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July 24, 2023

Using AI to evaluate film criticism – Lewis Beer

Introduction

Can ChatGPT evaluate the quality of insights in a student essay? In January 2023, Daisy Christodoulou published an article (Can ChatGPT mark writing?, No More Marking Blog) exploring this question. She tested ChatGPT using English essays, and found that while its grading and feedback were more or less aligned with her own, the AI was sometimes unable to spot fundamental problems in essays. Christodoulou offers some fascinating insights, but one thing she does not explore in any detail is the relationship between ChatGPT’s qualitative evaluation of an essay and the content of the essay itself.

In this post, I will share the results of my own brief experiment, in which I pasted two film reviews into ChatGPT and asked it to evaluate (and compare) the quality of insights in these reviews. My aim here was to use these texts as substitute ‘essays’, and consider how the AI-generated responses might help or hinder a marker in giving meaningful feedback.

The dialogues quoted from below were conducted on 23 March 2023, using the free ChatGPT 3.5. (I later repeated the dialogues with both this and ChatGPT 4.0, but found the responses from 23 March gave more substantial and interesting feedback. I will not discuss those later dialogues in this post.)

Review 1: Peter Bradshaw

To begin with, I asked ChatGPT to ‘Evaluate the quality of insight in the following review of the film Nomadland’, then pasted the text of Peter Bradshaw’s review of Nomadland from The Guardian. I wanted to see how it would respond to this vague request, in which I provided no criteria on which to base an evaluation.

The response did a good job of summarising the review using selected words from the text. For instance, at the end of its evaluation the chatbot said:

[Bradshaw] does offer a mild critique that the film could have been more explicit in its condemnation of the economic forces causing the characters' hardships. However, they still recognize the film's greatness and Chloé Zhao's exceptional filmmaking.

This is clearly pieced together from the following passage in Bradshaw’s review, in which I have highlighted the details used by ChatGPT:

[The film is] brought off with exceptional intelligence and style. Arguably it is not angry enough about the economic forces that are causing all this but it still looks superbly forthright. There is real greatness in Chloé Zhao’s film-making.

The paraphrasing techniques used here – transposing words like ‘exceptional’ and ‘greatness’ into slightly different contexts, and spelling out that ‘all this’ means ‘the characters’ hardships’ – are effective in ‘feedback’ terms, because they seem to indicate an understanding of the writer’s (i.e. student’s) argument. However, Bradshaw says the film is ‘not angry enough’, not that it is ‘not explicit enough’, and this may be a significant error.

In its final verdict on the review, ChatGPT said:

Overall, this review appears to be well-written and insightful, providing a thorough analysis of the film’s themes, characters, and style while also highlighting its strengths and potential weaknesses.

Without my having provided any criteria, the AI assumes that an insightful film review will explore themes, characters, and style, and that it will offer a balanced assessment of the film’s strengths and weaknesses.

More of ChatGPT’s ‘marking criteria’ emerged when I asked it to ‘Identify some ways in which the review could be improved’. It offered some valid points in response to this prompt. For example:

The review could have delved deeper into the film's technical aspects, such as its cinematography, editing, and sound design. While the review mentions the film's style and how it differs from traditional narrative-driven films, it could have gone into more detail about how the film achieves this style and what specific techniques the filmmakers used.

This is arguably an area where Bradshaw’s review falls short. Bradshaw does discuss how Nomadland differs from traditional narrative-driven films, and he does connect this with Zhao’s stylistic choices; in this part of his review, he might have discussed film-making technique in more detail. So the AI’s choice to structure feedback like this (‘While you did talk about x, you could have delved deeper into y...’) could be helpful for a student looking to improve their essay by building on their strengths.

On that note, here is another of ChatGPT’s constructive criticisms:

The review could have included a more critical evaluation of the film. While the review praises the film’s strengths, it could have offered a more balanced assessment that acknowledges both the film’s strengths and its weaknesses.

This picks up on the detail quoted above, from the AI’s initial evaluation, noting that Bradshaw’s critique of the film is ‘mild’. The AI has perhaps noticed that Bradshaw’s more negative language is limited to the very end of his review, and is couched in the word ‘arguably’. Again, if we imagine this as feedback being provided to a student, ChatGPT’s evaluations do a good job of mixing praise and criticism: ‘You balance your argument by acknowledging the film’s weaknesses, but you only do this briefly at the end – you could have included a more critical evaluation.’

Other responses, however, show ChatGPT’s limitations, and would constitute problematic essay feedback. For instance:

The review could have offered more specific examples of how the film explores its themes and characters. While the review mentions some of the film's themes, such as the impact of economic hardship on older Americans, it could have gone into more detail about how the film portrays these themes and how they are relevant to contemporary society.

This is not really a fair critique: Bradshaw does highlight specific examples of how the film explores ‘the impact of economic hardship on older Americans’, and he does allude to contemporary issues such as factory closures, the dominance of Amazon, and the importance of the tourist trade in this part of America:

...looking for seasonal work in bars, restaurants and – in this film – in a gigantic Amazon warehouse in Nevada, which takes the place of the agricultural work searched for by itinerant workers in stories such as The Grapes of Wrath.

Fern, a widow and former substitute teacher in Empire, Nevada – a town wiped off the map by a factory closure – who is forced into piling some possessions into a tatty van and heading off...

At times, the film looks like a tour of a deserted planet, especially when she heads out to the Badlands national park in South Dakota, where there is also tourist-trade work to be had.

ChatGPT also says:

The review could have provided more context for the film's production and reception. For example, the review could have mentioned the awards and critical acclaim that the film has received, or how it fits into Chloé Zhao's broader filmography.

Some of this is fair – the review was published after Nomadland’s Oscar success, so Bradshaw could have mentioned this – but it misses the contextual details Bradshaw includes about the film’s production:

Zhao was even allowed to film inside one of Amazon’s eerie service-industry cathedrals.

The movie is inspired by Jessica Bruder’s 2017 nonfiction book, Nomadland: Surviving America in the Twenty-First Century, and by the radical nomadist and anti-capitalist leader Bob Wells, who appears as himself.

The people she meets on the road are, mostly, real nomads who have vivid presences on screen.

