View in EDS HTML Full Text PDF Full Text

Exploring Relationship among Self-Regulated Learning, Self-Efficacy and Engagement in Blended Collaborative Context

Saved in:
Bibliographic Details
Title: Exploring Relationship among Self-Regulated Learning, Self-Efficacy and Engagement in Blended Collaborative Context
Language: English
Authors: Zhao, Shu-rong (ORCID 0000-0002-9321-1661), Cao, Cui-hong
Source: SAGE Open. Jan-Mar 2023 13(1).
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 11
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, College Students, Self Management, Learning, Independent Study, Learner Engagement, Self Efficacy, Cooperation, Blended Learning, Emotional Response, Student Behavior, Predictor Variables, Student Attitudes
Geographic Terms: China
DOI: 10.1177/21582440231157240
ISSN: 2158-2440
Abstract: Collaboration proves to be an effective way to facilitate students' engagement and solve the mostly-mentioned problems of blended learning (BL). For collaborative BL, self-regulated learning (SRL), self-efficacy and engagement are frequently referred to in BL studies and represent key elements of effective BL. Furthermore, the interaction of these elements correlates closely with the performance of students. There have been few research attempts to draw synergies and explore the relationship among these key elements. To address this gap, data were collected through a questionnaire and records on LMS from 125 students in a Chinese university. Descriptive analytics show that students fully recognize the positive effects of collaborative BL. Correlation analysis and regression analysis find that self-regulated learning (SRL) is significantly correlated with all factors except workload. It is also a significant predictor of behavioral engagement. Therefore, SRL proves to be central among the essential BL elements. Emotional engagement is significantly correlated to and interact with multiple key BL factors. Specifically, emotional engagement is a significant predictor of behavioral engagement. The findings offer insight into the interactions of key elements of BL and provide practical implications for improving BL learning design.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1376724
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwGdr-UGEzU_MizMTvDaj9k_AAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDL4nr5fqoA-e9Edz3wIBEICBm-TZHwrdZ7toZLR5PerINnjxY8aQPyFiWDqz1vJLSYAD0HT9wR6MJdAGGKMI3Sp4al3t4w6UYxSuDxlzoVLHWrUEaI3mRvnvd8_y1BzD3yL0gaXAykt66K8F1OQgWs2wBF8UZUJU_DQjb50XSnGzeHRDRvfZyRTSY1d1GYAVClF-39r6YrDcWJ4fjPdVbif-7u-bPihWqNb0qGyh
Text:
  Availability: 1
  Value: <anid>AN0162839143;[kbz6]01jan.23;2023Apr04.03:58;v2.2.500</anid> <title id="AN0162839143-1">Exploring Relationship Among Self-Regulated Learning, Self-Efficacy and Engagement in Blended Collaborative Context </title> <p>Collaboration proves to be an effective way to facilitate students' engagement and solve the mostly-mentioned problems of blended learning (BL). For collaborative BL, self-regulated learning (SRL), self-efficacy and engagement are frequently referred to in BL studies and represent key elements of effective BL. Furthermore, the interaction of these elements correlates closely with the performance of students. There have been few research attempts to draw synergies and explore the relationship among these key elements. To address this gap, data were collected through a questionnaire and records on LMS from 125 students in a Chinese university. Descriptive analytics show that students fully recognize the positive effects of collaborative BL. Correlation analysis and regression analysis find that self-regulated learning (SRL) is significantly correlated with all factors except workload. It is also a significant predictor of behavioral engagement. Therefore, SRL proves to be central among the essential BL elements. Emotional engagement is significantly correlated to and interact with multiple key BL factors. Specifically, emotional engagement is a significant predictor of behavioral engagement. The findings offer insight into the interactions of key elements of BL and provide practical implications for improving BL learning design.</p> <p>Keywords: collaborative blended learning; self-regulated learning; self-efficacy; engagement; workload; interaction</p> <hd id="AN0162839143-2">Introduction</hd> <p>Technological advancement and innovation turn to be a catalyst for dramatic change in education, which also gives rise to new approaches to learning ([<reflink idref="bib2" id="ref1">2</reflink>]). With this trend, blended learning gains momentum and turns very popular in higher education. Discussion on blended learning (BL) has switched from listing its benefits ([<reflink idref="bib1" id="ref2">1</reflink>]; [<reflink idref="bib33" id="ref3">33</reflink>]) to solving various problems in practice, from teachers' teaching design to learning design and students' learning behaviors ([<reflink idref="bib2" id="ref4">2</reflink>]), which are important components of learning analytics. Collaboration proves to be an effective way to facilitate students' engagement and solve the mostly-mentioned problems of BL, including isolation in online learning ([<reflink idref="bib21" id="ref5">21</reflink>]; [<reflink idref="bib49" id="ref6">49</reflink>]), inadequate preparation before face-to-face learning ([<reflink idref="bib26" id="ref7">26</reflink>]) and heavy workload ([<reflink idref="bib42" id="ref8">42</reflink>]). Collaboration is crucial to fruitful learning in blended context ([<reflink idref="bib3" id="ref9">3</reflink>]).</p> <p>In collaborative BL context, motivation, emotion, cognition and meta-cognitive strategies correlates closely with the performance of students ([<reflink idref="bib39" id="ref10">39</reflink>]). Students' SRL skills and the ability to collaborate with peers in learning have become essential abilities of the post-fourth industrial revolution era, and these abilities may affect students' academic performance ([<reflink idref="bib28" id="ref11">28</reflink>]). Self-efficacy influences online experience, learning status and student satisfaction ([<reflink idref="bib12" id="ref12">12</reflink>]). Learner engagement has been shown to be related to important educational results, including academic performance, persistence, satisfaction and community ([<reflink idref="bib16" id="ref13">16</reflink>]). Workload is one of the mostly-mentioned problems of BL ([<reflink idref="bib42" id="ref14">42</reflink>]), leading to unpleasant learning experience. All of these have become focus of discussion in this field. Various studies explored the influence of these key factors on learning achievements ([<reflink idref="bib28" id="ref15">28</reflink>]; [<reflink idref="bib29" id="ref16">29</reflink>]; [<reflink idref="bib56" id="ref17">56</reflink>]). Whereas these advances are recognized in those fields separately, there have been few research attempts to draw synergies from these fields and explore the relationship among these key elements. Even fewer takes emotional experience and emotional engagement of students into interaction with other key elements in BL context ([<reflink idref="bib19" id="ref18">19</reflink>]).</p> <p>In order to address the gap, this study takes SRL, self-efficacy and engagement as major variables, workload (the focus of complaint of BL) as additional variable, and explore their relations and interactions. We constructed a collaborative and blended learning design. After implementing it for a semester, we explore students' perceptions of the collaborative BL and the relationship among SRL, self-efficacy, engagement and workload through descriptive, correlation and stepwise regression analysis. This study offers an insight as to how the key elements of BL interact with each other. Such data and findings may offer practical implications for learning design for BL.</p> <p>To be specific, this study aims to address the following three research questions:</p> <p></p> <ulist> <item> RQ1: What are students' perceptions of collaborative BL?</item> <p></p> <item> RQ2: What are the relationships among SRL, self-efficacy and engagement?</item> <p></p> <item> RQ3: What are the implications of such relationships for the design and improvement of collaborative BL?</item> </ulist> <hd id="AN0162839143-3">Theoretical Background</hd> <p></p> <hd id="AN0162839143-4">Learning Analytics</hd> <p>With the increasing application of technology in education and the generation of various types of learning data, the use of learning analytics to understand and optimize the learning process and its environment has received increasing attention worldwide ([<reflink idref="bib38" id="ref19">38</reflink>]). Learning analytics refers to the measurement, collection, analysis and reporting of learner data and their environment, with the purpose of understanding and optimizing the environment in which learning occurs ([<reflink idref="bib15" id="ref20">15</reflink>]). Based on data from learning analytics, we can reshape support for learning process ([<reflink idref="bib7" id="ref21">7</reflink>]).