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.
Highlights
Introduction and Related ResearchOver the last decade, the notion of Big Data has been used to describe the nature and complexity of data across different sectors, notably in banking, government, business, healthcare and telecommunications [1]
We propose a Data Science approach to educational data focused on teaching analytics
This section talks about analysis of the simulated data as well as a discussion of the results, presented in a dashboard that tells the story to the teacher of how students’ perceive the teacher’s teaching effectiveness over a period of time
Summary
Introduction and Related ResearchOver the last decade, the notion of Big Data has been used to describe the nature and complexity of data across different sectors, notably in banking, government, business, healthcare and telecommunications [1]. It has been indicated that within the higher education sector, Big Data has enormous potential for transforming the sector, primarily, through the utilisation of analytics, data visualisation and security [3]. Despite increased demand for teachers to engage in inquiry, some limitations can hinder the adoption of Big Data in educational settings. Visualisation dashboards will help teachers with limited numerical knowledge to effortlessly understand and utilise teaching data [12,13]. There are challenges associated with the use of Big Data such as privacy and access to educational data [2,8,14,15] To address these challenges, educational data-driven approaches such as educational data mining and learning analytics have been proposed
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