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A recently published paper in the Proceedings of the National Academy of Sciences (PNAS), with the rather provocative title “Innovative teaching knowledge stays with users”, is currently making shockwaves across the scholarly teaching community in the USA and beyond (Lane, McAlpin et al. 2020).  Jointly authored by 12 researchers from four universities across the USA, the paper reports on a study to reveal the social networking characteristics of academics who use innovative teaching practices. Research participants were drawn from 9 departments representing three science disciplines at three research intensive universities in the United States. The three institutions in question are the University of South Florida, Boise State University, and the University of Nebraska–Lincoln.

In conducting the study, the researchers hoped that findings from the research would help to shed light on the diffusion process of innovative teaching practices within universities. Findings from the study suggest otherwise – when it comes to teaching innovation, there is little or no diffusion. This presents a conundrum to the scholarly teaching community, given, as it is, the immense amount of time, expertise and resources that have been put into the development and dissemination of innovative, student-centred pedagogies.

Innovative teaching knowledge stays with users: Overview and findings

The authors used a social networking survey to identify who amongst the research participants self-reported as having knowledge of, and routinely used, innovative teaching methods in their own practice. From this group, they conducted semi-structured interviews with 19 participants to find out which individuals they chose to speak about innovations in teaching, and why they preferred to speak to these individuals.  The researchers’ hypothesis was that academics knowledgeable and experienced in innovative teaching would talk mostly to those academics with little knowledge or experience of innovative teaching.

Contrary to expectations, findings from this study suggest that those with knowledge and experience of teaching innovation predominantly share their knowledge and expertise amongst themselves. In short, academics who are knowledgeable and experienced in innovative teaching are more likely to talk with colleagues who are also knowledgeable and experienced in innovative teaching. Reasons for this preference range from having similar teaching values, the need to share expertise and experience, being comfortable with one another, and down to the fact that it is convenient to speak to like-minded individuals. Less important were such aspects as shared teaching responsibilities, mentor/mentee relations, holding an important/relevant position, being on the same committee/conference/workshop, doing similar research or having similar appointment types.

In comparison, the study also found that academics with little or no knowledge and experience of innovative teaching were less likely to engage in conversations about teaching innovation, either amongst themselves, or with their more knowledgeable and experienced counterparts. Since conversations on teaching innovation are unlikely to take place between the more knowledgeable and the less knowledgeable, it follows that the diffusion of knowledge and expertise in innovative teaching is therefore unlikely.  

Diffusion of computational modelling across engineering modules: Findings and overview

The results correlate with the findings from a small study that I undertook to investigate the diffusion of computational modelling as a pedagogic tool across the Faculty of Engineering Sciences at University College London (UCL) (Nyamapfene 2019). This was after we had adopted computational modelling as the primary pedagogic tool in the first- and second-year engineering mathematics modules across the faculty (Nyamapfene 2016).

This study revealed that adoption of computational modelling pedagogies tended to be restricted to those academics who shared an interest in such pedagogies, and to those academics who had an active interest in student-centred learning and active learning methods. Such academics were more likely to be actively engaged in learning and teaching initiatives across the university, and they were more likely to express the view that their adoption of computational modelling was consistent with their views and philosophies of teaching. Some of these academics had actively contributed to the development and implementation of the Integrated Engineering Programme (IEP), the curriculum framework for undergraduate engineering programmes at UCL. For a brief overview of the IEP, see my November 6, 2017 blog piece entitled The UCL Integrated Engineering Programme: A Very Brief Guide (Nyamapfene 2017) and for a more detailed discussion, see our paper entitled “Faculty wide curriculum reform: the integrated engineering programme” in the European Journal for Engineering Education (Mitchell, Nyamapfene et al. 2019).