</p> <p>The typical feature of learning analytics is to promote the capability of learning participants to participate actively, rather than making decisions automatically ([<reflink idref="bib13" id="ref22">13</reflink>]; [<reflink idref="bib44" id="ref23">44</reflink>]). With this nature, learning analytics is increasingly used in higher education to understand and support student learning ([<reflink idref="bib51" id="ref24">51</reflink>]). In the early time, data of students on the LMS system were analyzed and linked with academic performance, but it was discovered that factors such as self-efficacy and motivation can better predict whether students will achieve satisfactory academic performance ([<reflink idref="bib7" id="ref25">7</reflink>]). Therefore, recent learning analytics focuses on the improvement of learners' learning effectiveness and learner support, for example, the cultivation of SRL learning.</p> <hd id="AN0162839143-5">SRL and Self-Efficacy</hd> <p>Students' SRL skills and the ability to collaborate with peers in learning have become essential abilities of the post-fourth industrial revolution era, and these abilities may affect students' academic performance ([<reflink idref="bib28" id="ref26">28</reflink>]). SRL is an integrated learning process, in which students, guided by a set of motivational beliefs, use planned behavioral, cognitive, and meta-cognitive activities to support the achievement of personal goals ([<reflink idref="bib41" id="ref27">41</reflink>]). It includes components of goal setting, environment management, task strategy, help seeking and task evaluation ([<reflink idref="bib9" id="ref28">9</reflink>]). SRL is a key factor in achieving a successful learning experience in online learning. Students with SRL behaviors usually have a more positive view of BL ([<reflink idref="bib28" id="ref29">28</reflink>]). Due to lack of SRL skills, many students cannot complete pre-class learning tasks, fail to learn and understand what should be learned online, and ultimately fail to prepare for the learning activities in class ([<reflink idref="bib22" id="ref30">22</reflink>]).</p> <p>Self-efficacy is defined as "people's judgments of their capabilities to organize and execute a course of action required to attain designated types of performances" ([<reflink idref="bib8" id="ref31">8</reflink>]). It influences online experience, learning status and student satisfaction ([<reflink idref="bib12" id="ref32">12</reflink>]). Under the BL context, [<reflink idref="bib43" id="ref33">43</reflink>] believes that self-efficacy covers online learning self-efficacy and social interaction self-efficacy. Previous studies have shown that, self-efficacy, as a student's intrinsic motivation, will affect online experience, learning status, learning interest and student satisfaction ([<reflink idref="bib6" id="ref34">6</reflink>]). It was also found that self-efficacy is a predictor of better academic performance ([<reflink idref="bib43" id="ref35">43</reflink>]).</p> <hd id="AN0162839143-6">Learner Engagement</hd> <p>Learner engagement is a general concept that focuses on the degree and quality of students' participation in academically meaningful activities ([<reflink idref="bib14" id="ref36">14</reflink>]). According to [<reflink idref="bib18" id="ref37">18</reflink>], in online and blended learning context, engagement falls into three dimensions, namely behavioral engagement, cognitive engagement and emotional engagement. Among them, behavioral engagement refers to a variety of activities, such as paying attention to learning, asking questions and participating in discussions in online learning. Cognitive engagement involves students' efforts in acquiring knowledge or skills in cognitive aspects during online learning. Emotional engagement is defined as learners' positive emotions toward teachers, peer learners and online learning ([<reflink idref="bib24" id="ref38">24</reflink>]). Previous research results have shown that students are highly diversified in their learning strategies, from students who are not engaged at all to those who continuously interact with resources ([<reflink idref="bib36" id="ref39">36</reflink>]). Learner engagement has been shown to be related to important educational results, including academic performance, persistence, satisfaction, and community ([<reflink idref="bib16" id="ref40">16</reflink>]). A successful BL should improve learner engagement, whether in or out of class ([<reflink idref="bib35" id="ref41">35</reflink>]). Learner engagement is essential in online and blended learning, but low participation has become a serious problem ([<reflink idref="bib19" id="ref42">19</reflink>]). It is necessary to explore the factors that affect or predict learner engagement, so as to optimize the learning design and ensure effective online and blended learning.</p> <hd id="AN0162839143-7">Collaboration in BL Context</hd> <p>Vygotsky's theory of social development believes that one cannot achieve acquisition in isolation ([<reflink idref="bib52" id="ref43">52</reflink>]). It is also found that motivation, positive attitudes toward learning and instructor guidance were enhanced with increased level of interaction ([<reflink idref="bib20" id="ref44">20</reflink>]). Interaction and collaboration bring deeper meaningful learning, better learning results, and attract increasing attention. It makes students believe in their capabilities and be responsible for their own learning. In group work, learners observe other's learning plan and reflect on and improve their own learning behavior ([<reflink idref="bib11" id="ref45">11</reflink>]).</p> <p>In BL context, previous studies on group activities have shown that peer collaboration is beneficial and successful ([<reflink idref="bib17" id="ref46">17</reflink>]). It is proved that peer collaboration connects online and offline learning activities ([<reflink idref="bib4" id="ref47">4</reflink>]), enhances learners' motivation and engagement ([<reflink idref="bib40" id="ref48">40</reflink>]). Through peer collaboration, learners know what others are doing and engage more in meaningful communication. [<reflink idref="bib47" id="ref49">47</reflink>] constructed a collaborative learning framework guided by Community of Inquiry model, which led to increased overall satisfaction and higher sense of social presence. In a word, collaboration has become one of the essential elements to ensure the effectiveness and quality of BL.</p> <hd id="AN0162839143-8">Correlations Among Key Factors of BL</hd> <p>SRL, self-efficacy, collaboration and engagement are popular issues of discussion in BL context. Many researchers and practitioners probe into their interaction patterns and seek implications for learning design. [<reflink idref="bib57" id="ref50">57</reflink>] finds that learning is mostly effective when students learn with their peers and demonstrate high degree of self-efficacy. [<reflink idref="bib28" id="ref51">28</reflink>] proves that peer collaboration acts as an important factor to enhance academic performance and influences remarkably SRL behavior in online learning. [<reflink idref="bib54" id="ref52">54</reflink>] finds that change in emotional state in the process of peer learning will lead to alteration in interaction pattern. [<reflink idref="bib40" id="ref53">40</reflink>] hold that social learning should be encouraged as it may reinforce student motivation and engagement. Study of [<reflink idref="bib32" id="ref54">32</reflink>] on relation between self-efficacy and engagement concludes that low engagement caused by technical issues does not influence negatively learner engagement.</p> <p>These studies explored the relationships of important BL elements in pairs, and there lacks comprehensive analysis which draws synergies of their relationships in a BL context. In this study, a collaborative BL learning model was designed and implemented for a semester. At the end of the semester, a study of the relationship among the above-mentioned factors was carried out, so as to explore their interaction patterns, and then to put forward relevant learning design suggestions for collaborative BL.</p> <hd id="AN0162839143-9">Research Design</hd> <p>Aiming at addressing the three research questions, a survey was conducted and data from learning management system were collected.