In a self-directed learning environment, students take tests anytime and anywhere to ascertain their mastery of learning. Traditionally, Computerised Adaptive Test (CAT) was used to automatically score and provide an ability estimate for each student. Although these ability estimates are good for summative purposes such as comparing of students or ranking students relative to the cohort, it may not be useful to teachers for formative assessment. To support teachers in the assessment and reporting of students’ progress and achievement in this big data environment, this paper presents an approach, illustrated using primary school Fractions, to transform CAT to provide teachers with both precise results as well as detailed information about students’ proficiency in Fractions in the form of profile descriptors. During our prototyping in schools, teachers generally found the reports useful in helping them identify student proficiency level in Fractions as well as customising their interventions to close individual student learning gaps.
Sub-themes: Reporting on progress and achievement using Big Data, Computerised Adaptive Testing, Assessment for Learning
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- November 18, 2018 Create Date
- November 18, 2018 Last Updated
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