This research examines the analysis of teaching-learning interactions in higher education teaching settings. To date, the majority of research in this area views the knowledge transfer process from the perspective of either instructor or student, leading to a lack of evaluation of the dynamic interaction that occurs between both parties during knowledge transfer. Yet, by its nature, teaching is a complex and dynamic process with many impacting factors. Therefore, this thesis argues that any knowledge transfer performance measurement system needs to be based on an analysis of the processes involved and have a high level of flexibility, simplicity and measurement built in. As a result, this research uses performance measurement tools that assist in analysing feedback from both student and instructor during the knowledge transfer process, in a combined qualitative and quantitative approach based on systems theory. This research has developed a multi-disciplinary framework that includes constructs and concepts from learning theories, knowledge transfer, performance measurement, systems theory and uses concepts from cognitive psychology as a linking tool. It has been designed to improve the efficiency of the analysis process, while increasing the supporting information available for use by stakeholders. The methodology includes best practice traits and checklists for knowledge transfer, key performance indicators in cost, quality and time, and an implementation methodology. The framework was found to facilitate the analysis of complex teaching setting processes, providing greater support for analysts in the identification of potential weaknesses and vulnerabilities than previously applied tools, which supports the identification of potential improvements and opportunities for corrective actions. The framework was validated using the Validation Square, and tested using three distinct theory-testing case studies: composite learning environment, classroom based lecture and large lecture theatre lecture. The case studies revealed that the framework provides a high level of detail, scalability, accuracy, reliability, and usability.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:654632 |
| Date | January 2015 |
| Creators | Gill, S. K. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/1464276/ |
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