At present, there are only a limited number of studies examining how to optimally construct cognitive diagnostic tests. The cognitive diagnostic index (CDI) and attribute-level discrimination index (ADI) have been proposed to assemble such tests. The CDI and ADI have been shown to be instrumental in constructing cognitive diagnostic tests when the attribute relationships are assumed to be nonhierarchical. For greater generality when designing cognitive diagnostic assessment, attribute hierarchy and the ratio of test length to the number of attributes (RTA) are two important factors to be considered. This article proposes modified indices that take into account attribute hierarchy and RTA. Simulation studies show that, under the deterministic input, noisy, "and" gate model (DINA) and the reduced version of the reparameterized unified model (rRUM), the proposed indices provide higher attribute and attribute pattern correct classification rates than the original indices.

One of the significant and distinguishing curriculum characteristics of STEM compared to other subjects domains is hands-on skill development. Hands-on training enables the enhancement of learning because it parallels with the concrete-to-abstract nature of cognitive development, providing additional sources of brain activation via kinaesthetic involvement and elevating students’ motivation and engagement. To bridge the gap between theory and practice, and advance unprepared students’ hands-on skills, this research proposed and implemented an innovative STEM course with the computer-assisted remedial learning system (CARLS) in the vocational high school experience in Taiwan. The effects of STEM course were examined through an experiment with learning performances hypotheses. A total of 32 students in a vocational high school in Taiwan voluntarily participated in this research and a one-group pre-test and post-test pre-experimental design was adopted. The results of this experimental course demonstrated that CARLS is effective and contributed in enhancing students’ knowledge, achievement and hands-on skill performance in this STEM course. Suggestions and implications for STEM education is also made for practitioners and educators. Key words: hands-on activities, STEM, Computer-Assisted Remedial Learning System, vocational high school.

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