Greetings from Singapore, where at the first of two workshops at Andragogy for Impact (AnfI) 2026, by the Singapore University of Social Sciences. The first is AI-Enabled Learning and Teaching for Adult Learners by Dr Lye Che Yee, and a gaggle of SUSS staff. The first challenge posed was how student will learn if AI just gives them the answer. One difference noted was that previous search systems required the student to provide keywords & search commands, whereas AI can understand natural language. I suspect this is an illusion. With early programming languages, such as COBOL, it was claimed business people could use it, programmers were not required. This was not the case, nor with later generation languages, when the same claims were made. Training was needed to understand the problem, express it well, document the process, & test the answers.
Dr Yee suggests that learners will need to learn a set of essential skills, even if AI does the hack work. These include communication skills, which is something one of my team's of ANU students system told me was lacking in my CV. One tricky skill which will be needed is judgement.
Associate Professor Wong showed how they redesigned a take home assignment to incorporate use of AI. The student has to explain how it was used with the techniques in the course. They argued it was not necessary to teach how to use LLMs, just as it is unnecessary to teach use of office software. I suggest this is incorrect. At primary school we were trained to make an emergency phone call. Just because people have technology, they do not necessarily know how to use it effectively. In fact Professor Wong's assessment design appears to be teaching how to use a LLM with detailed instructions. The key question for me is the students are trained in how to do this, or just given the assignment and expected to work that out themselves.
One incidental point which came up in discussion is that SUSS still subscribes to TurnItIn for checking student assignments, but has turned of the AI score as it is "nonsense".
We then moved on to adaptive drift, which I didn't quite get. But that might be because I got caught up chatting at morning tea, and was late back.
Back on track we were asked to try an application which used a simulated student to submit answers to typical questions, to be able to see how well LLMs could assess. The point here was that it is hard to simulate a weak student, as the LLM has so much knowledge. This is not an unusual problem, academics who have not been trained to teach have difficulty with struggling students. This suggests that software test techniques could be applied to reviewing assessment tasks. With software development it is routine to run an automated set of tests each time software is changed. The same could be done with assessment: the assignment changes and a series of tests are triggered, with simulated students submitting answers, which are then evulated.
Greetings from the ASCILITE 2024 conference at University of Melbourne. I had intended to go to a research talk, but I couldn't find the "Light Green Room", so I stumbled into a presentation from Turnitin on their AI detection tool (this is in a room with check green carpet & mould coloured roundell patterned wallpaper, so I don;t know what it is called).
Last year Turnitin released an AI writing report with paraphrase detection. This did not appear to work well and ANU decided to switch it off. But perhaps it is time to look at the product again and see if now works acceptable. One aspect I had difficulty understanding is Turnitin is addressing paper based assessment (are universities really returning to paper based assessment.
Turnitin are building a student composition space. The idea is the student writes their assignment within Turnitin. This is an implementation of the "show your work" approach. The tool will have a word processing function. With this approach you can see when the student did what.
Turnitin are also building an offline digital exam system. This is similar to standalone products already in use.
A new similarity report is al in the works for Turnitin. One small example is allowing for margin comments.
Like many, it appears my students will be studying away from the face to face classroom again in 2021, at least for the first part of the year, due to COVID-19. So I have been considering how to improve online delivery, and in particular how to improve the experience of assessment for students while deterring misconduct. One way may be to require students to "Show Your Work" in assessments.
Some Literature
As Nguyen, Keuseman and Humston (2020) note online assessment was suddenly a major concern due to COVID-19. Some techniques they explored for chemistry students were short answers and multiple-choice tests to investigate higher-order thinking. They also looked at more frequent lower-stakes tests. This is possible as they are easier to administer online than face to face. The frequency, the authors suggest, may help with student learning and the lower stakes reduce the temptation to cheat. Interestingly the frequency is also claimed to reduce procrastination, as students are forced to study to pass the frequent small tests, rather than wait until a large test.
