Computing and Information Systems - Research Publications
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ItemA Case Study on University Student Online Learning Patterns Across Multidisciplinary Subjects(ACM, 2024)This case study explores the online learning patterns of a cohort of first-year science students in two subjects: a compulsory science subject (SCIE) and an introductory programming subject (COMP), by analysing trace data from the Learning Management Systems (LMS). The methodology extends existing learning analytics techniques to incorporate temporal aspects of students' learning, such as session duration and weekly online behaviours. By examining over 82,000 learning actions, the research unveils significant variations in students' online learning strategies between subjects, offering deeper insights into these differences and their associated challenges. The study seeks to initiate broader discussions in learning analytics, emphasising the need to comprehend students' diverse online learning experiences and encouraging further exploration in future research.
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ItemNo Preview AvailableLearning with Style: Improving Student Code-Style Through Better Automated Feedback(ACM, 2024)This work introduces and evaluates ccheck, a lenient automatic grader and C style-checker, to guide students to improve their coding practices. Many computing classes rely heavily on autograders – software that automates grading and alleviates staff workload in classes with large enrollments. At best, autograders offer timely and consistent feedback to students. However, existing autograders primarily judge on functional correctness—they are generally strict and inflexible in marking beginner programming assignments. They tend not to provide feedback on programming style and structure, which instead requires delayed, tedious manual assessment. ccheck, the tool we introduce, aims to address this gap and provide more meaningful, real-time feedback with a pedagogical focus. We deploy ccheck in a class of 440 first-year computer science students. Teaching assistants employ the system for marking assistance, while students use the same system for self-evaluation prior to finalizing their submissions. Feedback was solicited through a survey of 76 students and a focus group of the teaching team. 82% of the students surveyed said that the system helped them learn good coding practices, while 75% emphasized that the feedback received from the system is meaningful and helpful. The teaching team focus group related to how they valued the automation of menial marking tasks, which enabled them to direct their time toward other meaningful feedback. Overall, we find that teaching, learning and student experiences are improved through the deployment of ccheck.
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ItemNo Preview AvailableMore than the sum of its parts: reflections on a networked program supporting curriculum innovation at a research-intensive university(ASCILITE, 2023)Curriculum renewal in higher education is a complex process involving multiple stakeholders across faculties, departments and supporting units with priorities and processes that commonly differ, adding to the complexity. Incentives for instructors to modify their curriculum include funded centralised programs that may also draw on the expertise of academic developers, learning designers and media producers. Here we reflect on our recent experiences working together across disparate academic and professional teams within a centrally funded curriculum renewal and innovation program in a large research-intensive university. One year after the implementation of a formalised network of supporting academic fellows, program reach significantly improved, and several projects implemented award-winning innovations. Early reflections on experiences across our supporting teams suggest that collaborative project work has contributed to more effective and innovative curriculum change initiatives. We propose a deeper investigation of these processes in a research project, to further inform curriculum innovation at research-intensive universities.
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ItemUniversity Learning Partnerships: Enhancing Learning, Enabling Innovation and Addressing Challenges in Schools(ASCILITE, 2023)Universities hold a significant advantage in the development of enterprising learning partnerships where internal and intentional collaboration can assist in addressing complex problems. Building a collaborative learning partnership between faculties enhances learning, removes blocks to enterprise and addresses industry problems. The learning partnership developed in this collaboration included academic staff from two different faculties, a team of students as well as input from external stakeholders including schools and pre-service teacher candidates. The approach included digital expertise and developmental pedagogies; with a shared outcome designed to meet the enterprise goals of the industry’s partner. The student team developed confidence and capacity in their ability to communicate with the industry partner as they were encouraged to be creative, equal participants with the agency to take risks and problem solve. The tangible outcome was the delivery of a minimum viable product that has potential to address issues around teacher shortage and other limits to school resourcing. Learning partnerships within universities facilitate authentic learning and encouraged enterprise
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ItemCreating a software application to help university educators to reflect on the cognitive complexity of their exam questions, using Bloom’s Taxonomy and automated classification(ASCILITE, 2023)Previous research has shown that many university educators struggle to accurately evaluate the cognitive complexity of exam questions (and overall exams) which they write, based on Bloom's Taxonomy. This can lead to concerns about the design of exams. Software tools could possibly assist educators via automated classification methods. This paper reports a work-in-progress project that is creating a software application (tool) to assist university educators with writing exams. We evaluate 3 methods of automated classification including keywords-based approach, OpenAI evaluation, and an existing algorithm. The tool is designed to be able to help educators to reflect on their exam, by providing educators with meaningful feedback on question complexity and the overall exam, assisting in exam design. The software tool developed in this study is expected to benefit educators by providing objective feedback and serving as a professional development resource.
