A Unified Learning Style Model for Technology-Enhanced Learning

2010 ◽  
Vol 8 (3) ◽  
pp. 65-81 ◽  
Author(s):  
Elvira Popescu
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Educational System ◽  
Critical Analysis ◽  
Learning Preferences ◽  
Social Aspects ◽  
Traditional Learning ◽  
Technology Enhanced Learning ◽  
Web Based ◽  
Enhanced Learning

This paper deals with the use of learning styles in technology-enhanced learning by introducing a “Unified Learning Style Model” (ULSM). The article aims at providing answers to three main questions: “What is ULSM?”, “Why do we need it?” and “How can we use it?” First, a critical analysis regarding learning styles is provided; the identified challenges are addressed by proposing the use of a new model, specifically designed for TEL use. This model integrates learning preferences extracted from several traditional learning style models, related to perception modality in a way for processing and organizing information, as well as motivational and social aspects. A detailed description of the ULSM components is provided together with its rationale and its advantages. The practical applicability of the model is also shown by briefly introducing an adaptive web-based educational system based on it (called WELSA).

Integrating Individual Differences in Adaptive Educational Systems

2012 ◽  
pp. 94-118
Author(s):  
Elvira Popescu
Keyword(s):  
Individual Differences ◽  
Learning Styles ◽  
Learning Style ◽  
Learning Preferences ◽  
Traditional Learning ◽  
Educational Systems ◽  
Technology Enhanced Learning ◽  
Web Based ◽  
Adaptive Educational Systems ◽  
The Individual

The chapter provides an overview of the individual differences that have an impact on the learning process and that are currently integrated in adaptive educational systems (AES). The focus is on one of these human factors in particular, namely learning style, which constitutes a popular source of adaptation in recent AES, but also one of the most controversial. The chapter includes a critical analysis of learning styles and their use in technology-enhanced learning settings, motivating the need for a Unified Learning Style Model (ULSM). This model integrates a carefully selected set of learning preferences extracted from several traditional learning style models, related to perception modality, way of processing and organizing information, as well as motivational and social aspects. The practical applicability of the model is also shown by briefly introducing an adaptive Web-based educational system built on it (called WELSA) and reporting the encouraging experimental results obtained so far. The use of ULSM in the emerging Web 2.0 context is also envisioned, by proposing the addition of a social learning dimension to WELSA.

scholarly journals A Social Web-Based Personalized Learning Environment: Proposing a PLE

2020 ◽  
Vol 8 (5) ◽  
pp. 3420-3424
Keyword(s):  
Learning Environment ◽  
Object Representation ◽  
Learning Preferences ◽  
Technology Enhanced Learning ◽  
Knowledge Modeling ◽  
Web Based ◽  
Personal Learning ◽  
Personalised Learning ◽  
E Learning ◽  
Enhanced Learning

The nature of e-learning has been advanced in the manner of how it structures with the advance of Web 2.0 and 3.0. Contemporary educational hypermedia is slowly but surely providing personalised user experience. Research in technology-enhanced learning is now more student oriented, in other words it is as a personalised learning environment. But, according to the progress of projects which has been published, it has been said that personal learning environment is left as a theory and the field has been faded. In this paper we have proposed our model by providing learners with three learning object representation options. In which users will have options to get either up to date content or mostly advanced content first or according to their learning preferences. Domain and knowledge modeling features are also detailed. Finally, empirical results for the affect values of the model were presented.

Personality Scales and Learning Styles

2013 ◽  
pp. 161-182
Author(s):  
Anshu Saxena Arora ◽  
Mahesh S. Raisinghani ◽  
Reginald Leseane ◽  
Lemaro Thompson
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Cognitive Abilities ◽  
Positive Impact ◽  
Human Mind ◽  
Learning Preferences ◽  
Web Based ◽  
Team Interaction ◽  
Learning Patterns ◽  
Academic Grades

There is a vast body of literature that indicates students not having homogeneous learning patterns. Just as the students vary, their learning styles, cognitive abilities, and learning preferences vary; similarly, instructors employ different teaching methods. Numerous researchers have hypothesized that when students’ unique learning dimensions are matched with similar teaching styles, it can have a significant positive impact in regards to students’ grasp on information, their satisfaction with the course, improved academic grades, and group/team interaction. However, it is rigorously debated what these dimensions are, if they are fixed or changeable, and which scale gives the most accurate purview into the various learning dimensions of students. This paper explores the behavior and learning style of the human mind and its capacity in different learning environment. The authors examine theory, similarities, differences, and implications of the five relevant learning models discussed in the paper. Analyzing and interpreting these learning styles and behaviors will help the reader employ the best scale or combination of scales that should be used in the creation of Web-based learning environments (WBLE) for students and adapting WBLE to their particular learning styles and preferences.

scholarly journals Technology-Enhanced Learning: An Optimal CPS Learning Application

