Modelling and Managing Student Satisfaction: Use of Student Feedback to Enhance Learning Experience

In 2014-15, following a call for expressions of interest open to its subscribers, QAA commissioned six small-scale primary research projects intended to encourage collaboration between providers and promote the formation of communities of practice. This report is one of two on the topic of the role of student satisfaction data in quality assurance and enhancement. The reports are not QAA documents, so we have respected the authors' approach in terms of style and presentation. We hope that you will read them with interest. Other topics in the series are the transition experiences of entrants to higher education from increasingly diverse prior educational experiences; and an impact study of the guidance documents for higher education providers published by QAA in 2013. The 2015 National Student Survey results released recently have highlighted that student satisfaction scores have not increased despite an increase in tuition fees (Havergal, 2015). Understanding the key enablers and barriers for integrating student satisfaction data with Quality Assurance (QA) and Quality Enhancement (QE) was a key focus of this small-scale research project. By combining a qualitative perspective (that is, literature review with integrated perspective of academics and academic-related staff) and quantitative perspective (that is, using a case study at the Open University (OU) as to what the key drivers of learning satisfaction were among 60,000 students), we have found five key challenges for HE. Most UK institutions now systematically collect learning satisfaction. Nonetheless, there remain several critics about the appropriateness of these questionnaires argue that most learning satisfaction instruments are teacher-centred, focusing on what the instructor does in the learning environment, rather than what learners actually do, how they engage and whether learning occurred. While many institutions have become reasonably skilled in collecting large amounts of student satisfaction data, making sense of rich data sources and acting upon the data is complex and cumbersome. Recently several studies have tried to close the loop. For example, Arbaugh (2014) and Rienties, Toetenel, and Bryan (2015) found across 40+ modules that learning design and teaching support in particular influenced learners' satisfaction. In our case study, using logistic regression modelling of 200 potential explanatory variables with 60K+ students we addressed the key drivers for students' learning satisfaction. Findings indicated that learning design had a strong and significant impact on overall satisfaction. Learners who were more satisfied with the quality of teaching materials, assessment strategies, and workload were significantly more satisfied with the overall …