A map-based visual analysis method for patterns discovery of mobile learning in education with big data

Zhou, Dongbo; Li, Hao; Liu, Sanya; Song, Bo; Hu, Tony

doi:10.1109/bigdata.2017.8258337

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…Two other studies for visual data mining can be found in [131] and [132]. The use of VA methods can help turn the features of education into a visible type of representation, with the ability of being seen and interpreted by means of variety of diagrams, charts, tables, infographics and other forms of visual factors [133]. For example, the activities have characteristic of geolocation which can be projected onto a map, while the resources of knowledge can also be converted into the map.…”

Section: E Visual Analyticsmentioning

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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Section: E Visual Analyticsmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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“…They addressed the benefits and challenges these stakeholders face. [14] They explore how big data mining that is based on the cloud can be applied to Indian education and research; then, they address the challenges, and the benefits can be obtained. They use "educational intelligence" as a term that includes terms like learning analytics, academic analytics, and big data analytics.…”

Section: Related Workmentioning

confidence: 99%

“…They explain the benefit of big data in Nurse Education, challenges, and potential dangers. [14] They propose PABED (Project Analyzing Big Education Data), which is an educational intelligence tool, using for analyzing big education data. N. Xu et.al [23] They study the big data using on the education they applied their study on the MOOC, which is a training platform.…”

Section: Related Workmentioning

confidence: 99%

Big Data Application and its Impact on Education

Khan

Alqahtani

2020

Int. J. Emerg. Technol. Learn.

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Big data is employed in widely different fields; we here study how education uses big data. We review the literature of the research about big data in education in the time interval from 2010 to 2020 then review the process of big educational data mining, the tools, and the applications of big data in education. This paper, with the help of these applications, explores the idea to improve the education process. Two methods are applied to validate education process and many parameters are discussed to complete the research.

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“…Chen et al [7] developed a visual system called DropoutSeer, which explores the learning activities of learners and classifies the factors leading to dropout behavior. Another study employed a visual analysis of a map-based approach to investigate learning behavior patterns of learners in a mobile learning environment [65]. In addition, many studies presented numerous innovative visualizations, to examine the behavioral patterns of learners in MOOCs courses, such as node-link diagram [58], thread river [18], novel glyphs [64], and calendar-based heatmap [43].…”

Section: Mooc Clickstream Visualizationmentioning

confidence: 99%

Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' courses

Mubarak

Cao

Zhang

2020

Comp Applic In Engineering

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The big data stored in massive open online course (MOOC) platforms have become a posed challenge in the Learning Analytics field to analyze the learning behavior of learners, and predict their respective performance, related especially to video lecture data, since most learners view the same online lecture videos. This helps to conduct a comprehensive analysis of such behaviors and explore various learning patterns in MOOC video interactions. This paper aims at presenting a visual analysis, which enables course instructors and education experts to analyze clickstream data that were generated by learner interaction with course videos. It also aims at predicting learner performance, which is a vital decision-making problem, by addressing their issues and improving the educational process. This paper uses a long short-term memory network (LSTM) on implicit features extracted from videoclickstreams data to predict learners' performance and enable instructors to make measures for timely intervention. Results show that the accuracy rate of the proposed model is 89%-95% throughout course weeks. The proposed LSTM model outperforms baseline Deep learning (GRU) and simple recurrent neural network by accuracy of 90.30% in the "Mining of Massive Datasets" course, and the "Automata Theory" accuracy is 89%.

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A map-based visual analysis method for patterns discovery of mobile learning in education with big data

Cited by 8 publications

References 30 publications

Big Educational Data & Analytics: Survey, Architecture and Challenges

Big Educational Data & Analytics: Survey, Architecture and Challenges

Big Data Application and its Impact on Education

Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' courses

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