Influencing Metacognition in a Traditional Classroom Environment Through Learning Analytics

Brown, Wayne J.; Kinshuk, Kinshuk

doi:10.1007/978-981-287-868-7_1

Cited by 12 publications

(9 citation statements)

References 2 publications

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“…This suggests that although the two main components of cognition are independent constructs, they are closely and significantly correlated in many ways. This is in line with earlier studies (Brown & Kinshuk, 2016;Sperling, Howard, Miller, & Murphy, 2002;Trainin & Swanson, 2005), wherein learners differed from each other due to their high or low performance in the knowledge and regulation of cognition. In addition, a high level of knowledge of cognition could compensate for low regulatory ability in the same way that high regulatory ability can compensate for low level of knowledge of cognition (Carvalho & Yuzawa, 2001).…”

Section: Discussionsupporting

confidence: 92%

“…It is reasonably assumed that knowledge and regulation of cognition mediate the effects of the task-induced involvement load on word learning. Although it has already been proposed that these two components are distinct but related (Brown & Kinshuk, 2016;Schraw, 2009), a further clarification on the mutual relationship of the two components is still needed. Armed with the above knowledge, we need to assess how these two constructs mediate the effects of task-induced involvement load on individuals' word learning.…”

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confidence: 99%

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The Effects of Task-induced Involvement Load on Word Learning and Confidence Judgments Mediated by Knowledge and Regulation of Cognition

2017

EDUC SCI-THEOR PRACT

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Citation: Teng, F. (2017). The effects of task-induced involvement load on word learning and confidence judgments mediated by knowledge and regulation of cognition. AbstractThe relationships between knowledge and regulation of cognition and how they interact to mediate the effects of task-induced involvement load on word learning and confidence judgments were investigated.The participants were 77 undergraduate English majors. They were required to complete a checklist on metacognition. Subsequently, they were assigned to complete three tasks with varying degree of involvement load. They were then required to rate their confidence in learning and using the target words. Pre-and post-study vocabulary tests were administered to measure improvement in word learning. According to the checklist on metacognition, the learners were assigned to four different ability groups. The results showed that knowledge of cognition is a good predictor of the confidence judgments, in the same way that regulation of cognition is a good predictor of the confidence judgments. In terms of learners' actual word learning performance, task-induced involvement load is mediated significantly by the regulatory competence but not by the knowledge of cognition. There is a discrepancy between learners' self-assessment and actual performance. Relevant educational implications are discussed.

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Section: Discussionsupporting

confidence: 92%

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confidence: 99%

The Effects of Task-induced Involvement Load on Word Learning and Confidence Judgments Mediated by Knowledge and Regulation of Cognition

2017

EDUC SCI-THEOR PRACT

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“…Yet, as Wong (2017) , are yet to be adequately studied in relation to learning analytics practices. However, collecting data about student factors, such as students' psychological or cognitive status, in an online learning environment has been found to be challenging (Brown & Kinshuk, 2016). The new findings on student persistence thus demonstrate a need for advancing data-intensive/dependent prediction and intervention approaches that take those persistence factors into account.…”

Section: Implications Of Student Persistence For Open Universitiesmentioning

confidence: 99%

Factors Related to Student Persistence in Open Universities

2019

IRRODL

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Student persistence has long been a major challenge for open universities. Despite the evolution of open education, an overall high student attrition rate remains. This paper examines the changes and trends in factors related to student persistence in open universities. It reviews the empirical studies from the 1970s to the 2010s which reported factors influencing student persistence. The relevant studies were searched from databases, including Scopus, Web of Science, and Google Scholar. Among the 108 studies collected, a total of 284 factors influencing student persistence were identified. The factors were categorised into student factors, institutional factors, and environmental factors. Their changes and trends over the years were examined. The results show that student factors were the most frequently studied over the years examined, with the major categories being students’ psychological attributes and outcomes. Institutional factors have been increasingly studied in recent decades, with the design and delivery of programmes and courses being the strongest category. Finally, environmental factors have been decreasingly examined, with factors related to students’ family and work being the two main categories. Based on the results, the implications for developing intervention and retention strategies for student persistence in open universities are discussed.

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“…Monitoring learning is a common practice that helps instructors regularly evaluate the effectiveness of their teaching to make subsequent instructional decisions and interventions. It is usually achieved using data that describe the learning experience, often reported explicitly through direct interaction with learners (e.g., face-to-face observations, self-report questionnaires and interviews) (Brown et al, 2016). In distant and online learning, these forms of interaction are most of the time intrusive and expensive, and do not fit well with the heterogeneity of learner profiles, nor do they quickly reflect changes in the learning settings (Thomas, 2014).…”

Section: Monitoring E-learningmentioning

confidence: 99%

Leveraging learners' activity logs for course reading analytics using session-based indicators

Sadallah

Encelle

Maredj

et al. 2020

IJTEL

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A challenge that course authors face when reviewing their contents is to detect how to improve their courses in order to meet the expectations of their learners. In this paper, we propose an analytical approach that exploits learners' logs of reading to provide authors with insightful data about the consumption of their courses. We first model reading activity using the concept of reading-session and propose a new and efficient session identification. We then elaborate a list of indicators computed using learners' reading sessions that allow to represent their behaviour and to infer their needs. We evaluate our proposals with course authors and learners using logs from a major e-learning platform. Interesting results were found. This demonstrates the effectiveness of the approach in identifying aspects and parts of a course that may prevent it from being easily read and understood, and for guiding the authors through the analysis and review tasks.

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Influencing Metacognition in a Traditional Classroom Environment Through Learning Analytics

Cited by 12 publications

References 2 publications

The Effects of Task-induced Involvement Load on Word Learning and Confidence Judgments Mediated by Knowledge and Regulation of Cognition

The Effects of Task-induced Involvement Load on Word Learning and Confidence Judgments Mediated by Knowledge and Regulation of Cognition

Factors Related to Student Persistence in Open Universities

Leveraging learners' activity logs for course reading analytics using session-based indicators

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