Investigating the relationships among teachers’ motivational beliefs, motivational regulation, and their learning engagement in online professional learning communities

Computers & Education 134 (2019) 145–155

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Computers & Education journal homepage: www.elsevier.com/locate/compedu

Investigating the relationships among teachers’ motivational beliefs, motivational regulation, and their learning engagement in online professional learning communities

T

Si Zhanga,∗, Qingtang Liub,∗∗ a b

School of Information Science and Engineering, Hunan First Normal University School of Educational Information Technology, Central China Normal University

A R T IC LE I N F O

ABS TRA CT

Keywords: Learning communities Computer-mediated communication Cooperative/collaborative learning Teaching/learning strategies

This study investigated the relationships among teachers' motivational beliefs, motivational regulation, and their learning engagement in online professional learning communities. More importantly, teachers' online learning behaviors were collected and used as indicates of learning engagement. 520 teachers involved in a training program were surveyed. The results of regression analyses showed that teachers' perceived task value positively predicted their online learning engagement. Moreover, teachers' motivational regulation played a partial mediating role in the predicting power of perceived task value to learning engagement. In addition, the first half of the mediating path among perceived task value, motivational regulation, and learning engagement was moderated by teachers' self-efficacy. The moderating effect on motivational regulation was higher for teachers with a high sense of self-efficacy than those with a low sense of self-efficacy. Therefore, it was suggested to assign learning tasks that meet the needs of teachers and pay attention to the cultivation of teachers’ self-efficacy beliefs to increase their learning engagement in online professional learning communities. The limitations and the future research directions were discussed.

1. Introduction Professional development is an important source of improving teachers' teaching ability, innovating teaching methods and ultimately improving students' learning performance (González & Skultety, 2018; Prenger, Poortman, & Handelzalts, 2017; Tondeur, Aesaert, Prestridge, & Consuegra, 2018). With the rapid development of information technology, teachers' professional development mode, learning content, learning resources and environments are in changing (Barnes, Zuilkowski, Mekonnen, & Ramos-Mattoussi, 2018; Fan, Wang, & Wang, 2011). Teachers' professional development mode has changed from the face-to-face mode to a blended mode combining online learning and classroom teaching practice (Jonker, März, & Voogt, 2018; Yeh, Huang, & Yeh, 2011). Online learning has broken the limits of time and space and teachers can access online learning activities anytime and anywhere, which effectively solves the conflict between teachers' work and learning (Chen, Chen, & Tsai, 2009; Ching & Hursh, 2014). The establishment of online professional learning community further promotes the collaboration and communication between teachers and experts (Koc, Peker, & Osmanoglu, 2009; Lee & Brett, 2015; Macià & García, 2016; Prenger et al., 2017). Although online learning has

∗

Corresponding author. Si Zhang, School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China. Corresponding author. Qingtang Liu, School of Educational Information Technology, Central China Normal University, Wuhan 430079, China. E-mail addresses: [email protected] (S. Zhang), [email protected] (Q. Liu).

∗∗

https://doi.org/10.1016/j.compedu.2019.02.013 Received 14 October 2018; Received in revised form 16 February 2019; Accepted 20 February 2019 Available online 25 February 2019 0360-1315/ © 2019 Elsevier Ltd. All rights reserved.