As with the previous criticism, ChatGPT has not acknowledged key details of the review in its initial assessment, so its critique is not balanced: it is like a marker who blames a student for ‘not doing x’ when the student in fact spent several paragraphs on ‘x’. (Human markers sometimes do this, of course.)

Review 2: Beatrice Loayza

I then asked ChatGPT, ‘Is the following review of the film Nomadland more incisive than the previous one?’, and pasted the text of Beatrice Loayza’s review of Nomadland, from Sight & Sound. Again, I deliberately did not provide any assessment criteria. ChatGPT’s answer was ‘yes’, for several reasons – some valid, some less so. First of all, it said, Loayza ‘provides a detailed analysis of the film's themes and cinematography, as well as the performance of Frances McDormand’. This is fair, and picks up on one of the criticisms of Bradshaw cited above (namely his lack of attention to technical aspects). Loayza comments on specific camera techniques, naming the cinematographer and describing the light effects he achieves. She also does more than Bradshaw to explain why McDormand’s performance is so effective.

ChatGPT picks up on another of its own criticisms of Bradshaw by praising Loayza’s critical perspective on the film:

However, the review also criticizes the film's lack of force and clarity in its insights into labor in the 21st century and the exploitation of older Americans. The author points out that the film's depiction of workers exploited by Amazon feels too easygoing and questions the film's liberal naivete in addressing the conditions of the nomadic lifestyle. Overall, the review provides a more nuanced and thoughtful analysis of the film.

This draws upon the following passage in Loayza’s review; again, I have highlighted phrases that ChatGPT seems to have picked up on:

[The film’s] insights into labour in the 21st century, and the exploitation of an older generation of Americans, lack force and clarity. At the very beginning of the film, Fern is employed by Amazon’s CamperForce programme, which provides base wages and free parking space to seasonal workers in their 60s and 70s. In 2020, Amazon doubled its profits during a global pandemic, which makes Zhao’s easygoing depiction of workers exploited by the company feel rather toothless. That the film aims to capture the ways in which a kind of working-class American outsider struggles without fully addressing the conditions of that struggle casts over it the shadow of a questionable liberal naivete.

To re-cap:

  • In its initial assessment of Bradshaw’s review, ChatGPT noted that his critique of the film was ‘mild’
  • In suggesting improvements, it built on this comment by recommending a more balanced approach
  • And in drawing a comparison with Loayza’s review, it notes her more substantial version of Bradshaw’s criticism.

At each stage, the AI appears to be drawing upon specific evidence from the texts, rather than just ‘hallucinating’ these evaluative comments.

Elsewhere in its comparison between Bradshaw and Loayza, however, ChatGPT did hallucinate some differences in order to justify its verdict. I will not cite these here, as this post is already very long, but the inaccuracies were of a similar kind to those in the summary of Bradshaw discussed in the previous section.

Final thoughts

If these film reviews were formative essays that I had to mark, I could use ChatGPT’s feedback to offer legitimate praise and criticism, suggest improvements, and judge the relative merits of the two essays in relation to each other. However, I would also notice that ChatGPT misses important details in these texts and draws some un-founded contrasts between them.

In the course of this experiment, I tried several variations on the above prompts. Here are some things to note if you want to try a similar experiment yourself:

  • I fed the reviews into ChatGPT several times, and in a different order. When I asked it to make a comparative evaluation, it tended to prefer the second review (even if this was Bradshaw’s). When I asked if it could reverse its comparative evaluation (i.e. ‘Can you argue that the other review is more insightful than the first?’), its responses varied: sometimes it doubled down on its first opinion, sometimes it conceded that an alternative opinion could be justified. Again, the reasons given for these opinions ranged from ‘valid’ to ‘hallucinatory’.
  • This post demonstrates what Chat-GPT is capable of in the hands of a technically ignorant, time-poor amateur like me, but by using the right prompts and follow-up prompts, it would no doubt be possible to collate more credible ‘essay feedback’, and then ask the AI to present and construct this in an appropriate way. Have a look at the other articles and resources linked to on the AI in Education Learning Circle webpage, try an experiment of your own, and share the results in the comments below. In particular, you might think about the learning outcomes and marking criteria specific to your discipline, or your module, and consider how you might train ChatGPT to use these in evaluating a piece of text.

July 17, 2023

Who Uses AI, How, and When?

By Matthew Voice, Department of Applied Linguistics, University of Warwick

As I mentioned in my previous WJETT blog post, I have participated in a strand of the ‘AI in Education’ learning circle during the last few months. Over the course of our strand’s meetings regarding the role of AI in formative feedback, our focus has primarily been on the nature of the emerging generative AI technology. Our research and conversation has a great deal to contribute with regards to what large language models mean for the future of formative feedback. However, given that these models are tools, it is worth reflecting on the user, too.

Our learning circle represent educators and students from a wide range of degree programmes, and motivations for engaging with higher education will vary from student to student across all courses. Throughout our discussions, conversation has largely focused on the role of AI in the improvement of formative feedback for students who are motivated by a desire for academic excellence. Given that students in this position will likely be motivated to engage with extracurricular activities within the university (e.g. joining the WIHEA as a student member, partaking in surveys of student experience or voluntary training workshops), their voices will perhaps be heard most clearly during our present conversations.

But the experiences of these students are not representative of our cohort as a whole, or indeed of all points across an individual’s university journey. Many students may view academic writing – either across their degrees, or for certain assignments or modules – as an obstacle to be overcome. In these cases, the objective of academic writing shifts away from excellence, and towards the production of work which will simply allow the student to attain a passing grade. Engagement with GenAI for these students might not mean refinement and improvement; it may simply be a resource for the fastest or most convenient means of content generation.

Integrating GenAI into teaching and formative feedback requires a recognition of this spectrum of experience and motivation. In my previous WJETT blog I recommended that future discussion and planning should consider the reasonableness and realism when supporting students to use and think critically about GenAI. By this, I mean:

1) Reasonableness: Is the task being asked of the GenAI (e.g. to produce formative feedback on a draft assignment) one that it is capable of achieving?

