Building and Using Multiple Stacks of Models for the Classification of Learners and Custom Recommending of Quizzes

Abstract

Recommending quizzes in e-Learning systems always represents a challenging task, as the quality of recommendations may have a high impact on the student’s progress. We propose a data analysis workflow based on building multiple stacks of models that use information from former students’ taken quizzes. The current implementation uses the RandomForest algorithm for building the models on a real-world dataset that has been obtained in a controlled environment. As preprocessing techniques, we have used normalization and discretization such that training data have been used for classification and regression tasks. At run-time, the models are queried for classifying the student and inferring an optimal quiz that is personalized for the student. We have evaluated the accuracy parametrized on the previous number of quizzes and found that a possible optimal timeframe for each class of students should be used and may provide more helpful quizzes.