Individual differences in basic cognitive processes and self-regulated learning: Their interaction effects on math performance

Musso, Mariel; Boekaerts, Monique; Segers, Mien; Cascallar, Eduardo

doi:10.1016/j.lindif.2019.03.003

Cited by 35 publications

(26 citation statements)

References 79 publications

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“…Reimann (2019) also concludes that self-regulated learning is a path to methodology and theory advancement. Musso et al (2019) established the connection between cognitive processes and self-regulated learning and its effect on mathematics performance, and this is applicable at the strategic level. This study differentiates itself from the existing research by combining teamwork and self-learning regulations to predict the online students' academic performance.…”

Section: Discussionmentioning

confidence: 99%

Do teamwork experience and self-regulated learning determine the performance of students in an online educational technology course?

et al. 2021

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This study uses the quantitative research approach to examine the connection between students’ teamwork experience, self-regulated learning, technology self-efficacy, and performance in an online educational technology course. Sixty-three (63) students participated in this study. The study data were collected through an online questionnaire that included background information, course satisfaction, motivation strategies for learning, and online technology self-efficacy, to study the variables’ interactions using quantitative research. To realize this study’s aims, multivariate regression and correlation approaches were employed to analyze the online students’ data. The multivariate regression analysis results show a relationship between self-regulated learning, the online course level, and the number of online courses that the students have completed. Right self-regulated learning strategies in online courses motivate students to strive for a good teamwork experience, leading to increased interest in online learning. In addition, the results also show that there is a relationship between satisfaction and the level of the online course. Achieving good grades makes the student more satisfied and improves the level of technology use. Finally, this study established a relationship between the students’ motivation and the online course level. Therefore, teachers and course designers should implement learning objects that promote students’ engagement and motivation in online learning environments.

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Section: Discussionmentioning

confidence: 99%

Do teamwork experience and self-regulated learning determine the performance of students in an online educational technology course?

et al. 2021

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“…1) (Nicol and Macfarlane-Dick 2006). Students may have individual differences in domain knowledge, strategy knowledge, and motivational beliefs that affect SRL behaviors and performance (Musso et al 2019;Winne 1996). For example, students need domain knowledge to correctly judge the difficulty of the task and the needed effort to Fig.…”

Section: Srl and Feedbackmentioning

confidence: 99%

An analysis of internal and external feedback in self-regulated learning activities mediated by self-regulated learning tools and open learner models

Chou

Zou

2020

Int J Educ Technol High Educ

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In self-regulated learning (SRL), students organize, monitor, direct, and regulate their learning. In SRL, monitoring plays a critical role in generating internal feedback and thus adopting appropriate regulations. However, students may have poor SRL processes and performance due to their poor monitoring. Researchers have suggested providing external feedback to facilitate better student SRL. However, SRL involves many meta-cognitive internal processes that are hidden and difficult to observe and measure. This study proposed a SRL model to illustrate the relationship among external SRL tools, internal SRL processes, internal feedback, and external feedback. Based on the model, this study designed a system with SRL tools and open leaner models (OLMs) to assist students in conducting SRL, including self-assessing their initial learning performance (i.e. perceived initial performance and monitoring of learning performance) after listening to a teacher’s lecture, being assessed by and receiving external feedback from the OLM (i.e. actual performance) in the system, setting target goals (i.e. desired performance) of follow-up learning, conducting follow-up learning (i.e. strategy implementation), and evaluating their follow-up learning performance (i.e. perceived outcome performance and strategy outcome monitoring). These SRL tools also externalize students’ internal SRL processes and feedback, including perceived initial, desired, and perceived outcome performances, for investigation. In addition, this study explores the impact of external feedback from the OLM on students’ internal SRL processes and feedback. An evaluation was conducted to record and analyze students’ SRL processes and performance, and a questionnaire was administered to ask students about their SRL processes. There are three main findings. First, the results showed that students often have poor internal SRL processes and poor internal feedback, including poor self-assessment, inappropriate target goals, a failure to conduct follow-up learning, and a failure to achieve their goals. Second, the results revealed that the SRL tools and external feedback from the OLM assisted most students in SRL, including monitoring their learning performance, goal-setting, strategy implementation and monitoring, and strategy outcome monitoring. Third, some students still required further support for SRL.

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“…They have outlined some cognitive and non-cognitive variables impacting academic performance in general and in some specific domains (e.g., Boekaerts, Pintrich, & Zeidner, 2000;De Corte, Mason, Depaepe, & Verschaffel, 2011;Zimmerman & Schunk, 2011). Cognitive variables include, but are not limited to, working memory capacity (WMC) (Engle 2002;Musso et al 2019), attention Riccio, Lee, Romine, Cash, & Davis, 2002), and learning strategies (Weinstein et al 1987). Among non-cognitive variables, research suggests sociodemographic background information variables (Jun 2005;Kovacic 2010), motivational/coping strategies (Boekaerts and Niemivirta 2000;Boekaerts 1997), and social support (Scott, Spielmans, & Julka, 2004).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Predicting key educational outcomes in academic trajectories: a machine-learning approach

2020

Self Cite

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Predicting and understanding different key outcomes in a student’s academic trajectory such as grade point average, academic retention, and degree completion would allow targeted intervention programs in higher education. Most of the predictive models developed for those key outcomes have been based on traditional methodological approaches. However, these models assume linear relationships between variables and do not always yield accurate predictive classifications. On the other hand, the use of machine-learning approaches such as artificial neural networks has been very effective in the classification of various educational outcomes, overcoming the limitations of traditional methodological approaches. In this study, multilayer perceptron artificial neural network models, with a backpropagation algorithm, were developed to classify levels of grade point average, academic retention, and degree completion outcomes in a sample of 655 students from a private university. Findings showed a high level of accuracy for all the classifications. Among the predictors, learning strategies had the greatest contribution for the prediction of grade point average. Coping strategies were the best predictors for degree completion, and background information had the largest predictive weight for the identification of students who will drop out or not from the university programs.

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Individual differences in basic cognitive processes and self-regulated learning: Their interaction effects on math performance

Cited by 35 publications

References 79 publications

Do teamwork experience and self-regulated learning determine the performance of students in an online educational technology course?

Do teamwork experience and self-regulated learning determine the performance of students in an online educational technology course?

An analysis of internal and external feedback in self-regulated learning activities mediated by self-regulated learning tools and open learner models

Predicting key educational outcomes in academic trajectories: a machine-learning approach

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