Abstract:
The foundations of this research derived from two separate directions: firstly methodological, and secondly, a problematic situation. Both were strongly driven by the “highs” and “lows” of the researcher’s experiences over several years working in the business sector in Thailand and overseas, combined with his passion for being part of education reform in Thailand.
Many students fail to complete their studies. Of domestic students starting an Honours/Master’s qualification at public providers in 1998, by the end of 2002 (5 years later), only 59% had completed their degrees successfully, 2% were still studying towards completion, while 39% had left without completing (Scott, 2004). Why was the completion rate so low? What were the problems that postgraduate students encountered while doing their theses? And how could we help improve the students’ performance?
This research compared and contrasted the two approaches, Theory of Constraints (TOC) and Appreciative Inquiry (AI), by applying them to improve this problematic situation area: Master’s thesis students’ issues. A Hybrid model, combining aspects of the two methods, was also developed and tested. A web-based survey was used to recruit 12 Victoria University of Wellington Master’s thesis students for individual interviews, allocating them into three similar groups of four: TOC, AI, and Hybrid. One interviewee from each group also took part in two coaching sessions (action research).
The outcomes yielded from the three methods revealed both the root causes of the students’ problems (TOC) and the root causes of their success (AI). Based on the two opposite approaches, and the hybrid model, the researcher developed and proposed guidelines for future postgraduate research students, their supervisors, and graduate school committees.
However, some limitations for TOC and AI were revealed: the time-consuming processes for full TOC analysis, and the fact that some tools were not user-friendly. To enhance AI’s performance, stress-free environments may be required. More research on applying TOC, AI, and the Hybrid model to individuals is therefore required in the future.
