Adaptive Electronic Textbooks

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Adaptive Electronic Textbooks were a topic of interests of Home members for many years. While the majority of works on electronic books is concerned with just coping the old format in the new media, we believe that moving textbooks to electronic form brings a number of interesting opportunities that go well beyond the functionality of traditional printed textbooks. In our work we primarily explored the opportunities to make an electronic book adaptive (i.e., personalized), interactive, and social. This paper presents different ideas in the area of adaptive electronic textbooks explored by Home members.

Subtopics

Concept-based Textbooks with Adaptive Navigation Support

This work focused on constructing the first adaptive textbooks. The personalization was based on advanced manual indexing of textbooks with concepts and relatively simple student modeling and personalization approaches.

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Sample papers:

  • Schwarz, E., Brusilovsky, P., and Weber, G. (1996) World-wide intelligent textbooks. In: Proceedings of ED-TELECOM'96 - World Conference on Educational Telecommunications, Boston, MA, June 17-22, 1996, AACE, pp. 302-307.
  • Brusilovsky, P., Schwarz, E., and Weber, G. (1997) Electronic textbooks on WWW: from static hypertext to interactivity and adaptivity. In: B. H. Khan (ed.) Web Based Instruction. Englewood Cliffs, New Jersey: Educational Technology Publications, pp. 255-261.
  • Weber, G. and Brusilovsky, P. (2001) ELM-ART: An adaptive versatile system for Web-based instruction. International Journal of Artificial Intelligence in Education 12 (4), 351-384.
  • Brusilovsky, P., Eklund, J., and Schwarz, E. (1998) Web-based education for all: A tool for developing adaptive courseware. In: H. Ashman and P. Thistewaite (eds.) Proceedings of Seventh International World Wide Web Conference, Brisbane, Australia, 14-18 April 1998, Elsevier Science B. V., pp. 291-300.

Automating the Construction of Adaptive Textbook

This work focused on automating several critical processes of adaptive book development that were done manually in the first generation research. This direction engages ontologies, advanced text processing and modeling, and machine learning.

Sample papers:

  • Sosnovsky, S., Brusilovsky, P., and Hsiao, I.-H. (2012) Adaptation "in the Wild": Ontology-based Personalization of Open-Corpus Learning Material. In: Proceedings of 7th European Conference on Technology Enhanced Learning (EC-TEL 2012), Saarbrücken, Germany, pp. 425-431.
  • Guerra, J., Sosnovsky, S., and Brusilovsky, P. (2013) When One Textbook is not Enough: Linking Multiple Textbooks Using Probabilistic Topic Models. In: D. Hernández-Leo, T. Ley, R. Klamma and A. Harrer (eds.) Proceedings of 8th European Conference on Technology Enhanced Learning (EC-TEL 2013), Paphos, Cypres, September 17-21, 2013, pp. 125-138.
  • Labutov, I., Huang, Y., Brusilovsky, P., and He, D. (2017) Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites. In: Proceedings of Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, ACM, pp. 907-915.
  • Thaker, K. M., Brusilovsky, P., and He, D. (2018) Concept Enhanced Content Representation for Linking Educational Resources. In: Proceedings of 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Santiago, Chile, December 3-6, 2018, IEEE, pp. 413-420.
  • Chau, H., Labutov, I., Thaker, K., He, D., and Brusilovsky, P. (2020) Automatic Concept Extraction for Domain and Student Modeling in Adaptive Textbooks. International Journal of Artificial Intelligence in Education 30.

Social Navigation for Online Textbooks

This work focused on social navigation as an alternative socially-driven mechanism to guide readers of online textbooks to the right content. Bringing analogy with recommender systems, the traditional knowledge-based approach was more like content-based filtering, while social navigation is more like collaborative filtering.

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Sample papers:

  • Brusilovsky, P., Chavan, G., and Farzan, R. (2004) Social adaptive navigation support for open corpus electronic textbooks. In: P. De Bra and W. Nejdl (eds.) Proceedings of Third International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH'2004), Eindhoven, the Netherlands, August 23-26, 2004, Springer-Verlag, pp. 24-33.
  • Farzan, R. and Brusilovsky, P. (2005) Social navigation support through annotation-based group modeling. In: L. Ardissono, P. Brna and A. Mitrovic (eds.) Proceedings of 10th International User Modeling Conference, Berlin, July 24-29, 2005, Springer Verlag, pp. 463-472.
  • Guerra, J., Parra, D., and Brusilovsky, P. (2013) Encouraging Online Student Reading with Social Visualization. In: Proceedings of 2nd Workshop on Intelligent Support for Learning in Groups at the 16th Annual Conference on Artificial Intelligence in Education, AIED 2013, Memphis, TN, USA, pp. 47-50.
  • Barria-Pineda, J., Brusilovsky, P., and He, D. (2019) Reading Mirror: Social Navigation and Social Comparison for Electronic Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 30-37.

