The purpose of this study is to investigate online pre-service teachers’ self-regulation in three types of online interaction and learner outcomes: perceived learning and satisfaction. The data were collected from 372 pre-service teachers enrolled in an online teacher training program. Descriptive statistics and path analysis were used to answer the proposed research questions. The results show that online pre-service teachers have perceptions of self-regulation in three types of online interaction and learner outcomes at a moderate level. Their perceptions do not vary depending on the demographics of age, gender, and employment status, except for perceived learning. Females perceived higher scores than males for perceived learning. The path analysis indicates the relationships among self-regulation in three types of online interaction and learner outcomes. As consistent with the prior research, the results imply that the improved self-regulation for interaction results in improved learner outcomes of perceived learning and satisfaction. However, the role of learner demographics is not generally significant in this research context.
Online education has become a mainstream way of education through its empowerment with interactive web technology. The adoption of online education surely lies behind the time and place flexibility it provides as can be observed in all definitions of distance education. For example, Moore and Kearsley (2011) define it as the instructional activities provided by an institutional organization via a communication medium, independent of time and place. This definition and other available ones imply the flexibility regarding how and when to interact with others and content. Although this independence is the most powerful aspect of online education contributing to its mainstream acceptance, it might restrict the opportunities for interaction of learners with instructors (Azevedo et al. 2008).
In this sense, self-regulation (SR) of learners plays a key role in the achievement of online education. A great deal of research has documented the significance of SR on learner outcomes (e.g. Broadbent 2017; Cho et al. 2017; Kizilcec et al. 2017). However, several scholars criticized SR research by noting that the use of SR in traditional education settings may limit its significance in online education due to the limited research on SR in online learner interactions (Broadbent and Poon 2015; Cho and Cho 2017; Cho and Kim 2013). Based on these critics, Cho and Cho (2017) revisited SR in online education by a particular emphasis on interaction. Therefore, the current study focused on a novel concept proposed by Cho and Cho (2017): SR in three types of online interaction, “Learner-Content”, “Learner-Instructor”, and “Learner-Learner”.
Self-regulation in Online Education
SR is defined as the self-direction and self-beliefs of learners to transform their potential into performance skills (Zimmerman 2008). As a result of the requirement for learner autonomy, SR skills are critical for the achievement of the learning outcomes in online education (Bol and Garner 2011). The SR skills include being able to use the strategies “such as comprehension monitoring, goal setting, planning, and effort management and persistence” (Pintrich and De Groot 1990, p.38). More specifically, the SR skills needed by the learners in online learning environments could be exemplified as self-monitoring and evaluating their performance regularly, and seeking help from their peers and instructors as needed (Cho and Cho 2017; Yukselturk and Bulut 2007).
Research indicated that successful online learners are the ones who can use SR strategies in their learning process (Hodges 2005; Yukselturk and Bulut 2007). Several studies conducted in online learning environments further confirmed that utilization of SR strategies is determinant in academic outcomes (e.g. Broadbent 2017; Cho et al. 2017; Cho and Shen 2013; Sun and Rueda 2012). In a systematic review of the literature on SR and academic achievement in online learning, Broadbent and Poon (2015) highlighted the positive relationship between SR strategies and academic outcomes. A relatively recent study by Kizilcec et al. (2017) demonstrated that learners with more SR skills tend to revisit learning materials more in massive open online courses. Similarly, Sun and Rueda (2012) found out that SR skills are significantly related to behavioural, emotional, and cognitive learner engagement in online courses. Studies also showed that SR in online environments plays a key role in the improvement of the sense of community (Cho et al. 2017) and the minimization of academic procrastination (Rakes and Dunn 2010).
Considering the significance of SR, the improvement of online learners’ use of SR strategies is a key issue in online education. In this regard, instructor scaffolding for the enhancement of SR strategies is recommended in relevant research studies (Cho and Kim 2013; Bol and Garner 2011; Hromalik and Koszalka 2018) as well as the identification of the influential interactive elements in online education (Delen and Liew 2016).