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ItemAuthorship Verification in Software Engineering Education: Forget ChatGPT and Focus on Students' Academic Writing Profiles(ASCILITE, 2023)The prevalence of academic misconduct, specifically contract cheating, is a rising concern in higher education institutions globally. Among the recent advancements, Generative Artificial Intelligence (genAI) has exacerbated the situation by offering authentically generated writings, making detection through traditional plagiarism tools ineffective. This paper explores the development and application of students' academic writing profiles, using a combination of word embedding (Word2Vec) and stylistic feature extraction techniques. By leveraging a Siamese neural network, our method focuses on recognising distinctive writing styles, a concept rooted in Authorship Verification (AV). Our approach's efficacy evaluates favourably against other AV methods and is tested against AI-generated texts deliberately designed to mimic student writing. The study emphasises the importance of understanding individual academic writing styles to identify outsourcing or AI-generated work effectively
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ItemAI-powered peer review process: An approach to enhance computer science students’ engagement with code review in industry-based subjects(ASCILITE, 2023)Code review is a common type of peer review in Computer Science (CS) education. It’s a peer review process that involves CS students other than the original author examining source code and is widely acknowledged as an effective method for reducing software errors and enhancing the overall quality of software projects. While code review is an essential skill for CS students, they often feel uncomfortable to share their work or to provide feedback to peers due to concerns related to coding experience, validity, reliability, bias, and fairness. An automated code review process could offer students the potential to access timely, consistent, and independent feedback about their coding artifacts. We investigated the use of generative Artificial Intelligence (genAI) to automate a peer review process to enhance CS students’ engagement with code review in an industry-based subject in the School of Computing and Information System, University of Melbourne. Moreover, we evaluated the effectiveness of genAI at performing checklist-based assessments of code. A total of 80 CS students performed over 36 reviews in two different weeks. We found our genAI-powered reviewing process significantly increased students’ engagement in code review and, could also identify a larger number of code issues in short times, leading to more fixes. These results suggest that our approach could be successfully used in code reviews, potentially helping to address issues related to peer review in higher education settings.
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ItemResearch profiles of Australian computing education authors: A scientometric analysis(IEEE, 2022)Computing education (CE) is a growing, but well-established field of research. However, relatively little is known about the research profiles of CE researchers: whether they tend to publish more educational or non-educational papers and when during their career they tend to commence CE research. Using a scientometric approach and data from Scopus, 189 CE authors from Australia were identified who had published in the field between 2018 and 2021. Their research publication history was then retrieved, and each publication was classified as educational or non-educational using a computer aided approach. It was found that CE researchers have diverse research profiles; well established researchers tended to have fewer educational papers, new researchers tend to have more educational papers, and that it is becoming more common to start a research career doing CE research. This has implications for how the research field may be viewed by university computing departments.
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ItemRedesigning Software Architecture and Design Curriculum to Promote Professional Skills Among Software Engineering Students: An Experience Report(AAEE, 2022)Context: Professional skills have become increasingly important in software engineering education; however, this is not always reflected in today’s teaching curricula (Petkovic et al., 2017). The Australian Computer Society report states software engineering and ICT students are lacking essential lifelong non-technical skills necessary to create successful software systems and that higher education institutions do not sufficiently assess professional skills as learning outcomes (ACS, 2019). Besides combining and promoting professional skills in software engineering and transmitting information, teachers in software engineering must also associate theory and practice (Matthews et al., 2012). Purpose or Goal: We redesigned the Software Design and Architecture final year compulsory subject curriculum, which is part of the Master of Software Engineering, to promote professional skills and self-regulated learning within the subject. This paper describes and evaluates our teaching initiatives. Approach or Methodoly/Methods: New teaching initiatives and subject redesign were aimed at better promoting technical and professional skills, and at supporting self-regulated learning among students. The initiatives were organised in 5 main stages (Kennedy, 2020): (i) assessment, (ii) getting the basics right, (iii) establishing our presence regularly, (iv) tutorials, (v) teaching and learning resources. We gathered information from Student Evaluation Survey (SES), and evidence from discussion board usage in 2020 and 2021 (updated subject version) to evaluate our methods. Actual or Anticipated Outcomes: Student Evaluation Survey results showed an increase in general satisfaction with the subject’s score (contents and delivery) increasing from 3.47 and 3.7 in 2018 and 2019, respectively, to 4.13 and 4 in 2020 and 2021 (scale goes from 1 to 5. The higher the score, the better the evaluation of the subject). These were the first two times this subject has received a score equal or above 4. Conclusions/Recommendations/Summary: Among the lessons learned, we can highlight that planning the subject in advance and working within an inclusive space that promotes continuous communication and collaboration between the teaching team and students are the primary activities for its success. In addition, we observed that clearer assessment guidelines and continuous feedback were able to promote professional and technical skills among students.
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ItemSame graph, different data: A usability study of a student-facing dashboard based on self-regulated learning theory(Australasian Society for Computers in Learning in Tertiary Education (ASCILITE), 2022)Student-facing learning analytics dashboards have the potential to reconnect students with their purpose for learning, reminding them of their goals and promoting reflection about their learning journey. However, far less is known about the specifics of the relationship between different types of visualisations and data presented in dashboards and their impact on students’ motivation. In this study, we used a Human-Centred Design method across three iterations to (1) understand how students prioritise similar visualisations when presenting different data (2) examine how they interact with these, and (3) propose a dashboard design that would accommodate students’ different motivational needs. In the first iteration, 26 participants ranked their preferred visualisations using paper prototypes; in the second iteration, a digital wireframe was created based on the results from the first iteration to conduct user tests with two participants; and in the third iteration, a high-fidelity prototype was created to reflect findings from the previous iterations. Overall, findings showed that students mostly valued setting goals and monitoring their progress from a multiple goals approach, and were reluctant about comparing their performance with peers due to concerns related to promoting unproductive competition amongst peers and data privacy. Implications for educators and learning designers are discussed.