Pedagogically Useful Extractive Summaries for Science Education

Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 177–184 Manchester, August 2008 Pedagogically Useful Extractive Summaries for Science Education Sebastian de la Chica, Faisal Ahmad, James H. Martin, Tamara Sumner Institute of Cognitive Science Department of Computer Science University of Colorado at Boulder sebastian.delachica, faisal.ahmad, james.martin, tamara.sumner@colorado.edu Abstract This paper describes the design and evaluation of an extractive summarizer for educational science content called COGENT. COGENT extends MEAD based on strategies elicited from an em- pirical study with science domain and in- structional design experts. COGENT identifies sentences containing pedagogi- cally relevant concepts for a specific sci- ence domain. The algorithms pursue a hybrid approach integrating both domain independent bottom-up sentence scoring features and domain-aware top-down fea- tures. Evaluation results indicate that COGENT outperforms existing summar- izers and generates summaries that closely resemble those generated by hu- man experts. COGENT concept invento- ries appear to also support the computa- tional identification of student miscon- ceptions about earthquakes and plate tec- tonics. 1 Introduction Multidocument summarization (MDS) research efforts have resulted in significant advancements in algorithm and system design (Mani, 2001). Many of these efforts have focused on summariz- ing news articles, but not significantly explored the research issues arising from processing edu- cational content to support pedagogical applica- tions. This paper describes our research into the application of MDS techniques to educational © 2008. Licensed under the Creative Commons At- tribution-Noncommercial-Share Alike 3.0 Unported license (http://creativecommons.org/licenses/by-nc- sa/3.0/). Some rights reserved. science content to generate pedagogically useful summaries. Knowledge maps are graphical representations of domain information laid out as networks of nodes containing rich concept descriptions inter- connected using a fixed set of relationship types (Holley and Dansereau, 1984). Knowledge maps are a variant of the concept maps used to capture, assess, and track student knowledge in education research (Novak and Gowin, 1984). Learning research indicates that knowledge maps may be useful cognitive scaffolds, helping users lacking domain expertise to understand the macro-level structure of an information space (O'Donnell et al., 2002). Knowledge maps have emerged as an effective representation to generate conceptual browsers that help students navigate educational digital libraries, such as the Digital Library for Earth System Education (DLESE.org) (Butcher et al., 2006). In addition, knowledge maps have proven useful for domain and instructional ex- perts to capture domain knowledge from digital library resources and to analyze student under- standing for the purposes of providing formative assessments (Ahmad et al., 2007). Knowledge maps have proven useful both as representations of knowledge for assessment purposes and as learning resources for presenta- tion to students. However, domain knowledge map construction by experts is an expensive knowledge engineering activity. In this paper, we describe our progress towards the automated generation of pedagogically useful extractive summaries from educational texts about a sci- ence domain. In the context of automated knowl- edge map generation, summary sentences corre- spond to concepts. While the detection of rela- tionships between concepts is also part of our overall research agenda, this paper focuses solely …

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