Teaching maths to pupils with different learning styles.

Recurso donde se analizan las diferentes formas en que se pueden enseñar las matemáticas a alumnos con distintos estilos de aprendizaje y cómo identificarlos. Ofrece de un modo práctico los posibles modos de comprender algunos de los conceptos necesarios para los exámenes de GCSE: el concepto de número, los modelos para la multiplicación y la división, el valor ordinal y decimales, fracciones, relación, proporción, porcentajes, el álgebra, forma, espacio, las medidas, los ángulos, círculos y manejo de datos. Incluye: ideas para utilizar en clase; propuestas para enseñar conceptos básicos de una manera visual; consejos prácticos y orientaciones; recursos fotocopiables.

Learning generators : NLP and learning styles in english text books. 'Generadores de aprendizaje : PNL y estilos de aprendizaje en libros de texto de ingl??s'

Resumen basado en el de la publicaci??n

Vermunt's learning styles : searching for portuguese college student's functioning. 'Estilos de aprendizaje de Vermunt : en busca del funcionamiento de los estudiantes portugueses en la educación superior'.

Resumen basado en el de la publicación

The impacts of learning styles, learning patterns and demographic factors on learners’ performance in a blended learning environment

Many studies have widely accepted the assumption that learning processes can be promoted when teaching styles and learning styles are well matched. In this study, the synergy between learning styles, learning patterns, and gender as a selected demographic feature and learners’ performance were quantitatively investigated in a blended learning setting. This environment adopts a traditional teaching approach of ‘one-sizefits-all’ without considering individual user’s preferences and attitudes. Hence, evidence can be provided about the value of taking such factors into account in Adaptive Educational Hypermedia Systems (AEHSs). Felder and Soloman’s Index of Learning Styles (ILS) was used to identify the learning styles of 59 undergraduate students at the University of Babylon. Five hypotheses were investigated in the experiment. Our findings show that there is no statistical significance in some of the assessed factors. However, processing dimension, the total number of hits on course website and gender indicated a statistical significance on learners’ performance. This finding needs more investigation in order to identify the effective factors on students’ achievement to be considered in Adaptive Educational Hypermedia Systems (AEHSs).

State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area.

A psychometric analysis of reliability and validity of the index of learning styles (ILS)

Prior literature showed that Felder and Silverman learning styles model (FSLSM) was widely adopted to cater to individual styles of learners whether in traditional or Technology Enhanced Learning (TEL). In order to infer this model, the Index of Learning Styles (ILS) instrument was proposed. This research aims to analyse the soundness of this instrument in an Arabic sample. Data were integrated from different courses and years. A total of 259 engineering students participated voluntarily in the study. The reliability was analysed by applying internal construct reliability, inter-scale correlation, and total item correlation. The construct validity was also considered by running factor analysis. The overall results indicated that the reliability and validity of perception and input dimensions were moderately supported, whereas processing and understanding dimensions showed low internal-construct consistency and their items were weakly loaded in the associated constructs. Generally, the instrument needs further effort to improve its soundness. However, considering the consistency of the produced results of engineering students irrespective of cross-cultural differences, it can be adopted to diagnose learning styles.

Learning styles vary among general surgery residents: analysis of 12 years of data

PURPOSE: Understanding the learning styles of individuals may assist in the tailoring of an educational program to optimize learning. General surgery faculty and residents have been characterized previously as having a tendency toward particular learning styles. We seek to understand better the learning styles of general surgery residents and differences that may exist within the population. METHODS: The Kolb Learning Style Inventory was administered yearly to general surgery residents at the University of Cincinnati from 1994 to 2006. This tool allows characterization of learning styles into 4 groups: converging, accommodating, assimilating, and diverging. The converging learning style involves education by actively solving problems. The accommodating learning style uses emotion and interpersonal relationships. The assimilating learning style learns by abstract logic. The diverging learning style learns best by observation. Chi-square analysis and analysis of variance were performed to determine significance. RESULTS: Surveys from 1994 to 2006 (91 residents, 325 responses) were analyzed. The prevalent learning style was converging (185, 57%), followed by assimilating (58, 18%), accommodating (44, 14%), and diverging (38, 12%). At the PGY 1 and 2 levels, male and female residents differed in learning style, with the accommodating learning style being relatively more frequent in women and assimilating learning style more frequent in men (Table 1, p < or = 0.001, chi-square test). Interestingly, learning style did not seem to change with advancing PGY level within the program, which suggests that individual learning styles may be constant throughout residency training. If a resident's learning style changed, it tended to be to converging. In addition, no relation exists between learning style and participation in dedicated basic science training or performance on the ABSIT/SBSE. CONCLUSIONS: Our data suggests that learning style differs between male and female general surgery residents but not with PGY level or ABSIT/SBSE performance. A greater understanding of individual learning styles may allow more refinement and tailoring of surgical programs.

The influence of learning styles, enrolment status and gender on academic performance of optometry undergraduates

Purpose: This cross-sectional study was designed to determine whether the academic performance of optometry undergraduates is influenced by enrolment status, learning style or gender. Methods: Three hundred and sixty undergraduates in all 3 years of the optometry degree course at Aston University during 2008–2009 were asked for their informed consent to participate in this study. Enrolment status was known from admissions records. An Index of Learning Styles (http://www4.nscu.edu/unity/lockers/users/f/felder/public/Learning-Styles.html) determined learning style preference with respect to four different learning style axes; active-reflective, sensing-intuitive, visual-verbal and sequential-global. The influence of these factors on academic performance was investigated. Results: Two hundred and seventy students agreed to take part (75% of the cohort). 63% of the sample was female. There were 213 home non-graduates (entrants from the UK or European Union without a bachelor’s degree or higher), 14 home graduates (entrants from the UK or European Union with a bachelor’s degree or higher), 28 international non-graduates (entrants from outside the UK or European Union without a bachelor’s degree or higher) and 15 international graduates (entrants from outside the UK or European Union with a bachelor’s degree or higher). The majority of students were balanced learners (between 48% and 64% across four learning style axes). Any preferences were towards active, sensing, visual and sequential learning styles. Of the factors investigated in this study, learning styles were influenced by gender; females expressed a disproportionate preference for the reflective and visual learning styles. Academic performance was influenced by enrolment status; international graduates (95% confidence limits: 64–72%) outperformed all other student groups (home non graduates, 60–62%; international non graduates, 55–63%) apart from home graduates (57–69%). Conclusion: Our research has shown that the majority of optometry students have balanced learning styles and, from the factors studied, academic performance is only influenced by enrolment status. Although learning style questionnaires offer suggestions on how to improve learning efficacy, our findings indicate that current teaching methods do not need to be altered to suit varying learning style preferences as balanced learning styles can easily adapt to any teaching style (Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review. London, UK: Learning and Skills Research Centre, 2004).

Adaptive E-learning Content Design and Delivery Based on Learning Styles and Knowledge Level

ACM Computing Classification System (1998): K.3.1, K.3.2.