Improving Education Predictions Through Reasoning by Analogy and Causal Relationships Applied to Smart Exploitation of Data

Abstract

To make predictions, one can use machine learning and/or knowledge-based approaches. Knowledge-based approaches focus on developing systems with reasoning capabilities to solve application problems. Traditionally, statistical techniques have been used, while more recently, machine learning techniques have been used to make predictions. Both types of techniques are based almost exclusively on the analysis of historical data. This paper proposes a model that combines knowledge engineering and intelligent data analysis, leveraging the causal relationship between a past event and its known consequences. By determining the similarity between a current analogous situation and the past event, the model infers what the consequences of the current situation might be. The main contribution is the combination of various knowledge engineering techniques to improve the prediction outcomes for certain events. The present approach not only relies on analysing historical data but also integrates smart data utilization, the identification of the most similar past event, and the prediction or definition of cause–effect rules based on causal inference. One use case is presented: predicting the percentage of students who are promoted to the next grade with all subjects passed over the four years of middle school. Applying statistical regression techniques, a predicted value of 68.67% was obtained. Applying the proposed model, a value of 62.85% was obtained. The actual value published by the Spanish Department of Education for the 2021–2022 school year was 63.95%. The prediction using statistical techniques deviated 7.3% from the actual value. The proposed method deviated only 1.7% from the actual value. The proposed method improved the prediction compared to the value obtained using statistical techniques.