Student Eye Gaze Tracking During MOOC Teaching

Yang, Fangfang; Jiang, Zengru; Wang, Chaojun; Jia, Zhiyang; Hirota, Kaoru

doi:10.1109/scis-isis.2018.00144

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…Recent papers choose to focus on one or a couple of these datasets: e.g. Student video-watching behavior [5], student performance [27], [28], student physical behavior [29], or discussion forum data [30]- [33]. Our work is evaluated on a similar dataset to [32] in that it provides information gathered on student message passing behavior in a discussion forum.…”

Section: A Related Workmentioning

confidence: 99%

Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

Sahay

Zhang

Yang

et al. 2023

IEEE/ACM Trans. Networking

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We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured interactions. While link prediction has been studied for general types of social networks, the evolution of SLNs over their lifetimes coupled with their dependence on which topics are being discussed presents new challenges for this type of network. To address these challenges, we develop a series of autonomous link prediction methodologies that utilize spatial and time-evolving network architectures to pass network state between space and time periods, and that models over three types of SLN features updated in each period: neighborhoodbased (e.g., resource allocation), path-based (e.g., shortest path), and post-based (e.g., topic similarity). Through evaluation on six real-world datasets from Massive Open Online Course (MOOC) discussion forums and from Purdue University, we find that our method obtains substantial improvements over Bayesian models, linear classifiers, and graph neural networks, with AUCs typically above 0.91 and reaching 0.99 depending on the dataset. Our feature importance analysis shows that while neighborhood and path-based features contribute the most to the results, post-based features add additional information that may not always be relevant for link prediction.

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Section: A Related Workmentioning

confidence: 99%

Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

Sahay

Zhang

Yang

et al. 2023

IEEE/ACM Trans. Networking

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“…The study aims to create a system that monitors the conditions of the e-learning in the MOOCs courses to determine if students watch the teaching videos or not by tracking the center of the iris. (Yang et al, 2018) [4]…”

Section: Support Vector Machine Deep Neural Networkmentioning

confidence: 99%

“…In addition, one study developed systems that work to monitor the learners in MOOCs courses to know if the student is watching the educational video or not [4]. This system achieved a big success since it determines the center of the iris.…”

Section: Face Aecognition Techniques That Support E-learning Systemsmentioning

confidence: 99%

“…1) The student's sitting position and the angles of the body are difficult to explain whether the body is tilted or otherwise to judge if the student watches the video online or not [4], [30]. 2) Lack of categorized reactions and feelings, as one of the expressions that do not find any similar classification which may appear [33], [35].…”

Section: The Common Face Recognition Obstacles In Real-timementioning

confidence: 99%

“…It is considered an unfriendly way of learning when it comes to the learner [3]. For example, it considers all learners equal regardless of their levels [4] during the learning session. Also, it does not care about the understanding rate for learners or their feelings during the sessions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence Techniques for Distance Education: A Systematic Literature Review

Aljarrah¹,

Ababneh²,

Karagozlu³

et al. 2021

TEM Journal

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In the current era, education, like other fields, relies heavily on big data. Moreover, artificial intelligence, including affective computing, is one of the most essential and popular technologies adopted by educational institutions to process and analyze big data. In this systematic review, many previous research types related to improving educational systems using artificial intelligence techniques were studied, such as: deep learning, machine learning, and affective computing. This systematic review aims to identify the gaps in students' emotional understanding in distance education systems. The world has recently witnessed the spread of educational processes for distance learning, especially in the university and the enormous open online courses (MOOCs). Besides, the COVID-19 pandemic has been involved in changing all educational processes to a distance learning system. The results indicated that these systems recorded a high success rate. However, the teacher does not fully understand the student’s emotional state during the educational session. It also lacks monitoring or monitoring during the electronic exams, which are electronic exams. So, it is a widespread problem in distance learning.

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A Review of Automatic Detection of Learner States in Four Typical Learning Scenarios

Wang

Chen

Wang

2022

Lecture Notes in Computer Science

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Student Eye Gaze Tracking During MOOC Teaching

Cited by 12 publications

References 12 publications

Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

Artificial Intelligence Techniques for Distance Education: A Systematic Literature Review

A Review of Automatic Detection of Learner States in Four Typical Learning Scenarios

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