Search

Worldwide, there are increasing concerns about postsecondary students' mental health and how student success is implicated. Previous research has established psychological well-being and self-regulated learning are important components of student success, however, there is a paucity of research examining the interplay between these factors during a semester-long course. In this study, 118 students in a learning-to-learn elective university course completed nine weekly online planning and reflection tools. Students planned for a study session, completed an academic engagement and a psychological well-being measure, then reflected on a challenge faced and described the strategy chosen to overcome that challenge. Findings revealed (a) students who reported always attaining their goals also reported higher overall psychological well-being, and (b) within-person patterns of psychological well-being and academic engagement over time may affect regulatory responses to challenge or vice versa. Implications for theory, research, and practice are discussed.

Student engagement is an important factor in academic performance and comprises four dimensions: behavioural, cognitive, emotional (Fredricks et al., 2004), and agentic (Reeve, 2013). Blended courses provide unique opportunities for instructors to use trace data collected during learning to understand and support student engagement. This mixed-methods case study compared the student engagement of two groups of students with a history of low prior academic achievement. The groups were (a) students who ultimately did well in the course and (b) students who did poorly. Data came from two primary sources: (a) log file data from the course LMS, and (b) trace data derived from authentic learning tasks. Data represented five indicators: (a) behavioural engagement, (b) cognitive engagement, (c) emotions experienced during learning, (d) agency or proactive approaches to studying, and (e) overall academic engagement. Findings indicated students who moved achievement groups showed higher levels of behavioural engagement, cognitive engagement, and agentic or proactive approaches to studying and overall engagement. Additionally, students who remained in the low achievement group showed higher levels of positive deactivating emotions (e.g., relief). Implications for future research on student engagement and designing teaching to increase engagement in blended courses are discussed.

The cognitive and social demands of collaboration can raise significant motivation challenges. Task progression relies on team members strategically taking control of the problems and adapting accordingly. Theory indicates that productive collaboration involves groups using three modes of regulation: self-regulation, co-regulation, and socially shared regulation. Despite research demonstrating the occurrence of all three modes in collaboration, it is unclear how these modes interact and how co-regulation supports the emergence of self- and shared-regulation of motivation. The study aimed to examine the role co-regulation played in dynamically stimulating the emergence of self- and shared-regulation of motivation. A cross-case comparison was conducted between two groups who experienced high levels of motivation challenges but achieved contrasting perceptions of the overall team learning productivity. During analysis, groups' dynamic regulatory processes within the online environment were visually represented using a tool called the Chronologically-ordered Representation for Tool-Related Activity (CORDTRA). Findings demonstrate that co-regulation of motivation may afford and thwart the emergence of self- and shared-regulation, and these processes interacted with the group's situational challenges and the regulatory skills group members possessed. Comparisons between the two groups indicated that groups' motivation regulation should (a) match the demands of the challenges at hand, (b) be positively supported by group members through co-regulation, and (b) involve a more varied strategic responses so that the group may continue to learn and co-construct knowledge effectively as a team.

This study examined prior knowledge and student engagement in student performance. Log data were used to explore the distribution of final grades (i.e., weak, good, excellent final grades) occurring in an elective undergraduate course. Previous research has established behavioral and agentic engagement factors contribute to academic achievement (Reeve, 2013). Hierarchical logistic regression using both prior knowledge and log data from the course revealed: (a) the weak-grades group demonstrated less behavioral engagement than the good-grades group, (b) the good-grades group demonstrated less agentic engagement than the excellent-grades group, and (c) models composed of both prior knowledge and engagement measures were more accurate than models composed of only engagement measures. Findings demonstrate students performing at different grade-levels may experience different challenges in their course engagement. This study informs our own instructional strategies and interventions to increase student success in the course and provides recommendations for other instructors to support student success.