Self-regulation in Three Types of Online Interaction
Interaction in online education can be defined as the mutual actions between the learner and other elements of online education. Moore (1989) distinguished three types of interaction by labelling them as “Learner–Content” (LC), “Learner–Instructor” (LI), and “Learner–Learner” (LL) interactions. He ascertained that LC interaction is the process, producing the outcome of cognitive changes in learners’ minds as they interact with the content. The next interaction type, LI interaction, is the process in which instructors promote learners’ interest and motivation; provide them with guidance, support, and encouragement. The last type of interaction is LL interaction. According to Moore (1989), this type of interaction is desired in online education since it is sometimes the most valuable learning resource and required part of instruction.
Interaction has been a current research issue in online education. The research efforts on the interaction types demonstrated their positive impact on learner outcomes (e.g. Alqurashi 2019; Kuo et al. 2013; Kuo et al. 2014a; 2014b; Paul et al. 2015; Swart et al. 2014). It has been revealed that interaction in online environment predicts such learner outcomes as satisfaction (Alqurashi 2019; Ekwunife-Orakwue and Teng 2014; Kuo et al. 2014a, 2014b; Paul et al. 2015; Shea et al. 2016; Swart et al. 2014), learner engagement (Bolliger Halupa 2018), social presence (Horzum 2015), and academic performance (Ekwunife-Orakwue and Teng 2014; Agudo-Peregrina et al. 2014; Shea et al. 2016).
Research on interaction additionally revealed that SR learning is the production of the internal factors (e.g. learners’ self-efficacy for interaction) and the external factors (e.g. interaction between learner and instructor) (Cho and Jonassen 2009). Thus, interaction regulation is a key issue to be considered in online education for the successful integration of the internal and external factors. Cho and Jonassen (2009) defined interaction regulation as learners’ capability to regulate their interaction with the instructor and other learners. Cho and Cho (2017) recently elaborated and conceptualized SR in three types of interaction proposed by Moore (1989). In other words, they conceptualized SR for LC, LI, and LL interactions in online learning by underlining the difference of SR in three types of interaction.
Several studies confirmed the impact of interaction regulation on learning outcomes. Cho et al. (2010) found out that interaction regulation is positively correlated with such learner outcomes as peer and instructor social presence, connection to a learning community, and perceived learning. Cho and Shen (2013) further indicated that interaction regulation is a predictor of the time spent by learners in online courses. Finally, Cho and Cho (2017) showed that learners’ SR in three types of interaction is correlated with their self-efficacy for learning and course satisfaction.
The literature concludes that the more learners have interaction regulation, the more positive outcomes will be obtained. For this aim, the research suggests that instructional scaffolding by online instructors is a key factor for the improvement of learners’ SR in three types of interaction. A study by Cho and Kim (2013) showed that learners’ demographics and their perceptions of mastering content, the importance of peer and instructor interaction, and instructor scaffolding for interaction are the predictors of their SR for interaction. Such a scaffolding and identification of the influential factors on individual and contextual factors require proper conceptualization and measurement of learners’ SR levels in three types of interaction.
Learner Satisfaction and Interaction in Online Education
Learner satisfaction is an instructional outcome that can be assumed as the indicator of the accomplishment in online education. For this reason, it has been commonly adopted in the literature to evaluate the quality of online courses and programs. Similarly, it has been also used in the studies focused on online interaction (Alqurashi 2019; Eom and Ashill 2016; Kara 2020; Kuo et al. 2013, 2014a, 2014b) and SR for interaction as a dependent variable (Cho and Cho 2017). Satisfaction can be defined as learner “perceptions of learning experiences and perceived value of a course” (Kuo et al. 2013, p.17). Several factors in online learning environments influence learner satisfaction such as instructor issues, course management, technology, and interactivity (Bolliger and Martindale 2004).
