients are presented as dashed lines. Biological sex, 1 = male, 2 = female. (Standardised solution; N = 226). *p < .05; **p < .01; ***p < .001

Relatedness

The model for the relatedness pathway (as shown in Fig. 2) was not supported by the data. After removing the non-significant hypothesised pathways (COVID-19 Interference → Authentic Happiness and Authentic happiness → Classroom Community) as suggested by (Kline, 2011), the modification indices indicated the inclusion of a direct path from COVID-19 interference to grades. This modified model was supported by the data (as shown in Fig. 5 and had an excellent fit), χ2/df = 1.360 (χ2 = 13.60, 10 df), p = 0.192; CFI = 0.989, NFI = 0.962, TLI = 0.977, RMSEA = 0.040 (95% CI = 0.000–0.088), AIC = 49.60 (AIC saturated 56.00).

Fig. 5.

Fig. 5

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The Relatedness Pathway Model. Note Beta coefficients greater than β =  ± .3 are presented in bold lines and negative beta coefficients are presented as dashed lines. Biological sex, 1 = male, 2 = female. (Standardised solution; N = 226). *p < .05; **p < .01; ***p < .001

Competency

The model for the competency pathway as shown in Fig. 3 was supported by the data, although three connections were non-significant. After removing the non-significant pathways (COVID-19 Interference → Harmonious Passion, COVID-19 → Conscientiousness, and Passion → Grades), the final model, as shown in Fig. 6, had excellent fit, χ2/df = 1.600 (χ2 = 19.20, 12 df), p = .084; CFI = 0.980, NFI = 0.949, TLI = 0.964, RMSEA = 0.052 (95% CI = 0.000–0.093), AIC = 51.20 (AIC saturated 56.00).

Fig. 6.

Fig. 6

Open in a new tab

The Competency Pathway Model. Note Beta coefficients greater than β =  ± .3 are presented in bold lines and negative beta coefficients are presented as dashed lines. Biological sex, 1 = male, 2 = female. (Standardised solution; N = 226). *p < .05; **p < .01; ***p < .001

Integrated model

The final model to be tested was the combination of the three previous models; however, this did not have a good fit. The integrated model was improved by removing the non-significant pathways, and by using the modification indices in several steps in line with the reviewed literature to ensure changes were theoretically sound, the final model is shown in Fig. 7. The integrated model had very good fit, χ2/df = 1.476 (χ2 = 72.32, 49 df), p = .017; CFI = 0.984, NFI = 0.953, TLI = 0.975, RMSEA = 0.046 (95% CI = 0.024–0.067), AIC = 156.32 (AIC saturated = 182.00).

Fig. 7.

Fig. 7

Open in a new tab

The Integrated Pathway Model. Note Beta coefficients greater than β =  ± .3 are presented in bold, and negative beta coefficients are presented as dashed lines. Biological Sex, 1 = male, 2 = female. (Standardised solution; N = 226). *p < .05; **p < .01; ***p < .001

The results suggest that 44% of the variance in students’ expectations of their future grades during COVID-19 restrictions is explained by the determinants of the integrated model. Squared multiple correlations (R2) represent the proportion of total variance on a variable that is accounted for by the predictors and are presented in Table 3.

Table 3.

Squared Multiple correlations, Standardised Direct, Indirect, and Total Effects for the Predictor Variables, COVID-19 Interference, Basic Psychological Needs, and Educational Factors on Grades for the Integrated Structural Model

Predictors Standardised beta coefficients effects on grades
R2 Direct Indirect Total Order of contribution to grades
COVID-19 interference (.09) – -.314 -.314 12
Autonomy (.60) – -.003 -.003 9
Relatedness (.11) – .225 .225 3
Competency (.52) .521*** .050 .572 1
Technology acceptance (.66) – .088 .088 8
Authentic happiness (.09) – .119 .119 7
Harmonious passion (.38) – .190 .190 4
Self-regulation of learning (.58) .183** - .183 5
Classroom community (.72) -.171* - -.171 11
Conscientiousness (.26) .258*** .037 .294 2
Age – .137 .137 6
Biological sex - -.073 -.073 10
Open in a new tab

N = 226. Order of contribution to grades, 1 = largest contributor to grades, 12 = largest hindrance to grades. p < .05; **p < .01; ***p < .001

Discussion

The present study aimed to assess the role of three core psychological needs in the process of learning, specifically in the online learning environment and, even more specifically, under conditions that forced students into this mode of learning due to COVID-19 restrictions. These three learning pathways, each originating with one of SDT’s well-known basic psychological needs and integrating educational factors were aligned by fundamental theories of learning. The proposed pathways were then used to empirically test the relationship between psychological needs and educational factors to investigate how these determinates could explain course grades during the impact of COVID-19 and beyond. The results of the individual pathway structural models support the hypotheses that relationships exist between the concept of specific need fulfilment and pathways of educational factors (H1a, H2a, and H3a). The results of the integrated pathway model provided supporting evidence of a more complex interrelationship between a student’s basic psychological needs and the educational factors that typically predict course grades (H4). COVID-19’s negative relationship to expected grades was larger than trait conscientiousness’s positive relationship, COVID-19 primarily impacted the social and more vulnerable state type factors (partially supporting, H1b, H2b, and H3b). This pattern of results is consistent with the evidence from preliminary COVID-19 research in which students repeatedly identified a lack of social interaction and a lack of appropriate skills as barriers to online learning (Aboagye et al., 2020; Anwar et al., 2020). Our findings highlight that students who perceive their learning environment to fulfil their basic psychological needs may be more likely to be inspired to proactively engage in a wide range of educational factors that enhance the processes of learning.

