Big data and learning analytics in higher education: Demystifying variety, acquisition, storage, NLP and analytics

Alblawi, Amal S.; Alhamed, Ahmad A.

doi:10.1109/icbdaa.2017.8284118

Cited by 33 publications

(22 citation statements)

References 13 publications

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“…In fact, student life is increasingly documented in digital dossiers consisting of academic, social, behavioral, and even emotional markers. And these dossiers encompass the data against which institutions run their descriptive and predictive analytics to develop insights, which feed back into the dossiers to further inform an array of actors about a given student (Alblawi and Alhamed 2017).…”

Section: Documenting Students In Datamentioning

confidence: 99%

Reconsidering data in learning analytics: opportunities for critical research using a documentation studies framework

Jones

McCoy

2018

Learning, Media and Technology

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In this article, we argue that the contributions of documentation studies can provide a useful framework for analyzing the datafication of students due to emerging learning analytics practices. Specifically, the concepts of individuals being "made into" data and how that data is "considered as" can help to frame vital questions concerning the use of student data in learning analytics. More specifically, approaches informed by documentation studies will enable researchers to address the sociotechnical processes underlying how students are constructed into data, and ways data about students are considered and understood. We draw on these concepts to identify and describe three areas for future research in LA. With the description of each area, we provide a brief analysis of current practices in American higher education, highlighting how documentation studies enables deeper analytical digging.

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Section: Documenting Students In Datamentioning

confidence: 99%

Reconsidering data in learning analytics: opportunities for critical research using a documentation studies framework

Jones

McCoy

2018

Learning, Media and Technology

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“…Samza is mainly used to address the large volumes for high rate stream data processing, and Spark is often used for off-line rapid Big data processing. In the context of Big data in education, some specific Big data architectures or frameworks [1]- [10] have been proposed for education. The authors in [1] proposed a distributed architecture for the information processing of Big education data and predicting student performance with and without sentiment analytics.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of Big data in education, some specific Big data architectures or frameworks [1]- [10] have been proposed for education. The authors in [1] proposed a distributed architecture for the information processing of Big education data and predicting student performance with and without sentiment analytics. The authors in [2] proposed a five-layered architecture termed the Concept Definition for Big Data Architecture for education.…”

Section: Introductionmentioning

confidence: 99%

“…Big data analytics is changing the educational industry and gives new opportunities for both learners and instructors. In general, there are three challenges for Big educational data analysis to be addressed: (1) The huge amount of data to be processed; (2) The complex and unstructured data analytics; and (3) The difficulty to find the hidden value in the Big education data in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

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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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“…: baseado em[Alblawi and Alhamed 2017] 1. A camada de acesso a dados deverá ser responsável pelos acessos aos bancos de dados existentes como os da plataforma Moodle, bases de sistemas acadêmicos, arquivos de sistemas governamentais e arquivos em formato Comma-Separated Values (CSV) ou Xtensible Markup Language (XML); para o trabalho deverá ser utilizada tecnologias tais como Sqoop, Rest API, Open Database Connectivity (ODBC) para as conexões apropriadas aos bancos de dados.…”

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Big Data com Learning Analytics para apoiar o Planejamento Pedagógico Acadêmico

Nogueira¹,

Francisco²,

Silva³

et al. 2018

Anais Do Workshop De Desafios Da Computação Aplicada À Educação (DesafIE!)

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O uso de tecnologias, como Ambientes Virtuais de Aprendizagem e Sistemas de Informações Educacionais, no ensino superior gerou dados estruturados e não estruturados espalhados em diferentes bases. A proposta que se pretende apresentar é, considerando essas condições, como usar técnicas de Learning Analytics (LA) para gerar informaçõesúteis para estudantes, professores e gestão acadêmica. A proposta inclui uma arquitetura que visa armazenar os dados das diferentes fontes em um Big Data e seu uso como insumo para LA. Assim, trazemos direcionamentos para uma possível solução em conformidade com a chamada do 7o DesafIE no período de 10 anos, envolvendo os temas recuperação da informação, LA e análise de dados em conteúdos educacionais online.

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Big data and learning analytics in higher education: Demystifying variety, acquisition, storage, NLP and analytics

Cited by 33 publications

References 13 publications

Reconsidering data in learning analytics: opportunities for critical research using a documentation studies framework

Reconsidering data in learning analytics: opportunities for critical research using a documentation studies framework

Big Educational Data & Analytics: Survey, Architecture and Challenges

Big Data com Learning Analytics para apoiar o Planejamento Pedagógico Acadêmico

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