</p> <hd id="AN0162839143-10">Context</hd> <p>Blended learning in this research took place in a Business English course offered by a university in Shandong Province, China. We created a BL model based on the Community of Inquiry model. For the learning of each chapter, the first section is for students to watch online videos and work in pairs to complete tasks that match the knowledge and skills involved in online learning (mostly essay questions). After completion of the relevant tasks, students were asked to upload the completed paper onto the Learning Management System (LMS). The second section is face-to-face learning, during which students participate in various practice activities under the guidance of teachers, usually in groups. The third is the after-class section, where the students complete comprehensive learning tasks in pairs, so as to consolidate and deepen the knowledge and skills learned, and develop cross-cultural communication skills. In this online and offline learning model, collaboration plays a leading role mainly in two periods: one is when students watch the videos before class, and the other occurs when students complete tasks after class. The specific learning arrangements are shown in Table 1.</p> <p>Graph</p> <p>Table 1. Characteristics of the Collaborative BL.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="left" /></colgroup><thead><tr><th align="left">Class frequency and time</th><th align="center">Twice a week, 90 min per session</th></tr></thead><tbody><tr><td>Total credits</td><td>4</td></tr><tr><td>Number of chapters</td><td>8</td></tr><tr><td>Number of students</td><td>125 (in pairs or in groups of 3)</td></tr><tr><td>Online resources</td><td>Recorded videos covering all 8 chapters</td></tr><tr><td>Pre-class activities</td><td>Watching online videos and completing learning tasks in pairs or in groups of 3</td></tr><tr><td>In-class activities</td><td>Practice, demonstrations, collaborative work</td></tr><tr><td>After class activities</td><td>Completing comprehensive tasks in pairs or in groups of 3</td></tr></tbody></table> </ephtml> </p> <p>The participants in this study are 125 students, of which 31 are of business English majors, and 94 of international economics and trade majors. All participants were between 18 and 22 years old, including 35 males and 90 females. One hundred eleven out of all 125 participants have previous blended learning experiences. The learning management system (LMS) records detailed information on learning behavior of each student, including time and frequency of watching online videos, short videos recording their peer interaction, performance in online tests, their task completion and feedback from teachers. In the 16th week of the term, a questionnaire was conducted to explore students' perception and relationships among key factors of the collaborative BL.</p> <hd id="AN0162839143-11">Instruments</hd> <p>A mixed research method was adopted to address the research questions. Questionnaire usually acts as a main method for investigating students' SRL, self-efficacy and engagement. In the past decade, various relevant questionnaires have been developed, revised and adopted, but the self-reporting data were argued to be too subjective. On the other hand, the widespread use of learning management systems in higher education institutions generates a large amount of tracking data that can be used for learning analytics. These detailed data provide objective and detailed information about the learning process of students, and may promote the understanding and optimization of the learning and learning environment in which learning occurs ([<reflink idref="bib45" id="ref55">45</reflink>]). However, relying solely on tracking data may only reveal part of the information, missing the underlying patterns of learning behavior and the interrelationship of BL elements ([<reflink idref="bib37" id="ref56">37</reflink>]). In order to solve the limitations of these two methods, this research combines questionnaire and LMS platform data, which complement each other and offer a more comprehensive understanding of students' learning experiences in blended learning environments.</p> <p>Besides demographic information, the questionnaire includes 1 open-ended question and 36 items of 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = not sure, 4 = agree and 5 = strongly agree). For the open-ended question, participants were asked to express their perceptions of the collaborative BL experience in one sentence. Likert scale questions explore the main elements of BL, including 4 dimensions, namely SRL, self-efficacy, engagement and workload. SRL items were adapted from [<reflink idref="bib9" id="ref57">9</reflink>], including three sub-scales: goal setting (3 items), task strategies (4 items) and help-seeking (4 items). Self-efficacy items were adapted from [<reflink idref="bib43" id="ref58">43</reflink>], covering two sub-scales, namely online learning self-efficacy (7 items) and social interaction self-efficacy (5 items). The engagement section was adapted from [<reflink idref="bib46" id="ref59">46</reflink>], including behavioral engagement (5 items) and emotional engagement (5 items). Workload section was developed by the instructors of this study. It went through pilot test and revision. Reliability and validity tests proved that this section was highly reliable and credible. Validity of the questionnaire is 0.917, and the reliability of each dimension exceeds 0.875. The details of the questionnaire are shown in Table 2.</p> <p>Graph</p> <p>Table 2. Descriptions of the 5-Scale Questionnaire.*</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="left" /><col align="char" char="." /></colgroup><thead><tr><th align="left">Scales</th><th align="center">Subscales</th><th align="center">Reliability</th></tr></thead><tbody><tr><td>SRL</td><td>Goal setting (3 items)</td><td>0.916</td></tr><tr><td /><td>Task strategy (4 items)</td><td>0.902</td></tr><tr><td /><td>Help seeking (4 items)</td><td>0.884</td></tr><tr><td>Self-efficacy</td><td>Online learning self-efficacy (7 items)</td><td>0.912</td></tr><tr><td /><td>Social interaction self-efficacy (5 items)</td><td>0.935</td></tr><tr><td>Engagement</td><td>Behavioral engagement (5 items)</td><td>0.907</td></tr><tr><td /><td>Emotional engagement (5 items)</td><td>0.939</td></tr><tr><td>Workload</td><td>3 items</td><td>0.875</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note.</emph> Validity: 0.909.</p> <hd id="AN0162839143-12">Data Collection and Analysis</hd> <p>The questionnaire was conducted in the 16th week of the collaborative BL implementation. Consent from all participants was obtained, and totally 125 valid questionnaires were retrieved.</p> <p>The LMS used in this course is Treenity (Zhihuishu.com), one of the most used online course management platforms for higher education in China. This research uses Treenity to record and identify students' BL learning behaviors, and obtain specific data about students' self-regulation, task completion, and behavioral engagement.</p> <p>SPSS 22.0 was used to analyze the collected data. In order to explore the first research question, a descriptive analysis of the various dimensions of the questionnaire and a word cloud analysis (by inputting all the data into Wordart.com) of the themes of the open-ended question were carried out to obtain students' perceptions of the collaborative BL. To explore the second question, we first conducted correlation analysis on SRL, self-efficacy and workload After that, a stepwise regression analysis was conducted on the above factors to explore their interaction. Stepwise regression was adopted to avoid multicollinearity, as the variables may correlate with each other. Finally, we put forward learning design suggestions for collaborative BL based on the results of the above data analysis.</p> <hd id="AN0162839143-13">Findings</hd> <p></p> <hd id="AN0162839143-14">Students' Perceptions of Collaborative BL and Learning Behavior</hd> <p>Descriptive analysis of the questionnaire data showed that the items of various scales including task strategy, goal setting, help-seeking, online learning self-efficacy, social interaction self-efficacy, behavioral engagement and emotional engagement all got high mean values (all exceed 4). In particular, items of the sub-scale of social interaction self-efficacy got a high mean value of 4.229. It shows that under the collaborative BL scenario created by this study, students show high degree of SRL, self-efficacy, engagement, and collaboration. The mean value of workload is 3.232, which is at a medium level. High workload has always been one of the main complaints of students against BL ([<reflink idref="bib42" id="ref60">42</reflink>]). This result shows that the collaborative BL in this study retained the workload within an acceptable degree. Specific analysis is shown in Table 3.