Nguyen, Keuseman and Humston (2020) also briefly looked at the use of an academic integrity pledge. However, they concluded this was only effective at an institution that has a culture of academic integrity. This seemed curiously circular reasoning: an institution that did not have a culture of academic integrity presumably would not have an integrity pledge.
To identify misconduct in online STEM courses, Sangalli, Martinez-Muñoz, and Cañabate (2020) analyzed the log from a learning management system of students undertaking exercises. They found co-occurrence of responses to exercises was an indicator of collusion, with pairs of students answering the same questions at the same time. Of course, this might give a false positive for students who are studying together.
Catalena (2020) was able to use the pattern of submission of student's coding assignments to identify plagiarism in a computer course. As students can submit repeatedly to a system that validates their code, it is possible to distinguish those who make incremental progress, from those who submit completed work. However, while submission of completed work without intermediate steps may indicate the student cheated by obtaining a correct answer from someone else it may be they used some other system for refining their answer, or they are exceptionally talented.
Pribela and Pracner (p. 99, 2017) propose building a system for students to create computer code, limiting the student's ability to copy from outside the system. This makes it possible to check the consistency of the student's work. It also prevents students from forgetting to include some element in their final assignment submission, as everything is in the system and accessible to the examiner. This seems a heavy-handed way to try to prevent misconduct and one which would stop students from using outside legitimate tools. The authors produced their temporal file system, based on Git, however, most of the benefit, I suggest, could be obtained by just using Git and allowing students import to it.
Dalziel (2008) suggests students prepare an ePortfolio, including the plans, notes, and comments to deter plagiarism. They point out that if entries are timestamped it is possible to see when entries are added. The author points out that the PebblePad ePortfolio tool has a link to the TurnItIn plagiarism detection tool. However, they don't report any results of using this approach.
Discussion
Students can be reluctant to start work on an assignment. Having a looming deadline, the student can be tempted to use old work of their own or someone else's, a form of poor academic practice or misconduct.
Students are urged to undertake their work methodically. However, they are only asked to submit the final product, not the steps taken to get there. This signals to the student that preparatory work is not of value. Students are therefore tempted to simply try to produce their final product, skipping steps in its production. Students may also become frustrated not understanding why they can't produce a polished result instantly, not realizing they are not the only ones who have to work through draft after draft.
An examiner competent in the discipline assisted by automated tools can look for signs of misconduct. However, this can be a time-consuming process which is stressful for staff as well as the accused student. Rather than look for poor practice, I suggest having students provide evidence of good practice.
A similar problem occurs with online examinations, where remote proctoring tools, such as ProctorU and Proctorio, have been employed. Those tools work reasonably well, but my colleagues at ANU Computer Science developed an alternative approach of self-invigilation for online examinations. Each student is encouraged to make recordings of themselves undertaking the exam, similar to those created by proctoring tools. The difference is the student makes the recording, rather than have software imposed on them. If the examiners raise concerns about who sat the exam, the student can present the video as evidence.
I suggest this self-invigilation process could be extended to assignments through a Show-Your-Work process, as Dalziel (2008) suggests. Rather than just submit their final completed assignment, the student would make available to the examiner all drafts, notes, and other work that was used in preparing the assignment. If there was doubt as to the author of the final work, the examiner could look to see if there was a consistent body of work by the student supporting it.
The student would be required to use a system that time stamped their work, and tracked any changes, not only of drafts of the final work but all notes used. It would then be possible to look for a consistent pattern of work over days or weeks.
This Show-Your-Work approach is already used in some group project-based courses in computer science at ANU. These require student teams to use project management software, such as GitLab. The examiner can see when work was uploaded to the system and who uploaded it, to see if this supports the team's claims.
Just as students can be videoed while undertaking an exam, a video record of all the time each student spent working on an assignment (or all the time they were studying) is technically feasible. However, this would be cumbersome to use, intrusive, and not necessary. An approach of logging all the work done on an assignment should be sufficient, as this is something the student should be doing anyway. Students in any field of study should be doing so in a methodical way, STEM, computer science, and engineering in particular.
