Analyzing undergraduate students' performance using educational data mining

Asif, Rameez; Merceron, Agathe; Ali, Syed Abbas; Haider, Najmi Ghani

doi:10.1016/j.compedu.2017.05.007

Cited by 464 publications

(320 citation statements)

References 7 publications

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“…This application is directly related to the student's participation in the educational process such as in terms of the analysis of learning styles or learning strategies, the analysis and improvement of student skills, and the motivation trends of students, among others. A second application area of LA [4,7,[22][23][24]32,35,38,44,45,47,57,62,63,66,69,72,76] Institutional database 15 [2,5,5,13,14,16,[39][40][41]48,52,53,67,68,79] Specific databases 13 [1,15,18,19,43,44,50,54,61,68,72,74,80] Not specified 7 [3,12,17,…”

Section: Areas Of Application Of La and Edm Results In Engineering mentioning

confidence: 99%

The use of tools of data mining to decision making in engineering education—A systematic mapping study

Buenaño-Fernández

Villegas-Ch

Luján‐Mora³

2019

Comp Applic In Engineering

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In recent years, there has been an increasing amount of theoretical and applied research that has focused on educational data mining. The learning analytics is a discipline that uses techniques, methods, and algorithms that allow the user to discover and extract patterns in stored educational data, with the purpose of improving the teaching‐learning process. However, there are many requirements related to the use of new technologies in teaching‐learning processes that are practically unaddressed from the learning analytics. In an analysis of the literature, the existence of a systematic revision of the application of learning analytics in the field of engineering education is not evident. The study described in this article provides researchers with an overview of the progress made to date and identifies areas in which research is missing. To this end, a systematic mapping study has been carried out, oriented toward the classification of publications that focus on the type of research and the type of contribution. The results show a trend toward case study research that is mainly directed at software and computer science engineering. Furthermore, trends in the application of learning analytics are highlighted in the topics, such as student retention or dropout prediction, analysis of academic student data, student learning assessment and student behavior analysis. Although this systematic mapping study has focused on the application of learning analytics in engineering education, some of the results can also be applied to other educational areas.

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Section: Areas Of Application Of La and Edm Results In Engineering mentioning

confidence: 99%

The use of tools of data mining to decision making in engineering education—A systematic mapping study

Buenaño-Fernández

Villegas-Ch

Luján‐Mora³

2019

Comp Applic In Engineering

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“…Página 55 / 67 para realizar estas tareas (Contreras, 2014;Marcano & Rodríguez, 2014;Asif, Merceron, Abbas, & Ghani, 2017).…”

Section: La Utilización De Procedimientos De Minería De Datos En La Eunclassified

Detección de patrones en competencias digitales manifestadas por estudiantes universitarios

Torres

Casillas

2018

Edutec-e

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RESUMENEl uso de tecnologías de la información y comunicación por estudiantes universitarios les genera competencias que implican en acciones para su aprendizaje. Bajo esta premisa, el objetivo del artículo es dar a conocer, con técnicas de análisis de minería de datos, los patrones identificados respecto a las competencias digitales procedimentales que manifiestan los estudiantes universitarios. La investigación fue cuantitativa. El conjunto de datos minable se obtuvo de un cuestionario validado con alpha de cronbach de 0.82 (bueno). Los resultados indican que tres clúster describen de forma óptima la relación entre competencias digitales y el desempeño académico que puede ser bueno o excelente. Para algunos estudiantes las tecnologías son incidentes en su desempeño académico, mientras que para otros no lo son. Palabras clave: Estudiantes universitarios, minería de datos, competencias digitales. ABSTRACT The use of information and communication technologies by university students generates competences that involve actions for their learning. Under this premise, the objective of this paper shows, using data mining techniques, the patterns identified in a data

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“…Decision trees prove to be flexible in that from the literature reviewed, decision trees [41] [42][43] [35][1] proved to be a relatively effective and popular method of problem solving with some studies [42] finding that decision trees produced more accurate results over other algorithms. A noted advantage of decision trees is that decision treed are simple and prompt data classifiers [1].…”

Section: B Decision Treesmentioning

confidence: 99%

Design and Development of an optimal algorithm to assign applicants to suitable teaching positions

Chishimba

Kunda

2018

zictjournal

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Resource allocation has always been an area of interest in the area of computing. Areas such as machine learning provide many solutions to the problem of resource allocation. The issue addressed in this study is the issue of optimal allocation of applicants (teachers) to positions in schools where their area of specialization will be better applied. We develop an algorithm that is able to allocate applicants to schools based on the applicant qualifications and the school's needs. We use the principles of resource allocation and machine learning in order to create an application to allocate applicants to schools where their qualifications are most suited. Methods used include classification techniques in machine learning, regression and similarity comparison. For the identification of subjects an applicant in proficient in, various machine learning algorithms are tested to determine which machine learning algorithm was best. The actual process of identifying which applicant qualifies for a school position is also tested against sequential assignment if applicants to schools. The results of this was the algorithm based assignment of applicants to schools which produced more accurate assignment of applicants to schools than the sequential assignment of applicants. The aim of this algorithm is to provide a solution to that automatically identifies the needs (subjects) of a school, determine which needs have a higher priority, identify the qualifications of the applicants and assign the applicants to the school according to the school's needs and the applicant's qualifications.

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Analyzing undergraduate students' performance using educational data mining

Cited by 464 publications

References 7 publications

The use of tools of data mining to decision making in engineering education—A systematic mapping study

The use of tools of data mining to decision making in engineering education—A systematic mapping study

Detección de patrones en competencias digitales manifestadas por estudiantes universitarios

Design and Development of an optimal algorithm to assign applicants to suitable teaching positions

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