Although the studies found that interaction is a predictor, they produced incongruent results regarding which interaction type is the most significant predictor of satisfaction. For example, while some studies found that LC interaction is the most significant predictor (Ekwunife-Orakwue and Teng 2014; Kuo et al. 2014a, 2014b), others revealed that LI interaction is the most significant one (Kara 2020; Paul et al. 2015; Swart et al. 2014). Besides, Zhang (2003) found out that LL interaction is the most significant predictor of learner satisfaction. Apart from these studies, Gray and DiLoreto (2016) revealed that satisfaction was not significantly affected by learner interaction, but affected by instructor presence. Paul et al. (2015) explain the incongruent results in this issue by stressing the differences in educational contexts such as content, learner characteristics, and educational level.
Perceived Learning and Interaction in Online Education
Perceived learning could be defined as learners’ self-report judgments about their learning. It is commonly adopted in the literature as a predictor of learning. For example, Rockinson-Szapkiw et al. (2016) showed that perceived learning is a predictor of learners’ final course grades. Baturay (2011) also found out that online learners’ perceived learning is highly related to their satisfaction with the online course. The research studies revealed that interaction is a predictor of perceived learning in online settings. For example, a study by Eom and Ashill (2016) indicated that three types of interaction have a significant impact on perceived learning. Similarly, Alqurashi (2019) found that LI and LC interactions are the significant predictors of perceived learning and LC interaction is the most influential one while LL interaction is not a significant predictor. Gray and Diloreto (2016) pointed out that learner interaction significantly influences their perceived learning. As different from these studies, Sebastianelli et al. (2015) found that course content is the most significant predictor of perceived learning; rather than LI and LL interactions.
Significance and Purpose of the Study
The present study was based on the call for further research on a relatively novel construct; SR in three types of interaction. The contribution of this study to the relevant literature is twofold: (1) enhancing the generalizability of the model proposed by Cho and Cho (2017) through its implementation in a different context, and (2) investigation of SR levels of learners in three types of interaction and its association with learner outcomes. Although Cho and Kim (2013) found out no significant association of learners’ demographics of age and gender with their regulation for interaction, they suggest further investigation of the association of these characteristics with interaction regulation. Considering the influence of learner demographics such as age (e.g. Huang et al. 2016) and gender (e.g. Ekwunife-Orakwue and Teng 2014) on interaction, it might be useful to investigate how SR in interaction and learner outcomes change depending on learner demographics. Besides, since the employment status of the learners causes challenges in online education (Kara et al. 2019), their employment status was also included in the analysis. Therefore, the current study aimed to investigate how SR in three types of interaction varies relying on learners’ age, gender, and employment status and its association with the commonly used learner outcomes in the SR and interaction research: perceived learning and satisfaction. Therefore, this study used these constructs as the learning outcomes to reveal the association of SR in three types of interaction with them. The following research questions were sought to be answered within this study:
What are learners’ perceived SR in three types of online interaction and learner outcomes?
How their perceptions of SR in three types of online interaction vary depending on their demographics?
What are the structural relations among learner perceptions of SR in three types of online interaction and learner outcomes of satisfaction and perceived learning?
The study was carried out through quantitative research design. For the first research question, descriptive statistics were used to reveal learners’ perceptions of SR in three types of online interaction and learner outcomes; namely perceived learning and perceived satisfaction. For the second research question, casual—comparative research design was used. The reasons and results of the differences between the groups are presented without an intervention. As for the third research question, a proposed model is tested with structural equation modeling. The hypothesized model was presented in Fig. 1.
The data were collected from 372 pre-service teachers enrolled in an online teaching certificate program offered by a public university. The university where the data were collected is one of the four universities offering online teaching certificate programs in Turkey. These certificate programs in Turkey are available for the students or graduates of the non-educational undergraduate programs since this certificate is a prerequisite in Turkey to work as teachers in schools. Therefore, the participants are also the students or graduates of other faculties such as art, letters, engineering, or administrative sciences. For this reason, the participants include both traditional university students and adults.
