Estimation of Test Scores Based on Video Viewing Behavior in the Programming MOOC Course

Furukawa, Masashi; Itsumura, Hiroshi; Yamaji, Katsuhiko

doi:10.1109/iiai-aai50415.2020.00039

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…For classifiers and predictors the main task was to predict the learning outcome using learner's activity data [27,68,122,133,143,165,173]. Other, but less frequent tasks were discipline classification [17,31,54,184,228], instructor behavior prediction [35,80,207,263], and dropout prediction [66,104].…”

Section: Discussionmentioning

confidence: 99%

“…Mubarak et al [173] trained a long-short-term memory network to predict the learning outcome as pass or fail based on learner's activity data (e.g., play, pause, speed, etc.). Furukawa et al [68] used the number of views, interval of video views (e.g., 0 to 19 days), and the first access to videos in a multiple regression predictor to evaluate whether the learner will pass or fail a MOOC course. Similarly, Korosi et al [122] predicted whether a learner will pass or fail a MOOC course using random forests and bagging based on characteristics related to platform usage (e.g., scrolling actions) and not representing the video itself.…”

Section: Classifiers and Predictorsmentioning

confidence: 99%

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A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

Navarrete¹,

Nehring²,

Schanze³

et al. 2023

Preprint

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People increasingly use videos on the Web as a source for learning, be it for daily tasks or in formal as well as informal educational settings. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning from computer science databases for the period from 2016 to 2021 using the PRISMA guidelines. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners' activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner's activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner's activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends in the development of design guidelines to produce learning videos.

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Section: Discussionmentioning

confidence: 99%

Section: Classifiers and Predictorsmentioning

confidence: 99%

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

Navarrete¹,

Nehring²,

Schanze³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The analysis of learning behavior is the same as in our previous study [13]. Since video viewing behavior stored in the log storage is thought to reflect the amount of learning and enthusiasm for learning, it was used as one of the learners' characteristics.…”

Section: Analysis Of Learning Behaviormentioning

confidence: 99%

“…Grasping the types of learners in the earlier stage is essential to realize such a process. We succeeded in estimating learners' differences by investigating the relationship between learners' video viewing behavior and test scores in the programming MOOC course [13]. This previous study analyzed video viewing behavior in detail and applied the multiple regression analysis to get insight into the characteristics of learners.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Test Scores based on Questionnaire and Video Viewing Behavior in the Programming MOOC Course

Furukawa

Itsumura

Yamaji

2022

International Journal of Learning Technologies and Learning Environments

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The Massive Open Online Courses (MOOCs) offer various types of learners the opportunity to attend university-level lectures. However, since many learners drop out during the learning process, the average MOOC completion rate is usually as low as 10%. For future improvement, MOOCs must grasp the learners' features in the earlier stage and provide appropriate support to each learner. This paper investigates the relationship between learners' characteristics and test scores in the programming MOOC course to recognize different types of learners. Video viewing behavior and the questionnaire information at the beginning of the lecture, i.e., age, programming skill, and keywords in the free description, are analyzed to characterize learners. As the results, it was observed that the repeated learning behavior and later join to the course relates to the higher and lower score, respectively. The information from the questionnaire improves the accuracy of pass/fail estimation before the third week. The characteristic cluster of learners, who could be rescued by offering appropriate support, was also obtained by multiple regression analysis results.

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Estimation of Test Scores Based on Video Viewing Behavior in the Programming MOOC Course

Cited by 2 publications

References 7 publications

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

Estimation of Test Scores based on Questionnaire and Video Viewing Behavior in the Programming MOOC Course

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