Modeling micro-interactions in self-regulated learning: A data-driven methodology

Yu, He; Harper, Simon; Vigo, Markel

doi:10.1016/j.ijhcs.2021.102625

Cited by 9 publications

(2 citation statements)

References 70 publications

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“…The classification results showed that organized and unorganized learning behaviors reflected different learning outcomes. Yu et al [25] used the interaction data of students' online learning to generate learning sequences at intervals of every 5, 30, and 40 min, and then explore the relationship between learning patterns and learning outcomes. Çebi et al [26] differentiate learning patterns into individual (micro-patterns) and collective (macro-patterns).…”

Section: Learning Pattern Analysismentioning

confidence: 99%

AI and Big Data in Education: Learning Patterns Identification and Intervention Leads to Performance Enhancement

Yang,

Lin,

Huang

et al. 2023

ITEL

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Improving learning outcomes is always one of the key objectives of learning analytics (LA) and educational data mining (EDM). In recent years, many Massive Open Online Courses (MOOC) have been deployed and making it easier to collect learners' data for further analysis. Naturally, leveraging AI to process such kind of big data becomes one of the main research streams to support education. In this paper, we collected data and defined student learning patterns by leveraging online courses on Python programming and we then verified if their learning performance was influenced by different learning patterns and interventions. We designed the intervention process, explored the impact of final learning outcomes, and analyze Self-Regulated Learning (SRL) abilities. From the experimental results, we share the learning outcomes and the difference in SRL with detailed explanation based on different groups.

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Section: Learning Pattern Analysismentioning

confidence: 99%

AI and Big Data in Education: Learning Patterns Identification and Intervention Leads to Performance Enhancement

Yang,

Lin,

Huang

et al. 2023

ITEL

View full text Add to dashboard Cite

show abstract

“…Interactive learning behavior (ILB): It occurs during the interactive reflection stage and is one of the key learning behaviors in e-learning, which has been proven to have a positive effect on the continuity and effectiveness of e-learning [32]. It is manifested by participating in seminars, posting on forums, replying to forums, asking the teacher questions, etc.…”

Section: E-learning Behavior Classification Model (Ebc Model)mentioning

confidence: 99%

SA-FEM: Combined Feature Selection and Feature Fusion for Students’ Performance Prediction

Sheng

et al. 2022

Sensors

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Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method.

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Interaction strategies in online learning: Insights from text analytics on iMOOC

Wang

Zhao

et al. 2022

Educ Inf Technol

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Modeling micro-interactions in self-regulated learning: A data-driven methodology

Cited by 9 publications

References 70 publications

AI and Big Data in Education: Learning Patterns Identification and Intervention Leads to Performance Enhancement

AI and Big Data in Education: Learning Patterns Identification and Intervention Leads to Performance Enhancement

SA-FEM: Combined Feature Selection and Feature Fusion for Students’ Performance Prediction

Interaction strategies in online learning: Insights from text analytics on iMOOC

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