Analysis of student behavior in learning management systems through a Big Data framework

Cantabella, Magdalena; Martínez‐España, Raquel; Ayuso, Belén López; Yáñez, Juan Antonio; Muñoz, Andrés

doi:10.1016/j.future.2018.08.003

Cited by 148 publications

(76 citation statements)

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“…Association rules have been used to discover relations between; admission system attributes in King Abdul-Aziz University (KAU) [30], the preliminary students knowledge [31], the specialties and student's interests [32], the factors that affect postgraduate study and assessment [33], and the courses and the failed students [34].…”

Section: Related Workmentioning

confidence: 99%

Factors Influencing Students Decisions to Enrollment in Sudanese Higher Education Institutions

Sedahmed¹,

Noureldien²

2019

IIM

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There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highest level of quality in a higher education system is by discovering knowledge from educational data such as students' enrollment data. Many mining tools that aim to discover exciting correlations, frequent patterns, associations, or casual structures among sets of items in educational data sets have been proposed. One of the widely used tools is association rules. In this paper, the Apriori algorithm is used to generate association rules to discover the importance and correlation between factors that influence student's decision to enroll in higher education institutions in Sudan. The algorithm is applied using a student's enrollment data set that was created using a questionnaire and 800 students enrolled in governmental and private sector universities as a sample. This paper classifies factors that influence enrollment into: student's demographic factors and four categories of enrollment related factors (Student and Society, Educational Institution, Admission, and Employment related factors), and determines the most influential factors in determining student's decision to enroll in Sudanese universities. The analysis result shows that the Educational Institution related factors (50%) and Admission related factors (40%) are strongly influencing students' enrollment decision, while the Employment related factors (10%) and Student and Society related factors (0%) have weak influence. The factors out of the 14 Educational Institution related factors that have a high impact are: reputation, diversity of study, quality of education, education facilities, and feasibility.

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Section: Related Workmentioning

confidence: 99%

Factors Influencing Students Decisions to Enrollment in Sudanese Higher Education Institutions

Sedahmed¹,

Noureldien²

2019

IIM

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“…Moodle [28] is one of the most popular open source LMS options available today. Other examples of LMS [29] are Canvas [151], Sakai [152], ATutor, Eliademy. Forma LMS, Dokeos and OpenOLAT.…”

Section: A Learning Management Systems (Lms)mentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario. INDEX TERMS Big data, learning technologies, educational data, learning analytics.

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“…Distintos autores han estudiado cómo influyen la motivación, los hábitos de estudio, la formación previa o las metodologías docentes y de aprendizaje en el rendimiento académico de los estudiantes de Histología (Bloodgood, 2012;Campos-Sánchez et al, 2012Selvig et al, 2015). Este trabajo demuestra que los resultados académicos de los estudiantes de OMH están estrechamente condicionados por el uso que hayan hecho de las herramientas y recursos del CV, lo cual está en línea con los hallazgos en otras disciplinas (Yu y Jo, 2014;Cerezo et al, 2016;Młynarska et al, 2016;Jenaro et al, 2018;Cantabella et al, 2019). Los resultados muestran que el alumnado de OMH accede al CV asiduamente, siendo su actividad mucho mayor a la media de la titulación y en clara dependencia con las actividades programadas, como se ha apuntado en estudios anteriores (Plendl et al, 2009;Álvarez et al, 2020a, b).…”

Section: Discusión Y Conclusionesunclassified

Análisis de los registros en Moodle para estudiar la actividad desarrollada por estudiantes de Histología Humana y su relación con el rendimiento académico

Vázquez

Carrére

Llatas

et al. 2020

Libro De Actas IN-RED 2020: VI Congreso De Innovación Edicativa Y Docencia en Red

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Este trabajo presenta los resultados obtenidos tras la extracción y análisis de los registros almacenados en Moodle correspondientes a la actividad desarrollada por los estudiantes de histología humana en el campus virtual. Mediante el lenguaje de programación R y el programa SPSS se estudió el comportamiento de los alumnos y las posibles diferencias entre estudiantes según su rendimiento académico. Los datos revelan que los estudiantes entran masivamente al campus virtual, con una actividad muy superior a la media de la titulación, y claramente dependiente del calendario académico. Su comportamiento no es homogéneo, observándose diferencias entre los grupos de alumnos según su nota final de teoría. El árbol de decisión obtenido al contrastar el uso efectuado por estudiantes que aprobaron por parciales y los aprobados en el examen final permite establecer diferencias estadísticamente significativas.

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Analysis of student behavior in learning management systems through a Big Data framework

Cited by 148 publications

References 10 publications

Factors Influencing Students Decisions to Enrollment in Sudanese Higher Education Institutions

Factors Influencing Students Decisions to Enrollment in Sudanese Higher Education Institutions

Big Educational Data & Analytics: Survey, Architecture and Challenges

Análisis de los registros en Moodle para estudiar la actividad desarrollada por estudiantes de Histología Humana y su relación con el rendimiento académico

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