Adaptive Navigation Support and Open Social Learner Modeling for PAL

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Description

The goal of this project is to leverage the power of open social learner modeling and adaptive navigation support in the context of the envisioned Personalized Assistant for Learning (PAL). The project is supported by the Distributed Learning Initiative contract W911QY13C0032.

Directions of work

The project focuses on both exploration and implementation of adaptive navigation support and open social learner modeling and pursues three directions of work

  • Exploring open social learner modeling interface for diverse learning content
  • Enhancing algorithms for personalized guidance using knowledge-based and social approaches
  • Developing architectural solutions and authoring tools to support open social learner modeling

Open Social Learner Modeling Interfaces

Our latest implementation of Open Social Learner Modeling (OSLM) interface is MasteryGrids system. Mastery Grids is both, a innovative OSML interface and an adaptive E-learning platform with integrated functionalities enabling multi-facet social comparison, open user modeling, and multi-type learning materials support. It presents and compares user learning progress and knowledge level (mastery) by colored grids, tracks user activities and feedbacks dynamically and provides flexible user-centered navigation across different content levels (e.g. topic, question) and different content types (e.g. question, example) of learning materials.

The Architecture

The architecture supporting Mastery Grids fulfills a major objective, portability, which is the ability to be integrated to other systems with little set up and modification. The architecture is modular and includes different software components:

  • backend Aggregate services communicating between the main interface and user modeling services,
  • backend user modeling services, and
  • backend content providing applications.

An overall architecture of the system can be sen in the next figure.

Architecture v1.png

Authoring Tools

The set of authoring tools developed for the project include tools for creating several kinds of smart learning content as well as tools to create adaptive courses that use this content

Content Authoring

This tool provides the interface for teachers to create and index annotated examples. more

Course Authoring

This tool provides the interface for teachers to create courses for Mastery Grids. more

Group Authoring

This tool provides the interface for teachers to define groups of students who can access the course. more

Authoring Portal

This is the central portal for providing access to the course authoring and group authoring tools. more

Systems

Mastery Grids

Mastery Grids is a visual-rich, interactive, adaptive E-learning system and framework with integrated functionalities enabling multi-facet social comparison, open social student modeling, and multi-type learning materials support. It presents and compares user learning progress and knowledge level (mastery) by colored grids, tracks user activities and feedbacks dynamically and provides flexible user-centered navigation across different content levels (e.g. topic, question) and different content types (e.g. question, example) of learning materials.

==>More

Aggregate

More


Links

Software sources and documentation is in GitHub. The Mastery Grids Interface, backend Aggregate and documentation can be found here. User model services can be found in here.

