This meta-analytic review explores the effects of self-assessment on students' self-regulated learning (SRL) and self-efficacy. A total of 19 studies were included in the four different meta-analyses ...conducted with a total sample of 2305 students. The effects sizes from the three meta-analyses addressing effects on different measures of SRL were 0.23, 0.65, and 0.43. The effect size from the meta-analysis on self-efficacy was 0.73. In addition, it was found that gender (with girls benefiting more) and certain self-assessment components (such as self-monitoring) were significant moderators of the effects on self-efficacy. These results point to the importance of self-assessment interventions to promote students’ use of learning strategies and its effects on motivational variables such as self-efficacy.
•Self-assessment effects on self-regulated learning (SRL) and self-efficacy were explored.•19 studies (2305 students) were included in four different meta-analyses.•Effects sizes from the three meta-analyses on SRL were 0.23, 0.65 and 0.43.•The effect size from the meta-analysis on self-efficacy was 0.73.
The authors propose a theoretical model linking emotions, self-regulated learning, and motivation to academic achievement. This model was tested with 5,805 undergraduate students. They completed the ...Self-Regulated Learning, Emotions, and Motivation Computerized Battery (LEM-B) composed of 3 self-report questionnaires: the Self-Regulated Learning Questionnaire (LQ), the Emotions Questionnaire (EQ), and the Motivation Questionnaire (MQ). The findings were consistent with the authors' hypotheses and appeared to support all aspects of the proposed model. The structural equation model showed that students' emotions influence their self-regulated learning and their motivation, and these, in turn, affect academic achievement. Thus, self-regulated learning and motivation mediate the effects of emotions on academic achievement. Moreover, positive emotions foster academic achievement only when they are mediated by self-regulated learning and motivation. The results are discussed with regard to the key role of emotions in academic settings and in terms of theoretical implications for researchers.
The purpose of this study is to determine the effect of self-regulated learning on student academic integrity with religiosity as the moderator variable. The approach used in this research is ...correlational causality. By using three measurement scales (self-regulated learning scale , academic integrity scale and religiosity) for 380 respondents , the results show that self-regulation learning affects academic integrity with religiosity as a moderator variable of 54.2 % . Based on the results obtained, it shows that the effect of self-regulated learning on academic integrity in students is in the form of a positive relationship, meaning that the higher the level of self-regulated learning in students, the higher the level of academic integrity . Religiosity as a moderator variable in this study illustrates that it can strengthen the influence of self- regulated learning variables on student academic integrity.
PurposeThis study explores a conceptual framework that addresses a school principal's self-regulated learning (SPSRL) as well as possible avenues for future conceptualization of, and research into ...this issue.Design/methodology/approachThe conceptual framework of SPSRL is based on an extensive literature review of the research on student’s and teacher’s self-regulated learning models.FindingsA novel conceptual and practical SPSRL framework for planning, performing, monitoring and self-reflection is elaborated.Research limitations/implicationsThis novel SPSRL conceptual framework provides school principals with a means to shape and develop processes, strategies and structures to monitor and evaluate their learning, enabling them to react effectively in uncertain and dynamic environments. This framework may open the way to future research into possible contributions of the SPSRL construct with other variables related to principal effectiveness. The suggested framework should be examined empirically in various sociocultural contexts, possibly substantiating its conceptual validity.Originality/valueThe SPSRL conceptual framework can improve school learning, which might connect the individual (the school principal) and organizational (teachers) learning levels.
The existing literature suggests that self-regulated learning (SRL) strategies are relevant to student grade performance in both online and blended contexts, although few, if any, studies have ...compared them. However, due to challenges unique to each group, the variety of SRL strategies that are implicated, and their effect size for predicting performance may differ across contexts. One hundred and forty online students and 466 blended learning students completed the Motivated Strategies for Learning Questionnaire. The results show that online students utilised SRL strategies more often than blended learning students, with the exception of peer learning and help seeking. Despite some differences in individual predictive value across enrolment status, the key SRL predictors of academic performance were largely equivalent between online and blended learning students. Findings highlight the relative importance of using time management and elaboration strategies, while avoiding rehearsal strategies, in relation to academic subject grade for both study modes.
•Few studies have online learner's and blended learners SRL strategy use & grade.•Online and blended learners differed in their SRL strategies use.•Individual predictive value of SRL strategies differed for online/blended learners.•Overall predictors of grade were equivalent for online and blended learners.•Time management/elaboration/rehearsal strategies key for predicting grade.
This study examined the extent to which instructional conditions influence the prediction of academic success in nine undergraduate courses offered in a blended learning model (n=4134). The study ...illustrates the differences in predictive power and significant predictors between course-specific models and generalized predictive models. The results suggest that it is imperative for learning analytics research to account for the diverse ways technology is adopted and applied in course-specific contexts. The differences in technology use, especially those related to whether and how learners use the learning management system, require consideration before the log-data can be merged to create a generalized model for predicting academic success. A lack of attention to instructional conditions can lead to an over or under estimation of the effects of LMS features on students' academic success. These findings have broader implications for institutions seeking generalized and portable models for identifying students at risk of academic failure.
