Recognising diverse learning styles in teaching and assessment of electronic engineering

Ayre, Mary; Nafalski, Andrew

doi:10.1109/fie.2000.897597

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…The second element, namely user profiles, comprise of users' interests, preferences, life experience, learning styles and customs, all of which are unique to an individual learner that has made his learning experience a personal journey. Over a period, the learners develop the ways they prefer to receive, process and present information and ideas, known as their learning styles (Ayre and Nafalski 2000). For instance, some people find it easier to understand a new concept by reading a textbook, whilst others prefer a verbal explanation.…”

Section: Discussionmentioning

confidence: 99%

CAN AVATARS REPLACE THE TRAINER? - A case study evaluation

Mahmood¹,

Ferneley²

2004

Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems

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E-learning implementations have become an important agenda item for academic and business institutions as an enabler to complement their education and training needs. However, many of the existing e-learning systems, present several limitations such as them being static, passive and consisting of a time-consuming set of services. This has highlighted the need for functionality, which allows more creativity, autonomy, and flexibility on behalf of the learner. The inclusion of avatar technology in e-learning environments has been of growing interest, aiming to encourage the learner to become more engaged and motivated whilst augmenting the use of human trainers. However, the empirical investigations on the effect of animated agents in teaching and learning has revealed diverse results in a continuum from avatars being helpful to them being distracting. This research has evaluated the utility of avatars. Unusually, the research has chosen a qualitative, interpretive approach with supporting case study data as the chosen research methodology. The justification for this research approach will be made and the initial findings will be presented together with a proposed conceptual framework.

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Section: Discussionmentioning

confidence: 99%

CAN AVATARS REPLACE THE TRAINER? - A case study evaluation

Mahmood¹,

Ferneley²

2004

Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems

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“…Learning styles in engineering education have been studied by (Al‐Holou & Clum 1999; Ayre & Nafalski 2000; Budhu 2002; Kuri & Truzzi 2002). Kuri and Truzzi (2002) use the Felder–Silverman model to assess the distribution of responses of freshmen engineering students using the ILS questionnaire, and they concluded that the use of learning styles to accommodate their teaching strategies to students has been shown to be effective in encouraging students.…”

Section: Related Workmentioning

confidence: 99%

Learning styles' recognition in e‐learning environments with feed‐forward neural networks

Villaverde

Godoy

Amandi

2006

Computer Assisted Learning

110

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People have unique ways of learning, which may greatly affect the learning process and, therefore, its outcome. In order to be effective, e-learning systems should be capable of adapting the content of courses to the individual characteristics of students. In this regard, some educational systems have proposed the use of questionnaires for determining a student learning style; and then adapting their behaviour according to the students' styles. However, the use of questionnaires is shown to be not only a time-consuming investment but also an unreliable method for acquiring learning style characterisations. In this paper, we present an approach to recognize automatically the learning styles of individual students according to the actions that he or she has performed in an e-learning environment. This recognition technique is based upon feed-forward neural networks.

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“…In essence, categorizing students' learning styles should not result in reduction in teaching diversity. Teaching methods that combine lectures, tutorials, case studies, small group discussions, and student support (tutorials) with the use of multimedia such as video, CD, computer software packages, and electronic communication, has shown improved student performance and satisfaction (Ayre and Nafalski, 2000). By using a variety of teaching methods, most learning styles are accommodated, while at the same time extending the experience of all the students of different modes of communication (Peng, 2002).…”

mentioning

confidence: 99%

Learning Styles among Students in an Advanced Soil Management Class: Impact on Students’ Performance

Eudoxie

2011

J. Nat. Resour. Life Sci. Edu

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Learning styles represent an integral component of the learning environment, which has been shown to differ across institutions and disciplines. To identify learner preferences within a discipline would aid in evaluating instructional resources geared toward active learning. The learning profiles of second‐year soil science students (n = 62) were investigated using the VARK and index of learning styles (ILS) questionnaires, at the mid‐semester stage. Student performance was measured using quality points (course work and final exam), which was also compared with to learning style preferences. The results showed that students were mostly multimodal (25%) and kinaesthetic (25%) for the VARK instrument. An almost balanced student profile was seen among the four dimensions for the ILS except between sequential and global preferences, which differed significantly (p < 0.05). Learning preference was not related to course performance. Students performed significantly better in course work compared with the final exam. Grouping students into “strong” and “balanced” profiles resulted in lower quality points for students labeled as having a strong preference. The data supported using a diverse set of instructional resources and complementary assessment, but how these and other attributes of the learning environment interact to facilitate better learning leaves much to be researched.

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Recognising diverse learning styles in teaching and assessment of electronic engineering

Cited by 8 publications

References 7 publications

CAN AVATARS REPLACE THE TRAINER? - A case study evaluation

CAN AVATARS REPLACE THE TRAINER? - A case study evaluation

Learning styles' recognition in e‐learning environments with feed‐forward neural networks

Learning Styles among Students in an Advanced Soil Management Class: Impact on Students’ Performance

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