IntelliDaM: A Machine Learning-Based Framework for Enhancing the Performance of Decision-Making Processes. A Case Study for Educational Data Mining

Nowadays, both predictive and descriptive modelling play a key role in decision-making processes in almost every branch of activity. In this article we are introducing
$IntelliDaM$
, a generic machine learning-based framework useful for improving the performance of data mining tasks and subsequently enhancing decision-making processes. Through its components designed for feature analysis, unsupervised and supervised learning-based data mining,
$IntelliDaM$
facilitates hidden knowledge discovery from data. Intensive research has been conducted in the field of
educational data mining
, as education institutions are interested in constantly adapting their educational programs to the needs of society by improving the quality of managerial decisions, course instructors’ decision-making, or information gathering for course design. The present work conducts a longitudinal educational data mining study by applying
$IntelliDaM$
to real data collected at Babeş-Bolyai University, Romania, for a Computer Science course. The problem of mining educational data has been thoroughly examined using the proposed framework, with the goal of analysing students’ performance. A very good performance has been achieved for the classification task (an
$F1$
score of around 92%), and the results also highlighted a statistically significant performance improvement by using a technique for selecting discriminative data features. The performed study confirmed that
$IntelliDaM$
could be a useful instrument in educational environments, particularly for improving decision-making processes, like designing courses, the setup of efficient examinations, avoiding plagiarism, or offering support regarding stress management.