Tag: academic-integrity

Old problem, new era

heducationist2 Comments

“Empirical studies suggest that a majority of students cheat. Longitudinal studies over the past six decades have found that about 65–87% of college students in America have admitted to at least one form of nine types of cheating at some point during their college studies”

(Yu et al., 2018)

Shocking? Yes. But also reassuring in its own way. When you are presented with something like that from 2018 (ie. pre chatgpt) you realise that this is not a newly massive issue; it’s the same issue with a different aspect, lens or vehicle. Cheating in higher education has always existed, but I do acknowledge that generative AI has illuminated it with an intensity that makes me reach for the eclipse goggles. There are those that argue that essay mills and inappropriate third party support were phenomena that we had inadequately addressed as a sector for a long time. LLMs have somehow opened a fissure in the integrity debate so large that suddenly everyone wants to do something about it. it has become so much more complex because of that but also that visibility could be seen positively (I may be reaching but I genuinely think there is mileage in this) not least because: 

1. We are actually talking about it seriously. 

2. It may give us leverage to effect long needed changes. 

The common narratives I hear are ‘where there’s a will, there’s a way’ and chatgpt makes the ‘way’ easier. The problem though, in my view, is that just because the ‘way’ is easier does not mean the ‘will’ will necessarily increase. Assuming all students will cheat does nothing to build bridges, establish trust or provide an environment where the sort of essential mutual respect necessary for transparent and honest working can flourish.  You might point to the stat at the top of this page and say we are WAY past the need to keep measuring will!  Exams, as I’ve argued before, are no panacea, given the long-standing issues of authenticity and inclusivity they bring (as well as being the place where students have shown themselves to be most creative in their subversion techniques!). 

In contrast to this, study after study is finding that students are increasingly anxious about being accused of cheating when that was never their intention. They report unclear and sometimes contradictory guidance, leaving them uncertain about what is and isn’t acceptable. A compounding  issue  is the lack of consistency in how cheating is defined. it varies significantly between institutions, disciplines and even individual lecturers. I often ask colleagues whether certain scenarios constitute cheating, deliberately using examples involving marginalised students to highlight the inconsistencies.  Is it ok to get structural, content or proof reading  suggestions from your family? How does your access to human support differ if you are a first generation, neurodivergent student studying in a new language and country? Policies usually say “no” but to fool ourselves that this sort of ‘cheating’ is not routine would be hard to achieve and even harder to evidence. The boundaries are blurred, and the lack of consensus only adds to the confusion.

To help my thinking on this I looked again at some articles on cheating over time (going back to 1941!) that I had put in a folder and badly labelled as per usual and selected a few to give me a sense of the what and how as well as the why and to provide a baseline to inform the context around the current assumptions about cheating. Yu et al. (2018) use a long established categorisation of types of cheating with a modification to acknowledge unauthorised digital assistance:

  1. Copying sentences without citation.
  2. Padding a bibliography with unused sources.
  3. Using published materials without attribution.
  4. Accessing exam questions or answers in advance.
  5. Collaborating on homework without permission.
  6. Submitting work done by others.
  7. Giving answers to others during an exam.
  8. Copying from another student in an exam.
  9. Using unauthorised materials in an exam.

The what and how question reveals plenty of expected ways of cheating, especially in exams but it is also noted where teachers / lecturers are surprised by the extent and creativity. Four broad types:

  1. Plagiarism in various forms from self, to peers to deliberate inappropriate practices in citation.
  2. Homework and assignment cheating such as copying work, unauthorised collaboration, or failing to contribute fairly.
  3. Other academic dishonesty such as falsifying bibliographies, influencing grading or contract cheating.
  4. In exams.