Self-regulation of learning involves developing metacognitive awareness (planning, monitoring, and evaluating) of (a) cognition-motivational beliefs, (b) behaviors-persistence, effort, engagement, and (c) affect-enjoyment, interest, and other emotions. Metacognitive awareness creates opportunities to exert metacognitive control as needed, which may involve sustaining or manipulating motivational cognition, behavior, and affect. By adopting a self-regulation perspective, this paper discusses the ways motivation develops within and across academic tasks and situations, as well as the ways learners can be supported to take control of their motivation in those contexts. Applying self-regulation principles in the practice of School Psychology means to consider the role of situation, context, and learners' socio-historical experiences while empowering learners to focus attention on things they can control.

Research that uses self-report measures to examine the complexity of self-regulated learning (SRL) and academic challenges for adolescents is limited. This study examined the psychometric property of the Self-Regulated Learning Profile and Self-Diagnostic (SRL-PSD) instrument and addressed the multi-components of SRL and academic challenges for adolescents. Participants were 358 adolescents from a Canadian middle school. The subscales of SRL-PSD were administered to students through LimeSurvey during a 25-min instructional session over two days. Results demonstrated the SRL-PSD was a reliable and valid self-report instrument to measure adolescents' SRL practices and academic challenges. Also, all types of SRL practices and academic challenges were significantly intercorrelated. Additionally, all types of SRL practices were positively associated with school engagement, whereas all types of academic challenges were negatively associated with school engagement. Overall, this study provides a validated self-report measure for educators and researchers to examine adolescents' SRL practices and academic challenges. [ABSTRACT FROM AUTHOR]

Difficulties with planning, such as negotiating task understandings and goals, can have a profound effect on regulation and task performance when students work collaboratively (Miller and Hadwin, "Computers in Human Behaviour", 52, 573-588, 2015a). Despite planning being a common challenge, teams often fail to identify strategies for addressing those challenges successfully. The purpose of this study was to examine the effect of team planning support in the form of awareness visualizations (quantified, nominal, and no visualization of individual planning perceptions summarized across group members) on the challenges students face during collaboration, and the ways they report regulating in the face of those challenges. Findings revealed differences across conditions. Individuals in the no visualization condition (a) rated planning as more problematic, and (b) were likely to encounter "doing the task, checking progress", and "group work" challenges when they encounter "planning challenges", (c) reported more "time" and "planning" main challenges compared to "doing the task" and "group work" challenges, and (d) reported that "planning" strategies (adopted as a team) were most effective for addressing planning challenges, followed by "teamwork" strategies which were less effective. In contrast, individuals belonging to groups who received one of the two visualizations (a) reported that both "planning" and "teamwork" strategies to be equally effective for addressing planning challenges, and (b) reported higher levels of success with their strategies than groups without a visualization support. Findings attest to the importance of supporting group planning with planning visualizations.

Collaboration in an online environment can be a socially and emotionally demanding task. It requires group members to engage in a great deal of regulation, where favourable emotions need to be sustained for the group's productive functioning. The purpose of this cross-case analysis was to examine the interplay of two groups' regulatory processes, regulatory modes, and socio-emotional interactions that contribute to or are influenced by emotions and socio-emotional climate perceived in the group. Specifically, this study compared a group of 4 students unanimously reporting a positive climate to a group of 4 students unanimously reporting a negative climate after completing a 90-min online text-based collaborative assignment. By drawing on two data channels (i.e., observed regulatory actions and socio-emotional interactions during collaboration and self-reported data about emotional beliefs and perceptions), four contrasting group features emerged: (a) incoming conditions served as a foundation for creating a positive collaborative experience, (b) regulation of emotions during initial planning, (c) negative emotions served as a constraint for shared adaptation in the face of a challenge, and (d) encouragement and motivational statements served as effective strategies for creating a positive climate. Implications for researching and supporting emotion regulation in collaborative learning are discussed.

Task-specific goals play a critical role in self-regulated learning, yet little research has examined students' self-set goals for authentic study sessions. We propose high-quality goals that are useful for guiding task engagement and evaluating progress are specific about (a) time, (b) actions, (c) standards, and (d) content. In Study 1, we examined characteristics of students' self-set goals. Five categories were created to describe students' goals relative to the features of a high-quality goal. Students rarely included specific information regarding actions, standards, or content. In Study 2, we examined patterns of change in quality of self-set goals across a semester in which students were in a learning-to-learn course. Improvements in goal quality were either inconsistent or non-existent. Implications of vague goals for regulating learning are discussed.

