Improving the Effectiveness of E-learning Videos by leveraging Eye-gaze Data
Received: 5 September 2023 | Revised: 26 September 2023 | Accepted: 4 October 2023 | Online: 5 December 2023
Corresponding author: Harita Ahuja
Abstract
Recent advances in technology strengthen remote and lifelong learning by integrating e-videos into teaching-learning pedagogy. Therefore, educational content developers are tasked with creating engaging and qualitative e-content. The shift in paradigm from offline to online teaching brings forth several issues regarding the quality of online learning materials and the missing dynamic interaction between instructors and learners. Leveraging contemporary artificial intelligence techniques to provide insights into methods for developing quality e-content is the need of the hour. This study showed that the pattern and duration of the eye gaze of the learner on the text, image, or instructor in the video reveal valuable insights, not only regarding the comprehension of the learner but also giving suggestions to improve video lectures. The results show that learners perform better when they spend more time looking at the instructor compared to the image and text on a frame. Therefore, just like classroom teaching, the presence of the instructor in the video is vital, as looking directly at the instructor while they are delivering the lecture encourages comprehension. Furthermore, by applying classification techniques to learner eye gaze data, it was possible to predict with 97% confidence whether the learner would answer the post-quiz correctly or not.
Keywords:
e-learning, prediction, machine learning, eye gaze data, classificationDownloads
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Copyright (c) 2023 Rakhi Saxena, Sunita Narang, Harita Ahuja
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