Predicting academic performance by considering student heterogeneity

Helal, Sumyea; Li, Jiuyong; Liu, Lin; Ebrahimie, Esmaeil; Dawson, Shane; Murray, Duncan; Long, Qi

doi:10.1016/j.knosys.2018.07.042

Cited by 153 publications

(130 citation statements)

References 17 publications

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“…Studies have validated the hypothesis that student subpopulation models trained only instances outpace those developed using all data. researches also showed that both registration & training action contributes to the accuracy of the identification of vulnerable students [12]. [17].…”

Section: Rajeev Sharma Sitendra Tamrakarmentioning

confidence: 99%

Efficient Pattern Generation using Declat Algorithm Based on Association Rule Mining in Educational Data Mining

Sharma,

Tamrakar

2020

IJEAT

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A new application called DM Educational Data Mining (EDM) involves data extraction and analysis from the classroom or area of education. In order for educators to deliver quality education to students, EDM integrates various educational information into its review. The EDM works by translating raw data from education systems invaluable information which could have a major effect on the study of education. The output of each student is measured from the database and must be sufficiently accurate to withstand changes in the academic record. Then we have transformed the overall arrangement into a modified relation for the adequacy of the Declat algorithm. The purpose of this work is to examine how prior researchers, as well as recent data mining trends in educational research, have dealt with data mining. In this paper, collected data comprised of 200 students. We define academic performance & impact of additional issues on the basis of these course’s last grades, indications of attendance, class tests, and term last answer substance add up to marks and so on. Here, we compare the FP-Growth and Eclat with Declat algorithm on the bases of confidence and support value in a relation of execution time & no. of patterns generated. This paper uses a declat algorithm to create patterns or delete effective patterns. Such patterns help to illustrate a growing student's success.

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Section: Rajeev Sharma Sitendra Tamrakarmentioning

confidence: 99%

Efficient Pattern Generation using Declat Algorithm Based on Association Rule Mining in Educational Data Mining

Sharma,

Tamrakar

2020

IJEAT

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“…In literature, various machine learning algorithms have been proposed and evaluated using real-world datasets. Researchers have analyzed students' heterogeneity for feature extraction [14]. Prediction models were implemented using four common machine learning algorithms, JRip, sequential minimal optimization, C4.5 and Naïve-Bayes.…”

Section: Introductionmentioning

confidence: 99%

Predicting Students’ Performance With School and Family Tutoring Using Generative Adversarial Network-Based Deep Support Vector Machine

et al. 2020

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It has been witnessed that supportive learning has played a crucial role in educational quality enhancement. School and family tutoring offer personalized help and provide positive feedback on students' learning. Predicting students' performance is of much interest which reflects their understanding on the subjects. Particularly it is desired students to manage well in fundamental knowledge in order to build a strong foundation for post-secondary studies and career. In this paper, improved conditional generative adversarial network based deep support vector machine (ICGAN-DSVM) algorithm has been proposed to predict students' performance under supportive learning via school and family tutoring. Owning to the nature of the students' academic dataset is generally low sample size. ICGAN-DSVM offers dual benefits for the nature of low sample size in students' academic dataset in which ICGAN increases the data volume whereas DSVM enhances the prediction accuracy with deep learning architecture. Results with 10-fold cross-validation show that the proposed ICGAN-DSVM yields specificity, sensitivity and area under the receiver operating characteristic curve (AUC) of 0.968, 0.971 and 0.954 respectively. Results also suggest that incorporating both school and family tutoring into the prediction model could further improve the performance compared with only school tutoring and only family tutoring. To show the necessity of ICGAN and DSVM, comparison has been made between ICGAN and traditional conditional generative adversarial network (CGAN). Also, the proposed kernel design via heuristic based multiple kernel learning (MKL) is compared with typical kernels including linear, radial basis function (RBF), polynomial and sigmoid. The prediction of student's performance with and without GAN is presented which is followed by comparison with DSVM and with traditional SVM. The proposed ICGAN-DSVM outperforms related works by 8-29% in terms of performance indicators specificity, sensitivity and AUC.

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“…La búsqueda de la calidad en la educación es uno de los objetivos medulares de las instituciones académicas. Para tal fin se despliegan estrategias y programas que tienen como finalidad mejorar el desempeño y permanencia de los estudiantes (Helal et al, 2018). Sin embargo, definir la mezcla adecuada de acciones y decisiones estratégicas que maximicen el desempeño estudiantil no es una tarea fácil, dados los diferentes factores influyentes.…”

Section: Introductionunclassified

Predicción del rendimiento académico por medio de técnicas de inteligencia artificial

2020

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El objetivo de este artículo es predecir el rendimiento académico de estudiantes de educación superior, a partir de diversos factores influyentes usando técnicas de inteligencia artificial (clasificadores). El estudio de tales factores, aunque ha sido ampliamente analizado desde enfoques cuantitativos y cualitativos, aun presenta oportunidades de investigación usando herramientas que provee la inteligencia artificial, particularmente en la predicción del rendimiento académico. Con los factores definidos (educacionales, familiares, socioeconómicos, de hábitos y costumbres, entre otros), se diseñó una metodología que permite entrenar un sistema capaz de clasificar a priori a un nuevo estudiante, en una de las cinco categorías predeterminadas de rendimiento académico. Esta clasificación permite a una institución educativa identificar con anticipación estudiantes con problemas potenciales de rendimiento académico. A partir de ello, se pueden desplegar acciones de acompañamiento y mitigación inmediatas. La metodología fue aplicada a una muestra de estudiantes de una universidad pública en Colombia, obteniendo un nivel de aciertos del 91.7%.

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Predicting academic performance by considering student heterogeneity

Cited by 153 publications

References 17 publications

Efficient Pattern Generation using Declat Algorithm Based on Association Rule Mining in Educational Data Mining

Efficient Pattern Generation using Declat Algorithm Based on Association Rule Mining in Educational Data Mining

Predicting Students’ Performance With School and Family Tutoring Using Generative Adversarial Network-Based Deep Support Vector Machine

Predicción del rendimiento académico por medio de técnicas de inteligencia artificial

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