https://orcid.org/0000-0001-8976-6202

What is phenomenography?

Phenomenography is a qualitative research approach that is increasingly used to investigate the differences, or the variations, in the way that  individuals within a specified population experience a particular phenomenon (Marton, 1981). For example, within education, phenomenography can be used to identify and quantify the different ways a given class of students experience a particular aspect of learning. More specifically, phenomenography my be defined as “a research method for mapping the qualitatively different ways in which people experience, conceptualize, perceive, and understand various aspects, and phenomena in the world around them” (Marton, 1986).

What are the origins of phenomenography?

Phenomenography originated from the research into  students’ experience  of  learning carried out at Goteborg University in Sweden in the 1970s by Marton and his colleagues (Tight, 2016). The main goal of this research was to understand, from the perspective of the students, the different ways they conceived of and went about their learning.

What are the assumptions underpinning phenomenography?

The key assumption underpinning phenomenography is  that there are only so many ways that a given population can perceive, understand or experience any given phenomenon  (Tight, 2016). The full range of different ways  that a given population experiences a phenomenon at any point in time is termed  the outcome space (Åkerlind, 2005). These different ways of experiencing a phenomenon can each be represented by a category of description, and these categories are hierarchically organised, with the higher-level categories encompassing the lower-level categories, and constituting a more developed way of understanding the phenomenon (Täks, Tynjälä, & Kukemelk, 2016; Tight, 2016).

Why is phenomenography appropriate for education research?

According to Marton and Booth (1998), learning comprises two aspects that are inextricably intertwined, namely the “what” aspect and the “how” aspect. The “what” aspect refers to the content of learning, and the “how” aspect refers to the way in which learning takes place. With respect to the “what” aspect, it is found that there is a qualitative variation in student learning outcomes. Similarly, with respect to the “how” aspect, it is found that there is a qualitative variation in learners’ approach to learning. Since phenomenography is a research method that focusses on determining variations in individuals’ perceptions and experiences of specified phenomena, it is well-suited to studying learners experiences and perceptions of learning.

When is it ideal to use phenomenography in education research?

Phenomenography can help educators to identify and foster learning approaches that facilitate a better understanding of the subject material that students are engaging with. This is consistent with research findings suggesting that different learning approaches lead to different learning outcomes (Marton, 1986).  As an example, Marton and his colleagues have used phenomenography  to identify and document differences in what students learn in specific learning tasks and to map these differences to the individual learning approaches that students adopt in the specified learning tasks (Marton, 1986). 

Phenomenography can also be used to uncover the different understandings that people have of specific phenomena and to sort them into conceptual categories. With respect to education, “learning, thinking, and understanding are dealt with as relations between the individual and that which he or she learns, thinks about, and understands” (Marton, 1986). Phenomenography enables us to understand these relationships, which, in turn, enables us to develop pedagogical interventions to improve the quality of student learning.  This is consistent with  Marton’s belief that  the goal of learning is to change  “the  way  a person  experiences,  conceptualizes,  or understands  a phenomenon” (Marton, 1981). 

How can phenomenography be incorporated into learning and teaching improvement?

Phenomenography is typically incorporated into learning and teaching improvement using this two-stage process (Åkerlind, 2008; Han & Ellis, 2019):

1. Use phenomenography to identify variations in student experiences of the learning and teaching environment, including their perceptions and understanding of taught concepts.
2. Implement strategies to shift students away from the less desirable variations to the more desirable ones, for example, by designing learning and teaching programmes that maximise students’ opportunities for discerning the full range of key features of the taught concepts.

What sort of research questions are addressed by phenomenography?

The typical research question addressed by  phenomenographic research is of the form (Booth, 1997):

• How do the group of people we are interested in understand, or experience, this or that concept or phenomenon before and/or after studying it? 

As an example, Bucks and Oakes (2011) used the following pair of research questions in their study which sought to uncover the different ways that first year engineering students understand different programming concepts:

• What are the qualitatively different ways that the conditional and repetition structures found in most programming languages are understood?
• What are the ways that first-year engineering students understand these concepts?

Another example of research questions used in phenomenographic studies, is the research question formulated by Daniel, Mann, and Mazzolini (2016) in their study of academics’ experiences of the university lecture:

• What are the different ways of experiencing lecturing?

Finally, a third example of typical  research questions used in phenomenographic studies is the pair of research questions that Fila (2017) used in in his study of  the ways that engineering students experience innovation during engineering projects:

• What are the qualitatively different ways engineering students experience innovation during their engineering projects?
• What are the structural relationships between the ways engineering students experience innovation?

Finally, what are the methods typically used in phenomenography?

Phenomenography is best viewed as a methodology whereby the actual methods used  in carrying out the research vary according to the specific question being addressed (Booth, 2001). Typical data collection methods include semi-structured interviews, open-ended questionnaires, written reports, video, think-aloud methods, and observation (Booth, 2001; Han & Ellis, 2019).