The amount of exam based cheating reported should really challenge assumptions about the security of exams at the very least and remind us that they are no panacea whether we see this issue through an ongoing or a chatgpt lens. Stevens and Stevens (1987) in particular share some great pre-internet digital ingenuity and Simpkin and McLeod (2006) show how the internet broadened the scope and potential. These are some of the types reported over time: 

  1. Using unauthorised materials.
  2. Obtaining exam information in advance.
  3. Copying from other students.
  4. Providing answers to other students.
  5. Using technology to cheat (using microcassettes, pre-storing data in calculators, mobile phones. Not mentioned but now apparently a phenomenon is use of bone conduction tech in glasses and/ or smart glasses).
  6. Using encoded materials (rolled up pieces of paper for example).
  7. Hiring a surrogate to take an exam.
  8. Changing answers after scoring (this one in Drake,1941)
  9. Collaborating during an exam without permission.

These are the main reasons for cheating across the decades I could identify (from across all sources cited at the end):

  1. Difficulty of the work. When students are on the wrong course (I’m sure we can think of many reasons why this might occur), teaching is inadequate or insufficiently differentiated.
  2. Pressure to succeed. ‘Success’ when seen as the principal goal can subdue the conscience.
  3. Laziness. This is probably top of many academics’ assumptions and it is there in the research but also worth considering what else competes for attention and time and how ‘I can’t be bothered’ may also mask other issues even in self-reporting. 
  4. Perception that cheating is widespread. If students feel others are doing it and getting away with it, it increases the cheating.
  5. Low risk of getting caught.
  6. Sense of injustice in systemic approach, structural inequalities both real and perceived can be seen as a valid justification. 
  7. External factors such as evident cheating in wider society. A fascinating example of this was suggested to me by an academic who was trained in Soviet dominated Eastern Europe who said cheating was (and remains) a marker of subversion so carries its own respectability)
  8. Lack of understanding of what is allowed and is not- students reporting they have not been taught this and degrees of cheating blurred by some of the other factors here- when does collaboration become collusion?
  9. Cultural influences. Different norms and expectations can create issues and this comes back to my point about individualised (or contextualised) definitions of what is and is not appropriate. 
  10. My own experiences, over 30 years, of dealing with plagiarism cases often reveals very powerful, often traumatic, experiences that lead students to act in ways that are perceived as cheating.

For each it’s worth asking yourself:

How much is the responsibility for this on the student and how much on the teacher/ lecturer and / or institution (or even society)?

I suspect that the truly willful, utterly cynical students are the ones least likely to self declare and are least likely to get caught. This furthers my own discomfort about the mechanisms we rely (too heavily?) on to judge integrity too.

This skim through really did make clear to me that cheating and plagiarism are not the simple concepts that many say they are. Also cheating in exams is a much bigger thing than we might imagine. The reasons for cheating are where we need to focus I think.  Less so the ‘how’ as that becomes a battleground and further entrenches ‘us and them’ conceptualisations.  When designing curricula and assessments the unavoidable truth is we need to do better by moving away from one size fits all approaches, by realising cultural, social and cognitive differences will impact many of the ‘whys’ and hold ourselves to account when we create or exacerbate structural factors that broaden likelihood of cheating. 

I am definitely NOT saying give wilful cheaters a free pass but all the work many universities are doing on assessment reform needs to be seen through a much longer lens than the generative AI one. To focus only on that is to lose sight of the wider and longer issue. We DO have the capacity to change things for the better but that also means that many of us will be compelled (in a tense, under threat landscape) to learn more about how to challenge conventions and even invest much more time in programme level, iterative, AI cognisant teaching and assessment practices. Inevitably the conversations will start with the narrow and hyped and immediate manifestations of inappropriate AI use but let’s celebrate this as leverage; as a catalyst.  We’d do well, at the very least, to reconsider how we define cheating, why we consider some incredibly common behaviours as cheating (is it collusion or is it collaboration for example or proof reading help from 3rd parties). Beyond that, we should be having serious discussions about augmentation and hybridity in writing: what counts as acceptable support? How does that differ according to context and discipline? It will raise questions about the extent to which writing is the dominant assessment medium, about authenticity in assessment and about the rationale and perceived value of anonymity. 

