You are here:

Towards Intelligent Test Sequencing, Enhancing Multiple Choice (MC) Tests in eLearning
PROCEEDINGS

, , , , Edward Waters College, United States

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Orlando, Florida, USA ISBN 978-1-880094-83-9 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA

Full Text

Abstract

Multiple choice (MC) tests are typically used for formative assessments in eLearning. However, the interaction bandwidth of traditional MC tests is severely limited; students can only make a tick on an answer option. Therefore the eLearning system needs to make assumptions to fill-in the gaps. Nevertheless, the system’s assumption may be wrong. A student may guess a correct answer or may make a careless mistake. If an eLearning system often makes stereo-typed pedagogical decisions based on wrong assumptions, eventually, the students would get frustrated. This research describes an artificial intelligent based methodology that could provide individualized test sequencing and personalized feedback.

Citation

Mohanarajah, S., Betton, S., Ikeokwu, F. & Sundaralingam, N. (2010). Towards Intelligent Test Sequencing, Enhancing Multiple Choice (MC) Tests in eLearning. In J. Sanchez & K. Zhang (Eds.), Proceedings of E-Learn 2010--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 554-559). Orlando, Florida, USA: Association for the Advancement of Computing in Education (AACE). Retrieved December 8, 2019 from .

© 2010 Association for the Advancement of Computing in Education (AACE)

References

View References & Citations Map
  1. Bloom, B.S. (1956). Taxonomy of educational objectives; the classification of educational goals. New York,, Longmans Green.
  2. Bush, M. (2001). "A Multiple-Choice test that rewards partial knowledge." Journal of Further and Higher Education 25(2): 157-163.
  3. Gardner-Medwin, A.R. (1995). "Confidence assessment in the teaching of basic science." Association of Learning Technology Journal 3: 80-85.
  4. Hopgood, A. (2000). Intelligent systems for engineers and scientists. Boca Raton, FL, CRC Press.
  5. Jameson, A. (1996). "Numerical Uncertainty Management in User and Student Modelling: An overview of Systems and Issues." User Modelling and User-Adapted Interaction 5. Katz, S., A. Lesgold, et al. (1994). Modelling the Student in Sherlock-II. Student modelling: the key to individualized knowledge-based instruction. J.E. Greer and G. McCalla. Berlin; New York, Springer-Verlag: 495-518.
  6. Zadeh, L. (1994). "Fuzzy logic, Neural networks, and soft computing." Communications of ACM 37(3): 77-84.-559 DASHDASH

These references have been extracted automatically and may have some errors. Signed in users can suggest corrections to these mistakes.

Suggest Corrections to References

Also Read