Abstract
Question answering (QA) is the automated process of answering general questions submitted by humans in natural language. QA has previously been explored within the educational context to facilitate learning, however the majority of works have focused on text-based answering. As an alternative, this paper proposes an approach to return answers as a concept map, which further encourages meaningful learning and knowledge organisation. Additionally, this paper investigates whether adapting the returned concept map to the specific question context provides further learning benefit. A randomised experiment was conducted with a sample of 59 Computer Science undergraduates, obtaining statistically significant results on learning gain when students are provided with the question-specific concept maps. Further, time spent on studying the concept maps were positively correlated with the learning gain.
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References
Katz, B. Annotating the world wide web using natural language. In: Fifth RIAO Conference on Computer Assisted Information Searching on the Internet (1997)
Gurevych, I., et al., Educational question answering based on social media content. In: Proceedings of the 2009 conference on Artificial Intelligence in Education, p. 133-140 (2009)
Novak, J.D., Canas, A.J.: The Theory underlying Concept maps and How to construct and use them. Florida Institute for Human and Machine Cognition (2006)
Dali, L., et al.: Question answering based on semantic graphs. In: Language and Technology Conference, Poznan (2009)
Bloom, B., et al., Taxonomy of educational objectives: the classification of educational goals. In: Handbook I: Cognitive Domain, New York, Toronto (1956)
Atapattu, T., Falkner, K. Falkner, N.: Acquistion of triples of knowledge from lecture notes: a natural langauge processing approach. In: 7th International Conference on Educational Data Mining, London (2014)
Atapattu, T., Falkner, K., Falkner, N.: Evaluation of Concept Importance in Concept Maps Mined from Lecture Notes: Computer vs Human. In 6th International Conference on Computer Supported Education, Barcelona (2014)
Graesser, A., Gordon, S., Brainerd, L.: QUEST: A model of question answering. Computers & Mathematics with Applications 23(6–9), 733–745 (1992)
Kolomiyets, O., Moens, M.: A survey on question answering technology from an information retrieval perspective. Information Sciences 181(24) (2011)
Manning, C., et al.: The stanford CoreNLP natural language processing toolkit. In: 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Baltimore (2014)
Miller, G.A., et al.: Introduction to WordNet: An On-line Lexical Database. International Journal of Lexicography 3(4), 235–244 (1990)
Canas, A.J., et al.: CmapTools: a knowledge modeling and sharing environment. In: First International Conference on Concept Mapping, Pamplona (2004)
Gallagher, B.: Matching structure and semantics: A survey on graph-based pattern matching. AAAI FS 6, 45–53 (2006)
Kenny, D.A.: Statistics for the Social and Behavioural Sciences (1987)
O’Donnell, A.M., et al.: Knowledge Maps as Scaffolds for Cognitive Processing. Educational Psychology Review 14(1), 71–86 (2002)
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Atapattu, T., Falkner, K., Falkner, N. (2015). Educational Question Answering Motivated by Question-Specific Concept Maps. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_2
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DOI: https://doi.org/10.1007/978-3-319-19773-9_2
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-19773-9
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