Convenience sampling strategy was used to select the participants. In this program, pre-service teachers participated in all courses online via an LMS, to which a web conferencing system was integrated for synchronous lessons, except the applied courses; namely, “Instructional Technology and Material Development”, “Teaching Methods” and “Teaching Practice”. The demographics of the participants are shown in Table 1.
According to Table 1, 249 (%66.9) of the participants are female, and 123 (%33.1) are male. Most of the participants’ ages ranged from 18 to 24. The participants at the age range of 25–30 (N = 47, %12.6) and 31–40 (N = 46, %12.4) are approximate to each other. There are only nine (%2.4) participants at the age of 40 and above. As for the employment status, most of the participants are unemployed. 272 (%73.1) of the participants are unemployed, 43 (%11.6) of them have part-time work, and 57 (%15.3) have full-time work.
Data Collection Instruments
The data were collected through an online questionnaire consisting of four parts. The first part is for collecting the demographic information about the participants such as age, gender, subject fields, and employment status. The rest of the questionnaire included the following scales:
The Online Self-regulation Questionnaire (OSRQ) in Three Types of Interaction
The original form of the scale was developed by Cho and Cho (2017) and adapted into Turkish language and culture by the researchers (Çakır et al. 2019). The scale is a 7-point Likert-type scale, on which 1 means “Strongly Disagree” and 7 means “Strongly Agree”. In the development study, the Cronbach alpha coefficients for the factors of SR in LI Interaction, SR in LC Interaction, and SR in LL Interaction were obtained as 0.94, 0.94, and 0.91, respectively. The fit indices were obtained as χ2 = 1223.35, CFI = 0.91, TLI = 0.90, SRMR = 0.06, and RMSEA = 0.07.
The adaptation study was conducted with a different sample than this study with the participation of 307 online pre-service teachers. The content validity was provided by Cho and Cho (2017). The language equivalency was ensured through back translation strategy with the support of two professionals of English Language Teaching. Both adapted and original versions of the scale involve 30 items and three factors (SR in LI Interaction, SR in LC Interaction, and SR in LL Interaction). The items in the factors of SR in LC interaction, SR in LI interaction, and SR in LL interaction was exemplified, respectively as follows: “Before starting an assignment, I plan out my work.”, “I ask my questions as clearly as possible for effective communication with the instructor.”, and “I plan my participation in online interaction with other students in advance.”.
Cronbach alpha coefficient of the total scale was obtained as 0.98 in the adaptation study. The Cronbach Alpha values for the factors of SR in LI Interaction, SR in LC Interaction, and SR in LL Interaction were obtained as 0.96, 0.96, and 0.95, respectively. According to the Field (2013), the alpha values greater than 0.70 indicate that the instrument is highly consistent. According to the results of the confirmatory factor analysis conducted to test the construct validity of the scale, the fit values (χ2/df:2.79; RMSEA = 0.07; SRMR = 0.05; NNFI = 0.092; CFI = 0.92; PNFI = 0.81) revealed that the adapted scale fits the collected data.
e-Satisfaction scale, developed by Gülbahar (2012), involved 29 items using a 5-point Likert-type scale, on which 1 means”Nearly Never” and 5 means”Nearly Always”. The scale includes four factors; namely, Communication and Usefulness, Instructional Process, Instructional Content, Interaction, and Evaluation. An example item from the scale is as follows: “Within the context of the course, I was able to quickly access all the information I was looking for.” The alpha coefficient of the scale was gathered as 0.97. According to the results of the confirmatory factor analysis conducted to test the construct validity of the scale, the fit values [χ2 (358, N = 2699) = 3278.64, p < 0.000, RMSEA = 0.064, S-RMR = 0.037, GFI = 0.90, AGFI = 0.88, CFI = 0.99, NNFI = 0.99, IFI = 0.99] revealed that the scale is a valid measurement tool.