Discrete pathways

The results of the three discrete pathways supported the hypothesised links between each of the individual psychological needs and educational factors, except for the proposed mediation effect of authentic happiness. This pattern of results is consistent with the large volume of educational research previously reviewed. However, the purpose of this study was to gain a holistic understanding of the interrelated complexity of these educational factors within the expanding digital domain. To this end, the integrated structural model fit superseded the discrete pathways enabling a real-world interpretation of educational data (Wrigley, 2019).

Integration of the discrete linear pathways

The integrated model provided empirical evidence of eight additional non-hypothesised mediating relationships and was consistent with the presupposition that all learning begins with relatedness (Trespalacios & Uribe-Florez, 2020; Van Fleet, 1979). Relatedness strongly predicted competency and mildly predicted autonomy. However, whereas previous research found competency to directly predict both harmonious passion and trait conscientiousness (Ruiz-Alfonso & León, 2019; Trautwein et al., 2009), the present study has shown these relationships may be mediated by autonomy and technology acceptance. Competency demonstrated the most considerable relationship with grades, providing empirical support for the benefit of reciprocal determinism (Bandura, 1983). This idea is further supported by the findings that demonstrate this phenomenon occurs cross-culturally (Williams & Williams, 2010) and in both adults and children (Talsma et al., 2018).

In the integrated model, autonomy did not predict academic success, possibly because of a suppressor effect. A suppressor is defined as a third variable that increases or decreases the regression coefficient between the predictor and outcome variable by its inclusion in a regression equation (Conger, 1974). Feasibly, the relationship between autonomy and grades has been suppressed by the classroom community, which also predicted a negative effect on grades. Indeed, the overall suppression of both variables may be linked to the construct overlap (multicollinearity) between relatedness and classroom community (Ho, 2006), in which one predictor variable is utilising redundant information leading to unstable regression coefficient estimates (Raykov & Marcoulides, 2012). Therefore, a possibility exists that overlapping questions between the relatedness scale and classroom community subscale of connectedness subsumed the classroom community’s positive relationship, which, in turn, suppressed autonomy. Thus, the direct regression coefficient between classroom community and grades should be treated with caution, particularly as both autonomy and classroom community displayed positive correlations with grades. However, despite of a suppression effect, these results are consistent with Boydie (2020), who found no relationship between a classroom community and grades, and Beachboard et al.’s (2011) work that indicated relatedness to academics is more consequential to grades than a sense of community.

Continuing, the integrated model revealed that authentic happiness predicted harmonious passion and trait conscientiousness. Part of being authentically happy is congruence between desires and actions (Seligman & Csikszentmihalyi, 2000); therefore, the connection between authentic happiness and harmonious passion is logical. However, the logic behind the association between authentic happiness and trait conscientiousness is not clear. The most compelling explanation for the current set of findings is that being authentically happy provides emotional resilience to the impact of phenomena such as COVID-19, a concept supported in the literature (Seligman et al., 2009; Yildirim & Belen, 2018) and aligns with research examining neural imaging that indicates emotional distractions disrupt the neural circuitry involved in goal-directed processing (Blair et al., 2007).

Interestingly, in the reviewed research, Nikou and Economides (2017) found that autonomy strongly predicted technology acceptance, while Fathali and Okada (2018) found it was moderately predicted by competency. The integrated results were consistent with both studies but aligned more strongly with Fathali and Okada (2018). Moreover, whereas past researchers (Liaw & Huang, 2013) found that technology acceptance predicted self-regulation of learning, the integrated model revealed harmonious passion may mediate this relationship. Findings reduce the paucity of educational literature surrounding harmonious passion (Ruiz-Alfonso & León, 2016, 2019) by demonstrating the possibility that harmonious passion may be a resilient motivational force that can predict students’ engagement of trait conscientiousness and self-regulation of learning. Evidence that harmonious passion predicts trait conscientiousness may be explained by the notion that trait conscientiousness may be stimulated by state-type factors (e.g., perseverance; Duckworth et al., 2007).

Consistent with the robust evidence supporting the influence of trait conscientiousness (e.g., Alkış & Temizel, 2018; Sorić et al., 2017; Trapmann et al., 2007; Vedel, 2014) the results of the present study revealed a strong relationship with expected grades. Greater conscientiousness also predicted self-regulation of learning. Indeed, all paths led to self-regulation of learning; out of eight educational factors, four indirectly and three directly predicted self-regulation of learning. The present results are consistent with research demonstrating that the digital domain necessitates higher levels of self-regulation than traditional classroom settings (Inan et al., 2017; Onah & Sinclair, 2017; Wandler & Imbriale, 2017). This finding may be explained by the idea that self-regulation of learning is a vital factor in psychological constructivism, optimally transforming new information into personal knowledge.

Age was a protective factor against COVID-19’s interference and positively predicted authentic happiness and competency. These results are consistent with the claims that authentic happiness increases with age (Tanzer, 2019) and that being older increases self-efficacy beliefs (Huang, 2013). Therefore, younger individuals may have experienced higher levels of negative affect and perhaps a less concrete self-concept, which would explain their significantly lower expected course grades. So too, females experienced significantly higher levels of COVID-19 interference than males, specifically, distractions from others in the home. The present results are consistent with Zhao et al.’s (2019) work that deals with the negative effect that gender role orientation may have in the workplace. However, in contrast to the effect of age, no sex difference was found in expected grades. This anomaly may be explained by females reporting higher levels of autonomy, and harmonious passion, which was also apparent in autonomous language learning (Varol & Yilmaz, 2010). The present study provides evidence to support the notion that females are more intrinsically motivated and passionate, resulting in a state of optimal functioning that is more resilient than males, which counterbalanced COVID-19’s impact.

