Characterising Student Engagement Modes through Low-Level Activity Patterns

Gledson, Ann; Apaolaza, Aitor; Barthold, Sabine; Günther, Franziska; Yu, He; Vigo, Markel

doi:10.1145/3450613.3456818

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…Examples of student characteristics modeled through the approach include motivation (Taylor et al, 2004 ), boredom and curiosity (Jaques et al, 2014 ), and engagement (Monkaresi et al, 2017 ). Interaction-based approach uses learning interaction data of students to build models for predicting various student characteristics such as valence and arousal (Salmeron-Majadas et al, 2018 ); motivation (Aluja-Banet et al, 2019 ); confusion, frustration, and eureka/delight (Graesser et al, 2008 ); and engagement (Gledson et al, 2021 ). Each of the two approaches has attracted attention in research.…”

Section: Introductionmentioning

confidence: 99%

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

Orji

Vassileva²

2022

Front. Artif. Intell.

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Student characteristics affect their willingness and ability to acquire new knowledge. Assessing and identifying the effects of student characteristics is important for online educational systems. Machine learning (ML) is becoming significant in utilizing learning data for student modeling, decision support systems, adaptive systems, and evaluation systems. The growing need for dynamic assessment of student characteristics in online educational systems has led to application of machine learning methods in modeling the characteristics. Being able to automatically model student characteristics during learning processes is essential for dynamic and continuous adaptation of teaching and learning to each student's needs. This paper provides a review of 8 years (from 2015 to 2022) of literature on the application of machine learning methods for automatic modeling of various student characteristics. The review found six student characteristics that can be modeled automatically and highlighted the data types, collection methods, and machine learning techniques used to model them. Researchers, educators, and online educational systems designers will benefit from this study as it could be used as a guide for decision-making when creating student models for adaptive educational systems. Such systems can detect students' needs during the learning process and adapt the learning interventions based on the detected needs. Moreover, the study revealed the progress made in the application of machine learning for automatic modeling of student characteristics and suggested new future research directions for the field. Therefore, machine learning researchers could benefit from this study as they can further advance this area by investigating new, unexplored techniques and find new ways to improve the accuracy of the created student models.

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