Personalised time-dependent learning

Time-dependent instruction appears to shape next-generation learning systems, where the value of instruction is as important as the time it takes to learn. The ability to grasp the exact knowledge required to accomplish a specific task, in the allotted time, is a key factor for organisations to remain economically competitive in the new knowledge society era. Time-constrained learning is a new concept which requires a multilevel cognitive organisation of knowledge to suit various learning profiles. This paper proposes an ontology-based authoring tool capable of mapping concepts to learning resources to different granularity levels, hence customising learning-delivery to time-constrained learners. The proposed framework in this paper distils knowledge to meet timeliness using a judicious application of real-time systems principles. This interdisciplinary learning design advocates progressive levels of learning which trade learning granularity with allocated instruction time. The paper provides performance and experimental studies using an evaluation model and a validation approach through use cases. The results show interesting performance tradeoffs in analysing the cognitive perception of time-dependent learning.