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• W4289821414 abstract "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 <i>IntelliDaM</i>, 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, <i>IntelliDaM</i> facilitates hidden knowledge discovery from data. Intensive research has been conducted in the field of <i>educational data mining</i>, 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&#x2019; decision-making, or information gathering for course design. The present work conducts a longitudinal educational data mining study by applying <i>IntelliDaM</i> to real data collected at Babe&#x015F;-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&#x2019; 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 <i>IntelliDaM</i> 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." @default.
• W4289821414 created "2022-08-05" @default.
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• W4289821414 date "2022-01-01" @default.
• W4289821414 modified "2023-10-17" @default.
• W4289821414 title "<i>IntelliDaM</i>: A Machine Learning-Based Framework for Enhancing the Performance of Decision-Making Processes. A Case Study for Educational Data Mining" @default.
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• W4289821414 doi "https://doi.org/10.1109/access.2022.3195531" @default.
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