Question Answering for Collaborative Learning with Answer Quality Predictor

Full Text (PDF, 606KB), PP.12-17

Views: 0 Downloads: 0

Author(s)

Kohei Arai 1,* Anik Nur Handayani 2

1. Information Science, Saga University – Japan

2. Electrical and Information Technology, State University of Malang – Indonesia

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2013.05.02

Received: 14 Feb. 2013 / Revised: 6 Mar. 2013 / Accepted: 11 Apr. 2013 / Published: 8 May 2013

Index Terms

E-Learning, Collaborative Learning, Question Answer, Knowledge, Answer Quality Predictor.

Abstract

The increasing advances of Internet Technologies in all application domains have changed life styles and interactions. With the rapid development of E-Learning, collaborative learning is an important for teaching, learning methods and strategies. Studies over the years shown that students had actively and more interactively involved in a classroom discussion to gain their knowledge. Students can ask their questions to the classroom discussion when they want to collaborate with others, asking one another for information, evaluating one another’s ideas. Therein, the activity allowing one question has many answer or information that should be selected. Every answer has a weighting and its very subjective to select it. In this paper, we introduce question answering for collaborative learning with answer quality predictor. By using answer quality predictor the quality of the information could be determined. Through the process of collaborative learning, the knowledge base will be enriched for future question answering. Further, not only the student could get answers form others but also provided by the system.

Cite This Paper

Kohei Arai, Anik Nur Handayani, "Question Answering for Collaborative Learning with Answer Quality Predictor", International Journal of Modern Education and Computer Science (IJMECS), vol.5, no.5, pp.12-17, 2013. DOI:10.5815/ijmecs.2013.05.02

Reference

[1]Dillenbourg, P. (1999). Collaborative Learning: Cognitive and Computational Approaches. Advances in Learning and Instruction Series. New York, NY: Elsevier Science, Inc.
[2]Chiu, M. M. (2000). Group problem solving processes: Social interactions and individual actions. Journal for the Theory of Social Behavior, 30, 1, 27-50.600-631.
[3]Chiu, M. M. (2008).Flowing toward correct contributions during groups' mathematics problem solving: A statistical discourse analysis. Journal of the Learning Sciences, 17 (3), 415 – 463
[4]Mitnik, R., Recabarren, M., Nussbaum, M., & Soto, A. (2009). Collaborative Robotic Instruction: A Graph Teaching Experience. Computers & Education, 53(2), 330-342.
[5]Chiu, M. M. (2008). Effects of argumentation on group micro-creativity. Contemporary Educational Psychology, 33, 383 – 402.
[6]Chen, G., & Chiu, M. M. (2008). Online discussion processes. Computers and Education, 50, 678 – 692
[7]Wang, C.C., Hung J.C., Yang C.Y., Shih T.K. (2006). An Apllication of Question Answering System for Collaborative Learning. IEEE Conference on ICDCSW’06
[8]Johnson, D.W. and Johnson R.T. (1999) Cooperation and competition: Theory and research. Edina. MN: Interaction Book Company
[9]Slavin, R. (1996) Research on cooperative learning and achievement: what we know, what we need to know. Contemporary Educational Pshchology, 21, 1, pp. 43-69.
[10]Gilrory, K.(2001). Collaborative e-learning: the right approach ([Online].Available at : http://www.ottergroup.com/otter-with- comments/right_approach.html).
[11]Lave, J., and Wenger, E. (1991) Situated Learning: Legitimate Peripheral Participation. Cambridge University Press, Cambridge
[12]Kuutti, K. (1991) The concept of activity as a basic unit of analysis for CSCW research. Proceedings of the Second European Conference on Computer-Supported Co-operative Work: EC-CSCW’91 (eds. L.J. Bannon, M. Robinson &K. Schmidt) pp. 249-264, Kluwer, Dordrecht.
[13]Watzlawick, P. (1967) Pragmatics of Human Communications: A Study of Interactional Patterns. Pathologies and Paradoxes. W.W. Norton, New York.
[14]Hwang GJ, Yin PY, Wang TT, Judy TR, Hwang GH, 2008, An enhanced Genetic Approach to Optimizing Auto-reply Accuracy of an E-learning System, Elsevier Journal, Computers and Education 51 (2008) 337-353
[15]Bahreininejad A, Alinaghi Tanaz, 2011, A Multi Agent Question Answering System for E-Learning and Collaborative Learning, nternational Journal of Distance Education Technologies, 9(2), 23-39, April-June 2011.
[16]Kohei Arai, Anik Nur Handayani, 2012, Question Answering System for an effective CollaborativeLearning, IJACSA Journal Vol.3 No.1
[17]Kohei A. Anik Nur Handayani, 2013, Predicting Quality of Answer in Collaborative Q/A Community, IJARAI Journal Volume 2 Issue 3.