2) Realism: Does our (educators, policy planners, researchers) understanding of engagement with GenAI reflect actual independent student use cases?

Assessing reasonableness through understanding what GenAI is capable of achieving will require ongoing review, and the development of support and training for staff in order to keep pace with developments. This, I think, has largely been the focus of the work done by the ‘AI in Education’ learning circle and the final report this group has produced. Going forward, we also need to consider how well we understand our students’ independent engagement with this sort of assistive technology. What tools and resources are they familiar with? How do they understand them? What do they do with them, and at what point(s) in their academic writing?

Grounding future policy and pedagogic resource development in relation to a realistic model of students’ use and understanding of GenAI will be a task as complex as anticipating the impact of future technological development in large language models. By acknowledging this, and undertaking this work, we best position ourselves to ensure that outputs and resources which arise from working groups such as ours will be meaningful to staff and students working across the university.


July 10, 2023

AI Feedback Systems: A Student Perspective – Mara Bortnowschi

The buzz is endless – AI is taking Higher Education by storm. Since the launch of ChatGPT, everyone seems to have an opinion, and rightfully so. It’s so new and we have yet to fully understand its potential and the impact it will have. Within academia, the general sentiment mostly surrounds concern for responsible use, as many students have heard their professors and lecturers warning them against its use. However, its rapid growth and widespread adoption demonstrate that it’s not going anywhere soon so instead of avoiding it, it should be given the time of day to understand the risks and challenges but also the opportunities it presents. Furthermore, I think the student voice in these discussions has been underrepresented, but really students can be the key to harnessing this technological advancement as an asset to enhancing learning and education.

The WIHEA group have already explored a number of subjects regarding AI in education from student perspectives that can be found on the group’s Artificial Intelligence in Education webpage. These have included emerging questions AI presents, the risks and ethics of academic integrity, evolving assessment styles to mitigate and integrate AI into assessment and how teaching may evolve. I will now explore some of the opportunities that are being presented with the widening availability and access to AI tools for students to enhance their learning and to generate formative feedback. While summative marking has been and continues to be required to be marked by human markers according to the UK Quality Code in Assessment (UKSCQA, 2018), formative feedback has more flexibility, and we are now presented with an opportunity to test and utilise the capabilities of these AI technologies in providing timely, constructive, and developmental feedback.

Existing feedback systems

This notion will be particularly explored with regards to formative elements of summative assessments. Feedback should allow a student to understand strengths and weaknesses of their work and if engaged with effectively, can be used to improve academic performance, and thus learning. Especially throughout the pandemic, we have seen the role of feedback change massively: as more of education has shifted online, reliance on formative assessments has increased as assessments for learning. This is in contrast to summative assessments which more so represent assessments of learning (Wyatt-Smith, Klenowski and Colbert, 2014). Formative assessments also are an opportunity for autonomous learning by developing one’s own skills and relying on self-motivation. It would also be fair to say that formative feedback can be self-assessment of sorts, as even though the formative feedback is generated externally, it is the engagement with, and learning you apply from it that will ultimately make a difference in each student’s performance.

AI generated feedback

So what could incorporation of AI in these feedback systems change? Well, the use of algorithms in generation of feedback is not an entirely new concept. Algorithms, such as Grammarly and Sketch Engine, have been around for a while and they can generate feedback on academic writing and are for the most part freely available, or students are granted access to them by their institutions. But with more complicated algorithms that use machine learning, we can apply them to provide specific and personalised feedback. To make this even more applicable, by integrating what could be different elements of summative marking criteria or rubrics, they could provide some of the most relevant feedback at a moment’s notice.

This application is indeed being explored right here at the University of Warwick. Isabel Fischer, a WBS professor, is trying to pilot a deep learning formative feedback tool that has the potential to provide more comprehensive feedback that was developed with WBS marking criteria at its core. By simply submitting a pdf or word document, the algorithm instantly produces a document of in depth feedback on the four aspects of WBS marking criteria. This could be just the start of developing similar department-specific feedback tools taking into account department-specific assignments, marking criteria, and writing styles for drafts of academic writing. While there are definitely some considerations to look out for, this is fascinating and shows great promise as a tool to increase student autonomy in adapting how they might approach assignments to still have the opportunity to personally benefit from formative feedback.

Considerations of using generative AI

The considerations I mentioned earlier are worth discussing as students are turning to generative AI technologies like ChatGPT more and more. While these technologies are being developed to simulate human intelligence, there are some things they are simply not capable of. For example, it lacks expressions or expressive language. If using them to generate feedback on your writing, you should be aware that they will not always be able to grasp the nuances or expressive language in that writing. In other words, any feedback you receive from AI should be approached critically. You decide what you implement from feedback you receive, and you are responsible for identifying and understanding what truly can improve your work. This is all part of the responsible use of AI, but really also goes for human generated feedback. Your assignment at the end of the day will still be marked by a human marker with in-depth subject-specific knowledge and skills that they are asking you to learn and demonstrate in your assignment. I think this is the quick, irresponsible and neglectful way people have been quick to exploit resources like ChatGPT, where they do not doubt any response it has generated and implement them into a piece of work, only to find that its references are wrong or entirely don’t exist. Firstly, this should not be the way we utilise it, as this is blatant plagiarism, but secondly, a critical approach should be used to (for example) verify references, and critically understand that the way AI answers can lack certain elements of context. Regardless, the point still stands: responsible applications of AI technologies should not be about using it to do your work, but using them to enhance or improve your outputs.

Engagement with AI technologies and feedback

A new level of engagement with AI has been seen since the release of ChatGPT and DALL-E. Perhaps this is rooted in the great advancement that this represented or, more sinisterly, the opportunity to exploit the technology to minimise workload. Regardless, everyone’s interest has been piqued, and the level of engagement has been massive, exceeding what anyone might have expected particularly from students. At the University of Warwick alone, students have made a total of 850,000 total site visits to ChatGPT in the first two months only on the university’s Wi-Fi (SWNS, 2023). I think it’s important to try to understand why this might be in order to be able to channel this traffic for good rather than just fear this alleged ‘cheating epidemic’ that the media has been dubbing it.