In summary, my study revealed that computational modelling pedagogies were most likely to be adopted by academics who already had an interest in innovative teaching methods, and by academics who subscribed to the IEP values and ethos. By the same token, the study suggests that academics who are less knowledgeable in computational modelling pedagogies, or who have little or no interest in these pedagogies, are least likely to adopt them in their own teaching practice. This is consistent with the findings by Lane, McAlpin et al. (2020) that conversations, and consequently, experimentation and adoption, tends to take place primarily amongst academics whose teaching values and approaches are consistent with the innovative approaches. In short, it is not enough to leave the spread of innovative teaching methods to natural diffusion processes.

Concluding remarks

The two studies above suggest that the diffusion of knowledge and expertise in innovative teaching methods tends to be restricted to those academics who have an intrinsic motivation and interest in the methods.  Those academics whose motivations and interests are elsewhere are unlikely to pay attention to these innovative pedagogies, let alone adopt them. How then can we resolve this situation?

Thirty years ago, Boyer (1990) made the observation that with respect to learning and teaching, the single most important consideration is the issue of faculty time. This is because academics tend to prioritise those activities that are highly prized in university reward systems. As he put it, “it’s futile to talk about improving the quality of teaching if, in the end, faculty are not given recognition for the time they spend with students.”  Even today, teaching remains underprioritised within higher education. For instance, a 2015 survey of teaching within UK engineering departments by the Royal Academy of Engineers (RAE) suggests that teaching quality tends to be relegated to a marginal role, with departments mostly preoccupied with research outputs and students numbers (Graham 2015).

As the RAE survey suggests, individual academic departments do not see a direct correlation between student numbers and the time and expertise invested in improving teaching quality. Student recruitment depends on several other factors such as the university brand and its location, and not just on the perceived quality of teaching. Given such a scenario, all that departments need to do to ensure an adequate level of student recruitment is to ensure that their teaching is reasonable, which, in practice, is a very low bar indeed. This is unlike research where there is a clearly discernible link between income and the invested time and expertise. As a result, departmental priorities, and, consequently, the reward structure in universities remains focused on research, and not on teaching quality.

However, as I noted in my June 14, 2017 blog piece, it is now retrogressive for universities to focus exclusively on research to the detriment of all the other things that universities need to be doing (Nyamapfene 2017). As I observed, the remit for the modern university has now expanded to include community engagement and enterprise (knowledge transfer and impact), over and above traditional research and education. This clearly calls for a diversified academic staff if the university is to successfully deliver its mandate across these multiple competing fronts. It is therefore pertinent that the reward system in universities should adequately, and equitably, reflect the multiplicity of academic career pathways that are now emerging.

References

Boyer, E. L. (1990). Scholarship reconsidered: Priorities of the professoriate, ERIC.

Graham, R. (2015). Does teaching advance your academic career?: perspectives of promotion procedures in UK higher education, Royal Academy of Engineering.

Lane, A. K., et al. (2020). “Innovative teaching knowledge stays with users.” Proceedings of the National Academy of Sciences: 202012372.

Mitchell, J. E., et al. (2019). “Faculty wide curriculum reform: the integrated engineering programme.” European Journal of Engineering Education: 1-19.

Nyamapfene, A. (2016). Integrating MATLAB Into First Year Engineering Mathematics: A Project Management Approach to Implementing Curriculum Change, IEEE.

Nyamapfene, A. (2017). “Progression for Teaching Only Academics in Research Intensive Universities: A Personal Perspective.” Engineering Learning and Teaching https://engineeringedu.press/2017/06/14/progression-for-teaching-only-academics-in-research-intensive-universities-a-personal-perspective/ Accessed 12 September, 2020 2020.

Nyamapfene, A. (2017). “The UCL Integrated Engineering Programme: A Very Brief Guide.” Engineering Learning and Teaching https://engineeringedu.press/2017/11/06/the-ucl-integrated-engineering-programme-a-very-brief-guide/ 2020.

Nyamapfene, A. (2019). Adoption of computational modelling in introductory engineering course modules: A case study. Proceedings of the 8th Research in Engineering Education Symposium, REES 2019-Making Connections, REES.