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Extending Moodle Functionalities to Adaptive Testing Framework
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, , , CAMRI Lab , Université du Québec à Montréal (UQAM), Canada ; , GDAC Lab, Canada ; , CAMRI Lab , Université du Québec à Montréal (UQAM), Canada

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Quebec City, Canada ISBN 978-1-880094-63-1 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA

Abstract

E-learning has advanced considerably in the last decades allowing the interoperability of different systems and different kinds of adaptation to the student's profile or the learning objectives. But, some of its aspects, such as E-testing are still in an early stage. As a consequence, most of the actual E-learning platforms offer only basic E-testing functionalities. In addition, most of those platforms present the tests in a traditional format despite their known limitations and precision problems. However, by making efficient use of well known techniques in artificial intelligence, existing psychometric theories and standards in E-learning, it could be possible to integrate adaptive and more informative E-testing functionalities in the actual E-learning platforms. In this paper, we will present some of the principles, the architectural elements and the algorithms used in an exploratory integration of adaptive testing functionalities within the Moodle platform.

Citation

Sodoke, K., Riopel, M., Raîche, G., Nkambou, R. & Lesage, M. (2007). Extending Moodle Functionalities to Adaptive Testing Framework. In T. Bastiaens & S. Carliner (Eds.), Proceedings of E-Learn 2007--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 476-482). Quebec City, Canada: Association for the Advancement of Computing in Education (AACE). Retrieved February 22, 2019 from .

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References

  1. Baker, F. (2001). The Basics of Item Response Theory. ERIC Clearinghouse on Assessment and Evaluation, University of Maryland
  2. Baker, F. (2004). Item response theory– Parameter estimation techniques. New York: Marcel Dekker.
  3. Carr, B. & Goldstein, I. (1977). Overlays: A theory of modelling for computer aided instruction, Technical Report AI Memo 406, MIT, Cambridge, MA.
  4. Wainer, H. (2000). Computerized adaptive testing: A primer. Mahwah, NJ: Lawrence Erlbaum Associates.
  5. Jonsson, A., Johns, J., Mehranian, H., Arroyo, I., Woolf, B., Barto, A., Fisher, D., and Mahadevan, S (2005). Evaluating the Feasibility of Learning Student Models from Data. American Association for Artificial Intelligence Workshop on Educational Data Mining.

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