Design Insights from the Implementation of a Student Result Processing System in Nigeria

The design of digital technologies is often influenced by the infrastructural, economic, and social realities of the environment of the designer. Thus, the Human Computer Interaction (HCI) community emphasize various strategies to learn from and about target users (Brown et al., 2010). However, products that are designed with the amount of detail recommended by the HCI community require a significant amount of time, energy and skill and as a result are expensive. The high cost of these services force individuals and organizations to resort to commercial products and it is often the case that commercial products that are successful for one group of people might be unsuitable for another (Johns et al., 2002). To address this problem, user groups often adapt the technology to suit their needs, use the technology in unintended ways or ultimately reject them. In this paper we present a case-study where locally developed technology was preferred over commercial solutions. We draw design insights from this experience on how we might design educational technologies while considering the culture of the target users. Keywords: Education, Result computation in Higher Education, Educational Software Tools, Digital Tools in Higher Education DOI: 10.7176/DCS/9-8-05 Publication date: August 31st 2019

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

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