Perceived Learning Scale
The scale, used to measure perceived learning, was developed by Rovai et al. (2009) and adapted into Turkish by Albayrak et al. (2014). The adapted version involved nine items using a 7-point Likert scale (1 = “Definitely False” to 7 = ”Definitely True”) and under three dimensions (Cognitive, Affective, Psychomotor). Items in the scale could be exemplified with the following one: “I trust myself so much through the issues I've learned.”
The Cronbach alpha coefficient of the adapted scale was obtained as 0.83. This value implies that the scale is adequately consistent. According to the results of the confirmatory factor analysis conducted to test the construct validity of the scale, the values for the fit indices [χ2/df = 1.43, χ2 = 32.84, df = 23,p. = 0.00389)],SRMR = 0.48, RMSEA = 0.059, AGFI = 0.90, GFI = 0.94, NFI = 0.90, NNFI = 0.94, and CFI = 0.96) revealed that the scale is a valid measurement instrument. Psychomotor dimension of the scale was excluded from the current study since the objectives of the courses within the online program, where the data were collected, do not cover psychomotor dimension of perceived learning.
Data Collection Procedure and Analysis
An online questionnaire was distributed to the online pre-service teachers via the learning management system on which the online courses are managed. The data were collected in the spring semester of 2018. The goal of the study was explained in detail at the beginning of the form for the participants to voluntarily participate in the study.
Self-regulation levels of the participants in three types of interaction were descriptively investigated to answer the first research question. Independent samples t test and multivariate analysis of variance (MANOVA) were used to reveal whether learners’ perceptions of SR in three types of online interaction and learner outcomes vary depending on their demographics. Structural Equation Modelling is used for the third research question to model the relationship between SR in three types of online interaction and learner outcomes. For the Structural Equation Modelling Analysis, AMOS 24 was used. For the other analyses, SPSS 20 was used.
RQ1: What are Learners’ Perceived SR in Three Types of Online Interaction and Learner Outcomes?
The findings regarding the learners’ perceived SR in three types of online interaction indicated that the mean score for the total scale (\(M\) = 5.05, SD = 0.82) is more than moderate (see Table 2). The factors within the scale showed similar findings. SR in LC Interaction had the highest mean score (\(M\) = 5.23, SD = 0.93), followed by SR in LI Interaction (\(M\) = 5.14, SD = 1.01). The lowest mean score was obtained for SR in LL Interaction (\(M\) = 4.77, SD = 0.93).
The findings for learner outcomes demonstrated that the mean scores are similarly more than moderate. The mean score for perceived learning was gathered as \(M\) = 4.51 (SD = 0.98) and the mean score for satisfaction was obtained as \(M\) = 3.27 (SD = 0.66).
RQ2: How Their Perceptions of SR in Three Types of Online Interaction Vary Depending on Their Demographics?
The results of the independent samples t test were used to investigate how participant perceptions vary depending on gender (see Table 3). According to the findings, SR in three types of online interaction and learner outcomes, except perceived learning, do not significantly change in terms of gender. It is revealed that perceived learning scores of females (M = 27.66, SD = 5.78) are higher than males (M = 25.88, SD = 5.89). As a result of the analysis conducted for the effect size, the value of 0.30 revealed that the gender effect is at a medium level. No significant difference was observed for other variables in terms of gender.
MANOVA results showed that SR levels in three types of online interaction do not significantly vary relying on employment status (F(2–369) = 1.230, Wilk’s Λ = 0.289, p > 0.05, η2 = 0.010). In addition, no significant difference was observed among the age categories (F(2–369) = 2.572, Wilk’s Λ = 0.060, p > 0.05, η2 = 0.019). It was determined that perceived learning and satisfaction scores do not significantly vary in terms of employment status (F(2–369) = 1.927, Wilk’s Λ = 0.104, p > 0.05, η2 = 0.010). Similarly, the mean scores of perceived learning and satisfaction do not significantly differ in terms of age (F(2–369) = 1.081, Wilk’s Λ = 0.372, p > 0.05, η2 = 0.009). These results implied that participants’ demographics generally do not have significant changes in their perceptions.