</p> <p>Graph</p> <p>Table 3. Descriptive Analysis of the Questionnaire.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Items</th><th align="center">No. of samples</th><th align="center">Min</th><th align="center">Max</th><th align="center">Mean</th><th align="center">Std. Deviation</th></tr></thead><tbody><tr><td>SRL-Task strategy</td><td>125</td><td>1</td><td>5</td><td>4.128</td><td>0.783</td></tr><tr><td>SRL-Help-seeking</td><td>125</td><td>1</td><td>5</td><td>4.069</td><td>0.798</td></tr><tr><td>SRL-Goal setting</td><td>125</td><td>2</td><td>5</td><td>4.011</td><td>0.865</td></tr><tr><td>Self-efficacy-Social interaction self-efficacy</td><td>125</td><td>3</td><td>5</td><td>4.229</td><td>0.727</td></tr><tr><td>Self-efficacy-Online learning self-efficacy</td><td>125</td><td>3</td><td>5</td><td>4.047</td><td>0.715</td></tr><tr><td>Engagement-Behavioral engagement</td><td>125</td><td>2</td><td>5</td><td>4.333</td><td>0.669</td></tr><tr><td>Engagement-Emotional engagement</td><td>125</td><td>2</td><td>5</td><td>4.139</td><td>0.829</td></tr><tr><td>Workload</td><td>125</td><td>1</td><td>5</td><td>3.232</td><td>0.861</td></tr></tbody></table> </ephtml> </p> <p>An open-ended question in the questionnaire enquires into students' perceptions of the collaborative BL. All the answers were collected and input into "Worddart" for word frequency analysis, the result of which is shown in Figure 1. Combining word frequency and students' detailed statements, the following preliminary conclusions can be drawn. "Online" and "courses" are the most frequently mentioned words of students. They expressed from various perspectives their views on online learning and face-to-face learning. They fully recognize the positive influence of the collaborative BL, and made suggestions, such as adding subtitles to the videos. Another word with a very high frequency is "hard work." In this sense, a considerable number of students thank the teachers for their guidance and the tremendous efforts they have made for the course design. On the other hand, some of them think that they have worked very hard in this course. "Learned a lot" and "acquired a lot" are also frequently mentioned words, which shows that students fully appreciate the positive role of collaborative BL. "Cooperate" was also referred to with high frequency. The participants said that they cooperate smoothly and pleasantly with their partners. Frequently referred-to words like "time," "busy" also indicate the workload for teachers and students brought by BL.</p> <p>Graph: Figure 1. Frequency of words in students' perception of the collaborative BL.</p> <p>Data from the Treenity platform shows that 98.6% of students can complete various tasks (questions on the task list) of each chapter on time. The time when student watch online videos and complete the task list vary on their personal habit. Some chose to complete that just after the last class, and some finished in the middle of the two classes. They rarely completed the tasks just before the next class, as they do not want to delay the progress of their peers in their pair discussion. This proves the supervising role of peer learning. According to data on Treenity, each student watches the video clips 3.21 times on average, which shows that students have devoted sufficient time in online learning, watching videos repeatedly before and after class. A phenomenon worth noting is that despite the high homework completion rate, the LMS platform shows that 15 students' video viewing progress is less than 100%, between 70% and 90%. This seems unreasonable, because video viewing is the prerequisite for completing the task list, and it is impossible to complete the task list without watching the video. The only explanation is that some students work hard in teams, while others may contribute little or avoid teamwork ([<reflink idref="bib50" id="ref61">50</reflink>]). The finding is similar to that of [<reflink idref="bib5" id="ref62">5</reflink>], who named it as social loafing and free riding. In the design of peer collaborative learning, some measures should be adopted to cope with social loafing and free riding, so as to give full play to the positive role of BL.</p> <hd id="AN0162839143-15">Relationships Among Key Factors in BL Context</hd> <p>In order to explore the relationships among the key factors in collaborative BL context, the Spearman correlation analysis was conducted. It showed that SRL, behavioral engagement, and emotional engagement have high correlation among one another (<emph>p</emph> <.01). There is no remarkable correlation between SRL and self-efficacy, between workload and all the positive factors listed in this study. The details of analysis are shown in Table 4.</p> <p>Graph</p> <p>Table 4. Spearman Correlation (Detail).</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th /><th align="center">SRL</th><th align="center">Self-efficacy</th><th align="center">WL</th><th align="center">Behavioral engagement</th><th align="center">Emotional engagement</th></tr></thead><tbody><tr><td rowspan="2">SRL</td><td>Coefficient</td><td>1</td><td /><td /><td /><td /></tr><tr><td><italic>p</italic> value</td><td /><td /><td /><td /><td /></tr><tr><td rowspan="2">Self–efficacy</td><td>Coefficient</td><td>.868<xref ref-type="table-fn" rid="tfn2">**</xref></td><td>1</td><td /><td /><td /></tr><tr><td><italic>p</italic> value</td><td>.000</td><td /><td /><td /><td /></tr><tr><td rowspan="2">WL</td><td>Coefficient</td><td>.078</td><td>–.011</td><td>1</td><td /><td /></tr><tr><td><italic>p</italic> value</td><td>.388</td><td>.904</td><td /><td /><td /></tr><tr><td rowspan="2">Behavioral engagement</td><td>Coefficient</td><td>.824<xref ref-type="table-fn" rid="tfn2">**</xref></td><td>.757<xref ref-type="table-fn" rid="tfn2">**</xref></td><td>–.000</td><td>1</td><td /></tr><tr><td><italic>p</italic> value</td><td>.000</td><td>.000</td><td>.996</td><td /><td /></tr><tr><td rowspan="2">Emotional engagement</td><td>Coefficient</td><td>.759<xref ref-type="table-fn" rid="tfn2">**</xref></td><td>.709<xref ref-type="table-fn" rid="tfn2">**</xref></td><td>–.008</td><td>.772<xref ref-type="table-fn" rid="tfn2">**</xref></td><td>1</td></tr><tr><td><italic>p</italic> value</td><td>.000</td><td>.000</td><td>.930</td><td>.000</td><td /></tr></tbody></table> </ephtml> </p> <p>2 <emph>p</emph> <.01.</p> <p>In order to obtain a deeper understanding of the interactions among the above-mentioned factors, we conducted stepwise regression analysis. Before performing the analysis, we are not clear whether there really exists interaction and how strong the interaction is. As such, the analyses were essentially exploratory. As behavioral engagement and emotional engagement are key indicators of successful BL, we assumed respectively behavioral engagement and emotional engagement as dependent variables, and other factors correlated to them as independent variables, and conducted stepwise regression analysis. The method was adopted because the above Spearman correlation analysis shows that there exist correlations among some of the variables. Stepwise regression can be used to screen and remove variables that cause multicollinearity ([<reflink idref="bib23" id="ref63">23</reflink>]).</p> <p>Taking behavioral engagement as dependent variable, and taking SRL, self-efficacy, workload, emotional engagement as independent variables, stepwise regression was performed. After automatic recognition by the model, the remaining two items are SRL and emotional engagement. The model passed the <emph>F</emph> test (<emph>F</emph> = 139.391, <emph>p</emph> =.000 <.05), indicating high degree of validity. In addition, the multicollinearity test of the model shows that the VIF values in the model are all less than 5, which means that there is no collinearity problem. The regression coefficient values of SRL and emotional engagement respectively are 0.493 (<emph>t</emph> = 8.046, <emph>p</emph> =.000 <.01) and 0.262 (<emph>t</emph> = 4.573, <emph>p</emph> =.000 <.01), which means that SRL and emotional engagement have significant positive impact on behavioral engagement. The independent variables can explain 69.6% change of behavioral engagement (as shown in Table 5).</p> <p>Graph</p> <p>Table 5. Stepwise Regression Analysis (behavioral Engagement as Dependent Variable).