Ontology-based Adaptive e-Textbook Platform for Student and Machine Co-Learning
This work addresses the need for more intelligent e-textbooks to enhance learning experiences for students, though it appears to be incremental in adding information retrieval capabilities to existing e-book frameworks.
The paper tackles the problem of limited adoption of electronic textbooks by developing an ontology-based adaptive platform that constructs knowledge graphs from e-book materials with minimal overhead and performs information retrieval through typed similarity queries using random walk. A case study demonstrates the applicability of the platform, showing promising advancements in e-textbooks.
The use of electronic textbooks (e-book) has been heavily studied over the years due to their flexibility, accessibility, interactivity and extensibility. Yet current shortcomings of e-book, which is often just a digitized version of the original book, does not encourage adoption. Consequently, this leads to a rethinking of e-book that should incorporate current technologies to augment its capabilities, where inclusion of information search and organization tools have shown to be favorable. This paper is on a preliminary work to add intelligence into such tools in terms of information retrieval. Construction of knowledge graph for e-book material with little overhead is first introduced. Information retrieval through typed similarity query is then performed via random walk. Case study demonstrate the applicability of the e-book platform, with promising application and advancement in the area of electronic textbooks.