Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data

Trakunphutthirak, Ruangsak; Lee, Vincent C. S.

doi:10.1177/07356331211048777

Cited by 23 publications

(12 citation statements)

References 65 publications

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“…Access to internet, Age, Economic dependence, Employment status, Ethnicity, Family Highest Education Level, Family income, Family size, Gender, Health status, House to university distance, Lives in town or village, Marital Status, Nationality, Parents job, Parents occupation, Parents qualification, Parent status, Total income [17], [22], [23], [26], [28]- [31], [35], [38], [41]- [43], [46], [48]- [55], [57]- [60], [63]- [65], [67], [68], [70]- [77] Leonardo…”

Section: Factor Variables Authors Who Consider Variables Of This Factormentioning

confidence: 99%

“…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%

“…Kumari [76] says in his work that the rise in the use of technology in education makes it necessary to take into account the variables of online learning, and shows that these variables help to classify students in a better way. Trakunphutthirak [65] states that the semester and the use of the internet significantly influence performance.…”

Section: As For the Factorsmentioning

confidence: 99%

“…Adekitan [40] seeks to predict the academic performance and the general average of students at the end of the fifth year of university, using the information available from the first three years. Trakunphutthirak [65] evaluates different characteristics and their effect on academic performance in the first ten weeks of the semester.…”

Section: As For the Approachmentioning

confidence: 99%

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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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Section: Factor Variables Authors Who Consider Variables Of This Factormentioning

confidence: 99%

Section: E-learning Factormentioning

confidence: 99%

Section: As For the Factorsmentioning

confidence: 99%

Section: As For the Approachmentioning

confidence: 99%

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revisión sobre la predicción del rendimiento académico mediante métodos de ensamble

Rodríguez¹,

Silva²,

Bravo³

2022

ing. Solidar

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show abstract

“…First, data analysis and psychological findings provide meaningful information and correlation between factors such as frustration, the COVID-19 pandemic, E-learning, and the performance evaluation of at-risk students [7][8][9]. Second, the prior students' performance prediction approaches used the psychological findings and data analysis contributions to optimize performance computation models [10][11][12][13][14]. ese various groups of studies are still limited in obtaining an optimized students' performance prediction system based on insightful frustration severity modeling [15][16][17][18][19].…”

Section: Introduction E Outbreak Of Covid-19 and E-learning With Massivementioning

confidence: 99%

Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

Ahmad

Aoun

ElAffendi

et al. 2022

Computational Intelligence and Neuroscience

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Recent articles reported a massive increase in frustration among weak students due to the outbreak of COVID-19 and Massive Open Online Courses (MOOCs). These students need to be evaluated to detect possible psychological counseling and extra attention. On the one hand, the literature reports many optimization techniques focusing on existing students’ performance prediction systems. On the other hand, psychological works provide insights into massive research findings focusing on various students’ emotions, including frustration. However, the synchronization among these contributions is still a black box, which delays the mathematical modeling of students’ frustration. Also, the literature is still limited in using insights of psychology and assumption-based datasets to provide an in-house iterative procedure for modeling students’ frustration severity. This paper proposes an optimization technique called the iterative model of frustration severity (IMFS) to explore the black box. It analyzes students’ performance via two modules. First, frustration is divided into four outer layers. Second, the students’ performance outcome is split into 34 inner layers. The prediction results are iteratively optimized under the umbrella of frustration severity layers through the outer and inner iterations. During validation, the IMFS achieves promising results with various evaluation measures.

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A critical review of data mining in education on the levels and aspects of education

Fuseini,

Missah

2024

QEA

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Purpose This systematic literature review aims to identify the pattern of data mining (DM) research by looking at the levels and aspects of education. Design/methodology/approach This paper reviews 113 conference and research papers from well-known publishers of educational data mining (EDM) and learning analytics-related research using a recognized literature review in computer science by Carrera-Rivera et al. (2022a). Two major stages, planning and conducting the review, were used. The databases of Elsevier, Springer, IEEE, SAI, Hindawi, MDPI, Wiley, Emerald and Sage were searched to retrieve EDM papers from the period 2017 to 2023. The papers retrieved were then filtered based on the application of DM to the three educational levels – basic, pre-tertiary and tertiary education. Findings EDM is concentrated on higher education. Basic education is not given the needed attention in EDM. This does not enhance inclusivity and equity. Learner performance is given much attention. Resource availability and teaching and learning are not given the needed attention. Research limitations/implications This review is limited to only EDM. Literature from the year 2017 to 2023 is covered. Other aspects of DM and other relevant literature published in EDM outside the research period are not considered. Practical implications As the current trend of EDM shows an increase in zeal, future research in EDM should concentrate on the lower levels of education to identify the challenges of basic education which serves as the core of education. This will enable addressing the challenges of education at an early stage and facilitate getting a quality education at all levels of education. Appropriate EDM techniques for mining the data at this level should be the focus of the research. Specifically, techniques that can cater for the variation in learner abilities and the appropriate identification of learner needs should be considered. Social implications Content sequencing is necessary in facilitating an easy understanding of concepts. Curriculum design from basic to higher education dwells much on this. Identifying the challenge of learning at the early stages will facilitate efficient learning. At the basic level of learning, data on learning should be collected by educational institutions just as it is done at the tertiary level. This will enable EDM to accurately identify the challenges and appropriate solutions to educational problems. Resource availability is a catalyst for effective teaching and learning. The attributes of a learner will enable knowing the true nature of the learner to determine the prospects of the learner. Originality/value This research has not been published in any journal. The information presented is the original knowledge of the authors. However, a pre-print of the work is in Research Square.

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Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data

Cited by 23 publications

References 65 publications

revisión sobre la predicción del rendimiento académico mediante métodos de ensamble

revisión sobre la predicción del rendimiento académico mediante métodos de ensamble

Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

A critical review of data mining in education on the levels and aspects of education

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