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

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 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 F 1 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.INDEX TERMS Data mining, Educational data mining, Machine learning, Students' performance analysis and prediction.

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

“…Learning Engagement [22], [34], [39], [40], [42], [43], [46], [49], [52], [53], [58], [67]- [70], [76], [87] In accordance with the findings presented in [23], academic performance pertains to the achievement of students in their pursuit of higher education, typically assessed through their academic grades or performance on examinations and assessments [18], [23], [35], [45], [64], [80], with particular emphasis on their initial year of study. Additionally, it encompasses the probability of students' persistence in pursuing further education or their decision to discontinue their academic pursuits.…”

“…The discipline of learning analytics encompasses various dimensions and focuses on the methodical gathering, examination, and presentation of data regarding learners and their educational context. The principal objective is to comprehend and enhance both the process of acquiring knowledge and the environment in which it occurs [34], [39], [40], [43], [52], [58], [69], using advanced artificial intelligence techniques [68], [76].…”

“…Data Source Paper 1 LMS [1], [9], [11]- [14], [19], [35]- [40], [43]- [45], [54], [58], [63], [65], [66], [68]- [71], [73], [74], [77], [79], [84], [87] 2 SIS [15], [17], [21], [22], [54]- [56], [62], [76], [78], [85], [86] 3 Hybrid/LMS and SIS [10], [18], [20], [23], [24], [34], [46]- [53], [57], [59]- [61], [72], [72], [75], [81], [82] 4 Other [41], [42],…”

“…Table 6 provides a concise summary of these findings. [13], [34], [35], [37]- [40], [42], [44], [45], [54], [62], [72], [76], [78], [86] 2 Behavioral [11], [19], [24], [43], [58], [60], [63]- [65], [69], [71], [73] 3 Academic and Behavioral [1], [10], [12], [14], [18], [46]- [49], [51], [53], [57], [68], [74], [77], [79], [80], [84] 4 Academic and Demographic [17], [36], [56] 5 Academic, Demographic and Pre-University…”

“…Learning Algorithm Paper 1 Support Vector Machine [1], [8], [9], [13], [20], [32], [34], [37], [38], [40], [45], [46], [48]- [53], [56], [59], [62], [66]- [69], [72]- [75], [77] 2 Random Forest [11], [14], [17], [18], [20], [23], [24], [38], [44], [47], [48], [50]- [52], [56], [57], [60], [63], [64], [66]- [69],…”