Real-world implications

The integrated results support the long-held view that the transmission of information hinges on inherent respect and trust between learner and academic (Van Fleet, 1979). This effect has been demonstrated more recently in first-year undergraduate students. In their study, Ambikairajah et al. (2019) found that brief 2-min conversations between academics and students significantly improved student perception of academic support. The integrated model extends the educational literature by demonstrating new mechanisms through which these positive relationships could foster learning throughout the complex pathways. For example, in the current study, relatedness directly predicted a student’s authentic happiness which, in turn, predicted educational factors not typically associated with social interaction, such as trait conscientiousness. The results also strongly imply that authentic happiness plays an important role in the processes of learning and supports the current trend of incorporating positive psychology into education (Dewaele et al., 2019; Norrish et al., 2013; Trask-Kerr et al., 2019). The finding that technology acceptance predicted harmonious passion was unexpected and demonstrated a unique motivational pathway. One interpretation of these findings is that as students use technology to acquire new information, it is a passion for the topic of study that increases the conscientious motivation to self-regulate learning. Thus, academics that are connected with their students and provide interesting and topical learning material are likely to inspire an autonomous and competent use of technology to increase passion and the conscientious use of strategies to construct personal knowledge.

The integrated results confirmed the stalwarts of educational research, that is, competency beliefs, trait conscientiousness, and self-regulation of learning were direct predictors of expectations of grades. Educational research supports the involvement of competency-beliefs in Bandura’s (1983) reciprocal determinism (Talsma et al., 2018; Williams & Williams, 2010). The integrated results indicate that this bidirectional self-influence predicts academic grades at a magnitude double that of trait conscientiousness. Therefore, academics providing competency support can feel a renewed faith in the efficacy of the zone of proximal development as an educational tool in the expanding digital domain. Finally, it may be time to re-examine the preconceived notion that conscientiousness is a stable trait factor. Consistent with Eisenberg et al.’s (2014) work that demonstrated self-regulation skills to be a core component in the development of more trait conscientiousness, the integrated model revealed harmonious passion might be the key to unlocking a student’s full conscientious potential. In sum, considering the discussed interrelationships, to assist students during COVID-19 and beyond, it is an academic’s relatedness to their students that may be the most influential factor in a student’s academic success.

Limitations and future research directions

Although strong theoretical support for the identified relationships, it is important to understand that model fit represents possibility and not causality. The self-report data this study relied upon may be vulnerable to common-method bias. Also, some students may have chosen to withdraw from studies during the pandemic, which may have resulted in a sample of determined students, or equally, a sample of students that had no other choice. The predictor variable COVID-19’s interference was based upon quantifiable physical environmental factors and is not a measure of negative affect or reduced wellbeing, which may range widely with an individual’s level of resilience and coping strategies. Although meeting the assumptions of multicollinearity (VIF less than 5; Becker et al., 2015), the use of the predictor variable of classroom community was questionable. The overlap between relatedness questions, such as ‘I feel connected with the people in the online courses who care for me, and for whom I care’ and classroom community questions, such as ‘I feel connected to others in the online courses’ should have been detected a priori to the investigation. A future research consideration may be that any effect from the development of a classroom community, like that of relatedness, is likely disseminated at the beginning of learning pathways. The strongest effect on grades can be explained by the theory of reciprocal determinism. Bandura (1983) points out that behaviour is regulated by the afterword contingency of an individual’s own actions, and this contingency is mutually derived from cognition, behaviour, and the environment. Future studies might adopt a longitudinal approach exploring students’ cognitions, behaviours, and learning environments with actual records of academic grades over time to ascertain educational factors and practices that may better create this state of positive and reciprocal self-influence. Despite these limitations, the current study uniquely contributes to the educational literature by providing a relatively comprehensive and theoretically driven model to explain how academics can enhance learning processes in the expanding digital domain.

Conclusion

Although COVID-19 is novel, there will always be change. Thus, research needs to determine proper ways to enhance students learning and inform pedagogical designs. The findings of this study have implications for academics working in the expanding digital domain by demonstrating the interrelated complexity between academic support of the core needs and educational factors in the processes of online student-centred learning. When academics implement supportive measures that fulfil a student’s psychological needs, it results in students being propelled by a feeling of relatedness, becoming more autonomous and self-efficacious, thus, optimally functioning, and proactively engaging in a wide range of educational factors that follow complex, interrelated pathways to academic success.

Acknowledgements

Nil

Biographies

Darrell Eckley

is studying a Master of Psychology (Clinical) at the University of the Sunshine Coast. He is a Provisional Psychologist and conducts academic research in education and transdiagnostic formulation.

Andrew Allen

PhD Candidate, is an Associate Lecturer in Clinical Psychology in the School of Health and Behavioural Sciences at the University of the Sunshine Coast. He is a registered Clinical Psychologist and conducts academic research in the areas of clinical psychology, cognitive psychology, and human sexuality.

Prudence Millear

Phd, is a Lecturer in Psychology at the University of the Sunshine Coast and Psychology Honours Program Coordinator. She conducts research in the areas of developmental psychology and occupational health psychology, with interests in factors that influence the success or not of relationships, developing and maintaining meaningful work, and healthy ageing.

