Monitoring student performance using data clustering and predictive modelling

Abstract

The current research is situated on context of teacher support in Virtual Learning Environments (VLE), specifically related to decision-making process. The knowledge about student performance is fundamental to solve pedagogical challenges and to define different ways in teaching and learning. Following this trend, VLE provides extra information by reports, graphs and interface's alert. However, there is demand of statistical tools to support the teacher decisions, because the learning analysis is limited to frequency analysis. This paper discusses an analytical approach to deal with e-learning data. Our focus is to identify groups of learners based on their answers. Therefore, this paper pointed out some main objectives: understand profiles of answers in order to guide a student to future learning activities, and identify which criteria, in each group, is the most relevant for the tutor's help. Several techniques are useful for e-learning issues. However, we focus in Educational Data Mining (EDM) methodology. Our paper selected data of an English e-learning course from PSLC repository for case study in validation step. Preprocessing techniques of EDM were applied on selected dataset. Initially, we removed incomplete, noisy and inconsistent data of sample. After the preprocessing, we executed two steps: clustering and prediction analysis. Firstly, we executed the clustering process, because we needed to identify student's group based on their answers. After understanding the groups, we predicted behaviors of students on each cluster, which were defined in the last step. In our research, it defines that the prediction will use a regression methodology, and the clustering will execute K-means algorithm. The study identified five student's groups based on their answer, such as Expert, Good, Regular, Bad and Criticism answers. Consequently, the prediction analysis defined that the score of tutor's help ("Avg Assistance Score") is the most interesting factor for our investigation. The approach executed the Stepwise Backward Regression, which is a semi-automated process of building a model by successively adding or removing variables based solely on the t-statistics of their estimated coefficients. Thus, other result is that the presence of the variables "Incorrect" and "Correct First Attempts" belongs to three regression models obtained by the approach. This work's findings presents knowledge about answers profiles of VLE students in two main perspectives. First, it analyzes the usage of Open Learning Data to characterize behavioral profiles of answers using multivariate analysis techniques. Second, our analysis contributes to expand present knowledge about how student performance changes the teacher decisions in VLE. This approach tends to be a useful tool in analytical process when the VLE system does not provide statistical tools.

Systematic mapping review on student’s performance analysis using big data predictive model

“…Similarly, a study by [9] analyze students' learning pattern and predict their final performance for a course in a university. In [13] identifies group of learners based on their answers and then guide them for future learning activities. In this study, different level of learner based on their competency received different set of learning activities.…”

From Transcripts to Insights for Recommending the Curriculum to University Students

“…Based on the literature review in 2013 [8], we consider two main groups: "Student Modeling" and "Decision Support Systems" in terms of EDM. Some widely used methods are regression and classification for predicting [9,38], but other methods have also been used such as clustering and feature selection for exploring patterns or emphasizing the interesting features [10,24].…”

Educational Data Mining for Classification of Students based on their Performance

“…In this proposed system, neural network toolbox is used prediction as it provides algorithms, pre-trained models, and apps to create, train, visualize, and simulate both shallow and deep neural networks to perform prediction and classification. In [9], Author has discussed about the classification method used for prediction with Decision tree classifiers and methods to solve the class imbalance problem. Decision tree classifier is used for classifying students according to their grades [9].…”

“…In [9], Author has discussed about the classification method used for prediction with Decision tree classifiers and methods to solve the class imbalance problem. Decision tree classifier is used for classifying students according to their grades [9]. In [10], Author has stated about the best model for classification of students.…”