In contrast to the older algorithm technologies that have been around, like for example the previously mentioned Grammarly and Sketch, which experienced much more moderate levels of engagement and use. Reasons vary from lack of awareness, to limited breadth of feedback to language, or to lack of confidence in the feedback they provide. AI has surpassed some of these limiting factors in that it is capable of generating a wider breadth of feedback that can include language, style, structure and more. The confidence in the feedback it produces is reassured by the continuous concern from educators. If professors are afraid AI technologies can be used to write entire assessments, then they must be capable of doing so.

Further benefits

As a result, we have seen students be a lot more open to trying to use ChatGPT, and I think we should utilise this eagerness in a way that encourages students to enhance their academic achievements. By introducing resources such as Isabel Fischer’s feedback tool or teaching students how to formulate prompts for ChatGPT to generate similar constructive feedback, we can guide a smooth integration of ChatGPT into Higher Education practices. And there are so many more benefits we have the potential to see. For one, this alleviates a massive workload off staff. If such tools are available to take care of the writing style and structure, staff’s role in formative feedback can remain more focused on content. The speed (or even instantaneity) with which AI can produce feedback also makes feedback more accessible. Furthermore, students can engage with it as many times as they like, inputting multiple drafts, as they are less limited by staff work capacity. Also, different students work on different timescales and with different approaches when faced with an assignment. This further widens accessibility to students that might start assignments later that what might normally be a formative deadline. Communicating these advantages is key in order to achieve these outcomes and to harness this technology towards enhancing educational experience for both staff and students.

Conclusion and personal experience

In my experience thus far with using ChatGPT, I have had mixed feelings. On the one hand, I am very apprehensive of the fact that its use is quite contentious at the moment, with some professors explicitly prohibiting its use or consultation. On the other hand, it is a resource that is available and it feels foolish not to use what is at your disposal. Throughout the research that went into this article and discussion with faculty members about its potential to provide feedback, I have been introduced to a very clearly constructive way to engage with ChatGPT, that seems to make both staff and students happy. While we are still in the early stages of understanding the potential and risks of generative AI technology, at the end of the day this is a tool that will have implications for Higher Education and we are being faced with the possibility of either embracing it, in various ways such as to generate formative feedback, or let it escape our control at the cost of academic integrity, because it is clear that prohibiting its use will not prevent people from exploiting it.

For further queries: marabortnowschi@yahoo.ca or mara.bortnowschi@warwick.ac.uk (may expire soon)

Reference List

SWNS (2023). University of Warwick fears cheating epidemic as data reveals huge number of students visiting AI website during exams. [online] Kenilworth Nub News. Available at: https://kenilworth.nub.news/news/local-news/university-of-warwick-fears-cheating-epidemic-as-data-reveals-huge-number-of-students-visiting-ai-website-during-exams-176836 [Accessed 19 Jun. 2023].

UKSCQA (2018). UK Quality Code for Higher Education Advice and Guidance Assessment. [online] Available at: https://www.qaa.ac.uk/docs/qaa/quality-code/advice-and-guidance-assessment.pdf?sfvrsn=ca29c181_4 [Accessed 16 Jun. 2023].

Wyatt-Smith, C., Klenowski, V. and Colbert, P. (2014). Assessment Understood as Enabling. The Enabling Power of Assessment, pp.1–20. doi:https://doi.org/10.1007/978-94-007-5902-2_1.


June 26, 2023

Using AI for Formative Feedback: Current Challenges, Reflections, and Future Investigation

By Matthew Voice, Applied Linguistics at the University of Warwick

One strand of the WIHEA’s working group for AI in education has focused on the role of AI in formative feedback. As part of this strand, I have been experimenting with feeding my own writing to a range of generative AI (ChatGPT, Google Bard, and Microsoft Bing), to learn more about the sorts of feedback they provide.

The accompanying presentation documents my observations during this process. Some issues, such as the propensity of AI to ‘hallucinate’ sources, are well-documented concerns with current models. As discourse on student use of AI begins to make its way into the classroom, these challenges might provide a basis for critical discussion around the accuracy and quality of the feedback produced by language models, and the need for student to review any outputs produced by LLMs.

Other common issues present different challenges for students using LLMs to elicit formative feedback. For instance, the prompt protocol in the presentation revealed a tendency for AI to provide contradictory advice when its suggestions are queried, leading to a confusing stance on whether or not an issue raised actually constitutes a point for improvement within the source text. When tasked with rewriting prompt material for improvement, LLMs consistently misconstrued (and therefore left absent) some of the nuances of my original review, in a fashion which changed key elements of the original argumentation without acknowledgement. The potential challenges for student users which arise from these tendencies is discussed in more detail in the presentation’s notes.

In addition to giving some indication of the potential role of LLMs in formative feedback, this task has also prompted me to reflect on the way I approach and understand generative AI as an educator. Going forward, I want to suggest two points of reflection for future tasks used to generate and model LLM output in pedagogical contexts. Firstly: is the task a reasonable one? Using LLMs ethically requires using my own writing as a basis for prompt material, but my choice to use published work means that the text in question had already been re-drafted and edited to a publishable standard. What improvements were the LLMs supposed to find, at this point? In future, I would be interested to try eliciting LLM feedback on work in progress as a point of comparison.

Secondly, is the task realistic, i.e. does it accurately reflect the way students use and engage with AI independently? The review in my presentation, for example, presupposes that the process of prompting an LLM for improvements to pre-written text is comparable to student use of these programmes. But how accurate is this assumption? In the Department of Applied Linguistics, our in-progress Univoice project sees student researchers interviewing their peers about their academic process. Data from this project might provide clearer insight into the ways students employ AI in their learning and writing, providing a stronger basis for future critical investigation of the strengths and limitations in AI’s capacity as a tool for feedback.