It’s interesting to read how over 80 years ago (Drake, 1941) many of the behaviours we witness today in both students and their teachers have 21st century parallels. Strict disciplinarian responses or ignoring it because ‘they’re only harming themselves’ being common. In other words, the underlying causes were not being addressed. To finish I think this sets out the challenge confronting us well:

“Teachers in general, and college professors in particular, will not be enthusiastic about proposed changes. They are opposed to changes of any sort that may interfere with long- established routines-and examinations are a part of the hoary tradition of the academic past”

(Drake, 1941, p.420)

Drake, C. A. (1941). Why students cheat. Journal of Higher Education, 12(5)

Hutton, P. A. (2006). Understanding student cheating and what educators can do about it. College Teaching, 54(1), 171–176. https://www.jstor.org/stable/27559254 

Miles, P., et al. (2022). Why Students Cheat. The Journal of Undergraduate Neuroscience Education (JUNE), 20(2):A150-A160 

Rettinger, D. A., & Kramer, Y. (2009). Situational and individual factors associated with academic dishonesty. Research in Higher Education, 50(3), 293-313. https://doi.org/10.1007/s11162-008-9116-5 

Simkin, M. G., & McLeod, A. (2010). Why do college students cheat?. Journal of Business Ethics, 94, 441-453. https://doi.org/10.1007/s10551-009-0275-x 

Stevens, G. E., & Stevens, F. W. (1987). Ethical inclinations of tomorrow’s managers revisited: How and why students cheat. Journal of Education for Business, 63(1), 24-29. https://doi.org/10.1080/08832323.1987.10117269 

Yu, H., Glanzer, P. L., Johnson, B. R., Sriram, R., & Moore, B. (2018). Why college students cheat: A conceptual model of five factors. The Review of Higher Education, 41(4), 549-576. https://doi.org/10.1353/rhe.2018.0025 

Gallant, T. B., & Drinan, P. (2006). Organizational theory and student cheating: Explanation, responses, and strategies. The Journal of Higher Education, 77(5), 839-860. https://www.jstor.org/stable/3838789 

AI3*: Crossing the streams of artificial intelligence, academic integrity and assessment innovation

heducationist2 Comments

*That’s supposed to read AI3 but the title font refuses to allow superscript!

Yesterday I was delighted to keynote at the Universities at Medway annual teaching and learning conference. It’s a really interesting collaboration of three universities: University of Greenwich, University of Kent and Canterbury Christchurch University. Based at the Chatham campus in Medway you can’t help but notice the history the moment you enter the campus. Given that I’d worked at Greenwich for five years I was familiar with the campus but, as was always the case when I went there during my time at Greenwich, I experienced a moment of awe when seeing the campus buildings again. It’s actually part of the Chatham Dockyard World Heritage site and features the remarkable Drill Hall library. The reason I’m banging on about history is because such an environment really underscores for me some of those things that are emblematic of higher education in the United Kingdom (especially for those that don’t work or study in it!)

It has echoes of cultural shorthands and memes of university life that remain popular in representations of campus life and study. It’s definitely a bit out of date (and overtly UK centric) like a lot of my cultural references, but it made me think of all the murders in the Oxford set crime drama ‘Morse’.  The campus locations fossilised for a generation the idea of ornate buildings, musty libraries and deranged academics. Most universities of course don’t look like that and by and large academics tend not to be too deranged. Nevertheless we do spend a lot of time talking about the need for change and transformation whilst merrily doing things the way we’ve done them for decades if not hundreds of years. Some might call that deranged behaviour. And that, in essence, was the core argument of my keynote: For too long we have twiddled around the edges but there will be no better opportunity than now with machine-assisted leverage to do the things that give the lie to the idea that universities are seats of innovation and dynamism. Despite decades of research that have helped define broad principles for effective teaching, learning, assessment and feedback we default to lecture – seminar and essay – report – exam across large swathes of programmes. We privilege writing as the principle mechanism of evidencing learning. We think we know what learning looks like, what good writing is, what plagiarism and cheating are but a couple of quick scenarios to a room full of academics invariably reveal lack of consensus and a mass of tacit, hidden and sometimes very privileged understandings of those concepts.

