Choose Your Own Question: Encouraging Self-Personalization in Learning Path Construction

• URL: http://arxiv.org/abs/2005.03818v1
• Date: Fri, 8 May 2020 01:53:04 GMT
• Title: Choose Your Own Question: Encouraging Self-Personalization in Learning Path Construction
• Authors: Youngduck Choi, Yoonho Na, Youngjik Yoon, Jonghun Shin, Chan Bae, Hongseok Suh, Byungsoo Kim, Jaewe Heo
• Abstract summary: We introduce Rocket, a Tinder-like User Interface for a general class of Interactive Educational System (IES)s. Rocket provides a visual representation of Artificial Intelligence (AI)-extracted features of learning materials, allowing the student to quickly decide whether the material meets their needs. Rocket enables self-personalization of the learning experience by leveraging the students' knowledge of their own abilities and needs.
• Score: 1.6505359493498744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning Path Recommendation is the heart of adaptive learning, the educational paradigm of an Interactive Educational System (IES) providing a personalized learning experience based on the student's history of learning activities. In typical existing IESs, the student must fully consume a recommended learning item to be provided a new recommendation. This workflow comes with several limitations. For example, there is no opportunity for the student to give feedback on the choice of learning items made by the IES. Furthermore, the mechanism by which the choice is made is opaque to the student, limiting the student's ability to track their learning. To this end, we introduce Rocket, a Tinder-like User Interface for a general class of IESs. Rocket provides a visual representation of Artificial Intelligence (AI)-extracted features of learning materials, allowing the student to quickly decide whether the material meets their needs. The student can choose between engaging with the material and receiving a new recommendation by swiping or tapping. Rocket offers the following potential improvements for IES User Interfaces: First, Rocket enhances the explainability of IES recommendations by showing students a visual summary of the meaningful AI-extracted features used in the decision-making process. Second, Rocket enables self-personalization of the learning experience by leveraging the students' knowledge of their own abilities and needs. Finally, Rocket provides students with fine-grained information on their learning path, giving them an avenue to assess their own skills and track their learning progress. We present the source code of Rocket, in which we emphasize the independence and extensibility of each component, and make it publicly available for all purposes.

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