Special Issue "Big Data and Data Science in Educational Research"

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (15 December 2018).

Special Issue Editor

Dr. Ben Kei Daniel
E-Mail Website
Guest Editor
Educational Technology and Research methodologies, Higher Education Development Centre, University of Otago, Dunedin, New Zealand
Interests: Artificial Intelligence in Education (AIED); Big Data and Learning Analytics; Data Science in Educational Research; Theory and Praxis of teaching of Research Methodology (Quan, Qual and MM)

Special Issue Information

Dear Colleagues,

The growing volume of data generated by machines, humans, applications, sensors and networks, together with the associated complexity of the research environment, requires innovation in educational research. In education, the ability to work with new forms of data and analytical tools can provide educational institutions with the knowledge they need to operate efficiently in highly technical and challenging research environments. Moreover, the growing research interest in Big Data and analytics in education suggests that the field of educational research is likely to become a data-intensive in the near future.

Educational Data Science(EDS) is a growing field of inquiry, primarily concerned with the extraction of information from a large and complex set of educational data, with the purpose of discerning valuable and actionable knowledge. EDS techniques can be applied to explore large quantities of data on students’ learning trajectories in learning management systems, social media interaction, faculty teaching practices. These data can be harvested and analysed to reveal useful patterns and insights to support better decisions relating to student learning, teaching and optimisation of institutional resources.

This Special Issue will present selected examples of the latest research on the application of Big Data and Data Science concepts, approaches, models, methods and methodologies in educational research. It will cover fundamental concepts and advanced Data Science approaches and analytic methods used in educational research, and ultimately open up opportunities to research and develop new analytical methods and techniques in Big Data applications in educational research.

Dr. Ben Kei Daniel
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Data Science in educational research
  • Learner and user modelling
  • Educational data mining
  • Technology-enhanced Educational
  • Big Data in Education
  • Learning Analytics
  • Teacher/Teaching Analytics
  • Sentiment analysis in learning environments
  • Dashboards and visualisation techniques in education
  • Social network analysis
  • Emergent educational research methods and techniques

Published Papers (1 paper)

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Open AccessArticle
Data Science Approach for Simulating Educational Data: Towards the Development of Teaching Outcome Model (TOM)
Big Data Cogn. Comput. 2018, 2(3), 24; https://doi.org/10.3390/bdcc2030024 - 10 Aug 2018
Cited by 6 | Viewed by 2275
The increasing availability of educational data provides the educational researcher with numerous opportunities to use analytics to extract useful knowledge to enhance teaching and learning. While learning analytics focuses on the collection and analysis of data about students and their learning contexts, teaching [...] Read more.
The increasing availability of educational data provides the educational researcher with numerous opportunities to use analytics to extract useful knowledge to enhance teaching and learning. While learning analytics focuses on the collection and analysis of data about students and their learning contexts, teaching analytics focuses on the analysis of the design of the teaching environment and the quality of learning activities provided to students. In this article, we propose a data science approach that incorporates the analysis and delivery of data-driven solution to explore the role of teaching analytics, without compromising issues of privacy, by creating pseudocode that simulates data to help develop test cases of teaching activities. The outcome of this approach is intended to inform the development of a teaching outcome model (TOM), that can be used to inspire and inspect quality of teaching. The simulated approach reported in the research was accomplished through Splunk. Splunk is a Big Data platform designed to collect and analyse high volumes of machine-generated data and render results on a dashboard in real-time. We present the results as a series of visual dashboards illustrating patterns, trends and results in teaching performance. Our research aims to contribute to the development of an educational data science approach to support the culture of data-informed decision making in higher education. Full article
(This article belongs to the Special Issue Big Data and Data Science in Educational Research)
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