Paper Annotation with Learner Models

Authors:
Tiffany Y. Tang;Gordon McCalla
Affiliations:
Dept. of Computing, Hong Kong Polytechnic University, Hong Kong, cstiffany@comp.polyu.edu.hk and Dept. of Computer Science, University of Saskatchewan, Canada, {yat751, mccalla}@cs.usask.ca;Dept. of Computer Science, University of Saskatchewan, Canada, {yat751, mccalla}@cs.usask.ca
Venue:
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Year:
2005

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Abstract

In this paper, we study some learner modelling issues underlying the construction of an e-learning system that recommends research papers to graduate students wanting to learn a new research area. In particular, we are interested in learner-centric and paper-centric attributes that can be extracted from learner profiles and learner ratings of papers and then used to inform the recommender system. We have carried out a study of students in a large graduate course in software engineering, looking for patterns in such “pedagogical attributes”. Using mean-variance and correlation analysis of the data collected in the study, four types of attributes have been found that could be usefully annotated to a paper. This is one step towards the ultimate goal of annotating learning content with full instances of learner models that can then be mined for various pedagogical purposes.