While it would be possible for a student to fake the assignment preparation process, it would be cumbersome. The student would have to take the working notes of someone else (or commission these to be written) and submit these to the online system over several days or weeks. If the student tried to enter all the working notes just before the deadline, this will be evident from the timestamps.
The student could alternatively not only get the work from someone else, but have them submit it for them. This would require handing over their ID and password to upload the material. Using two-factor authentication might help deter this, where the student would be required to enter a code sent to the mobile phone. Also, the possibility of a jail term for conspiracy and computer crime may deter some students.
Asking students to show the notes and drafts of their assignment is much the same as telling students on an assignment question to "show their work". Where a student doesn't show how they derived the final result, the examiner can reduce the grade. This doesn't require accusing the student of plagiarism.
If students are asked to show their work, they will need to be guided as to what is a reasonable quantity of work over what period. During my MEd studies, I created a journal using the institution's Mahara e-portfolio system, in which I recorded my impressions of the program. Also, I created a journal for each course, and one for my capstone e-Portfolio (in place of a thesis). In these, I made notes on references found, answers to study questions, and appended drafts of assignments. This was mostly to aid me in my work, and to have material to draw on for my reflective e-portfolio. However, I also kept this as evidence, if I was ever to be charged with academic misconduct. Fortunately, that never occurred, and my journals remain read only by me. I made an average of 100 postings per course, a total of 100,000 words. This was in addition to the assignments and capstone e-portfolio.
Based on my experience as a student, a reasonable guide would be 50 words of supporting material per percent of assessment. So an assignment worth 20% of the course assessment undertaken over two weeks would require 1,000 words of supporting notes in 10 posts. This would be in addition to ten drafts of the assignments, over at least five days of the two weeks.
References
Catalena, K. A. (2020). Mining Student Submission Information to Refine Plagiarism Detection (Doctoral dissertation). https://hdl.handle.net/1969.1/191583
Nguyen, J. G., Keuseman, K. J., & Humston, J. J. (2020). Minimize Online Cheating for Online Assessments During COVID-19 Pandemic. Journal of Chemical Education, 97(9), 3429-3435. URL https://pubs.acs.org/doi/pdf/10.1021/acs.jchemed.0c00790
Dalziel, C. (2008). Using ePortfolios to combat plagiarism. Hello! Where are you in the landscape of educational technology? Proceedings ascilite Melbourne 2008. URL https://ascilite.org/conferences/melbourne08/procs/dalziel.pdf
Pribela, I., & Pracner, D. (2017, January). A Temporal File System for Student's Assignments in The System Svetovid. In SQAMIA. URL https://perun.pmf.uns.ac.rs/sqamia/2017/download/sqamia2017-proc.pdf#page=99
Sangalli, V. A., Martinez-Muñoz, G., & Cañabate, E. P. (2020, April). Identifying Cheating Users in Online Courses. In 2020 IEEE Global Engineering Education Conference (EDUCON) (pp. 1168-1175). IEEE. URL https://doi.org/10.1109/EDUCON45650.2020.9125252
Greetings from the ASCILITE 2020 Conference Online, where I am taking part in a Turnitin Gradescope Workshop. Two papers I helped with are being presented tomorrow, but first I am brushing up my grading skills. Anu had TurnItIn and I have used their GradeMark tool, but that is much the same as online assignment marking with Moodle (apart from integration with TurnItIn's plagiarism detector). Hopefully Gradescope does more than this.
I worry that online and semi-automated grading tools may make the fixation students and teachers have with grades worse. These tools look objective and analytical, which grading is not. I am reasonably confident that I can decide that a piece of work is below, at, or above the required standard. However, I am not sure I can really say where the work sits on a seven point scale, let alone a mark out of 100.
