A Machine Learning Model to Predict the Performance of University Students

Canagareddy, Derinsha; Subarayadu, Khuslendra; Hurbungs, V.

doi:10.1007/978-3-030-18240-3_29

Cited by 4 publications

(7 citation statements)

References 2 publications

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“…In addition, the GPA is definitely the most used attribute and this is because it has a tangible value, which is directly related to the performance of students and that is why it is considered in 60% of the studies. On the other hand, the Total credit variable, as shown by [9] and [12] in their articles, is one of the attributes most correlated with the output variable (Pass or Fail).…”

Section: Academic Factorsmentioning

confidence: 99%

“…Article TP01 One-R [21] TP02 C4.5 [8], [28], [23], [1], [10], [16], [ 34] TP03 Naive Bayes (NB) [2], [21], [19], [8], [28], [25], [ 5] , [12], [23], [5], [31], [32], [1 ], [33], [10], [36], [40] TP04 Bayes Networks [19], [5] TP05 Decision trees [35], [17], [33], [24], [31] TP06 Neural Networks (MLPs) [2], [21], [8], [28], [17], [32], [5], [31], [15], [33], [36], [6], [40] TP07 Vector Support Machine (SVM) [2], [13], [12], [15],…”

Section: Code Techniquementioning

confidence: 99%

“…[2], [30], [18], [21], [32], [26], [12] , [5], [36], [4], [14], [6], [5] , [25] TP09 JRip Rule [21], [8] TP10 Logistic regression (LR) [2], [12], [11] TP11 ID3 [8], [5], [1], [20] TP12 Cart [1] TP13 RandomTr ee [21], [18], [35], [20] TP14 REPTree [18], [21], [36] TP15 K-Neighbors [13], [8], [31], [32] TP16 Minimum Sequential Optimizati on (SMO) [21], [11], [36] TP17 Random Forest (RF)…”

Section: Code Techniquementioning

confidence: 99%

“…[2], [13], [21], [35], [12], [25], [33] TP18 Logistic Classifier (LC) [12] TP19 Chi-square automatic interaction detection (CHAID)…”

Section: Code Techniquementioning

confidence: 99%

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Predicting Academic Performance through Data Mining: A Systematic Literature

Daza¹,

Guerra²,

Cervera³

et al. 2022

TEM Journal

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The main objective of this work is to make a systematic review of the literature on the prediction of the academic performance of university students by applying data mining techniques. For this purpose, an exhaustive search was carried out and after the analysis of the documentation collected, aspects such as: methodology, attributes, selection algorithms, techniques, tools, and metrics were considered, which served as the basis for the elaboration of this document. The results of the study showed that the most used methodology is KDD(database knowledge extraction), the most important attribute to achieve prediction is CGPA(academic performance), the most commonly used variable selection algorithm is InfoGain-AttributeEval, among the most efficient techniques are Naïve Bayes, Neural Networks (MLP) and Decision Tree (J48), the most used tools for the development of the models is the Weka software and finally the metrics necessary to determine the effectiveness of the model were Precision and Recall.

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Section: Academic Factorsmentioning

confidence: 99%

Section: Code Techniquementioning

confidence: 99%

Section: Code Techniquementioning

confidence: 99%

“…[2], [13], [21], [35], [12], [25], [33] TP18 Logistic Classifier (LC) [12] TP19 Chi-square automatic interaction detection (CHAID)…”

Section: Code Techniquementioning

confidence: 99%

See 2 more Smart Citations

Predicting Academic Performance through Data Mining: A Systematic Literature

Daza¹,

Guerra²,

Cervera³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

show abstract

“…(2017),Bergin et al. (2015),Bhegade and Shinde (2016),BydzovskA (2016),Canagareddy et al. (2019),Castro-Wunsch et al.…”

mentioning

confidence: 99%

A Critical Review of Data Mining for Education: What has been done, what has been learnt and what remains to be seen

Papadogiannis

Poulopoulos

Wallace

2020

International Journal of Educational Research Review

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This article provides a thorough review of educational data mining (EDM) in the period 2015-2019. Going beyond earlier review works, in this article we examine previous research from a variety of aspects, including the examined data, the algorithms used, the type of conclusions drawn, the educational level/setting of application and the actual exploitation of the results in the educational setting. Our findings indicate that tertiary education dominates the EDM domain, while minimal focus has been given to secondary education and almost none to primary education. Our finding, and suggestion, is that by focusing EDM on earlier education level the field can have a more profound impact on education and on society as a whole.

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A survey on educational data mining methods used for predicting students' performance

Xiao

2021

Engineering Reports

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Predicting students' performance is one of the most important issues in educational data mining (EDM), which has received more and more attention. By predicting students' performance, we can identify students' risk of academic failure and help instructors to take some actions such as guidance or interventions to help learners as early as possible, or carry out continual evaluation of learners as to optimize learning path or personalized learning resources recommendation. In this survey, we reviewed the 80 important studies on predicting students' performance using EDM methods in 2016-2021, synthesized the procedure of building prediction model of students' performance which contains four phases and 10 key steps, and compared and discussed the latest EDM methods used in all steps. We analyzed the challenges faced by previous studies in three aspects and put forward future suggestions on data collection, EDM methods used, and interpretation of prediction model. This survey provides a comprehensive understanding and practical guide for researchers in this field, and also provides direction for further research.

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A Machine Learning Model to Predict the Performance of University Students

Cited by 4 publications

References 2 publications

Predicting Academic Performance through Data Mining: A Systematic Literature

Predicting Academic Performance through Data Mining: A Systematic Literature

A Critical Review of Data Mining for Education: What has been done, what has been learnt and what remains to be seen

A survey on educational data mining methods used for predicting students' performance

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