Karina Tirsvad Rune

PhD, is a Lecturer in Psychology in the School of Health and Behavioural Sciences at the University of the Sunshine Coast. She is a Provisional Psychologist and conducts academic research in the areas of psycho-oncology, environmental psychology, and health behaviours.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Ethical approval for this project was obtained from the University of the Sunshine Coast Ethics Committee, approval number: S201243.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. Aboagye E, Yawson JA, Appiah KN. COVID-19 and e-learning: The challenges of students in tertiary institutions. Social Education Research. 2020 doi: 10.37256/ser.122020422. [DOI] [Google Scholar]
  2. Aithal PS, Aithal S. Building world-class universities: Some insights & predictions. International Journal of Management, Technology, and Social Sciences (IJMTS) 2019;4(2):13–35. [Google Scholar]
  3. Alkış N, Temizel TT. The impact of motivation and personality on academic performance in online and blended learning environments. Journal of Educational Technology and Society. 2018;21(3):35–47. [Google Scholar]
  4. Ambikairajah A, Ambikairajah R, Ambikairajah E. The impact of improving feelings of relatedness on motivation and engagement for tertiary students. International Journal of Mathematical Education in Science and Technology. 2019 doi: 10.1080/0020739X.2019.1703149. [DOI] [Google Scholar]
  5. Anwar M, Khan A, Sultan K. The barriers and challenges faced by students in online education during COVID-19 pandemic in Pakistan. Gomal University Journal of Research. 2020;36(1):52–62. [Google Scholar]
  6. Australian Bureau of Statistics. (2021). Education and Work, Australia.https://www.abs.gov.au/statistics/people/education/education-and-work-australia/latest-release
  7. Bandura A. Social learning theory. Prentice Hall; 1977. [Google Scholar]
  8. Bandura A. Temporal dynamics and decomposition of reciprocal determinism: A reply to Phillips and Orton. Psychological Review. 1983;90(2):166–170. doi: 10.1037/0033-295X.90.2.166. [DOI] [Google Scholar]
  9. Beachboard MR, Beachboard JC, Li W, Adkison SR. Cohorts and relatedness: Self-determination theory as an explanation of how learning communities affect educational outcomes. Research in Higher Education. 2011;52(8):853–874. doi: 10.1007/s11162-011-9221-8. [DOI] [Google Scholar]
  10. Becker JM, Ringle CM, Sarstedt M, Völckner F. How collinearity affects mixture regression results. Marketing Letters. 2015;26(4):643–659. doi: 10.1007/s11002-014-9299-9. [DOI] [Google Scholar]
  11. Blair KS, Smith BW, Mitchell DGV, Morton J, Vythilingam M, Pessoa L, Fridberg D, Zametkin A, Nelson EE, Drevets WC, Pine DS, Martin A, Blair RJR. Modulation of emotion by cognition and cognition by emotion. NeuroImage. 2007;35(1):430–440. doi: 10.1016/j.neuroimage.2006.11.048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Booker K. The role of instructors and peers in establishing classroom community. Journal of Instructional Psychology. 2008;35(1):12–16. [Google Scholar]
  13. Booker K. Connection and commitment: How sense of belonging and classroom community influence degree persistence for African American undergraduate women. International Journal of Teaching and Learning in Higher Education. 2016;28(2):218–229. [Google Scholar]
  14. Boydie, M. R. (2020). Predicting academic achievement based on sense of community among online public high school students (Publication Number 2429) [Doctoral dissertation, Liberty University]. https://digitalcommons.liberty.edu/doctoral/2429
  15. Broadbent J, Fuller-Tyszkiewicz M. Profiles in self-regulated learning and their correlates for online and blended learning students. Educational Technology Research and Development. 2018;66(6):1435–1455. doi: 10.1007/s11423-018-9595-9. [DOI] [Google Scholar]
  16. Bronfenbrenner U. The ecology of human development: Experiments by nature and design. Harvard University Press; 1979. [Google Scholar]
  17. Bronfenbrenner U, Ceci SJ. Nature-nurture reconceptualised in developmental perspective: A bioecological model. Psychological Review. 1994;101(4):568–586. doi: 10.1037/0033-295X.101.4.568. [DOI] [PubMed] [Google Scholar]
  18. Brown, T. H. (2006). Beyond constructivism: Navigationism in the knowledge era. On the Horizon, 14(3), 108–120. 10.1108/10748120610690681
  19. Browne M, Crudeck R. Alternative ways of assessing model fit. In: Bollen KA, Long JS, editors. Testing structural equation models. Sage; 1993. pp. 136–162. [Google Scholar]
  20. Burnham KP, Anderson DR. Practical use of the information-theoretic approach. In: Burnham KP, Anderson DR, editors. Model selection and inference. Springer New York; 1998. pp. 75–117. [Google Scholar]
  21. Byrne BM. Structural equation modeling with EQS and EQS/Windows: Basic concepts, applications, and programming. Sage; 1994. [Google Scholar]
  22. Cao W, Fang Z, Hou G, Han M, Xu X, Dong J, Zheng J. The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Research. 2020;287:1–5. doi: 10.1016/j.psychres.2020.112934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Carifio J, Perla R. Resolving the 50-year debate around using and misusing Likert scales. Medical Education. 2008;42(12):1150–1152. doi: 10.1111/j.1365-2923.2008.03172.x. [DOI] [PubMed] [Google Scholar]