What would be useful would be a way to evaluate the quality of grading, especially where multiple graders are used.
Despite being a vendor provided workshop, there was a minimum of sales pitch. However, it would have been useful to compare Gradescope to commonly used online assessment tools, such as the Quiz, Assignment and Workshop tools in Moodle.
Gradescope has something called a "dynamic rubric". This allows the grader to create a rubric during the marking. I have some difficulties with this idea. First of all conventional wisdom, and natural justice, say that the student should be provided with the rubric before the assessment, so they know how they will be graded. If the rubric is made up during marking, the student can't have had it before. Next, this removes the discipline from the assessment designer to think carefully about what they are trying to assess and how it will be marked. There could also be a lack of consistency if the rubric changes during the grading. However, if the grader has not been provided with any rubric, this would be better than nothing. It would also be useful where a problem is found with a question, and the expected answer is wrong.
Gradescope provides statistics overall and on individual questions, much the same as other online marking tools. What I had hoped for were ways for evaluating the quality of marking, between multiple graders, but there doesn't seem anything for this. There was also mention of the use of AI, but no details in the workshop. Overall Gradescope seems to have the features I would expect in any grading tool.
Before creating a complex marking scheme, educators need to stop and consider the purpose of the assessment and the cost and suffering they are causing students. In most cases school and university assessment is intended to find if the student's skills and knowledge are to a required standard. We want citizens and workers who can function in a modern society. There is no need to rank all the students, or mark them to within 1%, for this: pass/fail is sufficient.
Universities might like an easy way to rank applicants, but is that really the job of schools and are school assessments really much use for that? Similarly, university academics may want to use assessment to select students for advanced research courses, but few students will be undertaking these advanced courses, so is it worth the cost of administering this assessment for all students? Outside university, no one much cares about university grades: as long as you passed, that is all that matters.
One claim for the product is that the rubric can be customized by the examiner, as they do the marking. The product was demonstrated for engineering and computer science examinations, including for computer code.
There appear to be two distinct uses for the product: one is for marking traditional paper based examinations. The other is for digitally input long-form input, such as computer code.
This product is new to Turnitin, and integration with their copy detection function is to be added in early 2019. However, even in its current form the interface looks similar to Turnitin's GradeMark.
Starr (2016) reports on research into how the form of feedback students are given effects their engagement with it. In particular he looks at different ways to annotate when marking using the Turnitin product, and suggests individually composed text and audio comments are preferable to templates and rubrics. However, there may be an alternative explanation: bespoke comments take longer to compose, resulting in fewer, which students can better engage with.
Starr (2016)reports;
"It was found that GradeMark on-script ‘Bubble Comments’ and off-script ‘Voice Comments’ may both positively influence how valued learners feel and encourage and support their use of feedback for development by positively influencing their motivation and ability to engage. On-script ‘QuickMark Comments’ and off-script ‘General Comments’ and ‘Rubrics’ may negatively influence same." (Starr, 2016, p. 2)
The study (as the author admits) only looks at the form, not content of the feedback, nor the time required for different types of feedback. An assessor has only a fixed and limited amount of time to mark each assessment item. If providing feedback in a particular way takes longer, then the assessor will be able to provide less, or less considered feedback.
Turnitin's QuickMark provides a way to select from a pre-prepared set of comments. Similarly Rubrics make use of a structured template. Bubble and Voice comments are bespoke, composed each time (although there is no technical reason Turnitin could not provide a way to record a set of standard voice comments).
Starr's research suggests the bespoke comments are preferred by students, which is not surprising. However, does this take into account the additional time which it would take to prepare such comments? If it takes, for example, four times as long to compose a bespoke comment, the students will receive one quarter the quantity of comments. It may simply be that students are able to engage better when they receive fewer comments.
Correcting every error in a student paper is a waste to the marker's time and not helpful for student learning. I suggest more concise comments, in analogue or digital form, from templates or bespoke, text or audio.
