Unsupervised learning based mining of academic data sets for students’ performance analysis

Crivei, Liana Maria; Czibula, Gabriela; Ciubotariu, George; Dindelegan, Mariana

doi:10.1109/saci49304.2020.9118835

Cited by 14 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was found that 11 studies were based on the use of hybrid models to improve the accuracy of academic performance. These hybrid methods involve the integration of a set of algorithms (usually a pair) to achieve higher prediction accuracy compared to existing algorithms (Francis and Babu, 2019;Vora and Kamatchi, 2019;Karthikeyan et al, 2020;Crivei et al, 2020). According to (Fathian et al, 2016), hybrid models (as the 11 studies referred to) can be built using mainly bagging, boosting, and stacking approaches, these can increase performance because they can minimize classification errors and bias (Webb and Zheng, 2004) and to increase accuracy by 30% compared to any single model (Finlay, 2014).…”

Section: Methods and Algorithms That Predict Academic Performancementioning

confidence: 99%

“…The main difference between SL is that in UL the labels that the training set does not contain the labels that we like to predict. The study (Crivei et al, 2020), investigated the use of unsupervised machine learning methods, particularly principal component analysis and relational association rule mining to analyze academic performance. The authors proposed a new binary classification model called S PRAR (prediction of academic performance using relational association rules) to predict the outcome of a student in each academic discipline using Relational Association Rules (RAR).…”

Section: Unsupervised Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review Regarding the Prediction of Academic Performance

De-La-Cruz¹,

Rojas-Coaquira²,

Huerta³

et al. 2022

Journal of Computer Science

View full text Add to dashboard Cite

The prediction of students' academic performance is an area of great concern for universities and educational institutions since academic performance is one of the most important aspects of the learning process. To analyze this behavior, this study makes a critical analysis of the topic of interest and aims to review, analyze and summarize the latest research advances related to the prediction of academic performance. The systematic literature review method is applied to answer three questions: (1) What factors are determinants in predicting students' academic performance? ( 2) what methods are used to predict students' academic performance? and(3) what are the objectives and interests in predicting students' academic performance? After conducting the study of 50 outstanding articles, as results, we found that academic factor is the guideline for predicting academic performance; supervised machine learning is the most used technique, highlighting support vector machine, random forests, and neural networks; the most outstanding objectives for the application of prediction were: Student performance with 53%, risk of failure with 14%, search for student knowledge with 12%, avoid dropout with 12%, and decision making with 10%.

show abstract

Section: Methods and Algorithms That Predict Academic Performancementioning

confidence: 99%

Section: Unsupervised Machine Learningmentioning

confidence: 99%

A Systematic Review Regarding the Prediction of Academic Performance

De-La-Cruz¹,

Rojas-Coaquira²,

Huerta³

et al. 2022

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…CriveiGabriela et al (2020) [27] increasing attention in the field of Educational Data Mining has focused on the challenge of predicting student performance, and this work contributed to that body of research. More and more research shows that both carefully monitored and unsupervised learning methods can help teachers and students learn more about how education works and improve the way they teach and learn.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 96%

Analysis and Prediction of Student Performance by Using A Hybrid Optimized BFO-ALO Based Approach

Kumar

Ahuja

2022

IJRITCC

View full text Add to dashboard Cite

Data mining offers effective solutions for a variety of industries, including education. Research in the subject of education is expanding rapidly because of thebigquantityof student data that can be utilized to uncover valuable learning behavior patterns. This research presents a method for forecasting the academic presentation of students in Portuguese as well as math subjects, and it is describing with the help of 33 attributes. Forecasting the educationalattainment of students is the most popular field of study in the modern period. Previous research has employed a variety of categorization algorithms to forecast student performance. Educational data mining is a topic that needs a lot of research to improve the precision of the classification technique and predict how well students will do in school. In this study, we made a method to predict how well a student will do that uses a mix of optimization techniques. BFO and ALO-based popular optimization techniques were applied to the data set. Python was used to process all the files and conduct a performance comparison analysis. In this study, we compared our model's performance with various existing baseline models and examined the accuracy with which the hybrid algorithm predicted the student data set. To verify the expected classification accuracy, a calculation was performed. The experiment's findings indicate that the BFO-ALO Based hybrid model, which, out of all the methods, with a 94.5 percent success rate, is the preferred choice.

show abstract

“…Recent approaches in SPA addressed unsupervised RAR mining and SOMs [49], [50], to extract from academic data sets patterns and rules relevant for analysing students' academic performance. Other studies compare the ability of autoencoders and SOMs to find learning patterns in data sets related to students' performance in traditional and online environments [51], [52].…”

Section: Related Work On Students' Performance Analysis and Predictionmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Unsupervised learning based mining of academic data sets for students’ performance analysis

Cited by 14 publications

References 9 publications

A Systematic Review Regarding the Prediction of Academic Performance

A Systematic Review Regarding the Prediction of Academic Performance

Analysis and Prediction of Student Performance by Using A Hybrid Optimized BFO-ALO Based Approach

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

Contact Info

Product

Resources

About