All the educational organizations mainly aim at elevating the academic performance of students for improving the overall quality of education. In this direction, Educational Data Mining (EDM) is a rapidly trending research area that utilizes the essence of Data Mining (DM) concepts to help academic institutions figure out useful information on the Student Satisfaction Level (SSL) with the Online Learning process (OL) during COVID-19 lock-down. Different practices have been tried with EDM to predict students' behaviors to reach the best educational settings. Therefore, Feature Selection (FS) is typically employed to find the most relevant subset of features with minimum cardinality. As the predictive accuracy of a satisfaction model is significantly influenced by the FS process, the effectiveness of the SSL model is elaborately studied in this paper in connection with FS techniques. In this connection, a dataset was first collected online via a questionnaire of student reviews on OL courses. Using this datatset, the performance of wrapper FS techniques in DM and classification algorithms was analyzed in terms of fitness values. Ultimately, the goodness of subsets with different cardinalities is evaluated in terms of prediction accuracy and number of selected features by measuring the quality of 11 wrapper-based FS algorithms and the k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) as base-line classifiers. Based on the experiments, the optimal dimensionality of the feature subset was revealed, as well as the best method. The findings of the present study evidently support the well-known conjunction of the existence of minimum number of features and an increase in predictive accuracy. It is remarkable the relevancy of FS for highaccuracy SSL prediction, as the relevant set of features can effectively assist in deriving constructive educational strategies. Our study contributes a feature size reduction of up to 80% along with up to 100% classification accuracy on the adopted real-time dataset.
In this paper we propose a new approach based on text mining techniques for predicting student performance using LSA (latent semantic analysis) and K-means clustering methods. The present study uses free-style comments written by students after each lesson. Since the potentials of these comments can reflect student learning attitudes, understanding of subjects and difficulties of the lessons, they enable teachers to grasp the tendencies of student learning activities. To improve our basic approach using LSA and k-means, overlap and similarity measuring methods are proposed. We conducted experiments to validate our proposed methods. The experimental results reported a model of student academic performance predictors by analyzing their comments data as variables of predictors. Our proposed methods achieved an average 66.4% prediction accuracy after applying the k-means clustering method and those were 73.6% and 78.5% by adding the overlap method and the similarity measuring method, respectively.
Predicting students' academic achievement with high accuracy has an important vital role in many academic disciplines. Most recent studies indicate the important role of the data type selection. They also attempt to understand individual students more deeply by analyzing questionnaire for a particular purpose. The present study uses free-style comments written by students after each lesson, to predict their performance. These comments reflect their learning attitudes to the lesson, understanding of subjects, difficulties to learn, and learning activities in the classroom. To reveal the high accuracy of predicting student's grade, we propose Latent Semantic analysis (LSA) technique to overcome the problems caused by using statistically derived conceptual indices instead of individual words, then apply Artificial Neural Networks (ANN) model. We chose five grades instead of the mark itself as a student's result to predict their grade. The potentials of ANN for approximating extremely complex problems help us to develop an estimation model of student performance. Our proposed method averagely achieves 82.6% and 76.1% prediction accuracy and F-measure, respectively of students' grades.
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