Smart Learning Analytics

Boulanger, David; Seanosky, Jeremie; Kumar, Vive; Kinshuk, Kinshuk; Panneerselvam, Karthikeyan; Somasundaram, Thamarai Selvi

doi:10.1007/978-3-662-44188-6_39

Cited by 24 publications

(19 citation statements)

References 17 publications

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“…According to Boulanger et al (2015), a new concept of 'Education as a Service' is emerging as an approach to deal with the challenges of global and open markets. Educational resources in this approach are made easily accessible to global learners by delivering them as a service.…”

Section: Resultsmentioning

confidence: 99%

The design of smart educational environments

Salvat

2016

Smart Learn. Environ.

194

113

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This paper discusses the key characteristics of smart learning and the main challenges to be overcome when designing smart educational environments to support personalisation. In order to integrate smart learning environments into the learning ecosystem and educational contexts, innovative uses and new pedagogical approaches need to be implemented to orchestrate formal and informal learning. This contribution describes the main characteristics of smart learning and smart learning environments and sustains the relevance of taking the participation of future users into account during the design process to increase knowledge of the design and the implementation of new pedagogical approaches in smart learning environments.

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Section: Resultsmentioning

confidence: 99%

The design of smart educational environments

Salvat

2016

Smart Learn. Environ.

194

113

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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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“…The security model discussed in this paper arose from the need for a production-level security layer in building a learning analytics solution targeting the writing and coding learning domains to be used in various academic institutions worldwide [2][3][4][5]. With several institutions showing interest in the product, pilot experiments started.…”

Section: Introductionmentioning

confidence: 99%

Security and Privacy in Bigdata Learning Analytics

Seanosky

Jacques

Kumar

et al. 2016

Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC – 16’)

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In a growing world of bigdata learning analytics, tremendous quantities of data streams are collected and analyzed by various analytics solutions. These data are crucial in providing the most accurate and reliable analysis results, but at the same time they constitute a risk and challenge from a security standpoint. As fire needs fuel to burn, so do hacking attacks need data in order to be "successful". Data is the fuel for hackers, and as we protect wood from fire sources, so do we need to protect data from hackers. Learning analytics is all about data. This paper discusses a modular, affordable security model that can be implemented in any learning analytics platform to provide total privacy of learners' data through encryption mechanisms and security policies and principles at the network level.

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Smart Learning Analytics

Cited by 24 publications

References 17 publications

The design of smart educational environments

The design of smart educational environments

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

Security and Privacy in Bigdata Learning Analytics

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