A multifaceted data mining approach to understanding what factors lead college students to persist and graduate

Gopalakrishnan, Aparna; Kased, Rama; Yang, Hui; Love, Mary Beth; Graterol, Celia; Shada, Alycia

doi:10.1109/sai.2017.8252128

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…FCBF is a correlation based feature subset selection method, which is faster than other subset selection methods [20]. In EDM, FCBF practiced ranking the features of graduate students in United States universities, to detect the factors of high dropout rate and low graduation rate of four-year college students [21]. Authors of reference [22] applied FCBF in pre-processing stage to predict the student interactions in the intelligent learning environment, furthermore, the study recommended that FCBF would be competent on selecting features from students dataset, as in this kind of datasets the correlation between features are very crucial.…”

Section: Filter Feature Selection Algorithmmentioning

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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Section: Filter Feature Selection Algorithmmentioning

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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“…• 393 2018; Gopalakrishnan et al 2018;Febro 2019;Huang et al 2019;Dimic et al 2019;Enaro and Chakraborty 2020;Das et al 2020;Chaudhury and Tripathy 2020;Muchuchuti et al 2020;Chaves et al 2021]. Other methods employ wrapper approaches that use genetic algorithms (GA) to select the best set of attributes [Wafi et al 2019;Almasri et al 2020;Farissi et al 2020;Santos et al 2020].…”

Section: Introductionmentioning

confidence: 99%

“…. In[Gopalakrishnan et al 2018], an objective was to identify the main factors impacting university student dropout. The work proposes tests with: (i) CS-filtering algorithms;(ii) information gain; (iii) correlation; (iv) relief; (v) maximum relevance and minimum redundancy (mRMR); and (vi) Kruskal Wallise test.…”

mentioning

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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“…According to [6] one of the areas that have been put a lot of attention among researchers is education, which is the way or mechanism to predict students' graduation time [7]. Researchers have pointed out that students' performance was the most vital thing to be considered if the intention was to predict students' graduation time [8,9,10]. By analysing students' performance, students' graduation time can be forecasted; whether it was on time or not.…”

Section: Introductionmentioning

confidence: 99%

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

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Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students' achievement, university's performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students' academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students' graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naï ve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students' graduation time.

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A multifaceted data mining approach to understanding what factors lead college students to persist and graduate

Cited by 10 publications

References 15 publications

Role of FCBF Feature Selection in Educational Data Mining

Role of FCBF Feature Selection in Educational Data Mining

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

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

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