@inproceedings{7009864ffd5d4e22ae21d4627d71614f,
title = "Personalizing online educational tools",
abstract = "As more people turn to online resources to learn, there will be an increasing need for systems to understand and adapt to the needs of their users. Engagement is an important aspect to keep users committed to learning. Learning approaches for online systems can benefit from personalization to engage their users. However, many approaches for personalization currently rely on methods (e.g., historical behavioral data, questionnaires, quizzes) that are unable to provide a personalized experience from the start-of-use of a system. As users in a learning environment are exposed to new content, the first impression that they receive from the system influences their commitment with the program. In this position paper we propose a quantitative approach for personalization in online learning environments to overcome current problems for personalization in such environments.",
keywords = "Adaptive learning, Engagement, Intelligent tutoring systems, Learning styles",
author = "Lee, {Michael J.} and Bruce Ferwerda",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 1st ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, HUMANIZE 2017 ; Conference date: 13-03-2017",
year = "2017",
month = mar,
day = "13",
doi = "10.1145/3039677.3039680",
language = "English (US)",
series = "HUMANIZE 2017 - Proceedings of the 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, co-located with IUI 2017",
publisher = "Association for Computing Machinery, Inc",
pages = "27--30",
booktitle = "HUMANIZE 2017 - Proceedings of the 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, co-located with IUI 2017",
}