</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="center" colspan="2">Unstandardized Coefficients</th><th align="center">Standardized Coefficients</th><th align="center" rowspan="2"><italic>t</italic></th><th align="center" rowspan="2"><italic>p</italic></th><th align="center" rowspan="2">VIF</th><th align="center" rowspan="2"><italic>R</italic><sup>2</sup></th><th align="center" rowspan="2">Adj <italic>R</italic><sup>2</sup></th><th align="center" rowspan="2"><italic>F</italic></th></tr><tr><th /><th align="center"><italic>B</italic></th><th align="center">Std. Error</th><th align="center"><italic>Beta</italic></th></tr></thead><tbody><tr><td>Constant</td><td>1.244</td><td>0.188</td><td align="center">-</td><td>6.609</td><td>.000<xref ref-type="table-fn" rid="tfn3">**</xref></td><td align="center">-</td><td rowspan="3">0.696</td><td rowspan="3">0.691</td><td rowspan="3"><italic>F</italic> (2,122) = 139.391, <italic>p</italic> =.000</td></tr><tr><td>SRL</td><td>0.493</td><td>0.061</td><td>0.571</td><td>8.046</td><td>.000<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>2.021</td></tr><tr><td>Emotional engagement</td><td>0.262</td><td>0.057</td><td>0.325</td><td>4.573</td><td>.000<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>2.021</td></tr></tbody></table> </ephtml> </p> <p>3 <emph>Note</emph> **<emph>p</emph> <.01. <emph>N</emph> = 125.</p> <p>When taking emotional engagement as dependent variable, stepwise regression analysis shows the regression coefficient value of SRL is 0.373 (<emph>t</emph> = 3.544, <emph>p</emph> =.001 <.01), which means that SRL has a significant positive impact on emotional engagement. The regression coefficient value of behavioral engagement is 0.559 (<emph>t</emph> = 4.573, <emph>p</emph> =.000 <.01), which means that behavioral engagement will have a significant positive impact on Emotional engagement. The independent variables can explain 57.8% change of behavioral engagement (as shown in Table 6).</p> <p>Graph</p> <p>Table 6. Stepwise Regression Analysis (emotional Engagements as Dependent Variable).</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="center" colspan="2">Unstandardized coefficients</th><th align="center">Standardized coefficients</th><th align="center" rowspan="2"><italic>t</italic></th><th align="center" rowspan="2"><italic>p</italic></th><th align="center" rowspan="2">VIF</th><th align="center" rowspan="2"><italic>R</italic><sup>2</sup></th><th align="center" rowspan="2">Adj <italic>R</italic><sup>2</sup></th><th align="center" rowspan="2"><italic>F</italic></th></tr><tr><th /><th align="center"><italic>B</italic></th><th align="center">Std. error</th><th align="center"><italic>Beta</italic></th></tr></thead><tbody><tr><td>Constant</td><td>0.200</td><td>0.320</td><td align="center">-</td><td>0.625</td><td>.533</td><td align="center">-</td><td rowspan="3">0.578</td><td rowspan="3">0.571</td><td rowspan="3"><italic>F</italic> (2,122) = 83.401, <italic>p</italic> =.000</td></tr><tr><td>SRL</td><td>0.373</td><td>0.105</td><td>0.349</td><td>3.544</td><td>.001<xref ref-type="table-fn" rid="tfn4">**</xref></td><td>2.804</td></tr><tr><td>Behavioral engagement</td><td>0.559</td><td>0.122</td><td>0.451</td><td>4.573</td><td>.000<xref ref-type="table-fn" rid="tfn4">**</xref></td><td>2.804</td></tr></tbody></table> </ephtml> </p> <p>4 <emph>Note.</emph>* <emph>p</emph><0.05 **<emph>p</emph> <.01. <emph>N</emph> = 125.</p> <hd id="AN0162839143-16">Discussion and Implications</hd> <p></p> <hd id="AN0162839143-17">Optimizing Learning Design and Encouraging True in-depth Collaboration</hd> <p>Through descriptive analysis and word frequency analysis on open-ended questions, it finds that students fully recognize the utility of collaborative BL. Under this learning mode, their SRL, self-efficacy and engagement are all at a high level, and their learning burden remains at moderate level. The recognition of collaboration in BL context coincides with previous research findings. Research of [<reflink idref="bib55" id="ref64">55</reflink>] shows that peer collaboration improves engagement and extracurricular interaction, which in turn improve students' academic performance. [<reflink idref="bib28" id="ref65">28</reflink>] argued that the capability of students to learn collaboratively with their peers has a positive and significant impact on academic performance and on SRL. [<reflink idref="bib25" id="ref66">25</reflink>] found that group collaboration can reduce cognitive load. Our research further confirms that peer collaboration acts as an essential part in BL design, which deserves prime attention and should be encouraged. On the other hand, it is worth noting that interaction does not necessarily mean the improvement of learning effects. As [<reflink idref="bib10" id="ref67">10</reflink>] pointed out, the quality of interaction may be more important than quantity. Interaction is not necessarily collaborative. But once they collaborate, it has a positive impact on task completion ([<reflink idref="bib48" id="ref68">48</reflink>]). Under BL context, teachers should figure out the nature of pair or group interaction, that is, whether they are truly collaborative. Teachers should adopt a variety of activity designs, provide opportunities and encourage in-depth collaboration. In addition, modes of collaboration vary according to different situations. Therefore, teachers should encourage learners to participate in collaboration actively. However, when they find a collaborative model which is not conducive to the development of learners, teachers should promptly intervene in and offer guidance ([<reflink idref="bib54" id="ref69">54</reflink>]). As discovered in this research, free riding occurs in collaborative activities. Some students contribute little or avoid teamwork. Therefore, teachers need to accurately evaluate interaction or group activities in a BL context, and implement well-designed evaluation methods to avoid false collaboration.</p> <hd id="AN0162839143-18">Cultivating SRL Skills</hd> <p>Correlation analysis found that SRL is significantly correlated with all factors except workload. Stepwise regression analysis showed that SRL has significant positive impact on behavioral engagement and emotional engagement. Therefore, SRL proves to be central among the essential BL elements. Increased level of SRL can bring about an overall improvement of collaborative BL, which coincides with the finding of [<reflink idref="bib27" id="ref70">27</reflink>]. The research result of [<reflink idref="bib34" id="ref71">34</reflink>] shows that SRL is positively correlated with academic performance in online learning. Students with a high level of SRL will actively engage in learning and try their best to maintain learning motivation and achieve their learning goals. SRL mediates various elements in the collaborative BL context, and they work together on the learning effect. Hence it proves to be very important for the success of BL. However, in BL practice, especially in the online learning session, a considerable number of learners cannot complete pre-class learning tasks due to lack of SRL ([<reflink idref="bib27" id="ref72">27</reflink>]), which brings a series of negative impacts. Many learners have weak SRL skills, which makes it increasingly necessary for teachers to fully understand and intervene in the BL environment and cultivate students' SRL capabilities ([<reflink idref="bib30" id="ref73">30</reflink>]). SRL can be taught and can be learned through training ([<reflink idref="bib29" id="ref74">29</reflink>]). In BL implementation, teachers should conduct SRL training for students at the beginning of the course, or even during the preparation stage. Furthermore, in the learning design, teachers should develop mechanisms that are conducive to the promotion of SRL, and provide support in this sense to students as facilitators.