This is blog 14 in our diverse assessment series, the two most recent previous blogs can be found here:


June 22, 2023

Rethinking authentic assessment: work, well–being, and society by Jan McArthur

In this 2022 paper, Jan McArthur builds on “existing work on authentic assessment to develop a more holistic and richer concept that will be more beneficial to individual students and to the larger society of which they are part.” McArthur presents three key principles to help us rethink and broaden the concept of authentic assessment: 1) From real world/world of work to society; 2) From task performance to why we value the task; 3) From the status‑quo of real‑world/world of work to transforming society. If you are short on time, you might want to jump straight to page 8 where discussion of these three principles begins:

https://link.springer.com/article/10.1007/s10734-022-00822-y


June 19, 2023

Assessments: Capturing Lived Experience and Shaping the Future

Reflection on Project Outputs by Molly Fowler

Molly Fowler photo

This WIHEA funded co-creation project aimed to capture and explore student and staff perspectives on diverse assessment. Neither group were clearly able to define a diverse assessment strategy, but interestingly their feelings about assessment and ideas of how they can be improved were very similar. Students expressed a desire for greater choice, flexibility and equitable access to assessments. Equitable access encompasses a wide range of complex personal needs including language requirements, disability, neurodiversity, caring responsibilities, and the need to work alongside studies. Staff iterated many of the same concepts but framed their ideas around pedagogical models. There was a strong emphasis on learning from assessments on both sides and a widespread longing for a culture shift to design assessments that model a fair and fulfilling education. Student co-creation was seen as a necessary tool to expedite the shift towards embedding assessments as part of the learning journey.

I am a final year student on the Health and Medical Sciences BSc programme. My role as a student cocreator in this research project was to collect and analyse data from students and staff pertaining to their beliefs around assessment. In the analysis stage of the project, I mainly focused on collating and summarising the student data. I am new to conducting primary research and I have thoroughly appreciated this experience. I enjoyed the challenge of leading interviews and focus groups and deciding when to explore a statement further or manoeuvre back to the set questions. Gaining first-hand insight into the research process has augmented my ability to understand and extract key information from research papers which will be a life-long skill – and was particularly useful when I was conducting a systematic review for my dissertation. It has been very satisfying to observe my own personal development in this way.

This project has made me aware of my privilege in assessments as a neurotypical English speaker. I have been exposed to a range of different perspectives on assessment and I hope to be better equipped to identify problems and support those around me. For example, I was surprised to learn that international students feel more disadvantaged by multiple choice exams than essays, as MCQs often require a nuanced understanding of language and grammar. Similarly, I have always taken a pragmatic approach to assessments and centred my learning around them. I had not previously considered assessments as part of the learning journey or as a learning exercise. As I move into the next phase of my own education, I will try to extend my learning beyond assessments to gain knowledge that I can use in my profession. Undertaking this project has been an enriching experience as a student and as an individual. It has shaped my approach to my assessments, and I have become more aware of the complex needs of others who are completing the same assessment. Students and staff are calling for the same changes to assessment methodology, which can only be implemented if the University takes a holistic approach to restructuring assessments with students contributing to the process.

I look forward to bringing my knowledge from this assignment into my next research project. This is the 13th blog in our diverse assessment series. Previous blogs can be found here:

Blog 1: Launch of the learning circle (Isabel Fischer & Leda Mirbahai): https://blogs.warwick.ac.uk/wjett/entry/interested_in_diverse/

Blog 2: Creative projects and the ‘state of play’ in diverse assessments (Lewis Beer): https://blogs.warwick.ac.uk/wjett/entry/creative_projects_and/

Blog 3: Student experience of assessments (Molly Fowler): https://blogs.warwick.ac.uk/wjett/entry/a_student_perspective/

Blog 4: Assessment Strategy – one year after starting the learning circle (Isabel Fischer & Leda Mirbahai): https://blogs.warwick.ac.uk/wjett/entry/one_year_on/

Blog 5: Learnings and suggestions based on implementing diverse assessments in the foundation year at Warwick (Lucy Ryland): https://blogs.warwick.ac.uk/wjett/entry/learnings_suggestions_based/

Blog 6: How inclusive is your assessment strategy? (Leda Mirbahai): https://blogs.warwick.ac.uk/wjett/entry/blog_6_how/

Blog 7: Democratising the feedback process (Linda Enow): https://blogs.warwick.ac.uk/wjett/entry/democratising_the_feedback/

Blog 8: AI for Good: Evaluating and Shaping Opportunities of AI in Education (Isabel Fischer, Leda Mirbahai & David Buxton): https://blogs.warwick.ac.uk/wjett/entry/ai_for_good/

Blog 9: On ‘Opportunities of AI in Higher Education’ by DALL.E and ChatGPT (Isabel Fischer): https://blogs.warwick.ac.uk/wjett/entry/on_opportunities_of/

Blog 10: Pedagogic paradigm 4.0: bringing students, educators and AI together (Isabel Fischer): https://www.timeshighereducation.com/campus/pedagogic-paradigm-40-bringing-students-educators-and-ai-together

Blog 11: Ethically deploying AI in education: An update from the University of Warwick’s open community of practice (Isabel Fischer, Leda Mirbahai, Lewis Beer, David Buxton, Sam Grierson, Lee Griffin, and Neha Gupta): https://www.open.ac.uk/scholarship-and-innovation/scilab/ethically-deploying-ai-education

Blog 12: Building knowledge on the pedagogy of using generative AI in the classroom and in assessments (Isabel Fischer and Matt Lucas): https://blogs.warwick.ac.uk/wjett/entry/building_knowledge_on/

Join the Diverse Assessment Learning Circle: If you would like to join the learning circle please contact the co-leads: Leda Mirbahai, Warwick Medical School (WMS) (Leda.Mirbahai@warwick.ac.uk) and Isabel Fischer, Warwick Business School (WBS) (Isabel.Fischer@wbs.ac.uk). This LC is open to non-WIHEA members.


June 12, 2023

Building knowledge on the pedagogy of using generative AI in the classroom and in assessments

By Matt Lucas and Isabel Fischer (WBS)

Matt Lucas is a Senior Product Manager at IBM, and Isabel Fischer is an Associate Professor (Reader) of Information Systems at WBS (Warwick Business School). Isabel also co-convenes an IATL (Institute for Advanced Teaching and Learning) module. This blog represents their own opinions and not those of their employers.

