Abstract
Instructor-mediated training systems give end users direct control over content, increasing acceptance but introducing new technical challenges. Decreased opportunity for parameter estimation limits the utility of item-response or Bayesian approaches to adaptive assessment. We present four adaptive assessment algorithms that require little data about test item characteristics. Two algorithms present about half as many items as random selection before producing accurate skill estimates. These algorithms enable adaptive assessment in training settings where calibration data is sparse.
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Folsom-Kovarik, J.T., Wray, R.E., Hamel, L. (2013). Adaptive Assessment in an Instructor-Mediated System. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_61
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DOI: https://doi.org/10.1007/978-3-642-39112-5_61
Publisher Name: Springer, Berlin, Heidelberg
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