  24. Chen B, Vansteenkiste M, Beyers W, Boone L, Deci E, Kaap-Deeder J, Duriez B, Lens W, Matos L, Mouratidis A, Ryan R, Sheldon K, Soenens B, Petegem S, Verstuyf J. Basic psychological need satisfaction, need frustration, and need strength across four cultures. Motivation and Emotion. 2015;39(2):216–236. doi: 10.1007/s11031-014-9450-1. [DOI] [Google Scholar]
  25. Chen KC, Jang SJ. Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior. 2010;26(4):741–752. doi: 10.1016/j.chb.2010.01.011. [DOI] [Google Scholar]
  26. Conger AJ. A revised definition for suppressor variables: A guide to their identification and interpretation. Educational and Psychological Measurement. 1974;34(1):35–46. doi: 10.1177/001316447403400105. [DOI] [Google Scholar]
  27. Conti G, Heckman JJ. Understanding conscientiousness across the life course: An economic perspective. Developmental Psychology. 2014;50(5):1451–1459. doi: 10.1037/a0036426. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Cook RD, Weisberg S. Residuals and influence in regression. Chapman & Hall; 1982. [Google Scholar]
  29. Costa PT, McCrae RR. Neo personality inventory-revised (NEO PI-R) Psychological Assessment Resources; 1992. [Google Scholar]
  30. Costa PT, McCrae RR. The NEO Inventories. In: Archer RP, Smith SR, editors. Personality assessment. Routledge; 2008. pp. 213–245. [Google Scholar]
  31. Credé M, Phillips LA. A meta-analytic review of the motivated strategies for learning questionnaire. Learning and Individual Differences. 2011;21(4):337–346. doi: 10.1016/j.lindif.2011.03.002. [DOI] [Google Scholar]
  32. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of two theoretical models. Management Science. 1989;35(8):982–1003. doi: 10.1287/mnsc.35.8.982. [DOI] [Google Scholar]
  33. Deci EL, Ryan RM. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist. 2000;55(1):68–78. doi: 10.1037/0003-066X.55.1.68. [DOI] [PubMed] [Google Scholar]
  34. Department of Health. (2020). Coronavirus (COVID-19) restrictions. https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov-health-alert/coronavirus-covid-19-restrictions
  35. Dewaele JM, Chen X, Padilla AM, Lake J. The flowering of positive psychology in foreign language teaching and acquisition research. Frontiers in Psychology. 2019;10:1–13. doi: 10.3389/fpsyg.2019.02128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Dickinson L. Autonomy and motivation a literature review. System. 1995;23(2):165–174. doi: 10.1016/0346-251X(95)00005-5. [DOI] [Google Scholar]
  37. Duckworth AL, Peterson C, Matthews MD, Kelly DR. Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology. 2007;92(6):1087–1101. doi: 10.1037/0022-3514.92.6.1087. [DOI] [PubMed] [Google Scholar]
  38. Dumfart B, Neubauer AC. Conscientiousness is the most powerful noncognitive predictor of school achievement in adolescents. Journal of Individual Differences. 2016;37(1):8–15. doi: 10.1027/1614-0001/a000182. [DOI] [Google Scholar]
  39. Dunn JR, Schweitzer ME. Feeling and believing: The influence of emotion on trust. Journal of Personality and Social Psychology. 2005;88(5):736–748. doi: 10.1037/0022-3514.88.5.736. [DOI] [PubMed] [Google Scholar]
  40. Dziuban C, Graham CR, Moskal PD, Norberg A, Sicilia N. Blended learning: The new normal and emerging technologies. International Journal of Educational Technology in Higher Education. 2018;15(1):1–16. doi: 10.1186/s41239-017-0087-5. [DOI] [Google Scholar]
  41. Eisenberg N, Duckworth AL, Spinrad TL, Valiente C. Conscientiousness: Origins in childhood? Developmental Psychology. 2014;50(5):1331. doi: 10.1037/a0030977. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Elen J, Clark RE. Handling complexity in learning environments: Theory and research. Emerald Group Publishing; 2006. [Google Scholar]
  43. Fathali S, Okada T. Technology acceptance model in technology-enhanced OCLL contexts: A self-determination theory approach. Australasian Journal of Educational Technology. 2018;34(4):138–154. doi: 10.14742/ajet.3629. [DOI] [Google Scholar]
  44. Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods. 2007;39(2):175–191. doi: 10.3758/BF03193146. [DOI] [PubMed] [Google Scholar]
  45. Field A. Discovering statistics using IBM SPSS statistics. 5. Sage Publications; 2018. [Google Scholar]
  46. Gilal FG, Channa NA, Gilal NG, Gilal RG, Shah SMM. Association between a teacher’s work passion and a student's work passion: A moderated mediation model. Psychology Research and Behavior Management. 2019;12:889–900. doi: 10.2147/PRBM.S212004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Ho R. Handbook of univariate and multivariate data analysis and interpretation with SPSS. CRC Press; 2006. [Google Scholar]
  48. Hoyle RH. Structural equation modeling: Concepts, issues, and applications. Sage; 1995. [Google Scholar]
  49. Huang C. Gender differences in academic self-efficacy: A meta-analysis. European Journal of Psychology of Education. 2013;28(1):1–35. doi: 10.1007/s10212-011-0097-y. [DOI] [Google Scholar]
  50. Illeris K. How we learn: Learning and non-learning in school and beyond. Routledge; 2007. [Google Scholar]
  51. Illeris K. Contemporary theories of learning: Learning theorists… in their own words. Routledge; 2018. [Google Scholar]
  52. IBM Corp. (2017). IBM SPSS Statistics for Windows. In (Version 25.0) IBM Corp.