After two terms of including generative AI (GenAI) in my teaching and for assessments I am still building my knowledge and understanding around the pedagogy of using GenAI. Students seem to like the entertainment of playing around with music and art (e.g. DALL.E 2 and midjourney), creating images and also memes, with all of these being user-friendly for big screens and also for huddling around one laptop as part of teamwork. Text-outputs seems less intuitive for ‘collective use’: There does not seem to be yet an app available that allows for hands-on collaborative refinement of prompts (e.g. similar to students working on the same Google doc). And displaying a string of words on a shared screen clearly does not have the same entertainment value for students as ‘customers and consumers’.

In addition to a lack of entertainment value I also found that students seem to actually appreciate word-based GenAI (e.g. ChatGPT and Bard) as ‘their secret tool’ at their disposal and for them to use. They appear to appreciate it, if lecturers show them the exact prompts that they can copy that allows them to make the most of ‘their secret tool’. They seem less keen about having to be transparent about using the tool themselves and having to justify and critically reflect on usage. It not only means additional work, more importantly, they dislike the thought of the tool’s hidden power being exposed. They appear even less keen for lecturers to use GenAI for the lesson preparation and to be transparent about it because otherwise, what is the ‘perceived added value’ of attending the lecture if they could have just reviewed GenAI?

With this in mind, what are the skills that students can learn from using GenAI in the classroom and in assessments?

In the attached blog Matt Lucas and I suggest that by including innovative aspects into assessments, students can learn and practise four skills that are relevant for their future careers in a world disrupted by AI:

  1. Cognitive flexibility, abstraction and simplification

  2. Curiosity, including prompt engineering

  3. Personalisation, reflection and empathising to adapt to different audiences

  4. Critical evaluation of AI

For each of the four skills we explain in the attached blog the relevance for student learning with some illustrative examples, before outlining how we have incorporated these four skills into students’ assessments in the recent term.


May 30, 2023

Ungrading: more possibilities than some might think

Assessment is often the biggest cause of student anxiety and distress. Some have begun to explore ‘ungrading’ as a way to enhance the developmental rather than judgemental aspects of assessment. Ungrading can be implemented in various ways and is a process of decentring summative grades or marks. In this 17-minute video, Martin Compton from UCL explores the potential of ‘ungrading’ and the various ways that he has implemented elements of it.

https://www.youtube.com/watch?v=xdBYm8K_pVI&list=PLAbF8wnSF-e9H54nDtvsCXehw4xDS9xQb&index=2


April 03, 2023

Democratising the feedback process

Blog 7 of our Diverse Assessment Learning Series, based on a recent keynote address as part of the Diverse Assessments WIHEA Learning Circle

Linda Enow photo

By Dr Linda Enow, from Newman University, Birmingham UK

We need to rethink feedback in Higher Education (HE). To examine feedback in HE, this contribution engages with democratic principles, and through Audio-Visual feedback (AVF) interrogates current feedback practices. Some current gaps in feedback research are on understanding the nature of student engagement and interaction with feedback, relational constituents of feedback processes, and the role of technology in supporting feedback processes. This paper posits that power imbalance with feedback is a barrier to effective feedback engagement and interaction. Embedded in the, now challenged, conceptualisation of feedback giver and feedback recipient dynamic is implicit power imbalance. A further challenge for feedback is the over-reliance on written feedback within which is arguably entrenched the ‘product’ conceptualisation of feedback. Through the exploration of democratic and cognitive requirements of feedback, this contribution traces an outlook which values equitable relationships and emphasises the positioning of feedback as a process. Insights on feedback processes in this piece have emerged from empirical work on audio-visual feedback.

Where we are in HE with assessment design?

Significant strides are being taken to design diverse assessment tasks in HE. These tasks are designed with due consideration of the diverse student population and established knowledge of andragogy. Typically, assessment design aims to showcase inclusion. With graduate outcome requirements for HEIs in the UK, assessments subsequently engage with the more utilitarian constituent of seeking applicability in workplace settings. Perhaps assessments should be more dynamic, for instance generated from cohort identity and depth of knowledge of the practice requirements of students, rather than current static assessment practices. In any case, progress made with assessment design and the thinking behind assessment practices demonstrates the ongoing effort of Higher Education Institutes (HEIs) to be diverse in their practices, and this is welcome effort in the right direction.

As we diversify assessments, is feedback left behind?

In the context of assessment, the corresponding element of feedback is left behind. Written feedback remains the dominant feedback mode. A small proportion engages with audio-feedback, typically as a one-off. Audio-Visual Feedback (AVF) despite demonstrating its multimodal composition (see West & Turner, 2016) is minimally used compared to written feedback and audio feedback (Lowenthal, 2021; Nicol, 2012). Audio-visual feedback is inclusive, multimodal, precise, and retains its quality over time hence guaranteeing portability. The nature of AVF embeds verbal feedback with access to the written assignment, and the use of technology to enable precision. Verbalising naturally offers more depth in explanation with the added advantage of capturing relational constituents of feedback. AVF is a suitable format for the thinking or the cognitive composition of the feedback to become explicit (see Nicol, 2021). For all involved in the feedback process engagement and interaction are both enhanced. A summation of these strengths is reflected in Payne et al. (2022) positioning audio and video feedback modes as humanising.

We no longer ‘give’ feedback

Contemporary research on feedback processes tracks the evolution of feedback from the now defunct perception of information which was given to the students, to the contemporary understanding that; first of all feedback is a process, and secondly feedback is dialogic, ongoing and embedded with andragogical expectations, as well as aspirations of transferability and portability (e.g. de Kleijn, 2023; Winstone et al., 2022a). Equipped with the understanding that saying you are giving feedback is no longer acceptable (Winstone, et al., 2022b) from the andragogical perspective, this paper makes the case to re-think feedback processes from the bases of democratic principles. Educating, especially in HEIs in the UK, is based on foundational democratic principles imbued with a variety of duties, rights and responsibilities. Power imbalance is therefore in conflict with these democratic principles. From the position of evaluating HEIs in the UK, Winstone et al., (2022b: 1524) raises the concern that National Student Survey (NSS) questions “promote an outdated view of feedback as information transmitted from teacher to student in a timely and specific manner, largely ignoring the role of the student in learning through feedback processes”.

