A journey for teaching excellence: from teamwork to artificial intelligence

I was only a few months into my current post when I was asked to support the Global Engineering Challenge (GEC) and Engineering You're Hired (EYH) weeks. I was mesmerized. What struck me was how engineering background diversities were removed by having students from different departments working together to emphasize the learning of another fundamental skill: teamwork.

However, I felt that those two events were too isolated in the students’ undergraduate programme, and I thought that my teaching settings (i.e., laboratory activity in small groups throughout the academic year) would be ideal to have an ongoing and explicit engagement with teamwork. After attending a Belbin Team Role theory workshop [1] and gaining further knowledge during my PGCert, I developed some content about teamwork theory to integrate into my teaching. In particular, I embedded Tuckman’s Team Development Model [2] into my introductory speech before a laboratory for first-year students. Based on my interpretation of the students’ body language (e.g., no eye contact, looking unengaged), my attempt was a failure. Acknowledging my inexperience, I cherished the feedback from some colleagues. A senior colleague pragmatically spotted the issue, describing it as a demand-offer problem: first-year students enrolled to learn engineering and not pedagogical theory. Another colleague, based on his successful experience, suggested a gaming component to introduce this topic to students. Therefore, I considered removing the teamwork element from my introductory speech and adding it to the practical part of the session. However, this would have required a coordinated effort of the whole teaching team including the GTAs. Unfortunately, at that time, GTA performance in MEE was referred to as inconsistent and often poor in the yearly students' survey.

This became a pressing matter to address both for MEE, striving to meet the highest standards of TEF [3], and for myself. I realized that engaging students in non-engineering topics required a more professionalised teaching team. Thus, given the wide use I make of GTAs, the success of the teaching in the Structures Lab hinged not only on my ongoing training but also on the one of the GTAs.

After critically reviewing the literature about GTA training and contextualising the findings with the reality at the University, Faculty and Departmental levels, I decided to introduce a GTA training that is continuous and subject-specific and that supports the development of their teaching profile. My aim with this training was not only to support the institutional commitment to staff development [4] but also to allow GTAs to gain pedagogical skills essential for their future careers.

The new methodology consisted of an ongoing cycle of training-teaching-feedback. The training, based on microteaching [5], was run in two stages. The first one, individual, where, in turn, each GTA familiarised with pedagogical concepts contextualised to engineering practical teaching; and the second one, designed to create a “safe” environment for rehearsing among peers, where the trained GTA would lead the group session. The feedback, gathered as an online form, represented a critical element of the aforementioned cycle, as it provided GTAs with an opportunity for self-reflection but also allowed me to assess the training based on GTA self-efficacy [6].

The analysis of the feedback confirmed the effectiveness of the training method and was also instrumental to identify some hidden problems that I will need to address moving forward. For example, some GTAs underwent a “shock phase” in their second semester, from which the literature suggests they can exit only through continuous training [6]. Also, only a few GTAs seemed eager to develop their teaching profile. Making GTAs value their role as teachers may require a broader strategy possibly involving their supervisors to find the right balance between their teaching profile and the contrasting but primary research one. I believe that both issues stemmed from neither the cognitive nor the psychomotor domains of GTAs, but, conversely, from a not sufficiently high level in the affective one. They seek knowledge (as inferred from the feedback analysis), but what they probably need is a purpose.

Nonetheless, what alarmed me the most, was the poor engagement that GTAs had with feedback. While for the proposed training this was probably due to the unsuitable format, I also believe that this represents a symptom of a deeper and wider issue: some GTAs did not know how to provide feedback. As I was thinking about the root of the issue, a chat with a PGCert fellow, made me realized that there is a general expectation that everyone knows how to provide/receive feedback. Alas, this is far from the status quo. For example, I was indoctrinated to provide/receive feedback but I was never trained. I suspect neither are the GTAs or our students.

I have come full circle and I now understand that to teach teamwork to students I will need to focus not only inside my classroom but also tackle wider issues. On one hand, I will need a team of professional GTAs “practically” supported with ongoing training and “emotionally” empowered to recognise their value. On the other hand, more broadly, I will need to work towards a more effective strategy for teaching and deploying feedback that takes into account both the students need of almost real-time feedback and the current limited time that staff can dedicate to provide it. I believe that only technology can overcome this deadlock and specifically I look with interest at artificial intelligence (AI). The use of AI in education has grown over the past two decades [7], and, while challenges still exist, AI also offers a wealth of opportunities like the potential for providing verbal and oral feedback to students that is both personalised and just-in-time. While this will help students to customize their engagement with feedback, I also plan to explore peer feedback as a strategy to teach students (as well as GTAs) to provide feedback and act on it.

REFERENCES

1. Belbin, R.M., (2011). Management teams: Why they succeed or fail. Human Resource Management International Digest.
2. Nestor, R., (2013). Bruce Tuckman’s Team Development Model. [online]. [Viewed 15 June 2020]. Available at: https://www.lfhe.ac.uk/download.cfm/docid/3C6230CF-61E8-4C5E-9A0C1C81DCDEDCA2.
3. The University of Sheffield, (2016). Learning and Teaching at the University of Sheffield 2016- 2021 [online]. [Viewed 27 01 2020]. Available at: https://www.sheffield.ac.uk/polopoly_fs/1.661828!/file/FinalStrategy.pdf
4. Bagshaw, A. (2017). A Beginner’s Guide to the Teaching Excellence Framework [online]. [Viewed 27 01 2020]. Available at: https://wonkhe.com/blogs/a-beginners-guide-to-the-teaching-excellence-framework/
5. Calonge, D. S., Mark, K. P., Chiu, P. P., Thadani, D. R., & Pun, C. F. (2013). Extreme-Teaching-2 (XT²): Evaluation of an innovative semester-long intensive GTA training program based on microteaching. International Journal of Teaching and Learning in Higher Education, 25(1), pp. 129-143.
6. Chiu, P. H. P., & Corrigan, P. (2019). A study of graduate teaching assistants’ self-efficacy in teaching: Fits and starts in the first triennium of teaching. Cogent Education, 6(1), p. 1579964.
7. Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), pp. 582-599.