Kolb’s Learning Styles

Lahaie, Ulysses; Tittenberger, Peter

doi:10.1007/978-1-4419-1428-6_228

Cited by 24 publications

(32 citation statements)

References 10 publications

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“…In this study, the relationship between Latinos' learning styles, their academic achievement, and their demographic factors was explored using three surveys to collect and analyze data: The LSI developed by Kolb (2000), the PEPS developed by Dunn et al, (2003), and a demographic survey. The data were analyzed using descriptive and multivariate analysis of variance procedures employing the Statistical Package for the Social Sciences (SPSS) version 11.5.…”

Section: Methodsmentioning

confidence: 99%

“…Two instruments were used to collect data about students' learning styles, the Learning Style Inventory version 3.0 (Kolb, 2000), and the Productivity Environmental Preference Survey (Dunn et al, 2003). A hard copy of the two learning styles inventories, the LSI version 3.0 and the PEPS, were offered to students in both languages, Spanish and English.…”

Section: Lsi and Pepsmentioning

confidence: 99%

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The Relationship Between Latino Students' Learning Styles and Their Academic Performance

Torres

2013

Community College Journal of Research and Practice

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Section: Methodsmentioning

confidence: 99%

Section: Lsi and Pepsmentioning

confidence: 99%

The Relationship Between Latino Students' Learning Styles and Their Academic Performance

Torres

2013

Community College Journal of Research and Practice

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“…Learners also differ in their inclinations for how learning material is presented and how they intellectually receive and digest that material. That is, learners vary in their preferences of presentation styles, cognitive styles (field dependence/independence) (Riding and Cheema 1991), and learning styles (such as whether they are "doers," "watchers," "thinkers," or "feelers") (Kolb 1976(Kolb , 2004). Students will achieve higher learning performance if the learning content can be customized and offered according to their diverse learning needs (Beekhoven et al 2003;Chen and Mizoguchi 1999;Dewhurst et al 2000;Chen et al 2000;Smith 2001;McIlroy et al 2001;Riding and Cheema 1991;Wilson 2000;Su et al 2005).…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned in Sect. 1, the definition of LP may consist of content type preference including text, picture, audio, video, cognitive styles (including field dependence/independence (Riding and Cheema 1991)), and learning style (including Doer, Watcher, Thinker, and Feeler (Kolb 1976(Kolb , 2004). These preferences can also be extended into the LP.…”

Section: Extensibility Of the Personalized Learning Content Adaptatiomentioning

confidence: 99%

“…That is, not all students are the same, nor are their preferences for learning. Students, therefore, favor various ways to receive information and acquire knowledge, such as: content type preference (Gilbert and Han 1999;Stern and Woolf 2000), presentation style, cognitive styles (Riding and Cheema 1991), and learning style (Kolb 1976(Kolb , 2004, among others.…”

Section: Introductionmentioning

confidence: 99%

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A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments

Tseng

Lin

et al. 2011

User Model User-Adap Inter

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With the heterogeneous proliferation of mobile devices, the delivery of learning materials on such devices becomes subject to more and more requirements. Personalized learning content adaptation, therefore, becomes increasingly important to meet the diverse needs imposed by devices, users, usage contexts, and infrastructure. Historical server logs offer a wealth of information on hardware capabilities, learners' preferences, and network conditions, which can be utilized to respond to a new user request with the personalized learning content created from a previous similar request. In this paper, we propose a Personalized Learning Content Adaptation Mechanism (PLCAM), which applies data mining techniques, including clustering and decision tree approaches, to efficiently manage a large number of historical learners' requests. The proposed method will intelligently and directly deliver proper personalized learning content with higher fidelity from the Sharable Content Object Reference Model (SCORM)-compliant Learning Object Repository (LOR) by means of the proposed adaptation decision and content synthesis processes. Furthermore, the experimental results indicate that it is efficient and is expected to prove beneficial to learners.

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