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potential benefits some studies have found that teachers in online professional learning communities have uneven behavior distribution, less interaction, and poor learning continuity (Prenger et al., 2017; Tsiotakis & Jimoyiannis, 2016; Xing & Gao, 2018). As more and more teachers participate in professional development activities through online learning platforms (Beach, 2017; Donnelly & Boniface, 2013; Hou, 2015; Mu, Liang, Lu, & Huang, 2018; Tseng & Kuo, 2014), it is essential to understand the factors related to teachers’ learning engagement in online learning environments. Some studies attempt to improve teachers' learning engagement through external stimulation, such as online learning environment and activity designs (e.g., Beach, 2017; Lee & Brett, 2015; Rolando, Salvador, Souza, & Luz, 2014), learning analysis and feedback (e.g., Xing & Gao, 2018). In terms of internal factors, there is evidence suggesting a positive correlation between teachers' leaning engagement and their motivational factors (Durksen, Klassen, & Daniels, 2017; Gorozidis & Papaioannou, 2014). However, few studies have investigated the relationship between teachers' motivational factors and their learning engagement in online professional learning community. As for the measurement of teachers' learning engagement in online learning environments, self-report surveys, observations, and interviews are commonly used (Pimmer et al., 2019; Saini & Abraham, 2019). Although it is easier to measure teachers' learning engagement through these methods, the results have a certain degree of subjectivity (Henrie, Halverson, & Graham, 2015). In contrast, teachers’ learning behaviors on the online learning platform sheds light on their actual learning engagement (Chen et al., 2009; Xing & Gao, 2018; Yeh, 2010). The aim of this study was to investigate the relationships among teachers' motivational beliefs, motivational regulation, and their learning engagement in online professional learning communities. More importantly, teachers’ online learning behaviors were collected and used as indicators of learning engagement in this study. 2. Theoretical framework 2.1. Learning engagement The most obvious characteristic of online learning is the separation of learners and teachers in time and space. Therefore, it has become the focus of researchers to promote and maintain learners' learning engagement (Chen, 2017; Zhang, Meng, de Pablos, & Sun, 2019). Learning engagement refers to the degree to which learners actively participate in learning activities (Heflin, Shewmaker, & Nguyen, 2017). Learning engagement is identified as a critical factor for determining learners' learning performance in online learning environments (Blasco-Arcas, Buil, Hernández-Ortega, & Sese, 2013; Li & Baker, 2018; Phan, McNeil, & Robin, 2016) and is associated with many factors such as learners' self-efficacy, perceived task value, and teachers' teaching existence (Aristeidou, Scanlon, & Sharples, 2017; Jung & Lee, 2018). In terms of measurement of learning engagement, self-report surveys, observations, and interviews are commonly used methods (e.g., Chen, 2017; Henrie et al., 2015; Jung & Lee, 2018). For instance, Jung and Lee (2018) used a self-report questionnaire to collect students' data of self-efficacy, perceived usefulness and ease of use, teaching presence, learning engagement and persistence in Massive Open Online Courses. Based on the data collected from 306 students, the results of structural equation modeling revealed that students' self-efficacy, perceived usefulness and teaching presence were positively related with their learning engagement. With the development of information technology and the in-depth application of learning analytics in the education field, more and more studies collect learners' behaviors on online learning platform as indicators of learning engagement (e.g., Ding, Er, & Orey, 2018; Heflin et al., 2017; Kim, Park, Yoon, & Jo, 2016). For instance, Kim et al. (2016) constructed proxy variables to collect empirical evidence of students' learning engagement, such as the number of postings, posting length, discussion time per visit, and these variables were further used to detect and intervene students’ online learning behavior. The establishment of the online professional learning community supports teachers' collaborative learning which is a basic requirement of the social constructivist approaches (Hong, Lin, Chai, Hung, & Zhang, 2019; Xing & Gao, 2018). Given the relationship between learning engagement and learning performance, many studies have focused on teachers' learning engagement in the online learning community (e.g., in de Wal, den Brok, Hooijer, Martens, & van den Beemt, 2014; Saini & Abraham, 2019; Yeh, 2010; among others). For instance, Saini and Abraham (2019) developed a scale to measure learning engagement and investigated the effect of Facebook-based instructional approach on preservice teachers' learning achievement and engagement. Teachers' behavior recorded on the online learning platform also help to shed light on their learning engagement. For instance, Yeh (2010) counted the frequencies of teachers' discussions on a group discussion board, and used the behavioral data to determine teachers' roles in an online learning community. Tsiotakis and Jimoyiannis (2016) extracted log data from a web platform database to represent in-service teachers' participation and engagement in online learning communities. Based on the data, some critical factors related to teachers' presence in online learning communities were analyzed. In a similar vein, teachers’ learning engagement in online professional learning communities was measured and analyzed by several behavioral indicators in the present study, such as the number of logins, number of postings, number of resources submitted. 2.2. Relationship among perceived task value, self-efficacy and learning engagement Achievement motivation theories have been widely used to explain the reason why learners engage in or disengage from learning activities (e.g., Georgiou & Kyza, 2018; Nikou & Economides, 2016; Wang & Liou, 2018; Zainuddin, 2018). Among various theoretical frameworks of achievement motivation, the expectancy-value theory (EVT) is one of the influential theories. According to the EVT, motivational beliefs encompass one's perceptions of self-concept, intrinsic value and utility value of the task. Self-concept represents the expectation expressed by learners through the judgement of self-ability and perceived task difficulty. Intrinsic value represents an individual's interest in learning tasks, while utility value refers to whether a task is related to an individual's future plans or goals 146