How do we democratise feedback?

Democratic principles advocate freedoms, rights and responsibilities. If we think about feedback following democratic principles, we interrogate primarily the power imbalance in the lecturer-student relationship. The lecturer is in the powerful position of ownership of the feedback which they ‘give’ to the student (see Matthews, et al., 2021). The sense of ownership of the feedback content is taken away from the student and the student becomes a recipient. This power imbalance means the student has a skewed relationship with this feedback which is being imposed on them. What is the student to do with this feedback which has been given to them? What if the student does not identify with the feedback which has been given? Carless (2015:28) declares; impact on learning is limited unless students are actively engaging with feedback processes, and ultimately acting on feedback. Consensus on what these processes constitute is yet to be arrived at as De Kleijn (2023) requests clarity of the activities and strategies which must be applied in these feedback processes. When democratic principles are not followed, distance is created between the student and the feedback process.

In contrast to the lecturer-student feedback element, there is strong uptake of peer-feedback. Peer feedback (see Deneen & Hoo, 2023) supports feedback dialogue, evidences students’ feedback literacy and supports development of self-regulation skills. The strong interest in peer feedback is a result of the significant benefits. Nicol and McCallum (2022) assert that powerful insights are generated from student feedback, at times more powerful than teacher feedback. From the research which this contribution on democratising feedback draws on, the strength of peer feedback is being explored and understood from the position of power. There is the opportunity to redesign assessments to possibly capture and support these peer feedback partnerships linking them to assessment design. Moving away from the conceptualisation of feedback as product, to feedback as process serves to further democratisation of feedback. Careful consideration in incorporating peer feedback within this process contributes to improving feedback dialogue, and to establishing tangible steps. Continuing this dialogue using the audio-visual mode opens up relational spaces and further enhances feedback processes.

Audio-Visual Feedback (AVF)

The discursive nature of AVF assists in personalising feedback and modelling the expectation for engaging with feedback. The multimodal nature of audio-visual feedback (Lacković, and Popova, 2021) necessitates active designation of time to not only engage with feedback, but to interact with feedback. AVF supports the transition of feedback into a process. There is a challenge embedded in this; assessment design minimally factors in feedback avenues and suitable allocation of time for AVF. This is understandable as written feedback is dominant in HEIs in the UK. In order for AVF to attain its full potential, HEIs need to re-work their assessment and feedback policies. Re-working policies is definitely not to give more time to turnaround times; rather this is more time to work on effectively embedding AVF as a viable feedback avenue. AVF is not without its potential challenges. One example is large class sizes and staffing limitations in some HEIs. A potential solution is considering AVF for group assessments and group feedback. Teaching in HEIs is inherently multimodal; combining written, audio and audio-visual content. Why is feedback disproportionately in one format?

Some points to facilitate change
  1. Undertake an audit of feedback formats; recording written format, audio format, and audio-visual format.
  2. Interrogate power imbalance relating to feedback and explore institution-focused and relevant ways to improve the power imbalance.
  3. Co-design feedback processes in line with contemporary co-designing of assessments. Ensure feedback is not an add-on or an after-thought to assessments (see Ajjawi and Boud, 2018).
  4. Review assessment and feedback policies.
  5. Equity: establish feedback processes reflective of advances in learning, teaching and scholarship.
What next?

Feedback is a process involving students and lecturers in a shared space. Cognitive, sociocultural, social constructivist positions, amongst others are at play and space needs to be created for democratic principles. Alongside making sense of feedback, cognitive drivers pass judgement on the utility of feedback, and decision making guides portability of feedback. This contribution advocates the externalisation of these cognitive constituents as a precursor to democratising feedback. The argument is; the student has a right to know. Knowing in this way is empowering. Through democratisation, the student evolves from the position of a recipient to that of a partner in the feedback process. Partnership conjures images of ownership, responsibility and duties. Democratising feedback, through the medium of audio-visual feedback (AVF) removes the power imbalance, showcases the dialogic nature of feedback, and enhances subsequent portability of knowledge and skills. Whilst there is empirical work on feedback as a process, implementation in the structures of HEIs is yet to follow.

This is the 7th blog in our diverse assessment series. Previous blogs can be found here:

Blog 1: Launch of the learning circle (Isabel Fischer & Leda Mirbahai): https://blogs.warwick.ac.uk/wjett/entry/interested_in_diverse/

Blog 2: Creative projects and the ‘state of play’ in diverse assessments (Lewis Beer): https://blogs.warwick.ac.uk/wjett/entry/creative_projects_and/

Blog 3: Student experience of assessments (Molly Fowler): https://blogs.warwick.ac.uk/wjett/entry/a_student_perspective/

Blog 4: Assessment Strategy – one year after starting the learning circle (Isabel Fischer & Leda Mirbahai): https://blogs.warwick.ac.uk/wjett/entry/one_year_on/

Blog 5: Learnings and suggestions based on implementing diverse assessments in the foundation year at Warwick (Lucy Ryland): https://blogs.warwick.ac.uk/wjett/entry/learnings_suggestions_based/

Blog 6: How inclusive is your assessment strategy? (Leda Mirbahai): https://blogs.warwick.ac.uk/wjett/entry/blog_6_how/

Join the Diverse Assessment Learning Circle: If you would like to join the learning circle please contact the co-leads: Leda Mirbahai, Warwick Medical School (WMS) (Leda.Mirbahai@warwick.ac.uk) and Isabel Fischer, Warwick Business School (WBS) (Isabel.Fischer@wbs.ac.uk). This LC is open to non-WIHEA members.

References

Ajjawi, R. & Boud, D. (2018) 'Examining the nature and effects of feedback dialogue', Assessment & Evaluation in Higher Education, 43:7, 1106-1119, DOI: 10.1080/02602938.2018.1434128 https://doi.org/10.1080/02602938.2018.1434128

Carless, D. (2015) Excellence in university assessment: learning from award-winning practice. London: Routledge.

