A MOOC Video Viewing Behavior Analysis Algorithm

Luo, Yuping; Zhou, Guochang; Li, Jianping; Xiao, Xiao

doi:10.1155/2018/7560805

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Online education has become more and more popular, and MOOC learning behavior big data research [13, 14] has become more and more important. MOOC operators record learning behavior data for research that can help them improve their courses.…”

Section: Methodsmentioning

confidence: 99%

Study on MOOC scoring algorithm based on Chinese University MOOC learning behavior data

Luo

Zhou

et al. 2018

Heliyon

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Existing online learning evaluation methods do not accurately reflect learning effects, which only considers test and assignment scores. A comprehensive evaluation algorithm is proposed in this paper based on the big data of learning behavior. The conversion ratio is taken into account, which is defined by information entropy theory. The algorithm comprehensively considers the learner's multiple learning behaviors, such as viewing videos, doing exercises, taking exams, participating in discussions. The new evaluation algorithm can help learners understand the learning state and maintain their interest.

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Section: Methodsmentioning

confidence: 99%

Study on MOOC scoring algorithm based on Chinese University MOOC learning behavior data

Luo

Zhou

et al. 2018

Heliyon

Self Cite

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“…Their studies made an important contribution to instructional design to define key design principles for video production in the context of video learning at scale. In the past few years, as video use has continued to provide a main method for instruction, there have been wide explorations (Giannakos, 2013) of video length (Guo et al, 2014;Luo et al, 2018), speaking speeds of lecturers (Lemay & Doleck, 2020), segmentation of video lectures (Kay, 2012), variations in instructor audio streams (Kim et al, 2014), and video lecture types, such as voiceover presentations, lecture captures, picture-in-picture style, and Khan style (Chen & Wu, 2015;Hansch et al, 2015;Ilioudi et al, 2013;Kokoç et al, 2020;Sadik, 2016). Other studies have compared hand-drawn versus narration-over-PowerPoint (Chen & Thomas, 2020) or looked at screencast video lectures Swarts, 2012).…”

Section: Using Learning Analytics To Understand Video-based Learningmentioning

confidence: 99%

Video Features, Engagement, and Patterns of Collective Attention Allocation

2022

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Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible learning environments. This study selected “Fundamentals of C++ programming (Spring 2019)” on XuetangX as an example through which to observe the allocation patterns of attention within MOOC videos, as well as how video features and engagement correlate with the accumulation, circulation, and dissipation pattern of collective attention. The results showed that the types of instructions in videos predicted attention allocation patterns, but they did not predict the engagement of video watching. Instead, the length and whether the full screen was used in the videos had a strong impact on engagement. Learners were more likely to reach a high level of engagement in video watching when their attention had been circulated around the videos. The results imply that understanding the patterns and dynamics of attention flow and how learners engage with videos will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.

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“…In this work, we are focused instead on activity-based measurements from eLearning platforms, e.g., from discussion boards [38], [39]. In particular, we consider video watching behavior, typically recorded as sequences of events such as pause, play, and skip made by students interacting with lecture video players [40], [41]. A few works have investigated sequential pattern mining techniques for extracting subsequences of clicks from lecture video-watching [8], [16], [42], [43].…”

Section: Introductionmentioning

confidence: 99%

Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach

Chu¹,

Tenorio²,

Cruz³

et al. 2021

Preprint

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We study the problem of predicting student knowledge acquisition in online courses from clickstream behavior. Motivated by the proliferation of eLearning lecture delivery, we specifically focus on student in-video activity in lectures videos, which consist of content and in-video quizzes. Our methodology for predicting in-video quiz performance is based on three key ideas we develop. First, we model students' clicking behavior via time-series learning architectures operating on raw event data, rather than defining hand-crafted features as in existing approaches that may lose important information embedded within the click sequences. Second, we develop a self-supervised clickstream pre-training to learn informative representations of clickstream events that can initialize the prediction model effectively. Third, we propose a clustering guided meta-learning-based training that optimizes the prediction model to exploit clusters of frequent patterns in student clickstream sequences. Through experiments on three real-world datasets, we demonstrate that our method obtains substantial improvements over two baseline models in predicting students' in-video quiz performance. Further, we validate the importance of the pre-training and meta-learning components of our framework through ablation studies. Finally, we show how our methodology reveals insights on video-watching behavior associated with knowledge acquisition for useful learning analytics.

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A MOOC Video Viewing Behavior Analysis Algorithm

Cited by 6 publications

References 15 publications

Study on MOOC scoring algorithm based on Chinese University MOOC learning behavior data

Study on MOOC scoring algorithm based on Chinese University MOOC learning behavior data

Video Features, Engagement, and Patterns of Collective Attention Allocation

Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach

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