Students’ Performance Prediction Model Using Meta-classifier Approach

Hassan, Hasniza; Anuar, Syahid; Ahmad, Nor Ezzaty

doi:10.1007/978-3-030-20257-6_19

Cited by 18 publications

(13 citation statements)

References 23 publications

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“…The experimental results verify the effectiveness of its algorithm. From a methodological point of view, many studies belong to the research of hybrid model type (Sandoval et al, 2018;Yu et al, 2018a;Zhou et al, 2018;Akçapınar et al, 2019;Baneres et al, 2019;Hassan et al, 2019;Hung et al, 2019;Polyzou and Karypis, 2019). The underlying logic of this type of research is that the algorithms differ in their optimization search logic and find the most suitable algorithm for course failure prediction by comparison.…”

Section: Methods For Course Failure Predictionmentioning

confidence: 99%

“…Background features appear with a high frequency in studies of course failure prediction (Livieris et al, 2016;Hu et al, 2017;Tsiakmaki et al, 2018;Francis and Babu, 2019;Hassan et al, 2019;Hung et al, 2019;Yu et al, 2020). Livieris et al (2016) present a new user-friendly decision support tool for predicting students' performance concerning the final examinations of a school year, and they choose demographic features and historical academic performance as features.…”

Section: Background Featuresmentioning

confidence: 99%

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Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

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Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field.

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Section: Methods For Course Failure Predictionmentioning

confidence: 99%

Section: Background Featuresmentioning

confidence: 99%

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

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“…Table 3 shows the variables found that belong to the factor and the authors who used some of these for the development of their models. [28], [35], [45], [47], [49], [50], [55], [57], [59], [65], [70], [72]- [79] Source: Own work.…”

Section: E-learning Factormentioning

confidence: 99%

“…Assignments, Campus of study, Class, College/university integration, Course size, Extra paid classes, Extracurricular activities, Rank teacher, School's environment, Students in the course, Subjects, University support [28], [30], [35], [42], [43], [48]- [50], [53]- [56], [58], [59], [64], [68], [81] Source: Own work.…”

Section: Factor Variables Authors Who Consider Variables Of This Factormentioning

confidence: 99%

revisión sobre la predicción del rendimiento académico mediante métodos de ensamble

Rodríguez¹,

Silva²,

Bravo³

2022

ing. Solidar

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Introducción: El presente artículo es producto de la investigación “Métodos de ensamble para estimar el ren-dimiento académico de estudiantes de educación superior”, desarrollado en la Universidad Distrital Francisco José de Caldas en el año 2021 y se centra en la revisión de trabajos de investigación desarrollados en los últimos cinco años relacionados a la predicción del rendimiento académico utilizando algoritmos de ensamble.Objetivo: La revisión de la literatura tiene como objetivo identificar los algoritmos más utilizados y las variables más relevantes en la predicción del rendimiento académico.Metodología: Se realizó una revisión sistemática de la literatura en distintas bases de datos académicas (Science Direct, Scopus, SAGE Journals, EBSCO, ResearchGate, Google Scholar), utilizando ecuaciones de bús-queda construidas con palabras claves.Resultados: Se encontraron 54 artículos relacionados que cumplen con los criterios de inclusión de la revisión. Además, se encontraron beneficios en la aplicación de métodos de ensamble en la predicción del rendimiento académico. Conclusión: Se encontró que las variables más influyentes en el rendimiento académico corresponden al factor académico, el algoritmo utilizado que presenta mejores resultados es Random Forest, además de que fue el más utilizado, y que el uso de estos algoritmos es una herramienta precisa para predecir el rendimiento acadé-mico en cualquier etapa de la vida universitaria, y a su vez brindar la información para generar estrategias que permitan mejorar los indicadores de deserción y retención académica.

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“…The platforms involved MOOCs as EDx and distance learning platforms from universities e.g. Amrieh et al, (2015), Tang et al, (2015, Mahboob et al, (2017, Hassan et al, (2019). In addition, the scores in various tests were used as well as the demographic data of the students.…”

Section: Attributes Usedmentioning

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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Students’ Performance Prediction Model Using Meta-classifier Approach

Cited by 18 publications

References 23 publications

Educational Anomaly Analytics: Features, Methods, and Challenges

Educational Anomaly Analytics: Features, Methods, and Challenges

revisión sobre la predicción del rendimiento académico mediante métodos de ensamble

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

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