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Title |
Intelligent Automated Cognitive-Maturity Recognition System for Confidence Based E-learning |
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Author |
Imran Usman and Adeeb M. Alhomoud |
| Citation |
Vol. 21 No. 4 pp. 223-228 |
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Abstract |
As a consequence of sudden outbreak of COVID-19 pandemic worldwide, educational institutes around the globe are forced to switch from traditional learning systems to e-learning systems. This has led to a variety of technology-driven pedagogies in e-teaching as well as e-learning. In order to take the best advantage, an appropriate understanding of the cognitive capability is of prime importance. This paper presents an intelligent cognitive maturity recognition system for confidence-based e-learning. We gather the data from actual test environment by involving a number of students and academicians to act as experts. Then a Genetic Programming based simulation and modeling is applied to generate a generalized classifier in the form of a mathematical expression. The simulation is derived towards an optimal space by carefully designed fitness function and assigning a range to each of the class labels. Experimental results validate that the proposed method yields comparative and superior results which makes it feasible to be used in real world scenarios. |
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Keywords |
E-learning, cognitive maturity, Classification, Genetic Programming, confidence-based e-learning . |
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