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(Wigfield & Eccles, 2000). EVT calims that individual's expectations of success and perceived task value play an important role in explaining their decisions, persistence and performance related to achievement (Atkinson, 1964; Vernadakis, Kouli, Asimenia, Gioftsidou, & Antoniou, 2014; Wigfield & Eccles, 2000). The positive correlations among teachers' perceived task value, self-efficacy and learning engagement are supported by some empirical studies. For instance, Gorozidis and Papaioannou (2014) investigated the relationship between teachers' motivation and intentions regarding participation in training. Results showed that teachers' intrinsic motivation positively predicted their intentions to participate in training. Durksen et al. (2017) also found that there was a significant positive correlation between teachers' motivational beliefs and their learning engagement. Therefore, the present study assumes that teachers' perceived task value and self-efficacy are important factors related to their learning engagement in online professional learning communities. Teachers' perceived task value referred to teachers' perception of the importance of online learning tasks to their professional development. For instance, teachers perceived that learning tasks were very interesting (intrinsic value), or learning tasks helped to improve teaching abilities (utility value). Self-efficacy is another widely used representation of motivational beliefs. According to Bandura (1986), “perceived self-efficacy is defined as people's judgements of their capabilities to organize and execute courses of action required to attain designated types of performances” (p. 391). Self-efficacy represents learner's self-perceptions of capability to perform a target behavior. Since learner's self-concept and self-efficacy have much in common: an emphasis on perceived competence, an emphasis on the prediction of future motivation, emotion, and performance (Bong & Skaalvik, 2003; Marsh, 2007), the present study did not make a specifically distinction between self-concept and self-efficacy. Previous studies have revealed a significant relationship between learner's self-efficacy and learning performance (e.g., Chen, 2017; Wu, 2017; Yilmaz, 2016). For instance, Wu (2017) investigated the relationship among university students' learning performance, self-efficacy, perceived attention problems and self-regulation strategies by using a revised scale. Base on the data collected from 696 university students, the results from the multilevel structural equation model showed significant negative indirect relationship between students' self-efficacy and their learning performance. In the present study, teachers' self-efficacy referred to their perception of online learning ability based on past accomplishments, such as the abilities of watching video cases, participating in online discussion, or completing learning tasks. Research also showed a significant relation between teachers' self-efficacy perception and their learning performance (e.g., Choi, Cristol, & Gimbert, 2018; Hung, 2016; Kavanoz, Yüksel, & Özcan, 2015; Lee & Lee, 2014). For instance, Lee and Lee (2014) investigated pre-service teachers' self-efficacy beliefs for technology integration (SETI) after a coursework intervention. The results of hierarchical multiple regression showed a significant relationship between teachers' self-efficacy perception and their lesson planning practice. 2.3. Motivational regulation The self-determination theory is often used to explain learners' motivational regulation. The self-determination theory holds that the method of motivating students to value and self-regulate learning activities is to foster students' internalization and integration of values and behavioral regulations (Deci & Ryan, 1985; Ryan & Deci, 2000a). The self-determination theory on the one hand explains the related factors of variability in motivation and the undermining effects of learners' extrinsic motivation on their intrinsic motivation (Deci & Ryan, 1985), on the other hand specifies different forms of extrinsic motivation and contextual factors that either promote or hinder the internalization and integration of behavioral regulations (Ryan & Deci, 2000a). According to the self-determination theory, there are four types of extrinsic motivation: external regulation, introjected regulation, identified regulation, and integrated regulation (Ryan & Deci, 2000b). Internalization and integration refer to a process in which learners take in a regulation and transform the regulation into their own. Research has shown that learners' extrinsic motivation has distinct effects on their intrinsic motivation (e.g., Lee, Lee, & Hwang, 2015; Rezvani, Khosravi, & Dong, 2017; Ryan & Deci, 2000a). Based on a review of research on self-regulation learning, Schwinger and Stiensmeier-Pelster (2012) proposed a model to explain the process and structure of motivational regulation. In the model, motivational regulation starts from individual's weak level of motivation and then he (she) considers whether it is necessary to improve motivation. Next, he (she) uses a variety of strategies to regulate his (her) motivation to improve learning performance. This model also holds that individual's motivational regulation is related to situational factors, such as task characteristics, task environment, and personal factors, including prior knowledge, intelligence level, or interest. Motivational regulation is not only positively related to students' learning achievement and effort in the traditional classroom, but also can predict students' learning engagement in the online learning environment. For instance, Feng, Ye, Yu, Yang, and Cui (2018) found that individual's intrinsic motivation positively regulated the impacts of gamification artifacts on their participation. Mekler, Brühlmann, Tuch, and Opwis (2017) examined the effects of gamification elements on individual intrinsic motivation and learning performance. The results showed that extrinsic incentives could promote individual's learning performance. According to the motivational regulation model, teachers' motivational regulation was related to their motivational beliefs. If teachers believed that learning tasks were valuable and closely related to their professional development, they would actively regulate their behaviors to achieve positive learning engagement. Therefore, teachers’ motivational beliefs were not only directly related to their learning engagement, but also indirectly related to their learning engagement through the effect of motivational regulation (Erol & Kurt, 2017; Hrtoňová, Kohout, Rohlíková, & Zounek, 2015; Sánchez-Prieto, Olmos-Migueláñez, & García-Peñalvo, 2017). 2.4. Hypotheses development Literature reviews show that the task value perceived by teachers as learners is not only directly related to their learning engagement, but also indirectly related to their learning engagement through the motivational regulation. Teachers' self-efficacy is also related to their motivational regulation and learning engagement. In this study, we aimed to explore the relationships and functioning 147

