TY - GEN
T1 - Personalizing online educational tools
AU - Lee, Michael J.
AU - Ferwerda, Bruce
N1 - Funding Information:
This material is based upon work supported by the the National Science Foundation (NSF) Grant IIS 1657160 and Austrian Science Fund (FWF) Grant P25655. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or FWF.
Publisher Copyright:
© 2017 ACM.
PY - 2017/3/13
Y1 - 2017/3/13
N2 - 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.
AB - 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.
KW - Adaptive learning
KW - Engagement
KW - Intelligent tutoring systems
KW - Learning styles
UR - http://www.scopus.com/inward/record.url?scp=85017027984&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85017027984&partnerID=8YFLogxK
U2 - 10.1145/3039677.3039680
DO - 10.1145/3039677.3039680
M3 - Conference contribution
AN - SCOPUS:85017027984
T3 - HUMANIZE 2017 - Proceedings of the 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, co-located with IUI 2017
SP - 27
EP - 30
BT - HUMANIZE 2017 - Proceedings of the 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, co-located with IUI 2017
PB - Association for Computing Machinery, Inc
T2 - 1st ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, HUMANIZE 2017
Y2 - 13 March 2017
ER -