For small numbers of research participants, the semi-structured interview is often the preferred method since it provides rich and in-depth descriptions. For larger numbers of participants, the preferred method is the open-ended questionnaire since it is easier to administer and allows a wider range of experiences of a phenomenon to be captured (Han & Ellis, 2019). In practice, both methods are often used in conjunction as this allows both breadth and depth of variations to be covered in the data.

In line with most other qualitative methods, purposeful sampling in which the diversity of the sample is maximised to ensure a rich assortment of experiences is used (Booth, 2001; Daniel et al., 2016). The objective for this is to exhaust any variation in experience, and data collection is often extended until there is no further variation.

Data analysis consists of reading, analysing and categorising the collected data with the goal of identifying a set  of qualitatively distinct, logically related, ways of experiencing the phenomenon being investigated (Daniel et al., 2016).  This is an iterative process which continues until a stable set of distinct categories is obtained. Collectively, these categories constitute the outcome space of the phenomenographic study, and their dissemination is accompanied by descriptions of the essential aspects of each category, illustrated by pertinent extracts from the data (Booth, 2001).

References

Åkerlind, G. S. (2005). Variation and commonality in phenomenographic research methods. Higher Education Research & Development, 24(4), 321-334. doi:10.1080/07294360500284672

Åkerlind, G. S. (2008). A phenomenographic approach to developing academics’ understanding of the nature of teaching and learning. Teaching in Higher Education, 13(6), 633-644. doi:10.1080/13562510802452350

Booth, S. (1997). On Phenomenography, Learning and Teaching. Higher Education Research & Development, 16(2), 135-158. doi:10.1080/0729436970160203

Booth, S. (2001). Learning Computer Science and Engineering in Context. Computer Science Education, 11(3), 169-188. doi:10.1076/csed.11.3.169.3832

Bucks, G., & Oakes, W. (2011). Phenomenography as a Tool for Investigating Understanding of Computing Concepts.

Daniel, S., Mann, L., & Mazzolini, A. (2016). A phenomenography of lecturing. Paper presented at the 44th SEFI Conference, Tampere, Finland. http://sefibenvwh. cluster023. hosting. ovh. net/wp-content/uploads/2017/09/daniel-aphenomenography-of-lecturing-56_a. pdf.

Fila, N. D. (2017). A phenomenographic investigation of the ways engineering students experience innovation. Purdue University,

Han, F., & Ellis, R. A. (2019). Using Phenomenography to Tackle Key Challenges in Science Education. Frontiers in Psychology, 10(1414). doi:10.3389/fpsyg.2019.01414

Marton, F. (1981). Phenomenography ? Describing conceptions of the world around us. Instructional Science, 10(2), 177-200. doi:10.1007/bf00132516

Marton, F. (1986). Phenomenography—A Research Approach to Investigating Different Understandings of Reality. Journal of Thought, 21(3), 28-49. Retrieved from http://www.jstor.org/stable/42589189

Marton, F., & Booth, S. (1998). The Learner’s Experience of Learning. In The Handbook of Education and Human Development (pp. 513-541).

Täks, M., Tynjälä, P., & Kukemelk, H. (2016). Engineering students’ conceptions of entrepreneurial learning as part of their education. European Journal of Engineering Education, 41(1), 53-69. doi:10.1080/03043797.2015.1012708

Tight, M. (2016). Phenomenography: the development and application of an innovative research design in higher education research. International Journal of Social Research Methodology, 19(3), 319-338. doi:10.1080/13645579.2015.1010284

https://orcid.org/0000-0001-8976-6202

Looking for a journal in which to publish your Engineering Education Research (EER)? Look no further. The Research in Engineering Education Network (REEN) has set up a dedicated EER Journals page on their website listing some of the key journals in the field. What I like about this EER journal list is that, in addition to providing links to each journal, they also outline the scope of each journal, together with an indication of the types of papers that each journal accepts.

The journals currently listed on the REEN EER Journal page (as of today, 03 October 2020) are:

1. Advances in Engineering Education (AEE)                                       
2. Journal of Engineering Education (JEE)            
3. American Journal of Engineering Education (AJEE)                       
4. Journal of Engineering Education Transformations (JEET)
5. Australasian Journal of Engineering Education (AJEE)                   
6. Journal of International Engineering Education (JIEE)
7. Engineering Studies                                                                             
8. Journal of Pre-college Engineering Education Research (J-PEER)  
9. European Journal of Engineering Education (EJEE)                         
10. Journal of Women and Minorities in Science and Engineering (JWM)
11. IEEE Transactions on Education                                                         
12. International Journal of Engineering Education (IJEE)                    
13. Studies in Engineering Education (SEE)
14. International Journal of Engineering Pedagogy (iJEP)  
15. Journal of Civil Engineering Education (JCEE)

https://orcid.org/0000-0001-8976-6202

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.