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Fig. 1. The research model established in this study.

mechanisms among teachers’ perceived task value, self-efficacy, motivational regulation, and learning engagement. With this purpose, a research model was established as shown in Fig. 1. The research hypotheses are as follows: Hypothesis 1. In online professional learning communities, there is a significant positive correlation between teachers' perceived task value and their learning engagement. If teachers perceive that online learning tasks are closely related to their teaching practice and professional development, they will actively participate in learning activities and have a high level of learning engagement. On the contrary, if teachers perceive that learning tasks are less related to their teaching practice and professional development, they will participate in learning activities only for the purpose of completing learning tasks. As a result, they have a low level of learning engagement. Hypothesis 2. In online professional learning communities, teachers' self-efficacy plays a moderating role in the path from perceived task value to learning engagement. Teachers' learning engagement is not only related to their perceived task value, but also moderated by their self-efficacy. Teachers with a high perception of task value will have a higher level of learning engagement if they have a high sense of self-efficacy. On the contrary, teachers with a low perception of task value will have a lower level of learning engagement if they have a low sense of self-efficacy. Hypothesis 3. In online professional learning communities, teachers' motivational regulation plays a mediating role in the predicting power of perceived task value to learning engagement. Teachers' perceived task value is not only directly related to their learning engagement, but also indirectly related to their learning engagement through the motivational regulation. The mediating role of motivational regulation is mainly reflected in teachers' selection and application of motivational regulation strategies. The more appropriate teachers' motivational regulation strategies are, the higher their level of learning engagement will be. Hypothesis 4. In online professional learning communities, teachers' self-efficacy plays a moderating role in the first half (H4a) and second half (H4b) of the path among perceived task value, motivational regulation, and learning engagement. Teachers' perceived task value and self-efficacy are associated with their choice and application of motivational regulation strategies, and then further associated with their learning engagement. In the first half of the path among perceived task value, motivational regulation, and learning engagement, teachers' self-efficacy is associated with learning engagement by acting on the selection of motivational regulation strategies, and in the second half of the path, teachers' self-efficacy is associated with learning engagement by acting on the application of motivational regulation strategies.

3. Methodology 3.1. Research context and participants In 2014, the Ministry of Education of China launched a five-year teacher training program. All primary and secondary school teachers in China were required to participate in this program in batches and the online learning time requirement for each teacher was 120 h. In the training program, training institutions were required to establish online professional learning communities and implement web-based training. Each trainee was required to complete three tasks online: watching video cases, participating in online discussion, and submitting reflective diaries. The video cases were about how to use information technology tools to support classroom teaching. For instance, in the formative assessment stage of classroom teaching, different methods of using information technology tools to support students’ peer assessment activities were introduced. After watching video cases, the teachers in each online learning community discussed ways in which information technology tools could be used in classroom teaching activities. The discussion activity in each online learning community went through two phases: (1) A chief teacher posted a problem for discussion and uploaded relevant resources, and (2) all teachers in the online learning community discussed the problem and uploaded teaching materials, such as teaching plans, teaching slides, or teaching videos. The process of online discourse lasted for one month. Finally, each teacher submitted a diary reflecting his/her experience of the application of information technology in his (her) classroom teaching. A total of 2000 primary and secondary school teachers from a province in central China participated in the teacher training program which lasted from September 2015 to June 2016. In this study, the selection of participants was based on the principle of 148

