A Neuro-Fuzzy Model for E-Learning Activities
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Date
2017
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LAMBERT Academic Publishing
Abstract
E-learning system is an Information Technology-based platform adopted globally to complete or supplement the tradition mode of education. Most existing works e-learning system conceptualized on Learning Technology System Architecture (LTSA) Standard for content adaptability and interoperability but played less on instructional design theory and dynamic learners' activities monitoring. This study therefore extends LTSA with Instructional Design (ID) subsystem and a neutro-fuzzy artificial intelligent model for automatic interpretation of learner's activities.
The learning resource store in the LTSA was extended with a process for five selected instructional design theories (Gagnes, Dick and Carey, ADDIES, Merrills and Kemp). The extended LTSA was implemented on open-sourced Modular Object Oriented Distance Learning Environment (Moodle). The Students' Impression (SI) on the extended LTSA was investigated through Survey Monkey online platform using sixty (60) 300 level, B.Sc Computer Science students that offered System Analysis and Design (CSC 306) during the 2013/2014 session at Kwara State University, Malete as a case study. The effectiveness of the extended LTSA for the students under consideration was determined on the basis of mean score of the class examination grades. Furthermore, the evaluation process in the LTSA was extended to include diagnostic process, after which a six layer feed-forward neuro-fuzzy network model for learners' activities was developed and integrated. The developed neuro-fuzzy model was simulated in Matrix Laboratory editor. The performance of the developed model was evaluated by comparing the developed model with the existing Mamdani Fuzzy, Sugeno Fuzzy and Sigmoid neuro-fuzzy learner' activities models in terms of Mean Absolute Error (MAE) and Root Mean-Squared Error (RMSE). The complexity of the developed model was determined on the basis of accuracy, True Positive (TP), False Positive (FP), True Negative (TN) and False Negative (FN).
The effect of the extended LTSA on the academic performance of the students vielded 56.98 class mean score. The MAE and RMSE obtained for the developed model are 127.4 and 11.3 respectively while those of Mamdani Fuzzy are 420.9 and 20.6 respectively. Also, these values for Sugeno Fuzzy are 2190.5 and 46.9 respectively while those of Sigmoid neuro-fuzzy are 157.6 and 12.6 respectively. Furthermore, complexity of the developed model assumed an accuracy value of 0.9, TP value of 67.0. FP value of 9.0, TN value of 74.0 and FN of 0.0. The Triangle model which shares the same accuracy value of 0.9 with the developed model has TP value of 66.0, FP value of 18.0, TN value of 65.0 and FN of 1.0.
Consequently, the integrated ID-subsystem facilitates grounded instruction development that provides improved academic performance for the class. Also, the developed neuro-fuzzy model which outperformed the other three models on the complexity metrics offers seamless evaluation of learners' activities.