Advanced Student Modeling and Knowlege-Driven Recommendation for Adaptive Textbooks

This work direction focuses on more advanced approaches to model student knowledge in adaptive textbooks by monitoring all kinds of activities, including reading and question-answering. In turn, a more complete model of knowledge enables powerful recommendation approaches that consider user history, knowledge, and the current reading context

Sample papers:

  • Thaker, K., Huang, Y., Brusilovsky, P., and He, D. (2018) Dynamic Knowledge Modeling with Heterogeneous Activities for Adaptive Textbooks. In: Proceedings of the 11th International Conference on Educational Data Mining, Buffalo, USA, July 15-18, 2018, pp. 592-595.
  • Thaker, K., Carvalho, P., and Koedinger, K. (2019) Comprehension Factor Analysis: Modeling student’s reading behaviour: Accounting for reading practice in predicting students’ learning in MOOCs. In: Proceedings of 9th International Conference on Learning Analytics & Knowledge (LAK’19), Tempe, AZ, USA, March 4-8, 2019, pp. 111-115.
  • Thaker, K., Brusilovsky, P., and He, D. (2019) Student Modeling with Automatic Knowledge Component Extraction for Adaptive Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 95-102.
  • Thaker, K., Zhang, L., He, D., and Brusilovsky, P. (2020) Recommending Remedial Readings Using Student’s Knowledge State. In: Proceedings of 13th International Conference on Educational Data Mining, July 10-13, 2020, pp. 233-244.
  • Rahdari, B., Brusilovsky, P., Thaker, K., and Barria-Pineda, J. (2020) Using Knowledge Graph for Explainable Recommendation of External Content in Electronic Textbooks. In: Proceedings of Second Workshop on Intelligent Textbooks at 21st International Conference on Artificial Intelligence in Education (AIED 2020), July 6, 2020, CEUR, pp. 50-61.

Enhanced Structure Visualization for Electronic textbooks

This work focused on supporting user navigation in electronic textbooks with interactive visualization that reveals the textbook's topical or/and structural organization to be explored.

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Sample papers:

  • Brusilovsky, P. and Rizzo, R. (2002) Map-based horizontal navigation in educational hypertext. In: K. M. Anderson, S. Moulthrop and J. Blustein (eds.) Proceedings of 13th ACM Conference on Hypertext and Hypermedia (Hypertext 2002), College Park, MD, June 11-15, 2002, ACM, pp. 1-10.
  • Brusilovsky, P. and Rizzo, R. (2002) Map-based access to multiple educational on-line resources from mobile wireless devices. In: F. Paternò (ed.) Proceedings of 4th International Symposium on Mobile Human-Computer Interaction, Mobile HCI 2002, Pisa, Italy, September 18-20, 2002, Springer-Verlag, pp. 404-408.
  • Brusilovsky, P. and Rizzo, R. (2002) Using maps and landmarks for navigation between closed and open corpus hyperspace in Web-based education. The New Review of Hypermedia and Multimedia 9, 59-82.
  • Brusilovsky, P., Chavan, G., and Farzan, R. (2004) Social adaptive navigation support for open corpus electronic textbooks. In: P. De Bra and W. Nejdl (eds.) Proceedings of Third International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH'2004), Eindhoven, the Netherlands, August 23-26, 2004, Springer-Verlag, pp. 24-33.
  • Farzan, R. and Brusilovsky, P. (2005) Social navigation support through annotation-based group modeling. In: L. Ardissono, P. Brna and A. Mitrovic (eds.) Proceedings of 10th International User Modeling Conference, Berlin, July 24-29, 2005, Springer Verlag, pp. 463-472.
  • Ahn, J.-w., Farzan, R., and Brusilovsky, P. (2006) A two-level adaptive visualization for information access to open-corpus educational resources. In: P. Brusilovsky, J. Dron and J. Kurhila (eds.) Proceedings of Workshop on the Social Navigation and Community-Based Adaptation Technologies at the 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, Dublin, Ireland, June 20th, 2006, pp. 497-505.
  • Brusilovsky, P., Ahn, J.-w., and Farzan, R. (2007) Accessing Educational Digital Libraries through Adaptive Information Visualization. In: Proceedings of 10th DELOS Workshop on Personalized Access, Profile Management, and Context Awareness in Digital Libraries, PersDL 2007, Corfu, Greece, June 29-30, 2007.
  • Guerra, J., Parra, D., and Brusilovsky, P. (2013) Encouraging Online Student Reading with Social Visualization. In: Proceedings of 2nd Workshop on Intelligent Support for Learning in Groups at the 16th Annual Conference on Artificial Intelligence in Education, AIED 2013, Memphis, TN, USA, pp. 47-50.
  • Barria-Pineda, J., Brusilovsky, P., and He, D. (2019) Reading Mirror: Social Navigation and Social Comparison for Electronic Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 30-37.

Systems

ELM-ART - Adaptive and Interactive Electronic Textbook for LISP

About ELM-ART

InterBook - A framework to author and deliver adaptive electronic textbooks

About InterBook

Knowledge Sea

About Knowledge Sea

Systems

* Knowledge Sea
* Knowledge Sea II

ReadingCircle

Workshops

Online Books workshops

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