Abstract
Confidence Based Learning (CBL) is a newer method of teaching and learning system that not only identify the knowledge of a typical learner but also takes care of the confidence level in the knowledge for its practical use in professional world. Unlike, other e-learning systems, the CBL first assesses a learner and identifies the knowledge level and skill gaps and the limitation of confidence level. The authors in this manuscript propose a k-ary tree structure to represent tasks and Atomic Competencies (ACs) related to an Instructional Objective (IO). Structure of Learning Object (LO) and 2-dimensional assessment technique for CBL is available, but it lacks in proper trace of progressions. A mining technique using LRS for improvised identification of limitations of a learner is also suggested in the research. In this research proposal, the authors explain how a k-ary tree may be used for implementation of the tasks and ACs so that proper diagnosis can be performed, and a customized learning content can be provided. The authors proposed an optimization technique using Huffman/Dynamic Huffman Tree to quantify the progression towards mastery. The research article emphasizes of the progressive model towards the learners’ mastery. This not only provide a learning map to the learner but also help the course designer to trace the respective learners’ performance.
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Acknowledgment
The authors thank from the core of the heart, the colleagues of the Department of Computer Science and Engineering, NITTTR, Kolkata, India for assisting them to carrying out the present research activity. The authors express sincere gratitude for the assistance provided by the DST PURSE Scheme at University of Kalyani and acknowledge support from colleagues of Department of Computer Science and Engineering, University of Kalyani, Nadia, India.
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Chatterjee, R., Kar, S.P., Mandal, J.K. (2020). Managing Tasks in Confidence Based Learning Using K-ary Tree. In: Auer, M., Hortsch, H., Sethakul, P. (eds) The Impact of the 4th Industrial Revolution on Engineering Education. ICL 2019. Advances in Intelligent Systems and Computing, vol 1135. Springer, Cham. https://doi.org/10.1007/978-3-030-40271-6_2
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DOI: https://doi.org/10.1007/978-3-030-40271-6_2
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