The Development of a Data-Driven Framework to Measure Teaching Presence in a Learning Management System within a Hybrid Teaching and Learning Model: A Theory and Practice Enhancement Case Study

Abstract

This thesis proposes a data-driven approach to enhance Teaching Presence (TP) within the Community of Inquiry (CoI) framework. Traditional CoI methodology relies on asynchronous discussion tools in Learning Management Systems (LMS). However, this study expands the scope to include all LMS tools, integrating learning analytics (LA) for more comprehensive insights. Conducted at the University of Pretoria through an embedded case study design, the quantitative research aims to establish a new data-driven TP framework that holistically measures TP across the institution, transcending individual courses.

The study hypothesises that the CoI TP concept can be quantitatively measured using a data intelligence system to convert vast datasets into actionable insights. By empowering instructors with historical and real-time data, the framework seeks to inform their learning design (LD) practices, influence student engagement, and strengthen Learner Presence (LP). Through this quantitative approach, the research contributes to a shift in CoI TP research methodology, offering pragmatic and theoretical advancements. The thesis focuses on developing a Quantitative Teaching Presence Framework (QTPF) and applying it within a hybrid learning model to evaluate the adapted TP categories' effectiveness in providing longitudinal evidence of instructors' impact on TP and LP at the institutional level and to demonstrate the impact of the COVID-19 pandemic.
The thesis emphasises how a unique design-based research methodology contributes to developing a comprehensive, scalable, and data-driven TP framework that provides empirical insights into the teaching and learning environment. Findings highlight the significance of learning design patterns, TP trends, and student engagement, revealing the institutional capacity to adapt to changing pedagogical needs and the impact of the COVID-19 pandemic. The results demonstrate how a CoI QTPF enhances the understanding of TP and LP biographical variables, ultimately offering strategic value for developing policies, instructor training, and course design, particularly within a hybrid educational model. It exposes the value of the LMS for low and mid-performing students.

The study showcases that the proposed data-driven approach can offer a more nuanced and comprehensive understanding of TP and LP in higher education. The findings also reveal the benefits of a quantitative approach to TP, providing empirical evidence for instructors, course designers, and policy-makers. The research contributes to the broader field of CoI TP research, highlighting the potential of data analytics to enhance teaching and learning practices. Ultimately, the thesis informs future research and practice, emphasising the importance of data-driven approaches to enhance TP and LP in higher education.

Date of Award	13 Nov 2024
Original language	English
Awarding Institution	University of Bath
Supervisor	James Davenport (Supervisor) & Nelishia Pillay (Supervisor)