@inproceedings{4979dfa218a348cf99730277deef227f,
title = "Predicting Knowledge Gain for MOOC Video Consumption",
abstract = "Informal learning on the Web using search engines as well as more structured learning on Massive Open Online Course (MOOC) platforms have become very popular. However, the automatic assessment of this content with regard to the challenging task of predicting (potential) knowledge gain has not been addressed by previous work yet. In this paper, we investigate whether we can predict learning success after watching a specific type of MOOC video using 1) multimodal features, and 2) a wide range of text-based features describing the structure and content of the video. In a comprehensive experimental setting, we test four different classifiers and various feature subset combinations. We conduct a feature importance analysis to gain insights in which modality benefits knowledge gain prediction the most.",
keywords = "Knowledge gain, Resource quality, Web learning",
author = "Christian Otto and Markos Stamatakis and Anett Hoppe and Ralph Ewerth",
year = "2022",
doi = "10.48550/arXiv.2212.06679",
language = "English",
isbn = "9783031116469",
volume = "2",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "458--462",
editor = "Rodrigo, {Maria Mercedes} and Noburu Matsuda and Cristea, {Alexandra I.} and Vania Dimitrova",
booktitle = "Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners{\textquoteright} and Doctoral Consortium",
address = "Germany",
note = "23rd International Conference on Artificial Intelligence in Education, AIED 2022 ; Conference date: 27-07-2022 Through 31-07-2022",
}