•Predictive models in learning analytics need to account for instructional conditions.•Instructional conditions are based in the theory of self-regulated learning.•The study was conducted with a nine undergraduate blended learning (n=4139) courses.•Generalized predictive models were not suitable to inform practice and research.•Course specific models better detected variables of relevance for teaching practice.•Further implications for educational research and practice are discussed.
Self-Regulated Learning (SRL) is related to increased learning performance. Scaffolding learners in their SRL activities in a computer-based learning environment can help to improve learning ...outcomes, because students do not always regulate their learning spontaneously. Based on theoretical assumptions, scaffolds should be continuously adaptive and personalized to students' ongoing learning progress in order to promote SRL. The present study aimed to investigate the effects of analytics-based personalized scaffolds, facilitated by a rule-based artificial intelligence (AI) system, on students' learning process and outcomes by real-time measurement and support of SRL using trace data. Using a pre-post experimental design, students received personalized scaffolds (n = 36), generalized scaffolds (n = 32), or no scaffolds (n = 30) during learning. Findings indicated that personalized scaffolds induced more SRL activities, but no effects were found on learning outcomes. Process models indicated large similarities in the temporal structure of learning activities between groups which may explain why no group differences in learning performance were observed. In conclusion, analytics-based personalized scaffolds informed by students’ real-time SRL measured and supported with AI are a first step towards adaptive SRL supports incorporating artificial intelligence that has to be further developed in future research.
•Analytics-based scaffolds using trace data can support learning in real-time.•Personalized scaffolds induce metacognitive activities.•Personalized scaffolds most effective in promoting monitoring activities.•Students seldom plan and evaluate their learning and need more focused support.•Process models reveal possible explanation of missing effects on learning outcome.
The purpose of this study was to investigate whether students' self-reported SRL align with their digital trace data collected from the learning management system. This study took place in an ...upper-level college agriculture course delivered in an asynchronous online format. By comparing online students' digital trace data with their self-reported data, this study found that digital trace data from LMS could predict students' performance more accurately than self-reported SRL data. Through cluster analysis, students were classified into three levels based on their self-regulatory ability and the characteristics of each group were analyzed. By incorporating qualitative data, we explored possible explanations for the differences between students' self-reported SRL data and the digital trace data. This study challenges us to question the validity of existing self-reported SRL instruments. The three-cluster division of students' learning behaviors provides practical implications for online teaching and learning.
•Students' digital trace data from LMS in an online learning environment can reflect self-reported SRL data in some degree.•Students' digital trace data is more powerful in predicting students' academic performance than self-reported SRL data.•Important students' learning behavior variables are identified in an online learning environment.•Online students' learning behavior patterns are analyzed using cluster analysis.•Explanations for differences between students' self-reported SRL data and the digital trace data are explored and discussed.
Massive open online courses (MOOCs) require individual learners to be able to self-regulate their learning, determining when and how they engage. However, MOOCs attract a diverse range of learners, ...each with different motivations and prior experience. This study investigates the self-regulated learning (SRL) learners apply in a MOOC, in particular focusing on how learners' motivations for taking a MOOC influence their behaviour and employment of SRL strategies. Following a quantitative investigation of the learning behaviours of 788 MOOC participants, follow-up interviews were conducted with 32 learners. The study compares the narrative descriptions of behaviour between learners with self-reported high and low SRL scores. Substantial differences were detected between the self-described learning behaviours of these two groups in five of the sub-processes examined. Learners' motivations and goals were found to shape how they conceptualised the purpose of the MOOC, which in turn affected their perception of the learning process.
•We provide empirical investigation of learner motivations and learning strategies in MOOCs.•Substantial differences in learning behaviours were detected between participants with high and low SRL scores.•Learners’ motivations and goals shape their conceptualisation of a MOOC and the learning strategies they employ.•Learning in MOOCs cannot be fully understood by learning analytics alone and requires investigation of individual learners.
Beyond managing student dropout, higher education stakeholders need decision support to consistently influence the student learning process to keep students motivated, engaged, and successful. At the ...course level, the combination of predictive analytics and self-regulation theory can help instructors determine the best study advice and allow learners to better self-regulate and determine how they want to learn. The best performing techniques are often black-box models that favor performance over interpretability and are heavily influenced by course contexts. In this study, we argue that explainable AI has the potential not only to uncover the reasons behind model decisions, but also to reveal their stability across contexts, effectively bridging the gap between predictive and explanatory learning analytics (LA). In contributing to decision support systems research, this study (1) leverages traditional techniques, such as concept drift and performance drift, to investigate the stability of student success prediction models over time; (2) uses Shapley Additive explanations in a novel way to explore the stability of extracted feature importance rankings generated for these models; (3) generates new insights that emerge from stable features across cohorts, enabling teachers to determine study advice. We believe this study makes a strong contribution to education research at large and expands the field of LA by augmenting the interpretability and explainability of prediction algorithms and ensuring their applicability in changing contexts.
•SHAP exhibits two-fold utility in checking model stability and aiding study advice.•Success prediction models must be updated to ensure stable performance.•Changing learning contexts lead to distributional drift of LA indicators.•As the learning context changes, the importance of learning indicators shifts.•General activity and regularity indicators show the highest stability.
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