ANFIS application to infer student performance in English learning using affective factors
Teaching and learning practices are essentially meant to equip students with necessary competences. In assessing student learning, few teaching and learning practices consider the role of affective factors. Assessment relying solely on cognitive factors could impact teaching and learning effectiveness although marginal work exists exploring affective factors to enhance teaching and learning effectiveness. The current study considers using affective factors to infer student learning of English skills. Student affective factors, such as motivation, attitude, introversion, extroversion, anxiety, and self esteem are measured using questionnaires. The data obtained is then used to model student in learning using Adaptive Neuro Fuzzy Inference System (ANFIS) with a different set of parameters. Evaluation of the models is based on Mean Absolute Error (MAE) and Mean Squared Error (MSE) The result from the experiment shows that the model is able to infer student learning by 0.076 (MAE) and 0.008 (MSE) respectively. In addition, the proposed model that is not too general nor too complex and with fewer training iteration numbers shows the lowest error among other proposed models.
2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA SCIS)