Assessment Analytic Theoretical Framework Based on Learners’ Continuous Learning Improvement

Currently, university students are required to follow stringent curriculum structure regardless of their performance. Personalized learning is not being offered resulting the whole cohort must compy to a customized fixed curriculum design. This is because the designed curriculum does not take into account different students’ attainment. Furthermore, there is a mismatched between supply and demand of graduates’ skill sets to fulfil the requirement of industry. Due to these issues, employers face difficulties in finding suitable high-skilled worker which contributes to large number of unemployed graduates. Thus, a systematic intervention of students’ learning process is essential to construct informed and strategic responses in order to manage challenges and minimize skill mismatch, at the same time providing adequate fundamental knowledge. In this paper, an assessment analytics framework is proposed based on automated extracted skill sets from curriculum documents and individual performance to recommend adaptive learners’ learning system (ALLS). By preparing the graduates with the required industry skill sets, the graduates’ unemployment rate is envisaged to reduce.