Privacy-Preserving and Scalable Affect Detection in Online Synchronous Learning

Böttger, Felix; Cetinkaya, Ufuk; Mitri, Daniele Di; Gombert, Sebastian; Shingjergji, Krist; İren, Deniz; Klemke, Roland

doi:10.1007/978-3-031-16290-9_4

Cited by 4 publications

(2 citation statements)

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“…For instance, camera devices can be exploited to integrate the learner's emotions during the learning process, i.e. the learner facial expressions are assumed in the form of informal human feedback [82]. Another example concerns the interactions among peers in face-to-face lessons, which could be detected by recording audio or asking for explicit feedback from the teacher in the classroom.…”

Section: B Knowledge Representations For Knowledge Tracingmentioning

confidence: 99%

Hybrid Models for Knowledge Tracing: A Systematic Literature Review

Zanellati,

Mitri,

Gabbrielli

et al. 2024

IEEE Trans. Learning Technol.

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Knowledge Tracing is a well-known problem in AI for Education, consisting of monitoring how the knowledge state of students changes during the learning process and accurately predicting their performance in future exercises. In recent years, many advances have been made thanks to various machine learning and deep learning techniques. Despite their satisfactory performances, they have some pitfalls, e.g. modeling one skill at a time, ignoring the relationships between different skills, or inconsistency for the predictions, i.e. sudden spikes and falls across time steps. For this reason, hybrid machine-learning techniques have also been explored. With this systematic literature review, we aim to illustrate the state of the art in this field. Specifically, we want to identify the potential and the frontiers in integrating prior knowledge sources in the traditional machine learning pipeline as a supplement to the normally considered data. We applied a qualitative analysis to distill a taxonomy with three dimensions: knowledge source, knowledge representation, and knowledge integration. Exploiting this taxonomy, we also conducted a quantitative analysis to detect the most common approaches.

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Section: B Knowledge Representations For Knowledge Tracingmentioning

confidence: 99%

Hybrid Models for Knowledge Tracing: A Systematic Literature Review

Zanellati,

Mitri,

Gabbrielli

et al. 2024

IEEE Trans. Learning Technol.

View full text Add to dashboard Cite

show abstract

“…For instance, camera devices can be exploited to integrate the learner's emotions during the learning process, i.e. the learner facial expressions are assumed in the form of informal human feedback [82]. Another example concerns the interactions among peers in face-toface lessons, which could be detected by recording audio or asking for explicit feedback from the teacher in the classroom.…”

Section: B Knowledge Representations For Knowledge Tracingmentioning

confidence: 99%

Hybrid Models for Knowledge Tracing: a Systematic Literature Review

Zanellati¹,

Mitri²,

Gabbrielli³

et al. 2023

Preprint

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<p>Knowledge Tracing is a well-known problem often tackled through purely data-driven approaches. In recent years, many advances have been made in research with various machine learning and deep learning techniques. Despite their satisfactory performance, they have some pitfalls, e.g. modeling one skill at a time, ignoring the relationships between different skills, or inconsistency for the predictions, i.e. sudden spikes and falls across time steps. For this reason, hybrid machine-learning techniques have also been explored. With this systematic literature review, we aim to illustrate this field's state of the art. Specifically, we want to identify the potential and the frontiers in integrating prior knowledge sources in the traditional machine learning pipeline as a supplement to the normally considered data. We applied a qualitative analysis to distill a taxonomy with three dimensions: knowledge source, knowledge representation, and knowledge integration. Exploiting this taxonomy, we also conducted a quantitative analysis to detect the most common approaches.</p>

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