Clickstream-based outcome prediction in short video MOOCs

Korosi, Gabor; Eklund, Peter; Farkas, Richárd; Tóth, K

doi:10.1109/cits.2018.8440182

Cited by 5 publications

(2 citation statements)

References 8 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%

“…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. In a different way to determine learning success, Lallé and Conati [133] clustered learners into low and high achievers using characteristics such as the average proportion of videos watched per week.…”

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

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Towards the Readiness of Learning Analytics Data for Micro Learning

Lin

Sun

Shen

et al. 2019

Services Computing – SCC 2019

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With the development of data mining and machine learning techniques, data-driven based technologyenhanced learning (TEL) has drawn wider attention. Researchers aim to use established or novel computational methods to solve educational problems in the 'big data' era. However, the readiness of data appears to be the bottleneck of the TEL development and very little research focuses on investigating the data scarcity and inappropriateness in the TEL research. This paper is investigating an emerging research topic in the TEL domain, namely micro learning. Micro learning consists of various technical themes that have been widely studied in the TEL research field. In this paper, we firstly propose a micro learning system, which includes recommendation, segmentation, annotation, and several learning-related prediction and analysis modules. For each module of the system, this paper reviews representative literature and discusses the data sources used in these studies to pinpoint their current problems and shortcomings, which might be debacles for more effective research outcomes. Accordingly, the data requirements and challenges for learning analytics in micro learning are also investigated. From a research contribution perspective, this paper serves as a basis to depict and understand the current status of the readiness of data sources for the research of micro learning.

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An algorithm based on fuzzy ordinal classification to predict students’ academic performance

Gámez-Granados,

Esteban,

Rodriguez-Lozano

et al. 2023

Appl Intell

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Predicting students’ performance in distance courses is a very relevant task to help teachers identify students who need reinforcement or extension activities. Nevertheless, identifying the student’s progress is highly complicated due to the large number of students and the lack of direct interaction. Artificial intelligence algorithms contribute to overcoming this problem by automatically analyzing the features and interactions of each student with the e-learning platform. The main limitations of the previous proposals are that they do not consider a ranking between the different marks obtained by students and the most accurate models are usually black boxes without comprehensibility. This paper proposes to use an optimized ordinal classification algorithm, FlexNSLVOrd, that performs a prediction of student’s performance in four ranking classes (Withdrawn < Fail < Pass < Distinction) by generating highly understandable models. The experimental study uses the OULA dataset and compares 10 state-of-the-art methods on 7 different courses and 3 classical classification metrics. The results, validated with statistical analysis, show that FlexNSLVOrd has higher performance than the other models and achieves significant differences with the rest of the proposals. In addition, the interpretability of FlexNSLVOrd is compared with other rule-based models, and simpler and more representative rules are obtained.

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Clickstream-based outcome prediction in short video MOOCs

Cited by 5 publications

References 8 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

Towards the Readiness of Learning Analytics Data for Micro Learning

An algorithm based on fuzzy ordinal classification to predict students’ academic performance

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