Next Article in Journal
Exploiting Inter- and Intra-Base Crossing with Multi-Mappings: Application to Environmental Data
Previous Article in Journal
LPaaS as Micro-Intelligence: Enhancing IoT with Symbolic Reasoning
Article Menu
Issue 3 (September) cover image

Export Article

Open AccessArticle
Big Data Cogn. Comput. 2018, 2(3), 24; https://doi.org/10.3390/bdcc2030024

Data Science Approach for Simulating Educational Data: Towards the Development of Teaching Outcome Model (TOM)

Educational Technology, Higher Education Development Centre, University of Otago, Dunedin 9016, New Zealand
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 19 June 2018 / Revised: 2 August 2018 / Accepted: 3 August 2018 / Published: 10 August 2018
(This article belongs to the Special Issue Big Data and Data Science in Educational Research)
Full-Text   |   PDF [958 KB, uploaded 11 August 2018]   |  

Abstract

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. View Full-Text
Keywords: big data; data science; big data education; teaching analytics; dashboards; teaching output model; student evaluation of teaching; teaching practice big data; data science; big data education; teaching analytics; dashboards; teaching output model; student evaluation of teaching; teaching practice
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ndukwe, I.G.; Daniel, B.K.; Butson, R.J. Data Science Approach for Simulating Educational Data: Towards the Development of Teaching Outcome Model (TOM). Big Data Cogn. Comput. 2018, 2, 24.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Big Data Cogn. Comput. EISSN 2504-2289 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top