Simon Coupland

Dr Simon Coupland

Job: Principal Lecturer

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence (CCI)

Address: 羞羞视频, The Gateway, Leicester, LE1 9BH

T: +44 (0)116 207 8419

E: simonc@dmu.ac.uk

W: /cci

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Personal profile

Simon Coupland is a researcher working in the area of type-2 fuzzy logic. Simon has worked on the underpinning mathematics of the field making a number of important contributions. He also works on practical problems in this area including control, decision making and computing with words.

Research group affiliations

Centre for Computational Intelligence

Publications and outputs

dc.title: Enhancing Self-Determined Learning through Continuous vs. End-of-Module Assessment in Immersive 鈥楤lock鈥� Delivery dc.contributor.author: Coupland, Simon; Turgoose, Di
dc.title: A data-driven approach to student support using formative feedback and targeted interventions dc.contributor.author: Coupland, Simon; Fahy, Conor; Stuart, Graeme; Allman, Zoe dc.description.abstract: This chapter explores a data-driven approach to monitoring student progress and facilitating timely and effective formative feedback to improve student attainment. In undergraduate computer games programming, large coursework assignments are used to assess a student鈥檚 ability to synthesise a software solution to a given problem. There are challenges to providing formative feedback in a consistent and timely manner across a large cohort who are working independently, at differing speeds, meeting milestones at different times. Metadata automatically created by the students as they undertake their work is captured and processed and alerts the academic team to specific students who are likely to benefit from proactive academic support. The impact of this approach is evidenced through the distribution of grades achieved before and after this was introduced. The data shows students in the lower grade areas have improved attainment, whilst the attainment of higher-achieving students is largely unaffected. The chapter explains the specific technologies used and how the approach could be more broadly applied.
dc.title: Exploring the role of spiral/iterative learning in immersive education dc.contributor.author: Fahy, Conor; Coupland, Simon
dc.title: Continuous Feedback with Immersive Delivery dc.contributor.author: Coupland, Simon
dc.title: Student Co-Created Learning Experiences in Block Delivery dc.contributor.author: Coupland, Simon; Fahy, Conor dc.description.abstract: Co-creation is a collaborative process between students and staff. It aims to reposition students as active participants in the design and delivery of learning materials. In this article we propose a model for co-creation in block-delivery. This type of delivery is a departure from traditional, term-long modules, and the block mode we are concerned with is an academic year consisting of four 30 credit modules, where each module is delivered in a seven-week block. The proposed model focuses on embedding co-creation in traditional student voice processes, which typically happen at the end of each block. In our model, it is this end-of-module evaluation that provides the opportunity for students to contribute to the design of the learning experience for the next academic cycle. The student voice informs a set of action points for the following cycle and are fed back to the students. Our model requires a more active approach from the student, which in turn provides a more meaningful co-creation process. The model is evaluated in a pilot study with a cohort at level 6. We outline the design of this pilot, describe the results, and offer reflections on its implementation. dc.description: open access article
dc.title: A data-driven approach to student support using formative feedback and targeted interventions dc.contributor.author: Coupland, Simon; Fahy, Conor; Stuart, Graeme; Allman, Zoe dc.description.abstract: 羞羞视频 (羞羞视频) has approximately 30,000 registered students, primarily at its Leicester campus in the United Kingdom (UK) but also at campuses internationally, as well as UK-based and transnational education partners. Based in Leicester, 羞羞视频鈥檚 community was particularly hit by the impact of COVID-19, with Leicester being the first city to be placed in local lockdown, extending the period of lockdown beyond the broader national experience. The approaches described in this case study were motivated by the need to capture information about student progress in the lockdown-necessitated online environment but have been equally impactful in in-person classroom teaching. In the subject area of computer games programming (CGP) at levels 5 and 6, students are required to use theoretical underpinning to develop solutions to practical problems, often demonstrating mastery of learning through completing a single, large piece of coursework over a medium-long timeframe, usually three鈥揻ive months. Through the learning and assessment journey, students plan, meet, and reprioritise a series of dynamic sub-objectives. This all takes place during weekly timetabled workshops where most of the valuable learning occurs. These are student- and assessment-centred learning environments where learners, facilitated by tutors, incrementally develop their coursework projects. These workshops are natural opportunities to monitor engagement and to provide instant, formative feedback personalised to the learner and directly related to assessment. CGP as a discipline attracts students with a wide range of learning preferences and differences; the classical approach of 1鈥�1 in-person tutoring may not be the best approach for these students (Amoako et al., 2013). Additionally, the temporary move to online teaching necessitated by the COVID-19 pandemic meant this established approach was not possible. Continual support and feedback are critical in an online setting and facilitated through sustained interaction between tutor and learner (Gikandi et al., 2011). Maintaining this interactivity is important, and it has been observed that continual documentation and sharing of learner-created artefacts is a key feature of meaningful interactivity (Gikandi & Morrow, 2016). In response the CGP team have developed a suite of innovative tools and processes to facilitate the real-time monitoring of student progress through using digital artefacts and the metadata associated with these digital artefacts. This approach provides students with timely formative feedback at key milestones in their progress and facilitates interventions for students requiring additional support to fully engage for best attainment. This approach is grounded in constructivist theories of learning. The individual learner is at the centre of the process, and the feedback process is an iterative, continuous part of learning (Carless et al., 2011; Molloy, 2014).