</p> <hd id="AN0162839143-19">Reducing Workload and Providing Emotional Support for Students</hd> <p>The research findings indicate that emotional engagement is significantly correlated to and interact with multiple key BL factors, and this interaction ultimately determines the quality and effect of BL. Specifically, emotional engagement has significant positive impact on behavioral engagement, that is, the stronger the student's emotional engagement in learning, the more engaged they are in learning behavior. The finding coincides with that of [<reflink idref="bib53" id="ref75">53</reflink>], who believes that emotion stimulates students' attention and in turn boosts learning behavior (memory, problem solving, etc.). Emotion represents not only learning experience, but also the result of student-student interaction, teacher-student interaction. Aa a consequence, under the guidance of learning analytics, emotion-related elements deserve full attention in learning design. Instructors should stimulate positive emotions in students' learning experience with various attempts. In this respect, the role of teachers cannot be ignored. In the word frequency analysis listed above, the word "teacher" is frequently mentioned. In the BL situation, teachers should act as the curriculum designer and content expert in the pre-class session, the facilitator and monitor in the in-class session, and the researcher in the after-class session. The research of [<reflink idref="bib31" id="ref76">31</reflink>] verifies that teacher preparation and support play an important role in student engagement, especially emotional engagement. Teachers may provide students with emotional support by placing a high value on their constant efforts in completing online learning tasks. They may offer continuous positive feedback on every progress the students have made and encourage more peer scaffolding. As workload represents an inherent problem of blended learning, teachers should reduce the burden of students by encouraging in-depth collaboration and streamlining extracurricular learning, thereby reducing students' negative feelings about learning.</p> <hd id="AN0162839143-20">Limitations</hd> <p>This research explores the interaction among SRL, self-efficacy and engagement in the BL context, enriching the practical perspective of BL optimization. But the limitations of this study should be acknowledged. First, teachers with different backgrounds and different teaching styles may affect participants' views on collaborative BL. Hence future research should take teacher factors into consideration to better capture their interrelationships. Second, this study uses behavioral engagement and emotional engagement as dependent variables, but failed to include academic performance, an important outcome indicator, into the investigation of interrelationships. The reason is that the questionnaire was answered anonymously. It is impossible to match each person's score with the answer to the questionnaire. Third, the size of research samples needs to be expanded to better generalize the research results.</p> <hd id="AN0162839143-21">Conclusion</hd> <p>In order to make clear the effects of collaborative BL and explore the relationships among hot issues, this study constructed a collaborative BL design and explored students' perceptions, the relationship among SRL, self-efficacy and engagement through descriptive, correlation and stepwise regression analysis. Descriptive analytics and keyword frequency statistics show that students fully recognize the positive effects of collaborative BL. As to the interactions among key factors, correlation analysis and stepwise regression analysis find that SRL is significantly correlated with all factors except workload. SRL is a significant predictor of behavioral engagement. Therefore, SRL proves to be central among the essential BL elements. Emotional engagement is correlated to and interact with the key BL factors, and this interaction ultimately determines the quality and effect of BL. Specifically, emotional engagement is a significant predictor of behavioral engagement, that is, the stronger the student's emotional engagement in learning, the more engaged they are in learning behavior. The findings offer insight into the interactions of key elements for BL. Such results of learning analytics also provide practical implications for improving BL learning design. Suggestions were raised including optimizing learning design and encouraging true in-depth collaboration, cultivating SRL skills, reducing workload and providing emotional support for students.</p> <p>The more we learn about blended learning, the more it appears that such learning contexts are more diverse than imagined. Future study may take teacher factor and academic achievement into consideration, and carry out larger-scale learning experiment, so as to enrich the interrelation research.</p> <ref id="AN0162839143-22"> <title> References </title> <blist> <bibl id="bib1" idref="ref2" type="bt">1</bibl> <bibtext> Alammary A., Sheard J., Carbone A. (2014). Blended learning in higher education: Three different design approaches. Australasian Journal of Educational Technology, 30(4), 440–454. https://doi.org/10.14742/ajet.693</bibtext> </blist> <blist> <bibl id="bib2" idref="ref1" type="bt">2</bibl> <bibtext> Al-Husban N. A., Shorman S. (2020). Perceptions of Syrian student refugees towards blended learning: Implications for higher education institutions. International Journal of Emerging Technologies in Learning (iJET), 15(01), 45–60. https://doi.org/10.3991/ijet.v15i01.11431</bibtext> </blist> <blist> <bibl id="bib3" idref="ref9" type="bt">3</bibl> <bibtext> Anderson T., Garrison D. R. (1998). Learning in a networked world: New roles and responsibilities. In Gibson C. (Ed.), Learners in higher education (pp. 97–112). Atwood Publishing.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref47" type="bt">4</bibl> <bibtext> Anthony B., Kamaludin A., Romli A., Raffei A. F. M., Nincarean A/l Eh Phon D., Abdullah A., Ming G. L., Shukor N. A., Nordin M. S., Baba S. (2019). Exploring the role of blended learning for teaching and learning effectiveness in institutions of higher learning: An empirical investigation. Education and Information Technologies, 24(6), 3433–3466. https://doi.org/10.1007/s10639-019-09941-z</bibtext> </blist> <blist> <bibl id="bib5" idref="ref62" type="bt">5</bibl> <bibtext> Arnold N., Ducate L., Kost C. (2012). Collaboration or cooperation? Analyzing group dynamics and revision process in wikis. CALICO Journal, 29(3), 431–448. https://doi.org/10.11139/cj.29.3.431-448</bibtext> </blist> <blist> <bibl id="bib6" idref="ref34" type="bt">6</bibl> <bibtext> Artino A. R., Stephens J. M. (2009). Academic motivation and self-regulation: A comparative analysis of undergraduate and graduate students learning online. The Internet and Higher Education, 12(3-4), 146–151.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref21" type="bt">7</bibl> <bibtext> Axelsen M., Redmond P., Heinrich E., Henderson M. (2020). The evolving field of learning analytics research in higher education: From data analysis to theory generation, an agenda for future research. Australasian Journal of Educational Technology, 36(2), 1–7. https://doi.org/10.14742/ajet.6266</bibtext> </blist> <blist> <bibl id="bib8" idref="ref31" type="bt">8</bibl> <bibtext> Bandura A. (1986). Social foundations of thought and action. Prentice-Hall.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref28" type="bt">9</bibl> <bibtext> Barnard L., Lan W. Y., To Y. M., Paton V. O., Lai S. L. (2009). Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 12(1), 1–6.</bibtext> </blist> <blist> <bibtext> Bernard R. M., Abrami P. C., Borokhovski E., Wade C. A., Tamim R. M., Surkes M. A., Bethel E. C. (2009). A meta-analysis of three types of interaction treatments in distance education. Review of Educational Research, 79(3), 1243–1289. https://doi.org/10.3102/0034654309333844</bibtext> </blist> <blist> <bibtext> Chen J. W., Wang N., Li J. Y. (2017). A tentative analysis of construction of SPOC learning community. Journal of Xi'an International Studies University, 25(4), 58–61. https://doi.org/10.16362/j.cnki.cn61-1457/h.2017.04.012</bibtext> </blist> <blist> <bibtext> Cho M. H., Kim B. J. (2013). Students' self-regulation for interaction with others in online learning environments. The Internet and Higher Education, 17, 69–75. https://doi.org/10.1016/j.iheduc.2012.11.001</bibtext> </blist> <blist> <bibtext> Clow D. (2012). The learning analytics cycle: closing the loop effectively [Conference session]. 2nd International Conference on Learning Analytics and Knowledge, Vancouver. ACM. https://doi.org/10.1145/2330601.2330636</bibtext> </blist> <blist> <bibtext> Coates H. (2006). Student engagement in campus-based and online education: University connections. Routledge.</bibtext> </blist> <blist> <bibtext> Conole G., Gašević D., Long P., Siemens G. (2011). Message from the LAK 2011 General & Program Chairs [Conference session]. LAK' 11: Proceedings of the 1st International Conference on Learning Analytics and Knowledge. Association for Computing Machinery.</bibtext> </blist> <blist> <bibtext> Conrad D. L. (2010). Engagement, excitement, anxiety, and fear: Learners' experiences of starting an online course. American Journal of Distance Education, 16(4), 205–226. https://doi.org/10.1207/s15389286ajde1604_2</bibtext> </blist> <blist> <bibtext> Ellis R. A., Bliuc A.-M., Han F. (2021). Challenges in assessing the nature of effective collaboration in blended university courses. Australasian Journal of Educational Technology, 37(1), 1–14. https://doi.org/10.14742/ajet.5576</bibtext> </blist> <blist> <bibtext> Fredricks J. A., Blumenfeld P. C., Paris A. H. (2004). School engagement: potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109.