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Using attributes from task-technology fit theory, five algorithms—GLM, random forest, decision tree, gradient boosted tree, and support vector machine—are empirically compared for predictive performance. Results show task-technology fit supports higher accuracy, with gradient boosted tree outperforming the others. 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video-based learning  \nSuraya Masrom1, Rahayu Abdul Rahman2, Norhayati Baharun1, Syed Redzwan Sayed Rohani2,  \nAbdullah Sani Abd Rahman3  \n1Computing Sciences Studies, College of Computing, Informatics and Media, Universiti Teknologi MARA, Perak, Malaysia 2Faculty of Accountancy, Universiti Teknologi MARA, Perak, Malaysia 3Faculty of Sciences and Information Technology, Universiti Teknologi Petronas, Perak, Malaysia  \nArticle history:  \nReceived Oct 19, 2022 Revised Nov 27, 2022 Accepted Dec 9, 2022  \nKeywords:  \nMachine learning Prediction  \nTask-technology fit theory Video-based learning  \nCorresponding Author:  \nNowadays, various innovative educational and instructional tools have been created to deliver learning material including video content. One of the important issues with video-based learning is to devise effective teaching strategies to ensure higher level of learning can be achieved by the students. Getting insight and predicting the students’ video-based learning adoption will help the educators. Thus, this study aims to examine the potential of using machine learning prediction models on video-based learning adoption in higher education institutions. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT), and support vector machine (SVM) . The performance of each machine learning algorithm in predicting the students’ learning adoption with video-based learning has been observed based on the attributes of task-technology fit theory. The findings indicated that the task-technology fit is useful in helping the machine learning algorithm to achieve high accuracy in the prediction of video-based learning adoption. The GBT is the best outperforming algorithm, followed with RF and SVM. This paper presents a fundamental research framework useful for helping educators and researchers to enhance student interest and retention on video-based learning.  \nThis is an open access article under the CC BY-SA license.  \nSyed Redzwan Sayed Rohani  \nFaculty of Accountancy, Universiti Teknologi MARA 35400 Tapah Road, Perak, Malaysia  \nEmail: [syedr787@uitm.edu.my](syedr787@uitm.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe sudden outbreak called COVID-19 has changed education system dramatically. The social confinement enforcement introduced and implemented by most of the countries, led to the suspension of all education activities including conventional face to face teaching. During the challenges period, online learning is regarded as an alternative solution in ensuring the learning process yet minimize the health risk both educators and students as teaching is undertaken remotely and on digital platforms [1], [2] . In response to this new learning environment, educators create various innovative educational and instructional tools to deliver learning material including video content.  \nVideo content, also known as video-based learning [3] has been used in online learning for a longtime. The educators create video content as a teaching tool because it able to increase students’ knowledge and understanding [4] and improve their study habits [5] . Besides, unlike text-only content, video lectures  \nthat consists of multimedia elements that can give additional information to the students which eventually lead for better learning and retention [6] . In addition, video content has been used because it allows for practical and experiential learning especially on complex concept such as to simulate a laboratory experiment [7] or giving some practical demonstrations [8] .  \nIn line with the increasing demand and benefits offered by the video-based learning, studies on students’ intention and adoption of video contents prior COVID-19 outbreak also increase. However, the main findings of the prior studies are mixed and ","cbCaioI1ugbWsNeh","https://ap.wps.com/l/cbCaioI1ugbWsNeh","pdf",659816,"English","# Introduction\n## Video-based learning in online education\n## Motivation from COVID-19 and adoption research\n## Study contributions\n# Research Method\n## Data collection and datasets","[{\"question\":\"What problem does the study address in video-based learning?\",\"answer\":\"It addresses the need to predict students’ adoption of video-based learning and to support educators with more effective strategies for achieving higher learning outcomes.\"},{\"question\":\"Which machine learning algorithms are compared?\",\"answer\":\"The study empirically compares generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT), and support vector machine (SVM).\"},{\"question\":\"How does task-technology fit theory affect the prediction results?\",\"answer\":\"The findings indicate that task-technology fit is useful for helping the machine learning algorithms achieve high accuracy in predicting video-based learning adoption.\"}]","Machine learning with task-technology fit theory factors for predicting students’ adoption in video-based learning | PDF",1789642238]