Employing an undoubtedly questionable metaphor and unashamedly dated (1984) concept of ‘crossing the streams’ from the original Ghostbusters film, I argued that there are several parallels to the situation the citizens of New York first found themselves in way back when and not least the academics (initially mocked and defunded) who confront the paranormal manifestations in their Ghostbusters guises. First are the appearances of a trickle of ghosts and demons followed by a veritable deluge. Witness ChatGPTs release, the unprecedented sign ups and the ensuing 18 months wherein everything now has AI (even my toothbrush).   There’s an AI for That has logged 12,982 AIs to date to give an indication of that scale (I need to watch the film again to get an estimate on number of ghosts). Anyway, early in the film we learn that a Ghost catching device called a ‘Proton Pack’ emits energy streams but:


“The important thing to remember is that you must never under any circumstances, cross the streams.” (Dr Egon Spengler)

Inevitably, of course, the resolution to the escalating crisis is the necessity of crossing the streams to defeat and banish the ghosts and demons. I don’t think that generative AI is something that could or should be defeated and I definitely do not think that an arms race of detection and policing is the way forward either. But I do think we need to cross the streams of the three AIs: Artificial Intelligence; Academic Integrity and Assessment Innovation to help realise the long-needed changes.

Artificial Intelligence represents the catalyst not the reason for needing dramatic change.

Academic Integrity as a goal is fine but too often connotes protected knowledge, archaic practices, inflexible standards and a resistance to evolution.

Assessment innovation is the place where we can, through common language and understanding, address the concerns of perhaps more traditional or conservative voices about perceived robustness of assessments in a world where generative AI exists and is increasingly integrated into familiar tools along with what might be seen as more progressive voices who, well before ChatGPT, were arguing for more authentic, dialogic, process-focussed and, dare I say it, de-anonymised and humanly connected assessments.

Here is our opportunity. Crossing the streams may be the only way we mitigate a drift to obsolescence! MY concluding slide showed a (definitely NOT called Casper) friendly ghost which, I hope, connoted the idea that what we fear is the unknown but as we come to know it we find ways to shift from engagement (sometimes aggressively) to understanding and perhaps even an ‘embrace’ as many who talk of AI encourage us to do.

Incidentally, I asked the Captain (in my custom bot ‘Teaching Trek: Captain’s Counsel’) a question about change and he came up with a similar metaphor:

“Blow Up the Enterprise: Sometimes, radical changes are necessary. I had to destroy the Enterprise to save my crew in “Star Trek III: The Search for Spock.” Academics should learn when to abandon a failing strategy and embrace new approaches, even if it means starting over.”

In a way I think I’d have had an easier time if I’d stuck with Star Trek metaphors. I was gratified to note that ‘The Search for Spock’ was also released in 1984. An auspicious year for dated cultural references from humans and bots alike.

—————–

Thanks:

The conference itself was great and I am grateful to Chloe, Emma, Julie and the team for orgnaising it and inviting me.

Earlier in the day I was inspired by presentations by colleagues from the three universities: Emma, Jimmy, Nicole, Stuart and Laura. The student panel was great too- started strongly with a rejection of the characterisation of students as idle and disintersted and carried on forcefully from there! And special thanks too to David Bedford (who I first worked with something like 10 years ago) who uses an analytical framework of his own devising called ‘BREAD’ as an aid to informing critical information literacy. His session adapted the framework for AI interactions and it prompted a question which led, over lunch, to me producing a (rough and ready) custom GPT based on it.

I should also acknowledge the works I referred to: 1. Sarah Eaton whose work on the 6 tenets of post-plagiarism I heartily recommended and to 2. Cath Ellis and Kane Murdoch* for their ‘enforcement pyramid’ which also works well as one of the vehicles that will help us navigate our way from the old to the new.

*Recommendation of this text does not in any way connote acceptance of Kane’s poor choice when it comes to football team preference.