  53. Inan F, Yukselturk E, Kurucay M, Flores R. The impact of self-regulation strategies on student success and satisfaction in an online course. International Journal on E-Learning. 2017;16(1):23–32. [Google Scholar]
  54. Jacobson, M. J., Levin, J. A., & Kapur, M. (2019). Education as a complex system: Conceptual and methodological implications. Educational Researcher, 48(2), 112–119. 10.3102/0013189X19826958
  55. Johnson GM, Cooke A. Self-regulation of learning and preference for written versus audio-recorded feedback by distance education students. Distance Education. 2016;37(1):107–120. doi: 10.1080/01587919.2015.1081737. [DOI] [Google Scholar]
  56. King RB. Sense of relatedness boosts engagement, achievement, and well-being: A latent growth model study. Contemporary Educational Psychology. 2015;42:26–38. doi: 10.1016/j.cedpsych.2015.04.002. [DOI] [Google Scholar]
  57. Kirk GS, Raven JE, Schofield M. The presocratic philosophers: A critical history with a selection of texts. Cambridge University Press; 1983. [Google Scholar]
  58. Kline RB. Principles and practice of structural equation modeling. Guilford; 2011. [Google Scholar]
  59. Krueger RF, Johnson W. Behavioral genetics and personality: A new look at the integration of nature and nurture. In: John OP, Robins RW, Pervin LA, editors. Handbook of personality: Theory and research. The Guilford Press; 2008. pp. 287–310. [Google Scholar]
  60. Liaw SS, Huang HM. Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers and Education. 2013;60(1):14–24. doi: 10.1016/j.compedu.2012.07.015. [DOI] [Google Scholar]
  61. Maslow AH. A theory of human motivation. Psychological Review. 1943;50(4):370–396. doi: 10.1037/h0054346. [DOI] [Google Scholar]
  62. McNiff J, Aicher T. Understanding the challenges and opportunities associated with online learning: A scaffolding theory approach. Sport Management Education Journal. 2017;11(1):13–23. doi: 10.1123/smej.2016-0007. [DOI] [Google Scholar]
  63. Morcom VE, MacCallum JA. Getting personal about values: Scaffolding student participation towards an inclusive classroom community. International Journal of Inclusive Education. 2012;16(12):1323–1334. doi: 10.1080/13603116.2011.572189. [DOI] [Google Scholar]
  64. Nahyun K, Hana S. Personality, traits, gender, and information competency among college students. Malaysian Journal of Library and Information Science. 2017;16(1):87–107. [Google Scholar]
  65. Niemiec CP, Ryan RM. Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory and Research in Education. 2009;7(2):133–144. doi: 10.1177/1477878509104318. [DOI] [Google Scholar]
  66. Nikou SA, Economides AA. Mobile-based assessment: Integrating acceptance and motivational factors into a combined model of self-determination theory and technology acceptance. Computers in Human Behavior. 2017;68:83–95. doi: 10.1016/j.chb.2016.11.020. [DOI] [Google Scholar]
  67. Norman G. Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education: Theory and Practice. 2010;15:625–632. doi: 10.1007/s10459-010-9222-y. [DOI] [PubMed] [Google Scholar]
  68. Norrish JM, Williams P, O'Connor M, Robinson J. An applied framework for positive education. International Journal of Wellbeing. 2013;3(2):147–161. doi: 10.5502/ijw.v3i2.2. [DOI] [Google Scholar]
  69. Onah D, Sinclair J. Assessing self-regulation of learning dimensions in a stand-alone MOOC platform. International Journal of Engineering Pedagogy. 2017;7(2):4–21. doi: 10.3991/ijep.v7i2.6511. [DOI] [Google Scholar]
  70. Park SY. An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Journal of Educational Technology and Society. 2009;12(3):150–162. [Google Scholar]
  71. Pelletier LG, Séguin-Lévesque C, Legault L. Pressure from above and pressure from below as determinants of teachers’ motivation and teaching behaviors. Journal of Educational Psychology. 2002;94(1):186–196. doi: 10.1037/0022-0663.94.1.186. [DOI] [Google Scholar]
  72. Piaget J. Psychology and epistemology. Grossman; 1973. [Google Scholar]
  73. Pintrich, P. R. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ).https://files.eric.ed.gov/fulltext/ED338122.pdf
  74. Pogorskiy, E., Beckmann, J. F., Joksimović, S., Kovanović, V., & West, R. (2018). Utilising a virtual learning assistant as a measurement and intervention tool for self-regulation in learning. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) (pp. 846–849). 10.1109/TALE.2018.8615130
  75. Raykov T, Marcoulides GA. A first course in structural equation modeling. Routledge; 2012. [Google Scholar]
  76. Reeve J, Jang H, Carrell D, Jeon S, Barch J. Enhancing students’ engagement by increasing teachers’ autonomy support. Motivation and Emotion. 2004;28(2):147–169. doi: 10.1023/B:MOEM.0000032312.95499.6f. [DOI] [Google Scholar]
  77. Richardson M, Abraham C, Bond R. Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin. 2012;138(2):353–387. doi: 10.1037/a0026838. [DOI] [PubMed] [Google Scholar]
  78. Roberts BW, Hill PL, Davis JP. How to change conscientiousness: The sociogenomic trait intervention model. Personality Disorders: Theory, Research, and Treatment. 2017;8(3):199–205. doi: 10.1037/per0000242. [DOI] [PubMed] [Google Scholar]
  79. Roberts BW, Lejuez C, Krueger RF, Richards JM, Hill PL. What is conscientiousness and how can it be assessed? Developmental Psychology. 2014;50(5):1315–1330. doi: 10.1037/a0031109. [DOI] [PubMed] [Google Scholar]