A demo view of MasteryGrids can be accessed here

Publications

  • Gonzalez-Brenes, J. P., Huang, Y., and Brusilovsky, P. (2013) FAST: Feature-Aware Student Knowledge Tracing. In: Proceedings of NIPS 2013 Workshop on Data Driven Education, Lake Tahoe, NV, December 10, 2013, also available at http://lytics.stanford.edu/datadriveneducation/papers/gonzalezetal.pdf.
  • Hosseini, R. and Brusilovsky, P. (2013) JavaParser: A Fine-Grain Concept Indexing Tool for Java Problems. In: Proceedings of The First Workshop on AI-supported Education for Computer Science (AIEDCS) at the 16th Annual Conference on Artificial Intelligence in Education, AIED 2013, Memphis, TN, USA, July 13, 2013, pp. 60-63, also available at https://sites.google.com/site/aiedcs2013/proceedings.
  • Hosseini, R., Brusilovsky, P., and Guerra, J. (2013) Knowledge Maximizer: Concept-based Adaptive Problem Sequencing for Exam Preparation. In: Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013), Memphis, USA, pp. 848-851.
  • Brusilovsky, P., Baishya, D., Hosseini, R., Guerra, J., and Liang, M. (2013) KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model. In: Proceedings of 2013 IEEE 13th International Conference on Advanced Learning Technologies, Beijing, China, July 15-18, 2013, pp. 275-279.
  • Brusilovsky, P. (2014) Addictive Links: Engaging Students through Adaptive Navigation Support and Open Social Student Modeling (Keynote talk). In: Proceedings of WWW 2014 Workshop on Web-based Education Technologies, Seoul, Korea, April 8, 2014.
  • Huang, Y., Xu, Y., and Brusilovsky, P. (2014) Doing More with Less: Student Modeling and Performance Prediction with Reduced Content Models. In: V. Dimitrova, et al. (eds.) Proceedings of 22nd Conference on User Modeling, Adaptation and Personalization (UMAP 2014), Aalborg, Denmark, July 7-11, 2014, Springer Verlag, pp. 338-349. ([1])
  • Hosseini, R. and Brusilovsky, P. (2014) Example-Based Problem Solving Support Using concept Analysis of Programming Content. In: S. Trausan-Matu, K. Boyer, M. Crosby and K. Panourgia (eds.) Proceedings of 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI, USA, June 5-9, 2014, Springer International Publishing, pp. 683-685. ([paper|http://link.springer.com/chapter/10.1007%2F978-3-319-07221-0_106]
  • Hosseini, R., Vihavainen, A., and Brusilovsky, P. (2014) Exploring Problem Solving Paths in a Java Programming Course. In: Proceedings of Psychology of Programming Interest Group Annual Conference, PPIG 2014, Brighton, UK, June 25-27, 2014, pp. 65-76, also available at http://www.sussex.ac.uk/Users/bend/ppig2014/7ppig2014_submission_18.pdf.
  • González-Brenes, J. P., Huang, Y., and Brusilovsky, P. (2014) General Features in Knowledge Tracing to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 84-91, also available at http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf.
  • Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) Increasing Adoption of Smart Learning Content for Computer Science Education. In: Proceedings of Proceedings of the Working Group Reports of the 2014 on Innovation \&\#38; Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57, available at http://dx.doi.org/10.1145/2713609.2713611.
  • Khajah, M. M., Huang, Y., González-Brenes, J. P., Mozer, M. C., and Brusilovsky, P. (2014) Integrating Knowledge Tracing and Item Response Theory: A Tale of Two Frameworks. In: I. Cantador, M. Chi, R. Farzan and R. Jäschke (eds.) Proceedings of Workshop on Personalization Approaches in Learning Environments (PALE 2014) at the 22th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014, Aalborg, Denmark, July 11, 2014, CEUR, pp. 7-12, also available at http://ceur-ws.org/Vol-1181/pale2014_paper_01.pdf.
  • Yudelson, M., Hosseini, R., Vihavainen, A., and Brusilovsky, P. (2014) Investigating Automated Student Modeling in a Java MOOC. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 261-264, also available at http://educationaldatamining.org/EDM2014/uploads/procs2014/short%20papers/261_EDM-2014-Short.pdf.
  • Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Parameterized Exercises in Java Programming: Using Knowledge Structure for Performance Prediction. In: Proceedings of The second Workshop on AI-supported Education for Computer Science (AIEDCS) at 12th International Conference on Intelligent Tutoring Systems ITS 2014, Honolulu, Hawaii, June 6 2014, also available at http://d-scholarship.pitt.edu/21915/.
  • Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Predicting Student Performance in Solving Parameterized Exercises. In: S. Trausan-Matu, K. Boyer, M. Crosby and K. Panourgia (eds.) Proceedings of 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI, USA, June 5-9, 2014, Springer International Publishing, pp. 496-503, also available at http://d-scholarship.pitt.edu/21916/.
  • Guerra, J., Sahebi, S., Lin, Y.-R., and Brusilovsky, P. (2014) The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 153-160, also available at http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/153_EDM-2014-Full.pdf.
  • Loboda, T., Guerra, J., Hosseini, R., and Brusilovsky, P. (2014) Mastery Grids: An Open Source Social Educational Progress Visualization. In: S. de Freitas, C. Rensing, P. J. Muñoz Merino and T. Ley (eds.) Proceedings of 9th European Conference on Technology Enhanced Learning (EC-TEL 2014), Graz, Austria, September 16-19, 2014 (Best paper award).
  • Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) Increasing Adoption of Smart Learning Content for Computer Science Education. In: Proceedings of Proceedings of the Working Group Reports of the 2014 on Innovation \&\#38; Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57, also available at http://dx.doi.org/10.1145/2713609.2713611.