</bibtext> </blist> <blist> <bibtext> Gao J., Zhao B., Xiong Y., Gan J. (2021). Optimization design of the online learning environment for ethnic college students: The perspective of the emotional participation. Interactive Learning Environments, 29(8), 1288–1300. https://doi.org/10.1080/10494820.2019.1636077</bibtext> </blist> <blist> <bibtext> Garrison D. R., Kanuka H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105. https://doi.org/10.1016/j.iheduc.2004.02.001</bibtext> </blist> <blist> <bibtext> George A.-J., McEwan A., Tarr J.-A. (2021). Accountability in educational dialogue on attrition rates: Understanding external attrition factors and isolation in online law school. Australasian Journal of Educational Technology, 37(1), 111–132. https://doi.org/10.14742/ajet.6175</bibtext> </blist> <blist> <bibtext> Hao Y. (2016). Middle School Students' flipped learning readiness in foreign language classrooms: Exploring its relationship with personal characteristics and individual circumstances. Computers in Human Behavior, 59, 295–303. https://doi.org/10.1016/j.chb.2016.01.031</bibtext> </blist> <blist> <bibtext> Jaccard J., Turrisi R. (2003). Interaction effects in multiple regression (Vol. 72). SAGE.</bibtext> </blist> <blist> <bibtext> Jung Y., Lee J. (2018). Learning engagement and persistence in massive open online courses (MOOCS). Computers & Education, 122(7), 9–22. https://doi.org/10.1016/j.compedu.2018.02.013</bibtext> </blist> <blist> <bibtext> Karabulut-Ilgu A., Yao S., Savolainen P., Jahren C. (2018). Student Perspectives on the flipped-classroom approach and collaborative problem-solving process. Journal of Educational Computing Research, 56(4), 513–537. https://doi.org/10.1177/0735633117715033</bibtext> </blist> <blist> <bibtext> Kim M. K., Kim S. M., Khera O., Getman J. (2014). The experience of three flipped classrooms in an urban university: An exploration of design principles. The Internet and Higher Education, 22, 37–50. https://doi.org/10.1016/j.iheduc.2014.04.003</bibtext> </blist> <blist> <bibtext> Lai C.-L., Hwang G.-J. (2016). A self-regulated flipped classroom approach to improving students' learning performance in a mathematics course. Computers & Education, 100, 126–140. https://doi.org/10.1016/j.compedu.2016.05.006</bibtext> </blist> <blist> <bibtext> Lim C. L., Ab Jalil H., Ma'rof A. M., Saad W. Z. (2020). Peer learning, self-regulated learning and academic achievement in blended learning courses: A structural equation modeling approach. International Journal of Emerging Technologies in Learning (iJET), 15(3), 110–125. https://doi.org/10.3991/ijet.v15i03.12031</bibtext> </blist> <blist> <bibtext> Lin J.-W., Lai Y.-C., Lai Y.-C., Chang L.-C. (2016). Fostering self-regulated learning in a blended environment using group awareness and peer assistance as external scaffolds. Journal of Computer Assisted Learning, 32, 77–93. https://doi.org/10.1111/jcal.12120</bibtext> </blist> <blist> <bibtext> Lodge J., Panadero E., Broadbent J., de Barba P. (2018). Supporting self-regulated learning with learning analytics. In Lodge J., Horvath J. C., Corrin L. (Eds.), Learning analytics in the classroom (1st ed., pp. 45–55). Routledge.</bibtext> </blist> <blist> <bibtext> Ma J., Han X., Yang J., Cheng J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: The role of the instructor. The Internet and Higher Education, 24, 26–34. https://doi.org/10.1016/j.iheduc.2014.09.005</bibtext> </blist> <blist> <bibtext> Manwaring K. C., Larsen R., Graham C. R., Henrie C. R., Halverson L. R. (2017). Investigating student engagement in blended learning settings using experience sampling and structural equation modeling. The Internet and Higher Education, 35, 21–33. https://doi.org/10.1016/j.iheduc.2017.06.002</bibtext> </blist> <blist> <bibtext> Mirriahi N., Alonzo D., Fox B. (2015). A blended learning framework for curriculum design and professional development. Research in Learning Technology, 23, 1–14. https://doi.org/10.3402/rlt.v23.28451</bibtext> </blist> <blist> <bibtext> Narciss S., Proske A., Koerndle H. (2007). Promoting self-regulated learning in web-based learning environments. Computers in Human Behavior, 23, 1126–1144. https://doi.org/10.1016/j.chb.2006.10.006</bibtext> </blist> <blist> <bibtext> O'Flaherty J., Phillips C. (2015). The use of flipped classrooms in higher education: A scoping review. The Internet and Higher Education, 25, 85–95. https://doi.org/10.1016/j.iheduc.2015.02.002</bibtext> </blist> <blist> <bibtext> Pardo A., Gasevic D., Jovanovic J., Dawson S., Mirriahi N. (2019). Exploring student interactions with preparation activities in a flipped classroom experience. IEEE Transactions on Learning Technologies, 12(3), 333–346. https://doi.org/10.1109/tlt.2018.2858790</bibtext> </blist> <blist> <bibtext> Pardo A., Han F., Ellis R. A. (2016). Exploring the relation between self-regulation, online activities, and academic performance: A case study [Conference session]. Sixth International Conference on Learning Analytics & Knowledge (LAK'LA), April 25–29, 2016, Edinburgh, UK (pp.422–429). ACM. https://doi.org/10.1145/2883851.2883883</bibtext> </blist> <blist> <bibtext> Pontual Falcão T., Ferreira Mello R., Lins Rodrigues R. (2020). Applications of learning analytics in Latin America. British Journal of Educational Technology, 51, 871–874. https://doi.org/10.1111/bjet.12978</bibtext> </blist> <blist> <bibtext> Ramirez-Arellano A., Bory-Reyes J., Hernández-Simón L. M. (2019). Emotions, motivation, cognitive–metacognitive strategies, and behavior as predictors of learning performance in blended learning. Journal of Educational Computing Research, 57(2), 491–512. https://doi.org/10.1177/0735633117753935</bibtext> </blist> <blist> <bibtext> Sallam M. H., Martín-Monje E., Li Y. (2022). Research trends in language MOOC studies: A systematic review of the published literature (2012-2018). Computer Assisted Language Learning, 35, 764–791. https://doi.org/10.1080/09588221.2020.1744668</bibtext> </blist> <blist> <bibtext> Schunk D. H., Zimmerman B. J. (Eds.) (2012). Motivation and self-regulated learning: Theory, research, and applications. Routledge.</bibtext> </blist> <blist> <bibtext> Senn G. J. (2008). Comparison of face-to-face and hybrid delivery of a course that requires technology skills development. Journal of Information Technology Education, 7, 267–283. https://doi.org/10.28945/189</bibtext> </blist> <blist> <bibtext> Shen D., Cho M. H., Tsai C. L., Marra R. (2013). Unpacking online learning experiences: online learning self-efficacy and learning satisfaction. The Internet and Higher Education, 19, 10–17. https://doi.org/10.1016/j.iheduc.2013.04.001</bibtext> </blist> <blist> <bibtext> Siemens G., Baker R. S. J. D. (2012). Learning analytics and educational data mining: Towards communication and collaboration [Conference session]. 2nd International Conference on Learning Analytics and Knowledge. ACM Press. https://doi.org/10.1145/2330601.2330661</bibtext> </blist> <blist> <bibtext> Siemens G., Gasevic D. (2012). Guest editorial-Learning and knowledge analytics. Educational Technology & Society, 15(3), 1–2. https://<ulink href="http://www.researchgate.net/publication/279555980%5fGuest%5fEditorial%5f-%5fLearning%5fand%5fKnowledge%5fAnalytics">www.researchgate.net/publication/279555980%5fGuest%5fEditorial%5f-%5fLearning%5fand%5fKnowledge%5fAnalytics</ulink></bibtext> </blist> <blist> <bibtext> Skinner E., Furrer C., Marchand G., Kindermann T. (2008). Engagement and disaffection in the classroom: Part of a larger motivational dynamic? Journal of Education & Psychology, 100(4), 765–781. https://doi.org/10.1037/a0012840</bibtext> </blist> <blist> <bibtext> So H. J., Brush T. A. (2008). Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors. Computers & Education, 51(1), 318–336. https://doi.org/10.1016/j.compedu.2007.05.009</bibtext> </blist> <blist> <bibtext> Storch N. (2001). How collaborative is pair work? ESL tertiary students composing in pairs. Language Teaching Research, 5(1), 29–53. https://doi.org/10.1177/136216880100500103</bibtext> </blist> <blist> <bibtext> Trespalacios J., Lowenthal P. (2019). What do they really like? An investigation of students' perceptions of their coursework in a fully online educational technology program. Australasian Journal of Educational Technology, 35(5), 60–78. https://doi.org/10.14742/ajet.4364</bibtext> </blist> <blist> <bibtext> Van Den Bossche P., Gijselaers W. H., Segers M., Kirschner P. A. (2006). Social and cognitive factors driving teamwork in collaborative learning environments: Team learning beliefs and behaviors. Small Group Research, 37(5), 490–521. https://doi.org/10.1177/1046496406292938</bibtext> </blist> <blist> <bibtext> Viberg O., Hatakka M., Bälter O., Mavroudi A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027</bibtext> </blist> <blist> <bibtext> Vygotsky L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.