As theory and research in self-regulated learning (SRL) advance, debate continues about how to measure SRL as strategic, fine-grained, dynamic adaptations learners make during and between study sessions. Recognizing learners' perceptions are critical to the strategic adaptations they make during studying, this research examined the unique contributions of self-report data for understanding regulation as it develops over time. Data included (a) scores on the Regulation of Learning Questionnaire (RLQ) completed in the first and last few weeks of a 13-week course and (b) diary-like Weekly Reflections completed over 11 weeks. Participants were 263 undergraduate students in a course about SRL. First, exploratory factor analysis resulted in a five-factor model of the RLQ with factors labeled Task Understanding, Goal Setting, Monitoring, Evaluating, and Adapting. Second, latent class analysis of Time 1 and 2 RLQ scores revealed four classes: "emergent regulators," "moderate regulators," "high regulators with emergent adapting," and "high regulators". Finally, in-depth qualitative analysis of Weekly Reflections resulted in group SRL profiles based on a sub-sample of participants from each RLQ class. Qualitatively, these groups were labeled: "unengaged regulators," "active regulators," "struggling regulators", and "emergent regulators". Quantitative and qualitative SRL profiles were juxtaposed and similarities and differences discussed. This paper explicates and discusses the critical importance of sampling self-reports of SRL over time and tasks particularly in contexts where regulation is developing.

This study examined undergraduate students' reports of emotions and emotion regulation during studying from a self-regulated learning (SRL) perspective. Participants were 111 university students enrolled in a first-year course designed to teach skills in SRL. Students reflected on their emotional experiences during goal-directed studying episodes at three times over the semester. Measures included self-evaluations of goal attainment, emotion intensity ratings and open-ended descriptions of emotion regulation strategies. Findings generally revealed that positive emotions were positive predictors and negative emotions were negative predictors of self-evaluations of goal attainment, although positive emotions were associated with larger changes in self-evaluations. Boredom was analysed separately and was found to be a positive predictor at the between-person level but not a predictor at the within-person level. Finally, students reported (a) enacting a variety of strategies to regulate their emotions and (b) using a different strategy more often than the same strategy from one study session to the next.

This study examined the nature of confidence judgments associated with personal goal setting during undergraduate studying episodes. Calibration was examined between paired judgments of confidence and self-evaluations of goal attainment made over nine consecutive weeks. Participants were 170 students enrolled in a first-year undergraduate course designed to promote self-regulated learning. Learners completed nine weekly Personal Planning Tools in which they (a) set a personal academic goal for university studying in the upcoming week, (b) judged their confidence for attaining that goal (JOC), and (c) self-evaluated their success in attaining that goal after the study episode. Multilevel linear modeling indicated that (a) JOCs were better calibrated with self-evaluations of current goal attainment than past goal attainment, (b) calibration did not improve over the nine weeks, but learners did become less overconfident, and (c) learners who were performing better at university tended to be better calibrated. (Contains 8 tables and 1 figure.)

Despite intensive research in computer-supported collaborative learning (CSCL) over the last decade, there is relatively little research about how groups and individuals in groups engage, sustain, support, and productively regulate collaborative processes. This article examines the role of regulatory processes in collaborative learning and how CSCL environments can be used for shared regulation of learning. First, we establish the importance of regulation processes and introduce three types of regulation contributing to successful collaboration: self-, co-, and socially shared regulation of learning. Second, we illuminate two strands of seemingly diverse research that lay an important foundation for supporting and researching regulation in CSCL contexts establishing that (a) computer-based pedagogical tools used to successfully support regulation in individual learning contexts can be leveraged for collaborative task contexts, and (b) computer-based tools for supporting collaborative knowledge construction can be leveraged for supporting regulatory processes. Finally, we draw on emerging research in our own programs of research to demonstrate how regulation can be supported and researched in CSCL environments. The article concludes by charting a course for future CSCL research focused on supporting regulated learning in collaborative task contexts. (Contains 2 figures and 1 table.)