  80. Rocchi MA, Pelletier LG, Lauren Couture A. Determinants of coach motivation and autonomy-supportive coaching behaviours. Psychology of Sport and Exercise. 2013;14(6):852–859. doi: 10.1016/j.psychsport.2013.07.002. [DOI] [Google Scholar]
  81. Ross A, Talmage CA, Searle M. Toward a flourishing neighborhood: The association of happiness and sense of community. Applied Research in Quality of Life. 2019;14(5):1333–1352. doi: 10.1007/s11482-018-9656-6. [DOI] [Google Scholar]
  82. Rovai AP. Building classroom community at a distance: A case study. Educational Technology Research and Development. 2001;49(4):33–48. doi: 10.1007/BF02504946. [DOI] [Google Scholar]
  83. Rovai AP. Development of an instrument to measure classroom community. The Internet and Higher Education. 2002;5(3):197–211. doi: 10.1016/S1096-7516(02)00102-1. [DOI] [Google Scholar]
  84. Ruiz-Alfonso Z, León J. The role of passion in education: A systematic review. Educational Research Review. 2016;19:173–188. doi: 10.1016/j.edurev.2016.09.001. [DOI] [Google Scholar]
  85. Ruiz-Alfonso Z, León J. Teaching quality: Relationships between passion, deep strategy to learn, and epistemic curiosity. School Effectiveness and School Improvement. 2019;30(2):212–230. doi: 10.1080/09243453.2018.1562944. [DOI] [Google Scholar]
  86. Sanli E, Celik SB, Gencoglu C. Validity and reliability of the authentic happiness scale. Khazar Journal of Humanities and Social Science. 2019;22(1):5–20. doi: 10.5782/2223-2621.2019.22.1.5. [DOI] [Google Scholar]
  87. Santini ZI, Jose PE, York Cornwell E, Koyanagi A, Nielsen L, Hinrichsen C, Meilstrup C, Madsen KR, Koushede V. Social disconnectedness, perceived isolation, and symptoms of depression and anxiety among older Americans (NSHAP): A longitudinal mediation analysis. The Lancet Public Health. 2020;5(1):62–70. doi: 10.1016/S2468-2667(19)30230-0. [DOI] [PubMed] [Google Scholar]
  88. Schaefer T, Fabian CM, Kopp T. The dynamics of online learning at the workplace: Peer-facilitated social learning and the application in practice. British Journal of Educational Technology. 2020;51(4):1406–1419. doi: 10.1111/bjet.12894. [DOI] [Google Scholar]
  89. Schumacker RE, Lomax RG. A beginner’s guide to structural equation modeling. Psychology Press; 2004. [Google Scholar]
  90. Seligman ME. Authentic happiness: Using the new positive psychology to realise your potential for lasting fulfillment. Simon and Schuster; 2004. [Google Scholar]
  91. Seligman MEP, Csikszentmihalyi M. Positive psychology. American Psychologist. 2000;55(1):5–14. doi: 10.1037/0003-066X.55.1.5. [DOI] [PubMed] [Google Scholar]
  92. Seligman MEP, Ernst RM, Gillham J, Reivich K, Linkins M. Positive education: Positive psychology and classroom interventions. Oxford Review of Education. 2009;35(3):293–311. doi: 10.1080/03054980902934563. [DOI] [Google Scholar]
  93. Sigmundsson H, Haga M, Hermundsdottir F. The passion scale: Aspects of reliability and validity of a new 8-item scale assessing passion. New Ideas in Psychology. 2020;56:1–6. doi: 10.1016/j.newideapsych.2019.06.001. [DOI] [Google Scholar]
  94. Sijtsma K. On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika. 2009;74(1):107–120. doi: 10.1007/s11336-008-9101-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Sivan E. Motivation in social constructivist theory. Educational Psychologist. 1986;21(3):209–233. doi: 10.1207/s15326985ep2103_4. [DOI] [Google Scholar]
  96. Skinner, B. F. (1950). Are theories of learning necessary? Psychological Review, 57(4), 193–216. 10.1037/h0054367 [DOI] [PubMed]
  97. Sorić I, Penezić Z, Burić I. The big five personality traits, goal orientations, and academic achievement. Learning and Individual Differences. 2017;54:126–134. doi: 10.1016/j.lindif.2017.01.024. [DOI] [Google Scholar]
  98. Steiger JH. Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research. 1990;25(2):173–180. doi: 10.1207/s15327906mbr2502_4. [DOI] [PubMed] [Google Scholar]
  99. Sulea C, van Beek I, Sarbescu P, Virga D, Schaufeli WB. Engagement, boredom, and burnout among students: Basic need satisfaction matters more than personality traits. Learning and Individual Differences. 2015;42:132–138. doi: 10.1016/j.lindif.2015.08.018. [DOI] [Google Scholar]
  100. Tabachnick BG, Fidell LS, Ullman JB. Using multivariate statistics. Pearson; 2007. [Google Scholar]
  101. Talsma K, Schüz B, Schwarzer R, Norris K. I believe, therefore I achieve (and vice versa): A meta-analytic cross-lagged panel analysis of self-efficacy and academic performance. Learning and Individual Differences. 2018;61:136–150. doi: 10.1016/j.lindif.2017.11.015. [DOI] [Google Scholar]
  102. Tanzer JR. Developing authentic happiness: Growth curve models to assess lifelong happiness. The Journal of Positive Psychology. 2019 doi: 10.1080/17439760.2019.1689419. [DOI] [Google Scholar]
  103. Thoonen EE, Sleegers PJ, Peetsma TT, Oort FJ. Can teachers motivate students to learn? Educational Studies. 2011;37(3):345–360. doi: 10.1080/03055698.2010.507008. [DOI] [Google Scholar]
  104. Ting YL. Tapping into students’ digital literacy and designing negotiated learning to promote learner autonomy. The Internet and Higher Education. 2015;26:25–32. doi: 10.1016/j.iheduc.2015.04.004. [DOI] [Google Scholar]