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stratified sampling and 520 teachers were selected as the participants according to the proportion of primary and secondary schools, years of service and gender. Among the participants, there were 239 secondary school teachers (46%) and 281 primary school teachers (54%). The number of female teachers was 311 (59.8%), and that of male teachers was 209 (40.2%). These teachers’ average years of service was 18.5. They had participated in many teacher training programs and were very familiar with the training process and online learning platform. All the 520 teachers completed the online learning tasks on time. 3.2. Instrument development and validation Teachers’ perceived task value, self-efficacy, and motivational regulation were measured by self-reported scales. 3.2.1. Task value scale The perceived task value scale was revised from Dağhan and Akkoyunlu's (2016) study that originally contained 3 items and used a 7-point Likert scale, of which 1 represented strongly disagree and 7 represented strongly agree. The combined reliability of the original scale was 0.8838. In this study, the original 3 items were modified and the background conditions of online learning was added. The revised 3 items were: (1) online learning tasks keep me updated, (2) online learning tasks meet my requirements for knowledge, learning and development, (3) online learning tasks play a key role in improving my teaching ability. The results of exploratory factor analysis on the modified scale showed that the factor loads of the 3 items were 0.921, 0.934, and 0.903 respectively, the interpretation rate of perceived task value was 84.52%, and the reliability coefficient (Cronbach’ alpha) of the scale was 0.906. 3.2.2. Self-efficacy scale The self-efficacy scale was adapted from Artino and Mccoach's (2008) study that originally contained 7 items and used a 7-point Likert scale. The reliability coefficient (Cronbach’ alpha) of the original scale was 0.89. In this study, the original items were modified and the background information of online learning community was added. For example, one of the items was that “I'm sure I can understand the most difficult learning materials in the online learning community”, another item was that “Even without the help of the teacher educator, I can complete the online learning task excellently”. The results of exploratory factor analysis on the modified scale showed that the factor loads of the 7 items were all above 0.78, the interpretation rate of self-efficacy was 76.12%, and the reliability coefficient (Cronbach’ alpha) of the scale was 0.84. 3.2.3. Motivational regulation scale The motivational regulation scale was adapted from Liou and Kuo's (2014) study. In the original scale, there were 5 sub-scales related to motivational regulation, namely, active learning strategies (8 items), learning environment stimulation (6 items), learning goal-orientation (7 items), learning self-regulation-triggering (3 items), and learning self-regulation-implementing (5 items). The total reliability coefficient (Cronbach’ alpha) of the original scale was greater than 0.7, and the reliability coefficient of each sub-scale (Cronbach’ alpha) was greater than 0.80. In this study, the background information of online learning community was added into 5 sub-scales. For example, one of the items was “When new learning tasks appear in the online learning community, I connect them with my teaching practice.” Another item was “I am willing to participate in learning tasks in the online learning community because many of my colleagues will discuss teaching problems together.” All the items in the 5 sub-scales of the original scale were used in this study and the version of motivational regulation scale of this study consists of 29 items and used a 7-point Likert scale. The results of exploratory factor analysis on the 5 sub-scales were Χ2/df = 2.749, RMSEA = 0.058, RMR = 0.052, CFI = 0.988, TLI = 0.979. The reliability coefficient (Cronbach’ alpha) of the 5 sub-scales were 0.884, 0.881, 0.858, 0.9, and 0.84. 3.3. Data collection and analyses Ethical approvals for participant recruitment and gaining their consent to use online data were gained from the hosting institution. 520 teachers' demographic information (teacher ID, gender, years of service, training score, etc.) and online learning behavior were collected from the database of the online learning platform, i.e. login time, postings, resources uploading, etc. Questionnaires were distributed through the internet (https://www.wjx.cn/) from July to September 2016. A total of 515 questionnaires were collected, 514 of which were valid and the effective rate was 98.8%. The quantitative statistics of learning engagement was conducted over two stages. In the first stage, all the teachers' log data were exported and analyzed by the first author who created open codes based on the kinds of behavior representative of the learning engagement recorded on the online learning platform. For example, posting behavior that indicated teachers were engaged in online discussion was selected and coded. At the end of this stage, all the open codes were clustered as primary codes and the newly obtained codes were validated by checking the behavior data against the codes. In the second stage, 20% participants were randomly selected and their behaviors were coded independently by the second author. Two coders (the first and second authors) negotiated the results in detail after they completed their coding and achieved an inter-rater reliability of 0.81 (Cohen's Kappa). Then, the first author encoded the rest of the data. Teacher ID was used to connect and organize the questionnaire and learning engagement data. All teachers' names were pseudonyms. SPSS 21.0 was used for data statistical analysis. Firstly, a multiple collinearity test was conducted among latent variables of the questionnaire. There was no multi-collinearity problem because the variance expansion factor of each latent variable was less than 1 (Hair, Anderson, Tatham, & Black, 2006). Secondly, the mean and central values of each variable in the research model were calculated and stored as new variables for subsequent correlation analysis and regression analysis. Finally, in the model validation 149

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Table 1 Descriptive statistics of learning engagement. Items Number of logins Number of video cases watched Reflective diaries submitted Assignments submitted Resources (lesson plan, teaching slides, etc.) submitted Number of posts Number of replies