RQ3: What are the Structural Relations Among Learner Perceptions of SR in Three Types of Online Interaction and Learner Outcomes of Satisfaction and Perceived Learning?
Correlations among the variables were examined before the structural model was tested. Significant positive correlations were observed among all variables (see Table 4). The highest correlation was observed between SR in LI interaction and SR in LC interaction (r = 0.665, p < 0.001). The lowest correlation was observed between SR in LI interaction and perceived learning (r = 0.363, p < 0.001).
To answer the third research question, a path analysis was conducted to identify and model the relationships among online learners’ SR perceptions in three types of online interaction and outcomes of perceived learning and satisfaction. The hypothesized model significantly fits the collected data: χ2/df = 3.133, SRMR = 0.034, CFI = 0.98, TLI = 0.95, RMSEA = 0.076, PNFI = 0.46. The structural model with the obtained regression weights is presented in the Fig. 2. According to the regression weights, SR in LI interaction has a positive effect on learner satisfaction and perceived learning, but this effect is not significant. The other interaction types have a positive and significant effect on perceived learning and satisfaction. In other words, SR in LC interaction and SR in LL interaction significantly contribute to the prediction of perceived learning and learner satisfaction. In the overall model, the regression weights indicated that the effect of SR in LC interaction on perceived learning has the highest effect (β = 0.29). It is followed by the effect of SR in LL interaction on satisfaction (β = 0.25) and SR in LL interaction on perceived learning (β = 0.21).
Due to the significant change in perceived learning in terms of gender, the model was separately tested for males and females. The test conducted for females provided almost similar findings with the overall model (χ2/df = 2.730, SRMR = 0.036, CFI = 0.97, TLI = 0.93, RMSEA = 0.084, PNFI = 0.45). The analysis with females showed that SR in LI interaction positively correlated with their perceived learning and satisfaction, but it did not significantly predict the outcomes. Their SR in LC and SR in LL interactions significantly predicted their perceived learning and satisfaction. As for the male participants, the tested model indicated that only SR in LL interaction significantly predicted their perceived learning and satisfaction (χ2/df = 2.119, SRMR = 0.049, CFI = 0.97, TLI = 0.93, RMSEA = 0.096, PNFI = 0.45).
SR in three types of online interaction has been recently elucidated by Cho and Cho (2017) and requires further investigation for understanding of learners’ SR in online environments. This study aimed to investigate online pre-service teachers’ perceptions of SR in three types of online interaction and its association with learner outcomes. The study illustrated that the learners perceived SR in three types of online interaction at a moderate level. The descriptive findings were quite approximate with the prior study by Cho and Cho (2017). However, SR in LC interaction had the highest score while Cho and Cho (2017) found that SR in LI interaction is the highest. One explanation might be the online class sizes in the study context that impedes the opportunity for learners to satisfactorily interact with the instructors. In both studies, learner perceptions of SR in LL interaction showed the lowest perception probably due to the lack of opportunities or encouragements for learners to engage in deep level of interaction with each other (Cho and Cho 2017). Online class size again might block instructors’ contribution to LL interaction.
