In education diagnoses, diagnosing misconceptions is important as well as diagnosing skills. However, traditional cognitive diagnostic computerized adaptive testing (CD-CAT) is usually developed to diagnose skills. This study aims to propose a new CD-CAT that can simultaneously diagnose skills and misconceptions. The proposed CD-CAT is based on a recently published new CDM, called the simultaneously identifying skills and misconceptions (SISM) model (Kuo, Chen, & de la Torre, in press). A new item selection algorithm is also proposed in the proposed CD-CAT for achieving high adaptive testing performance. In simulation studies, we compare our new item selection algorithm with three existing item selection methods, including the Kullback–Leibler (KL) and posterior-weighted KL (PWKL) proposed by Cheng (2009) and the modified PWKL (MPWKL) proposed by Kaplan, de la Torre, and Barrada (2015). The results show that our proposed CD-CAT can efficiently diagnose skills and misconceptions; the accuracy of our new item selection algorithm is close to the MPWKL but less computational burden; and our new item selection algorithm outperforms the KL and PWKL methods on diagnosing skills and misconceptions.

References

Cheng, Y. (2009). When cognitive diagnosis meets computerized adaptive testing: CD-CAT. Psychometrika, 74(4), 619–632. doi: 10.1007/s11336-009-9123-2

Kaplan, M., de la Torre, J., & Barrada, J. R. (2015). New item selection methods for cognitive diagnosis computerized adaptive testing. Applied Psychological Measurement, 39(3), 167–188. doi:10.1177/0146621614554650

Kuo, B.-C., Chen, C.-H., & de la Torre, J. (in press). A cognitive diagnosis model for identifying coexisting skills and misconceptions. Applied Psychological Measurement.

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