  105. Trapmann S, Hell B, Hirn JOW, Schuler H. Meta-analysis of the relationship between the big five and academic success at university. Journal of Psychology. 2007;215(2):132–151. doi: 10.1027/0044-3409.215.2.132. [DOI] [Google Scholar]
  106. Trask-Kerr K, Chin TC, Vella-Brodrick D. Positive education and the new prosperity: Exploring young people’s conceptions of prosperity and success. Australian Journal of Education. 2019;63(2):190–208. doi: 10.1177/0004944119860600. [DOI] [Google Scholar]
  107. Trautwein U, Lüdtke O, Roberts BW, Schnyder I, Niggli A. Different forces, same consequence: Conscientiousness and competence beliefs are independent predictors of academic effort and achievement. Journal of Personality and Social Psychology. 2009;97(6):1115–1128. doi: 10.1037/a0017048. [DOI] [PubMed] [Google Scholar]
  108. Trespalacios J, Uribe-Florez LJ. Developing online sense of community: Graduate students’ experiences and perceptions. The Turkish Online Journal of Distance Education TOJDE. 2020;21(1):57–72. doi: 10.17718/tojde.690340. [DOI] [Google Scholar]
  109. Tucker B. The flipped classroom. Education next. 2012;12(1):82–83. [Google Scholar]
  110. Vallerand RJ, Blanchard C, Mageau GA, Koestner R, Ratelle C, Léonard M, Gagné M, Marsolais J. Les passions de l'ame: On obsessive and harmonious passion. Journal of Personality and Social Psychology. 2003;85(4):756–767. doi: 10.1037/0022-3514.85.4.756. [DOI] [PubMed] [Google Scholar]
  111. Van Fleet A. Learning to teach: The cultural transmission analogy. Journal of Thought. 1979;14(4):281–290. [Google Scholar]
  112. Varol B, Yilmaz S. Similarities and differences between female and male learners: Inside and outside class autonomous language learning activities. Procedia - Social and Behavioral Sciences. 2010;3:237–244. doi: 10.1016/j.sbspro.2010.07.038. [DOI] [Google Scholar]
  113. Vedel A. The big five and tertiary academic performance: A systematic review and meta-analysis. Personality and Individual Differences. 2014;71:66–76. doi: 10.1016/j.paid.2014.07.011. [DOI] [Google Scholar]
  114. Vygotsky LS. Mind in society: The development of higher psychological processes. Harvard University Press; 1978. [Google Scholar]
  115. Vygotsky LS. Thinking and speech (N. Minick, Trans.) In: Rieber RW, Carton AS, editors. The collected works of LS Vygotsky. Plenum Press; 1987. pp. 39–285. [Google Scholar]
  116. Wandler JB, Imbriale WJ. Promoting undergraduate student self-regulation in online learning environments. Online Learning. 2017;21(2):1–16. doi: 10.24059/olj.v21i2.881. [DOI] [Google Scholar]
  117. Wang C, Zhao H. The impact of COVID-19 on anxiety in Chinese university students. Frontiers in Psychology. 2020;11(1168):1–8. doi: 10.3389/fpsyg.2020.01168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  118. Wang JCK, Ng BLL, Liu WC, Ryan RM. Can being autonomy-supportive in teaching improve students’ self-regulation and performance? In: Liu WC, Wang JCK, Ryan RM, editors. Building autonomous learners. Springer; 2016. pp. 227–243. [Google Scholar]
  119. WHO. (2020). WHO Director-General's opening remarks at the media briefing on COVID-19 - 11 March 2020. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
  120. Wilcox RR. Fundamentals of modern statistical methods: Substantially improving power and accuracy. 2. Springer; 2010. [Google Scholar]
  121. Williams T, Williams K. Self-efficacy and performance in mathematics: Reciprocal determinism in 33 nations. Journal of Educational Psychology. 2010;102(2):453. doi: 10.1037/a0017271. [DOI] [Google Scholar]
  122. Williamson B. Making markets through digital platforms: Pearson, edu-business, and the (e)valuation of higher education. Critical Studies in Education. 2020 doi: 10.1080/17508487.2020.1737556. [DOI] [Google Scholar]
  123. Wong J, Baars M, Davis D, Van Der Zee T, Houben GJ, Paas F. Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human-Computer Interaction. 2019;35(5):356–373. doi: 10.1080/10447318.2018.1543084. [DOI] [Google Scholar]
  124. Wrigley T. The problem of reductionism in educational theory: Complexity, causality, values. Power and Education. 2019;11(2):145–162. doi: 10.1177/1757743819845121. [DOI] [Google Scholar]
  125. Xiao S, Yao K, Wang T. The relationships of self-regulated learning and academic achievement in university students. SHS Web of Conferences. 2019;60:1–4. doi: 10.1051/shsconf/20196001003. [DOI] [Google Scholar]
  126. Yildirim M, Belen H. The role of resilience in the relationships between externality of happiness and subjective well-being and flourishing: A structural equation model approach. Journal of Positive School Psychology. 2018;3(1):62–76. [Google Scholar]
  127. Yilmaz R. Knowledge sharing behaviors in e-learning community: Exploring the role of academic self-efficacy and sense of community. Computers in Human Behavior. 2016;63:373–382. doi: 10.1016/j.chb.2016.05.055. [DOI] [Google Scholar]
  128. Zimmerman BJ. Self-regulated learning and academic achievement: An overview. Educational Psychologist. 1990;25(1):3–17. doi: 10.1207/s15326985ep2501_2. [DOI] [Google Scholar]
  129. Zimmerman BJ, Bandura A. Impact of self-regulatory influences on writing course attainment. American Educational Research Journal. 1994;31(4):845–862. doi: 10.3102/00028312031004845. [DOI] [Google Scholar]

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