M 2.25 10.73 2.53 3.27 4.88 2.16 5.04

SD 1.25 1.19 1.72 0.87 3.06 1.52 7.58

Maximum 6 13 15 4 21 9 46

Minimum 1 8 2 2 0 1 0

phase, the moderated mediation effect was examined by using the Process plug-in written by Hayes (http://www.afhayes.com). 4. Results 4.1. Quantitative statistics of teachers’ learning engagement in online professional learning communities Teachers' learning engagement was measured by 7 indicators of online learning behaviors, as shown in Table 1. The factor loadings of the indicators were all above 0.7, the interpretation rate of learning engagement was 65.54%, and the reliability coefficient (Cronbach’ alpha) was 0.823. 4.2. Significant correlations among teachers’ perceived task value, self-efficacy, motivational regulation and their learning engagement The correlations among perceived task value, self-efficacy, motivational regulation, and learning engagement were calculated, and the results were shown in Table 2. Teachers' perceived task value was significantly correlated with their self-efficacy, learning engagement and motivational regulation. Moreover, there were significant correlations among teachers’ self-efficacy and their motivational regulation and learning engagement. In addition, there was also a significant correlation between teachers' motivational regulation and learning engagement. 4.3. A moderated mediation model existed among teachers’ perceived task value, self-efficacy, motivational regulation, and their learning engagement In online professional learning communities, the relationship among teachers' perceived task value, self-efficacy, motivational regulation and their learning engagement needed to be further tested. According to the testing method of moderated mediation model (Hayes, 2013), the first step was to test whether the direct relationship between perceived task value and learning engagement was moderated by self-efficacy, the regression equation (1) was established. (1)

Y= c0 + c1 X+ c2 U+ c3 UX + e1

In equation (1), Y represents learning engagement, X represents perceived task value, U represents self-efficacy, and UX represents the interaction between perceived task value and self-efficacy. c0 , c1, c2 and c3 are the coefficients, and e1 represents the residual. If c3 is significant, it indicates that self-efficacy moderates the direct relationship between perceived task value and learning engagement. The second step was to test whether there was a mediating effect of motivational regulation and whether the mediating effect was moderated by self-efficacy. Regression equations (2) and (3) were established.

M= a0 + a1 X+ a2 UX + e2

(2)

Y= c′0 + c′1 X+ c′2 U+ c′2 UX + b1 M+ b2 UM + e3

(3)

In equations (2) and (3), M represents motivational regulation. The meanings of Y, X, and U in equations (2) and (3) are the same as those in equation (1). In equation (3), UM represents the interaction between self-efficacy and motivational regulation. The coefficients in equations (2) and (3) were tested in order. The significances of a1 and a3 in equation (2) were tested first, and then the significances of b1 and b2 in equation (3) were tested. Combined with the results of equations (2) and (3), if a1was not equal to 0 (the Table 2 Correlation analysis of variables. Variables

M

SD

1

2

3

4

1. 2. 3. 4.

4.91 5.13 4.26 4.40

1.26 1.13 0.89 1.69

1 .79** .55** .73**

1 .52** .75**

1 .52**

1

Perceived task value Self-efficacy Motivational regulation Learning engagement

Note. N = 514. *p < 0.05, **p < 0.01, ***p < 0.001. 150

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Table 3 Results of model testing. Variable

Perceived task value Self-efficacy Perceived task value * self-efficacy Motivational regulation Motivational regulation * selfefficacy F R2

Equation 1 (Learning engagement)

Equation 2 (Motivational regulation)

Equation 3 (Learning engagement)

β

t

95%CI

β

t

95%CI

β

t

95%CI

.35 .478 .006

7.76*** 10.37*** .206

[.26,.44] [.39,.57] [-.04,.53]

.40 .26 .13

6.79*** 4.29*** 3.08**

[.17,.31] [.08,.23] [.02,.10]

.31 .65

6.64*** 4.99***

[.22,.39] [.39,.90]

.12 -.21

3.56*** −1.648

[.09,.31] [-.14,.01]

267.43*** .61

84.958*** 0.33

211.407*** 0.624

value reaches statistical significance) and b2 was not equal to 0, it meant that self-efficacy moderated the path from motivational regulation to learning engagement. If a3 was not equal to 0 and b1 was not equal to 0, it meant that self-efficacy moderated the path from perceived task value to motivational regulation. If a3 was not equal to 0 and b2 was not equal to 0, it meant that self-efficacy moderated the first half and second half of the path among perceived task value, motivational regulation, and learning engagement. Mean and central values of perceived task value, self-efficacy, motivational regulation, and learning engagement were calculated and then the Process plug-in was used to calculate the parameters of the research model, as shown in Table 3. The results of equation (1) showed that perceived task value was positively correlated with learning engagement (β = 0.35, t = 7.76, p < 0.001). The interaction between perceived task value and self-efficacy had no significant correlation with learning engagement (β = 0.006, t = 0.206, p = 0.837), which indicated that a moderated mediation model could be established. The results of equation (2) showed that perceived task value was positively correlated with motivational regulation (β = 0.40, t = 6.79, p < 0.001) and self-efficacy was positively correlated with motivational regulation (β = 0.26, t = 4.29, p < 0.001). Moreover, the interaction between perceived task value and self-efficacy also had a significant correlation with motivational regulation (β = 0.13, t = 3.08, p = 0.002). The results of equation (3) showed that perceived task value (β = 0.31, t = 6.64, p < 0.001), self-efficacy (β = 0.65, t = 4.99, p < 0.001) and motivational regulation (β = 0.12, t = 3.56, p < 0.001) all were positively correlated with learning engagement. However, the interaction between motivational regulation and self-efficacy had no significant correlation with learning engagement (β = −0.21, t = −1.648, p = 0.092). In the results of the model testing, a3 was not equal to 0 and b1 was not equal to 0, indicating that there was a moderated mediation model among perceived task value, self-efficacy, motivational regulation, and learning engagement, as shown in Fig. 2. Teachers' motivational regulation played a partial mediating role between perceived task value and learning engagement, and the first half of the mediating path was moderated by teachers’ self-efficacy. The testing results of research hypotheses were shown in Table 4.

