Abstract
In today’s generation of MOOCs, videos are fundamental to the student learning experience. Due to the prominence of video content in MOOCs, production staff and instructional designers invest significant time and resources in creating these videos. With instructional videos, they want to increase student involvement. Without formative assessment, however, actual engagement is hard to quantify. Nonetheless, a large number of students necessitates a larger pool of questions; to address this issue, we considered mixing machine-learning methods with automatic natural language processing in order to expand the number of questions evaluated and ensure their validity. To accomplish this, we implemented a methodology that generates questions automatically from video transcripts. Following each course comes an evaluation issue, which is typically a multiple-choice question designed to measure a student's comprehension of the video's material. machine-generated questions performed comparably to human-generated questions when it came to judging skill and resemblance. Additionally, the findings indicate that the majority of the questions generated improve e-assessment when the new technology is applied.
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Bachiri, Ya., Mouncif, H. (2022). Increasing Student Engagement in Lessons and Assessing MOOC Participants Through Artificial Intelligence. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2022. Lecture Notes in Business Information Processing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-06458-6_11
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