</bibtext> </blist> <blist> <bibtext> Weiss R. P. (2000). Emotion and learning. Training and Development, 54(11), 45–48.</bibtext> </blist> <blist> <bibtext> Xu J. F., Zhang S. S. (2019). A review of peer interaction research methods. Journal of PLA University of Foreign Languages, 42(6), 1–10+157 https://kns.cnki.net/kcms/detail/41.1164.H.20191202.1322.034.html</bibtext> </blist> <blist> <bibtext> Xu L. J., Yu S. Q., Chen S. D., Ji S. P. (2021). Effects of the flipped classroom model on student performance and interaction with a peer-coach strategy. Educational Studies, 47(3), 292–311. https://doi.org/10.1080/03055698.2019.1701991</bibtext> </blist> <blist> <bibtext> Yun H., Park S., Kim D., Jung E., Yoon M. (2020). The influence of academic level and course delivery mode on the use of motivational regulation strategies and learning engagement. Australasian Journal of Educational Technology, 36(3), 89–103. https://doi.org/10.14742/ajet.5879</bibtext> </blist> <blist> <bibtext> Zimmerman B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Education & Psychology, 81(3), 329–339. https://doi.org/10.1037/0022-0663.81.3.329</bibtext> </blist> </ref> <ref id="AN0162839143-23"> <title> Footnotes </title> <blist> <bibtext> Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> ORCID iD Shu-rong Zhao</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-9321-1661</bibtext> </blist> </ref> <aug> <p>By Shu-rong Zhao and Cui-hong Cao</p> <p>Reported by Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib33" firstref="ref3"></nolink> <nolink nlid="nl2" bibid="bib21" firstref="ref5"></nolink> <nolink nlid="nl3" bibid="bib49" firstref="ref6"></nolink> <nolink nlid="nl4" bibid="bib26" firstref="ref7"></nolink> <nolink nlid="nl5" bibid="bib42" firstref="ref8"></nolink> <nolink nlid="nl6" bibid="bib39" firstref="ref10"></nolink> <nolink nlid="nl7" bibid="bib28" firstref="ref11"></nolink> <nolink nlid="nl8" bibid="bib12" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib16" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib29" firstref="ref16"></nolink> <nolink nlid="nl11" bibid="bib56" firstref="ref17"></nolink> <nolink nlid="nl12" bibid="bib19" firstref="ref18"></nolink> <nolink nlid="nl13" bibid="bib38" firstref="ref19"></nolink> <nolink nlid="nl14" bibid="bib15" firstref="ref20"></nolink> <nolink nlid="nl15" bibid="bib13" firstref="ref22"></nolink> <nolink nlid="nl16" bibid="bib44" firstref="ref23"></nolink> <nolink nlid="nl17" bibid="bib51" firstref="ref24"></nolink> <nolink nlid="nl18" bibid="bib41" firstref="ref27"></nolink> <nolink nlid="nl19" bibid="bib22" firstref="ref30"></nolink> <nolink nlid="nl20" bibid="bib43" firstref="ref33"></nolink> <nolink nlid="nl21" bibid="bib14" firstref="ref36"></nolink> <nolink nlid="nl22" bibid="bib18" firstref="ref37"></nolink> <nolink nlid="nl23" bibid="bib24" firstref="ref38"></nolink> <nolink nlid="nl24" bibid="bib36" firstref="ref39"></nolink> <nolink nlid="nl25" bibid="bib35" firstref="ref41"></nolink> <nolink nlid="nl26" bibid="bib52" firstref="ref43"></nolink> <nolink nlid="nl27" bibid="bib20" firstref="ref44"></nolink> <nolink nlid="nl28" bibid="bib11" firstref="ref45"></nolink> <nolink nlid="nl29" bibid="bib17" firstref="ref46"></nolink> <nolink nlid="nl30" bibid="bib40" firstref="ref48"></nolink> <nolink nlid="nl31" bibid="bib47" firstref="ref49"></nolink> <nolink nlid="nl32" bibid="bib57" firstref="ref50"></nolink> <nolink nlid="nl33" bibid="bib54" firstref="ref52"></nolink> <nolink nlid="nl34" bibid="bib32" firstref="ref54"></nolink> <nolink nlid="nl35" bibid="bib45" firstref="ref55"></nolink> <nolink nlid="nl36" bibid="bib37" firstref="ref56"></nolink> <nolink nlid="nl37" bibid="bib46" firstref="ref59"></nolink> <nolink nlid="nl38" bibid="bib50" firstref="ref61"></nolink> <nolink nlid="nl39" bibid="bib23" firstref="ref63"></nolink> <nolink nlid="nl40" bibid="bib55" firstref="ref64"></nolink> <nolink nlid="nl41" bibid="bib25" firstref="ref66"></nolink> <nolink nlid="nl42" bibid="bib10" firstref="ref67"></nolink> <nolink nlid="nl43" bibid="bib48" firstref="ref68"></nolink> <nolink nlid="nl44" bibid="bib27" firstref="ref70"></nolink> <nolink nlid="nl45" bibid="bib34" firstref="ref71"></nolink> <nolink nlid="nl46" bibid="bib30" firstref="ref73"></nolink> <nolink nlid="nl47" bibid="bib53" firstref="ref75"></nolink> <nolink nlid="nl48" bibid="bib31" firstref="ref76"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1376724
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Exploring Relationship among Self-Regulated Learning, Self-Efficacy and Engagement in Blended Collaborative Context
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Shu-rong%22">Zhao, Shu-rong</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9321-1661">0000-0002-9321-1661</externalLink>)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Cui-hong%22">Cao, Cui-hong</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22SAGE+Open%22"><i>SAGE Open</i></searchLink>. Jan-Mar 2023 13(1).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 11
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Management%22">Self Management</searchLink><br /><searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Independent+Study%22">Independent Study</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Efficacy%22">Self Efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperation%22">Cooperation</searchLink><br /><searchLink fieldCode="DE" term="%22Blended+Learning%22">Blended Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Emotional+Response%22">Emotional Response</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/21582440231157240
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2158-2440
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Collaboration proves to be an effective way to facilitate students' engagement and solve the mostly-mentioned problems of blended learning (BL). For collaborative BL, self-regulated learning (SRL), self-efficacy and engagement are frequently referred to in BL studies and represent key elements of effective BL. Furthermore, the interaction of these elements correlates closely with the performance of students. There have been few research attempts to draw synergies and explore the relationship among these key elements. To address this gap, data were collected through a questionnaire and records on LMS from 125 students in a Chinese university. Descriptive analytics show that students fully recognize the positive effects of collaborative BL. Correlation analysis and regression analysis find that self-regulated learning (SRL) is significantly correlated with all factors except workload. It is also a significant predictor of behavioral engagement. Therefore, SRL proves to be central among the essential BL elements. Emotional engagement is significantly correlated to and interact with multiple key BL factors. Specifically, emotional engagement is a significant predictor of behavioral engagement. The findings offer insight into the interactions of key elements of BL and provide practical implications for improving BL learning design.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2023
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1376724
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1376724
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/21582440231157240
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Self Management
        Type: general
      – SubjectFull: Learning
        Type: general
      – SubjectFull: Independent Study
        Type: general
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Self Efficacy
        Type: general
      – SubjectFull: Cooperation
        Type: general
      – SubjectFull: Blended Learning
        Type: general
      – SubjectFull: Emotional Response
        Type: general
      – SubjectFull: Student Behavior
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Exploring Relationship among Self-Regulated Learning, Self-Efficacy and Engagement in Blended Collaborative Context
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhao, Shu-rong
      – PersonEntity:
          Name:
            NameFull: Cao, Cui-hong
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-electronic
              Value: 2158-2440
          Numbering:
            – Type: volume
              Value: 13
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: SAGE Open
              Type: main
ResultId 1