Selecting Relevant Educational Attributes for Predicting Students’ Academic Performance

Abid, Abir; Kallel, Ilhem; Blanco, Ignacio J.; BenAyed, Mounir

doi:10.1007/978-3-319-76348-4_63

Cited by 10 publications

(11 citation statements)

References 18 publications

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“…A total of 120 documents were returned, of which 33 articles were selected because they had FS in educational data as their main objective. This set of works utilized a diverse set datasets, namely: student questionnaires [Ahmed et al 2019], data stemming from e-learning platforms [Govindasamy and Velmurugan 2019;Jalota and Agrawal 2021], school databases [Abid et al 2018;Sokkhey and Okazaki 2020] and university graduation departments [Ahmed et al 2020;Chaves et al 2021]. The selected articles can be grouped in accordance with the FS method employed, namely: (i) filtering methods; (ii) wrapper approaches; (iii) embedded approaches; (iv) methods for feature extraction; and (v) FS comparison studies, as summarized in Table I.…”

Section: Related Workmentioning

confidence: 99%

A Genetic Algorithm with Flexible Fitness Function for Feature Selection in Educational Data: Comparative Evaluation

Albuquerque

Tarrataca

Brandão

et al. 2022

jidm

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Educational Data Mining is an interdisciplinary field that helps understand educational phenomena through computational techniques. The databases of educational institutions are usually extensive, possessing many descriptive attributes that make the prediction process complex. In addition, the data can be sparse, redundant, irrelevant, and noisy, which can degrade the predictive quality of the models and affect computational performance. One way to simplify the problem is to identify the least important attributes and omit them from the modeling process. This can be performed by employing attribute selection techniques. This work evaluates different feature selection techniques applied to open educational data and paired alongside a genetic algorithm with a flexible fitness function. The methods and results described herein extend a previously published paper by: (i) describing a larger set of computational experiments; (ii) performing a hypothesis test over different classifiers; and (iii) presenting a more in-depth literature revision. The results obtained indicate an improvement in the classification process.

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Section: Related Workmentioning

confidence: 99%

A Genetic Algorithm with Flexible Fitness Function for Feature Selection in Educational Data: Comparative Evaluation

Albuquerque

Tarrataca

Brandão

et al. 2022

jidm

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“…The DS33 datasets is a Portages secondary students school dataset. The dataset is has been used in different EDM studies [10][11][12]. This is dataset of 395 students taking Mathematics subject.…”

Section: Ds33mentioning

confidence: 99%

“…Whereas, [9] claims CFS subset evaluator as the best feature selection method for predicting the final semester examination performance of students. According to [10] there is no common feature selection method which can be accurate for all datasets even for a common domain. So that there is a need to figure out the important feature selection methods for predicting the performance of students.…”

Section: Introductionmentioning

confidence: 99%

Role of FCBF Feature Selection in Educational Data Mining

Zaffar

Hashmani

Savita

et al. 2020

Mehran Univ. res. j. eng. technol.

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The Educational Data Mining (EDM) is a very vigorous area of Data Mining (DM), and it is helpful in predicting the performance of students. Student performance prediction is not only important for the student but also helpful for academic organization to detect the causes of success and failures of students. Furthermore, the features selected through the students’ performance prediction models helps in developing action plans for academic welfare. Feature selection can increase the prediction accuracy of the prediction model. In student performance prediction model, where every feature is very important, as a neglection of any important feature can cause the wrong development of academic action plans. Moreover, the feature selection is a very important step in the development of student performance prediction models. There are different types of feature selection algorithms. In this paper, Fast Correlation-Based Filter (FCBF) is selected as a feature selection algorithm. This paper is a step on the way to identifying the factors affecting the academic performance of the students. In this paper performance of FCBF is being evaluated on three different student’s datasets. The performance of FCBF is detected well on a student dataset with greater no of features.

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“…Whereas, [8] claims CFS subset evaluator as the best feature selection method for predicting the final semester examination performance of students. According to [9] there is not one common feature selection method which can be accurate for all datasets even for a common domain. There is a need to focus on the feature selection algorithms in the area of predicting the performance of students.…”

Section: Introductionmentioning

confidence: 99%