dc.title: Assessing the Provenance of Student Coursework dc.contributor.author: Coupland, Simon dc.description.abstract: The Higher Education sector is mobilising vast resources in its response to the use of Generative AI in student coursework. This response includes institutional policies, training for staff and students and AI detection tools. This paper is concerned with one aspect of this fast-moving area; the assessment of the provenance of a piece of written student coursework. The question of the provenance of student work is a surprisingly complex one, which, in truth can only ever be answered by the student themselves. As academics we must understand the difference between checking for plagiarism and generative AI use. When assessing a student's possible use of generative AI there is no ground truth for us to test against and this makes the detection of AI use a completely different problem to plagiarism detection. A range of AI detection tools are available, some of which have been adopted within the sector. Some of these tools have high detection rates, however, most suffer with false positive rates meaning institutions would be falsely accusing hundreds of students per year of committing academic offences. This paper explores a different approach to this problem which complements the use of AI detection tools. Rather than examining the work submitted by a student, the author examines the creation and editing of the that work over time. This gives an understanding how a piece of work was written, and most importantly how it has been edited. Inspecting a documents history requires that it is written on a cloud-based platform with version history enabled. The author has created a tool which sits on top of the cloud-based platform and integrates with the virtual learning environment. The tool records each time a student digitally touches their work, and the changes are recorded. The tool interface gives an overview for a cohort, with the ability to delve more deeply into an individual submission. The result is an easily accessible interactive history of a document during its development, giving some kind of provenance to that document. This history of construction and editing, shows how a piece of written work has been crafted over time, providing useful evidence of academic practice. Data on the points where students digitally touch their work can also be useful beyond questions of academic practice. The Author gives an example of using a data-driven approach to give formative feedback and discusses how data-driven approaches could become common in teaching practice.
dc.title: Sprinting into blocks: what computing, AI and gaming academics learned dc.contributor.author: Allman, Zoe; Coupland, Simon; Khuman, A. S.; Fahy, Conor; Attwood, Luke
dc.title: Developing and delivering in block: Reflections one year in dc.contributor.author: Allman, Zoe; Coupland, Simon; Attwood, Luke; Fahy, Conor; Hasshu, Salim; Khuman, A. S.; Shell, Jethro
dc.title: Authentic assessment supporting curriculum and delivery mode transformation dc.contributor.author: Allman, Zoe; Coupland, Simon; Fahy, Conor dc.description.abstract: 羞羞视频 is embracing significant transformation as curriculum and delivery mode transitions into an intensive block model approach. The Computer Games Programming (CGP) team were particularly innovative in their approach (Jones, 2022), completely revisiting curriculum sequencing and assessment methods to facilitate the best learning journey for students, and responding to employer and sector skills needs. This presentation highlights two examples of authentic assessments emerging from university-wide transformation. The digital economy requires graduates equipped with a set of digital skills which are practice based. CGP had been moving away from traditional written exams towards large, in-depth coursework which students produce over a term or academic year. In this approach digital skills are implicitly assessed, for example using source control metadata to assess students鈥� capabilities with a specific tool chain. This requires examination of digital footprints over the term. Therefore, the model needed revisiting to facilitate block delivery and explicitly assess these skills through face-to-face practical assessments we call driving tests, replicating assessments that are commonplace in other disciplines (Snodgrass et al, 2014; Kent-Waters et al, 2018). The depth of student knowledge is examined with a professional conversation, replicating assessment methods used in teacher and lecturer training (Britt et al, 2001). A driving test involves a student sitting with a tutor whilst being asked to perform a number of sequential pre-scripted tasks. Students are marked on the breadth of tasks they complete and the manner in which they complete them. The student is given immediate and personalised verbal feedback and an overall mark. The student leaves a digital trail which is used for moderation. Professional conversations introduce further diversity in assessment in level 6. These conversations supplement a practical assessment component and assess descriptors which can be difficult to evaluate in more traditional formats, for example identifying emerging issues at the forefront of the subject, and systematically identifying personal learning needs. Preparatory, co-created conversations highlighted that current level 6 learners would value this 鈥榯echnical interview鈥� format as the conversation allow learners to naturally demonstrate their understanding of the subject without additional coursework documentation/production. Students value these approaches that facilitate authentic demonstration of practical skills with tutor support and instant verbal feedback. As these assessment methods embed, there is ongoing consideration of whether these should be time limited activities; our experience suggests it should not as to date students have required different amounts of time to complete the task whilst demonstrating competency.

Research interests/expertise

Understanding the performance capabilities of type-2 fuzzy logic.

Improving the computational performance of type-2 fuzzy logic systems.

Assessing other extensions to type-1 fuzzy sets and systems such as triangular type-2 fuzzy sets and non-stationary fuzzy systems.

The application of type-2 fuzzy logic to real-world problems.

Areas of teaching

MSc Computing/IT/ISM Introduction to computer systems.

Occasional lectures to MSc CIR on fuzzy logic, neural networks and recent advances in research.

Qualifications

PhD in Computer Science

BSc (Hons) Computing

Courses taught

IMAT3404 Mobile Robots

Honours and awards

Joint Winner IEEE CIS Pre-college Education subcommittee Video Competition, 2012.

IEEE Transactions on Fuzzy Systems Outstanding Paper Award, 2009.

British Computer Society Machine Intelligence Award Winner, 2008.

Membership of professional associations and societies

IEEE Member

Externally funded research grants information

FuzzyPhoto, AHRC, 01/11/12 – 31/10/14, CI, Stephen Brown.