Case study 1 – Source: Powell, N. et al. (2003). Dyslexia and Learning Computer Programming. Leeds Metropolitan University. http://www.ics.ltsn.ac.uk/pub/italics/Vol3-2/dyslexia.htm
The following information comes from an interview with M, a final year multimedia student.
M has dyslexia and experiences difficulties with his programming, he confirms that the most immediate impact of dyslexia on his programming is associated with memory and spelling. He finds it hard to remember details of the code and its ordering. He also finds that getting the case of variables correct and mixing up letters in long variable names is problematic, and finds that coloured syntax and predictive typing features embedded in Integrated Development Environments have been a great help with this.
M Does not feel that his dyslexia has any compensations, however he does consider that his visualization skills are a great benefit to him both in programming and more generally. In fact, his principal concern is not programming but learning either in lectures or from textbooks. He prefers to attend lectures as he finds seeing and hearing the information more effective than reading it from notes or a book. He advocates either filming or recording lectures, as this makes use of his preferred communication channels and also facilitates the reviewing of more difficult aspects of the lecture.
Case Study 2 – Source: Conroy, Gerard. Delivering an Inclusive Curriculum Using Specialist Software. UMIST, Department of Computation. IN Successful student diversity (HEFCE 2002/48). http://www.hefce.ac.uk/pubs/hefce/2002/02_48/cases.htm
The case study illustrates how a lecturer has taken positive action to improve student retention, which was a recognised problem with the original programme. By exploiting information technology designed for use by disabled students, a programme has been created that encourages all students to reach their full potential.
UMIST is a research-led, technology and engineering university based in the centre of the city. It has approximately 6,500 students of whom nearly 30 per cent are international students. About 3 per cent of students declare a disability. (Disability includes dyslexia and all disabilities and medical conditions classified under the UCAS codes.)
Promotion of disability issues is a gradual process supported by staff training events. These are primarily intended for academic staff, yet mainly attended by non-academic staff. The recent establishment of Departmental Disability Co-ordinators, self-assessment questions for auditing the curriculum, and four working parties to consider compliance with the latest legislation on disability have contributed to the overall increase in disability awareness.
The Department of Computation has approximately 1,100 students including postgraduates. Approximately 3 per cent have declared a disability, and a significant number of students with physical and sensory disabilities enrol on programmes. There is a moderate level of disability awareness within the department, promoted by the departmental disability co-ordinator and more recently by an increase in disabled students.
Assessment Policy and Practice
There is no university-wide policy on examinations for disabled students, but support and provision for disabled students within the Department of Computation is high and assessment methods are totally flexible. There is a wealth of best practice in the department, thought this is not documented.
The accepted informal procedure is that any modifications to examination and assessment procedures recommended by the university’s disability and learning support adviser should be accepted and organised. As this role is undertaken by a senior lecturer from within the Department of Computation, there has been general acquiescence with this practice. Either the Central Examination Office or the academic department pays for any additional costs for assessment modifications.
Drivers for Change
By far the most influential driver for change is the increasing number of disabled students studying at UMIST and the associated impact of a critical mass. Other internal drivers include the departmental disability co-ordinator and the university’s disability and learning support adviser who is a champion for disability issues. The main external driver is the legislation (SENDA).
Teaching of a module on machine learning within the artificial intelligence course has been designed and organised entirely around the needs of disabled students.
The rationale is to enable disabled students to receive comparable learning experiences with their contemporaries, and in the process make the coursework more usable to all students. An underlying assumption is that disabled learners to not have a separate learning style to non-disabled learners; they just fall along a continuum of learner differences. By providing a variety of flexible teaching methods this will accommodate learner differences.
Each three-hour teaching period is divided into lectures and reviews or small group discussions. Each lecture starts with a review of the previous lecture and the learning outcomes achieved. In light of this, the learning outcomes for the current session are agreed. A 30-minute lecture proceeds, followed by a 15-minute small group discussion to clarify understanding. There is a 10-minute break before the next 15-minute group sessions. Another 30-minute lectures is followed by a short group discussion to clarify understanding and the session is completed by a 50-minute group session, usually to undertake specific activities.
Handouts are provided to explain the learning outcomes and how they should be achieved. The handouts are available in alternative formats including electronic formats of any diagrams or lecture materials.
Specialist Software for Dyslexic Students
A specialist software package has been used, designed for dyslexic students. Called Inspiration, this presents an overview of course content as a diagram, with images and narrative. This can be particularly useful to many dyslexic students who rely on visual rather than auditory memory for learning. It is also an effective revision technique for many students.
Prior to this approach being adopted, there were general problems with students’ low attendance. The module leader was concerned that the learning outcomes contained many implicit skills that needed to be made explicit. While developing the self-assessment questions for auditing the curriculum, the importance of explicit learning outcomes and different methods of achieving them became apparent. The new inclusive design and delivery of the machine learning module is an attempt to break down barriers.
Links with Institutional Strategy
There is not a departmental strategy for teaching and learning or a widening participation. The approach has been developed independently, although it is linked to the new SENDA requirements and to the university teaching and learning strategy. This aims to "enable students to develop their full potential….through providing a learning experience of highest quality" and to "ensure that the student experience is more active and less passive, with a focus on managing diversity through student-centred learning, in order to help improve retention". The dissemination of good practice is a feature of the strategy and therefore, if successful, this approach should be promoted throughout the institution.
Monitoring and Evaluation
The success of the department’s approach will be evaluated by quantifiable evidence such as any increase in attendance levels, and by qualitative evaluation gained from student feedback forms and focus groups of disabled students. Evaluation conducted so far indicates an increase in attendance levels and generally positive feedback from all students on the new format. The diagrammatic illustration of the structure of the course was particularly welcomed, and this software will be used more extensively in the remainder of the course.
The modifications made in the design and delivery of the module will hopefully benefit all students as well as disabled students. However it is too early to provide any conclusive evidence, which will be gained following a summative evaluation.
Adapting the programme is not an onerous tasks – it just takes a little more thought. The delivery method is more intensive and requires a higher level of concentration from the lecturer. However, the benefits gained – with instant feedback from students and much more participative interaction – outweigh the extra effort required.