4.4. The moderating mechanism of self-efficacy Model testing showed that self-efficacy played a moderating role in the path from perceived task value to motivational regulation. However, the moderating mechanism of self-efficacy was unclear, so it was necessary to conduct a simple slope test on perceived task value and self-efficacy. The specific testing method was as follows. Those teachers whose perception of self-efficacy were one standard deviation above the average were selected as a sample of teachers with high self-efficacy and those whose perception of selfefficacy were one standard deviation below the average were selected as a sample of teachers with low self-efficacy. Then the predicting power of perceived task value to motivational regulation in the two samples were investigated. The results showed that the perceived task value of teachers with high self-efficacy had higher predictive power to motivational regulation (B simple = 0.297, SE = 0.042, p < 0.001), while the perceived task value of teachers with low self-efficacy had a lower predictive power to motivational regulation (Bsimple = 0.181, SE = 0.038, p < 0.001). Perceived task value was regarded as the independent variable and motivational regulation as the dependent variable, the moderating mechanism of self-efficacy was shown in Fig. 3.

Fig. 2. Results of the model testing. 151

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Table 4 Results of hypotheses testing. Hypotheses

Results

Hypothesis 1. In online professional learning communities, there is a significant positive correlation between teachers' perceived task value and their learning engagement. Hypothesis 2. In online professional learning communities, teachers' self-efficacy plays a moderating role in the path from perceived task value to learning engagement. Hypothesis 3. In online professional learning communities, teachers' motivational regulation plays a mediating role in the predicting power of perceived task value to learning engagement. Hypothesis 4. In online professional learning communities, teachers' self-efficacy plays a moderating role in the first half (H4a) and second half (H4b) of the path among perceived task value, motivational regulation, and learning engagement.

Supported Not supported Supported H4a was supported

Fig. 3. The moderating mechanism of self-efficacy.

5. Discussion Based on the expectancy-value theory and self-determination theory, a moderated mediation model was established in this study. Regression analyses were used to investigate the relationships among teachers’ motivational beliefs, motivational regulation, and their learning engagement in online professional learning communities and some meaningful conclusions were drawn. First, teachers' perceived task value was positively associated with their learning engagement. Research Hypothesis 1 was supported. This result indicated that teachers were more likely to engage in online learning activities if they believed that online learning task were relevant to their teaching practice and professional development. This result was consistent with the stipulation of the expectancy-value theory that perceived task value was a predictive variable of individual learning engagement and academic performance (Vernadakis, Kouli, Tsitskari, Gioftsidou, & Antoniou, 2014; Wigfield & Eccles, 2000). In online professional learning communities, the more valuable the online tasks perceived by teachers were, the more time and energy they would invest in online learning. On the contrary, if teachers believed that online learning tasks had little value for their professional development, they would have a low degree of learning motivation and a low level of learning engagement. In order to improve teachers' level of learning engagement, it is suggested that the learning tasks posted in online professional learning communities are closely related to teachers' actual needs (Louws, Meirink, van Veen, & van Driel, 2017). A bottom-up approach, such as using questionnaire surveys or interviews, maybe a good way to obtain teachers' actual learning needs. Before posting learning tasks, the organizers of online professional learning communities can conduct a survey or interview to understand teachers' actual learning needs, then they choose the learning tasks that most members are interested in to heighten teachers’ level of learning engagement. Second, teachers' motivational regulation played a partial mediating role in the predicting power of perceived task value to learning engagement in online professional learning communities. Teachers' perception of task value was associated with their motivational regulation strategies, and then further associated with their learning engagement. Research Hypothesis 3 was supported. This result indicated that compared with the perceived task value, teachers' self-regulation in the online learning process was a proximal factor related to their learning engagement (Prestridge, 2019). On the one hand, this result was consisted with the stipulation of the expectancy-value theory that perceived task value was a predictive variable of individual's learning engagement and learning performance. In online professional learning communities, the more valuable the tasks perceived by teachers were, the higher their interest and enthusiasm for online learning would be. Then, these teachers would use more motivational regulation strategies to improve and heighten their learning engagement, which in turn led to higher perceived task value. Correspondingly, when teachers perceived that the current learning task had little value, they would have a low degree of learning motivation and use few motivational regulation strategies. As a result, they would have a low level of learning engagement. On the other hand, this result was consisted with the stipulation of the self-regulation model that progressive relationship existed between individual's motivational beliefs and regulating activities (Schwinger & Stiensmeier-Pelster, 2012). The first step of the self-regulation model was individual's motivational beliefs, such as perceived task value, self-efficacy. The second step was individual's motivational regulation, that was, individual regulated their cognitive and metacognitive processes (Uzun & Kilis, 2019). Therefore, the results of this study not only 152