Deneen, C. C. & Hoo, H-T. (2023) 'Connecting teacher and student assessment literacy with self-evaluation and peer feedback', Assessment & Evaluation in Higher Education, 48:2, 214-226, DOI: 10.1080/02602938.2021.1967284 https://doi.org/10.1080/02602938.2021.1967284

de Kleijn, R. A. M. (2023) 'Supporting student and teacher feedback literacy: an instructional model for student feedback processes', Assessment & Evaluation in Higher Education, 48:2, 186-200, DOI: 10.1080/02602938.2021.1967283

Lacković, N. & Popova, B. (2021) 'Multimodality and socio-materiality of lectures in global universities’ media: accounting for bodies and things', Learning, Media and Technology, 46:4, 531-549, DOI: 10.1080/17439884.2021.19286


February 20, 2023

Blog 6: How inclusive is your assessment strategy?

By Leda Mirbahai (Warwick Medical School)

Leda Mirbahai photo

Assessments are a fundamental part of student experience - with students learning by doing, i.e. by engaging with assessment tasks and then, after submission through the feedback they receive on their performance and progress Internal and External Examiner often ensure that assessment strategies are reliable, effective, and accurate, however, whether they are inclusive is often overlooked.

Considering the diversity of our student population, it is clear that ‘one size fits all’ approach to assessment design and delivery is not an inclusive assessment strategy which links with diversification of our assessment approaches (a point that we will come back to shortly). The Quality Assurance Agency (QAA), UK’s quality code for higher education, lists 10 guiding principles on expectations and practices for assessment which are:

  1. Assessment methods and criteria are aligned to learning outcomes and teaching activities.
  2. Assessment is reliable, consistent, fair and valid.
  3. Assessment design is approached holistically.
  4. Assessment is inclusive and equitable.
  5. Assessment is explicit and transparent.
  6. Assessment and feedback are purposeful and supports the learning process.
  7. Assessment is timely.
  8. Assessment is efficient and manageable.
  9. Students are supported and prepared for assessment.
  10. Assessment encourages academic integrity.

As mentioned, a criteria of good assessment ‘strategy’ is to be inclusive and equitable. If one really reflects on the 10 principles, they are all interlinked. For example, it is very difficult to demonstrate an assessment strategy that is inclusive where assessment loads are not manageable for our students or students don’t receive equitable level of support. With Toolkits such as ‘Embedding inclusive assessment reflective toolkit’, a project funded by QAA, becoming available we can reflect on the assessment strategy of our courses and programmes by considering how well we align to attributes of an inclusive assessment. The toolkit starts to encourage a triangulation critic of our assessment approaches by involving major stakeholders from students, academics and leaders in the process and asking some key reflective questions. Reflecting on the 9 attributes of inclusive assessment as mentioned by this toolkit, most questions are basically reflecting on the principles of good assessment, which in my view indicates that if your assessment aligns to the principles, it should promote inclusivity.

As an academic staff leading the assessment strategy for a new UG course at WMS, I have been involved in the planning and delivery of a course level assessment strategy. As the course is new, it has removed some of the challenges of trying to alter and adjust existing assessments for individual modules. This raises an important concept; assessment strategies should be seen at course/programme level. Going back to principles of good assessments, we need to demonstrate how our assessments map to course level, year level and module level learning outcomes as well as ensuring students are supported and prepared for assessments. This highlights the gradual building of our learner’s skills and knowledge in a spiral curriculum and enabling them to receive continuous feedback on their progress in a meaningful way. Learners need to be able to visualise and reflect on their progress across the programme and to achieve this, our assessments in one module (skills, attribute and knowledge) need to meaningfully build on the previous modules; hence programme level approach being more desirable.

Using diverse assessments is a great way of acknowledging that ‘one size fits all’ approach to assessment design and delivery is not an inclusive assessment strategy. However, this also doesn’t mean that we should sprinkle our assessment programmes with as many different modes of assessments possible. Introducing too many different modes of assessments that are not revisited or are not utilising or building on skills and knowledge of a learner would just add to the level of stress encountered by our learner as it means our students need to learn a new and unfamiliar assessment approach just to use it once! Therefore, diversification should be achieved in a meaningful way rather than just for the sack of introducing new assessment modes.

Finally, in my view one of the most important aspects of a good assessment strategy is continuous reflection and improvement. There is no fixed perfect assessment strategy as our learners and their requirements are constantly changing. An assessment strategy that is developed and never revised will soon become unfit for the purpose it was developed. I know this as I am already making a list of changes that we need to embed into our assessment strategy and the course is only 3 years old! So I leave you with one question. How inclusive is your assessment strategy?

References

The Quality Assurance Agency (QAA) for Higher Education (2018) UK Quality Code for Higher Education advice and guidance: Assessments.

Embedding inclusive assessment-Reflective toolkit (2022), a QAA funded project. Developing a Set of Inclusive Assessment Design Attributes for use Across the Higher Education Sector (qaa.ac.uk)

This is the 6th blog in our diverse assessment series. Previous blogs can be found here:

Blog 1: Launch of the learning circle (Isabel Fischer & Leda Mirbahai): https://blogs.warwick.ac.uk/wjett/entry/interested_in_diverse/

Blog 2: Creative projects and the ‘state of play’ in diverse assessments (Lewis Beer): https://blogs.warwick.ac.uk/wjett/entry/creative_projects_and/

Blog 3: Student experience of assessments (Molly Fowler): https://blogs.warwick.ac.uk/wjett/entry/a_student_perspective/

Blog 4: Assessment Strategy – one year after starting the learning circle (Isabel Fischer & Leda Mirbahai): https://blogs.warwick.ac.uk/wjett/entry/one_year_on/

Blog 5: Learnings and suggestions based on implementing diverse assessments in the foundation year at Warwick (Lucy Ryland): https://blogs.warwick.ac.uk/wjett/entry/learnings_suggestions_based/

Join the Diverse Assessment Learning Circle: If you would like to join the learning circle please contact the co-leads: Leda Mirbahai, Warwick Medical School (WMS) (Leda.Mirbahai@warwick.ac.uk) and Isabel Fischer, Warwick Business School (WBS) (Isabel.Fischer@wbs.ac.uk). This LC is open to non-WIHEA members.


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  • Very interesting, thank you for sharing. Great CPD reflection. by Joel Milburn on this entry
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