Time-Dependent Performance Prediction System for Early Insight in Learning Trends

Abstract

K students' learning trends is relevant to diagnose learning performance and early detect situations where teachers' intervention would be most effective. Prediction systems represent one of the bests tools for this purpose. Predicting performance is the basis for student diagnostics, learning trends projection and early detection. Most performance prediction systems output numerical grades or performance class memberships. Research tends to focus on prediction accuracy. Accuracy is relevant, because it helps improving diagnostics, but it should not be confused with the main goal: improving learning. To help teachers improve student performance many other aspects can be considered: more accessible prediction data, better graphical representations, methods for detecting learning trends and most suitable moments for intervention, etc. Most of these improvements rely on the ability to consider learning data evolution over time. This is particularly relevant due to cumulative nature of learning and so it is one of the main characteristics considered in this work. This work is an empirical research in the search for practical systems to help teachers in their guidance duties. It relays on teachers receiving in-depth information on student learning trends during semester. This information is elaborated from an automatic system which yields predictions on expected student performance. Main contribution of this work is a custom-designed practical prediction system. Main innovations of the proposed system are its time-dependent nature and the use of probabilistic predictions. The proposed system delivers by-weekly probabilistic performance predictions and analytical timedependent graphs that help gaining insight in students’ learning trends. The proposed system is tested during a complete semester in the subject Mathematics I at the University of Alicante. Data gathered is used as initial evidence to empirically test the system and results are shown and discussed. Usefulness, convenience and advantages of the time-dependent nature of learning data are also tested and discussed. As an additional consequence derived from these tests, some initial methods for selecting the best moments for teacher intervention are proposed and discussed. Performance predictions are shown as point graphs over time, along with calculated trends. This information is summarized and organized to help teachers explore and analyse student learning performance efficiently. Some case examples are presented and analysed using these graphs, showing their potential to help teachers understand beyond raw data. Teachers can use this information to diagnose students, understand learning trends, early detect intervention situations and act accordingly to help students improve their learning results. This research considers only learning trend diagnosis and detection of most suitable moments for teacher intervention. Intervention strategies and their results are out of scope. This paper is structured in seven sections. Section II analyses some relevant background works. First, several reviews which describe the most appropriate techniques in prediction are presented. Then, some related works on early detection and on providing insightful, graphical representations are explained. Lastly, a discussion drawing conclusions of this review is performed. As a result, research questions are proposed in section III. A custom automated learning system, in which the proposed prediction system is included, is presented in section IV. Section V explains how data from the system is used to perform student diagnosis and to select the best intervention moment. Section VI analyses some Time-Dependent Performance Prediction System for Early Insight in Learning Trends