On the development of a computer-assisted testing system with genetic test sheet-generating approach

Since the last decade, computer-assisted testing has proven to be an efficient and effective way to evaluating students´ learning status such that proper tutoring strategies can be adopted to improve their learning performance. A good test will not only help the instructor evaluate the learning status of the students, but also facilitate the diagnosis of the problems embedded in the students´ learning process. One of the most important and challenging issues in conducting a good test is the construction of test sheets that can meet various assessment requirements. A previous study has indicated that selecting test items to best fit multiple assessment requirements can be formulated as a mixed integer programming model. The problem is known to be NP-hard in the literature and, hence, computational challenges hinder the development of efficient solution methods. As a sequel, we instead seek quality approximate solutions in a reasonable time. Two approximation methods based upon a genetic approach are developed. Statistics from a series of computational experiments indicate that our approach is able to efficiently generate near-optimal combinations of test items that satisfy the specified requirements or constraints.