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affirmed the importance of motivational regulation in the online learning environment, but also further revealed the relationships between motivation factors and online learning engagement. Third, teachers' self-efficacy played a moderating role in the first half of the mediating path among perceived task value, motivational regulation, and learning engagement. Research Hypothesis 2 was not supported and research Hypothesis 4 was partially supported. For teachers with high self-efficacy, their perceived task value had a strong indirect predictive power of learning engagement through motivational regulation, while for teachers with low self-efficacy, their perceived task value had a weak indirect predictive power to learning engagement through motivational regulation. In general, teachers with high self-efficacy would have higher learning engagement when they perceived high task value. Two reasons may account for this finding. First, according to the social cognitive theory (Bandura, 1997; Lin & Chang, 2018), teachers with high self-efficacy would regard difficulties encountered in the online learning process as learning opportunities, and they would invest more energy and adopt more effective motivational regulation strategies. While teachers with low self-efficacy tended to avoid tasks that were prone to failure. These teachers used less motivational regulation strategies and invested less effort into online learning. Second, the finding that teachers’ self-efficacy played a moderating role could be explained from the perspective of attribution theory (Hung, Chang, & Hwang, 2011; Weiner, 1985). Teachers with high self-efficacy believed that their self-efficacy can be changed through efforts. These teachers tended to attribute the results of their actions as factors under their control, such as effort. They tend to adopt more self-regulatory strategies. However, teachers with low self-efficacy believed that the main factor related to their learning results was their own ability, that is, teachers with high ability could succeed without effort. These teachers attributed the results of their actions to external factors beyond their control, such as luck and circumstances. As a result, they were more passive to adopt motivational regulation strategies in the online learning process. In online professional learning communities, teachers' perceived task value and self-efficacy were mutually promoting. Compared with the teachers with low self-efficacy, the teachers with high self-efficacy became more active and more engaged in online learning with the use of motivational regulation strategies. Therefore, in the teacher training program, teacher trainers can actively improve the perceived task value of teachers with low self-efficacy, and also pay attention to the cultivation of those teachers' self-efficacy beliefs. 6. Conclusions, limitations and future research As a main way of teachers' professional development, online training is facing with the problem of teachers' insufficient participation and a low level of learning engagement. The conclusions of the present study are helpful for understanding the relationship among teachers' motivational beliefs, motivational regulation, and their learning engagement in online professional learning communities, that is, under what conditions and in what ways teachers' perceived task value is associated with their learning engagement. However, there are still some limitations in this study. First, the sample size of the questionnaire survey was small, so it was difficult to extend the conclusions of this study to other online professional learning communities. Second, although self-efficacy did not play a moderating role in the path from perceived task value to learning engagement, the precedence relationship between self-efficacy and perceived task value had not been verified. Thirdly, although very intuitive quantitative indicators were used to represent teachers' learning engagement, the time intervals of behaviors closely related to learning engagement were not considered. Thus, more research is needed on the correlation between teachers' leaning engagement and their motivational factors. Of specific interest is the precedence relationship between self-efficacy and perceived task value. In addition, more research needs to be done on the behavior indicators of measuring teachers' learning engagement in online professional learning communities. In addition, compared with the general online learning environment, the online professional learning community has its own characteristics, such as the patterns of learning activities, learning requirements, and mentor. This study only investigated the relationship among teachers' motivational beliefs, motivational regulation and learning engagement. Future research can further investigate the relationship between the interaction of teachers' motivational beliefs and characteristics of the online professional learning community, and teachers’ learning engagement so as to establish personalized intelligent online learning communities to meet individual teachers' personality characteristics. Acknowledgments This work was supported by the Hunan Provincial Natural Science Foundation of China (Grant No. 2018JJ3097). References Aristeidou, M., Scanlon, E., & Sharples, M. (2017). Profiles of engagement in online communities of citizen science participation. 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