The study secondly demonstrated that learner characteristics including gender, age, and employment status do not cause a significant change in learners’ perceptions of SR in three types of online interaction. In this regard, this study produced similar findings with several studies on interaction change in terms of gender (e.g. Bolliger and Halupa 2018; Vasiloudis et al. 2015) and age (e.g. Ekwunife-Orakwue and Teng 2014; Vasiloudis et al. 2015). Although it was assumed that learners’ employment status might cause educational challenges for them, the study revealed no significant impact of employment status on their SR perceptions in three types of online interaction. Learners’ satisfaction and perceived learning did not significantly change depending on their demographics, except gender. The perceived learning scores of the males were significantly higher than the females. Since perceived learning includes cognitive and affective dimensions, it is possible that their perceptions might be influenced by the gender differences in other antecedents of perceived learning or course performance such as cognitive (Kang et al. 2014) or instructor presence (Russo and Benson 2005).
Finally, the study indicated that learners’ perceptions of SR in three types of online interaction are significantly and positively correlated with learner outcomes of perceived learning and satisfaction. The tested model (see Fig. 2) showed the structural relationship among these constructs. This result is consistent with the prior studies on the association of interaction with perceived learning (Alqurashi 2019; Eom and Ashill 2016; Kara 2020; Gray and Diloreto 2016) and satisfaction (e.g. Alqurashi 2019; Cho and Cho 2017; Eom and Ashill 2016; Kara 2020; Kuo et al. 2013, 2014a, 2014b). All elements except SR in LI interaction significantly predicted the learner outcomes. The model showed that SR in LC interaction makes the greatest contribution to the prediction of perceived learning, followed by SR in LL interaction. Besides, SR in LL interaction made the greatest contribution to the prediction of learner satisfaction, followed by SR in LC interaction. Cho and Cho (2017), as incongruent with the current study, found that all elements except SR in LL interaction significantly predicted learner outcomes of self-efficacy for learning and satisfaction. The non-significance obtained for the impact of SR in LI interaction on both learner outcomes is likely the result of the limited interaction between the instructors and learners owing to the large online class sizes considering the influence of LI interaction on SR learning (Cho and Jonassen 2009). Accordingly, another implication might be that this limited interaction caused learners’ need for higher level of SR in interaction between them and content. For this reason, scaffolding by instructors for the enhancement of SR strategies of learners as suggested by the several previous studies (e.g. Cho and Kim 2013; Bol and Garner 2011; Hromalik and Koszalka 2018) is highly recommended for the improvement of learner outcomes based on the results of the current study. The findings from the path analysis suggest for practice that online instructors are particularly required to encourage learners to interact with each other through discussions or collaborative learning tasks for the enhancement of LL interaction and to organize materials and learning tasks in a way that improves LC interaction.
The contribution of this study to the literature of online learning is twofold. First, the results of the study enhanced the generalizability of a novel conceptualization of SR in three types of interaction by implementing it in a different online learning context and with a different population. The study produced approximately similar results with the previous study by Cho and Cho (2017) in the context of online teacher training. Second, the study revealed the level of SR in three types of online interaction as perceived by online pre-service teachers and its association and impact on learner outcomes. It was concluded that the improvement in learners’ SR in three types of online interaction might yield improved learner outcomes in online education. It was also concluded that the contextual issues such as instructor actions and online class sizes would be the determinants of SR in three types of online interaction and they have a key role in the achievement of the learner outcomes.
Limitations and Recommendations for Future Research
The study has several limitations. The participants are limited with online pre-service teachers and findings are limited with the context of a single university. The inclusion of more heterogeneous participants (e.g. online students from diverse subject fields, online graduate and undergraduate students) and replication of this study in other contexts might enhance the generalizability of both the findings obtained in this study and the conception of SR in three types of online interaction. The study draws attention to the influence of contextual elements in the findings. Future studies might focus on how contextual issues affect SR in three types of online interaction and learner outcomes.
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Kara, M., Kukul, V. & Çakır, R. Self-regulation in Three Types of Online Interaction: How Does It Predict Online Pre-service Teachers’ Perceived Learning and Satisfaction?. Asia-Pacific Edu Res 30, 1–10 (2021). https://doi.org/10.1007/s40299-020-00509-x
- Self-regulation in three types of online interaction
- Online self-regulation
- Perceived learning
- Online teacher training