Matching cognitive styles to computer-based instruction : an approach for enhanced learning in electrical engineering

An approach aimed at enhancing learning by matching individual students' preferred cognitive styles to computer-based instructional (CBI) material is presented. This approach was used in teaching some components of a third-year unit in an electrical engineering course at the Queensland University of Technology. Cognitive style characteristics of perceiving and processing information were considered. The bimodal nature of cognitive styles (analytic/imager, analytic/verbalizer, wholist/imager and wholist/verbalizer) was examined in order to assess the full ramification of cognitive styles on learning. In a quasi-experimental format, students' cognitive styles were analysed by cognitive style analysis (CSA) software. On the basis of the CSA results the system defaulted students to either matched or mismatched CBI material. The consistently better performance by the matched group suggests potential for further investigations where the limitations cited in this paper are eliminated. Analysing the differences between cognitive styles on individual test tasks also suggests that certain test tasks may better suit certain cognitive styles.

Personalizing the design of computer-based instruction to enhance learning

This paper reports two studies designed to investigate the effect on learning outcomes of matching individuals' preferred cognitive styles to computer-based instructional (CBI) material. Study 1 considered the styles individually as Verbalizer, Imager, Wholist and Analytic. Study 2 considered the bi-dimensional nature of cognitive styles in order to assess the full ramification of cognitive styles on learning: Analytic/Imager, Analytic/ Verbalizer, Wholist/Imager and the Wholist/Verbalizer. The mix of images and text, the nature of the text material, use of advance organizers and proximity of information to facilitate meaningful connections between various pieces of information were some of the considerations in the design of the CBI material. In a quasi-experimental format, students' cognitive styles were analysed by Cognitive Style Analysis (CSA) software. On the basis of the CSA result, the system defaulted students to either matched or mismatched CBI material by alternating between the two formats. The instructional material had a learning and a test phase. Learning outcome was tested on recall, labelling, explanation and problem-solving tasks. Comparison of the matched and mismatched instruction did not indicate significant difference between the groups, but the consistently better performance by the matched group suggests potential for further investigations where the limitations cited in this paper are eliminated. The result did indicate a significant difference between the four cognitive styles with the Wholist/Verbalizer group performing better then all other cognitive styles. Analysing the difference between cognitive styles on individual test tasks indicated significant difference on recall, labelling and explanation, suggesting that certain test tasks may suit certain cognitive styles.

Slum free India program. Report on the findings and recommendations of QUT BEE workshop to investigate strategies to deliver better outcomes

As a result of a broad invitation extended by Professor Martin Betts, Executive Dean of the Faculty of Built Environment and Engineering, to the community of interest at QUT, a cross-disciplinary collaborative workshop was conducted to contribute ideas about responding to the Government of India’s urgent requirement to implement a program to re-house slum dwellers. This is a complex problem facing the Indian Ministry of Housing. Not only does the government aspire to eradicate existing slum conditions and to achieve tangible results within five years, but it must also ensure that slums do not form in the future. The workshop focused on technological innovation in construction to deliver transformation from the current unsanitary and overcrowded informal urban settlements to places that provide the economically weaker sections of Indian society with healthy, environmentally sustainable, economically viable mass housing that supports successful urban living. The workshop was conducted in two part process as follows: Initially, QUT academics from diverse fields shared current research and provided technical background to contextualise the challenge at a pre-workshop briefing session. This was followed by a one-day workshop during which participants worked intensively in multi-disciplinary groups through a series of exercises to develop innovative approaches to the complex problem of slum redevelopment. Dynamic, compressed work sessions, interspersed with cross-functional review and feedback by the whole group took place throughout the day. Reviews emphasised testing the concepts for their level of complexity, and likelihood of success. The two-stage workshop process achieved several objectives:  Inspired a sense of shared purpose amongst a diverse group of academics  Built participants’ knowledge of each other’s capacity  Engaged multi disciplinary team in an innovative design research process  Built participants’ confidence in the collaborative process  Demonstrated that collaborative problem solving can create solutions that represent transformative change.  Developed a framework of how workable solutions might be developed for the program through follow up workshops and charrettes of a similar nature involving stakeholders drawn from the context of the slum housing program management.

Sequential nonlinear manifold learning

The computation of compact and meaningful representations of high dimensional sensor data has recently been addressed through the development of Nonlinear Dimensional Reduction (NLDR) algorithms. The numerical implementation of spectral NLDR techniques typically leads to a symmetric eigenvalue problem that is solved by traditional batch eigensolution algorithms. The application of such algorithms in real-time systems necessitates the development of sequential algorithms that perform feature extraction online. This paper presents an efficient online NLDR scheme, Sequential-Isomap, based on incremental singular value decomposition (SVD) and the Isomap method. Example simulations demonstrate the validity and significant potential of this technique in real-time applications such as autonomous systems.