2019 ◽  
Vol 11 (16) ◽  
pp. 4415 ◽  
Author(s):  
Yu-Hung Chien
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Soft Power ◽  
Collaborative Problem Solving ◽  
Technology Enhanced Learning ◽  
Effective Learning ◽  
Computer Agent ◽  
Collaborative Interactions ◽  
The Impact ◽  
Enhanced Learning

Collaborative problem-solving (CPS) is highly valued in the sustainability of learning to foster the key soft power of talent for the future. In this study, a CPS learning application was built to train and assess individuals with the aim of increasing CPS skills. For effective learning to take place, several issues need to be carefully considered, and these were investigated while testing the proposed application. This study examined the impact of collaborative interactions (CIs) (human–computer agent (HCA) and human–human (HH) interactions) on the CPS performance of students. Gender and learning styles, which may have interaction effects with CIs on CPS performance, were also explored. The results show that the students’ CPS performance in HCA was significantly greater than that in HH. The interaction effect between gender and CI was not significant. The impact of learning style on CPS performance in HH was not significant. In contrast, in HCA, students with verbal, global, and reflective learning styles performed significantly better on CPS tasks than did students with visual, sequential, and active learning styles. Finally, we discussed the optimal ways to teach CPS and the practical effects of a CPS learning application.

Personality Scales and Learning Styles

2011 ◽  
Vol 1 (1) ◽  
pp. 29-49 ◽  
Author(s):  
Anshu Saxena Arora ◽  
Mahesh S. Raisinghani ◽  
Reginald Leseane ◽  
Lemaro Thompson
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Cognitive Abilities ◽  
Positive Impact ◽  
Human Mind ◽  
Learning Preferences ◽  
Web Based ◽  
Team Interaction ◽  
Learning Patterns ◽  
Academic Grades

There is a vast body of literature that indicates students not having homogeneous learning patterns. Just as the students vary, their learning styles, cognitive abilities, and learning preferences vary; similarly, instructors employ different teaching methods. Numerous researchers have hypothesized that when students’ unique learning dimensions are matched with similar teaching styles, it can have a significant positive impact in regards to students’ grasp on information, their satisfaction with the course, improved academic grades, and group/team interaction. However, it is rigorously debated what these dimensions are, if they are fixed or changeable, and which scale gives the most accurate purview into the various learning dimensions of students. This paper explores the behavior and learning style of the human mind and its capacity in different learning environment. The authors examine theory, similarities, differences, and implications of the five relevant learning models discussed in the paper. Analyzing and interpreting these learning styles and behaviors will help the reader employ the best scale or combination of scales that should be used in the creation of Web-based learning environments (WBLE) for students and adapting WBLE to their particular learning styles and preferences.

scholarly journals A Web-based Instrument to Initialize Learning Style: An Interactive Questionnaire Instrument

2018 ◽  
Vol 13 (12) ◽  
pp. 238 ◽  
Author(s):  
Doniyorbek Qambaralievich Ahmadaliev ◽  
Chen Xiaohui ◽  
Murodjon Abduvohidov
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Learning Objects ◽  
Learning Preferences ◽  
Web Based ◽  
Learning And Teaching ◽  
Effective Learning ◽  
Initial Learning ◽  
Study Results ◽  
Very High

Being able to detect and address individual learners’ learning preferences can be a basis for effective learning and teaching. Many researches are contributing to define the potential differences of individual preferences in learning. In this paper, we present an interactive Web-based instrument to initiate students’ learning style. The instrument uses learner’s interaction with learning objects as hint and representing them as the students’ learning style. By applying the instrument, we have been able to detect initial learning styles accurately. Evaluation of our experimental results showed high precision. Besides, very high satisfactory feedbacks were received from students. Based on the mentioned benefits and study results, our method has potential influence on defining individual’s preference in learning

David Kolb's Experiential Learning Machine

1993 ◽  
Vol 24 (1) ◽  
pp. 46-62 ◽  
Author(s):  
Richard Hopkins
Keyword(s):  
Experiential Learning ◽  
Learning Styles ◽  
Learning Style ◽  
Critical Analysis ◽  
Learning Machine

AbstractThis article is a review of David Kolb's program of work on learning styles and experiential learning, which I find to be a problematic instance of psychologism. I argue that Kolb's approach ignores the process nature of experience and that attractive as it may be instrumentally, it ultimately breaks down under the weight of its structuralist reductions. Kolb attempts to account for experiential learning without a coherent theory of experience, such as might have been found in phenomenology, which he virtually ignores. Thus, Kolb neglects the constitutive effects of the noetic-noemic corelationship and the intentional reality of the person. I contrast Kolb's formulations with John Dewey's much more resilient conception of "habit" and close with a critical analysis of various ways in which Kolb's learning-style instruments are used for aggressive intervention in people's lives.

An empirical investigation on learning and teaching lean manufacturing

2020 ◽  
Vol 62 (3) ◽  
pp. 339-354
Author(s):  
Guilherme Luz Tortorella ◽  
Rogério Miorando ◽  
Diego Fettermann ◽  
Diego Tlapa Mendoza
Keyword(s):  
Graduate Students ◽  
Learning Styles ◽  
Learning Style ◽  
Teaching Methods ◽  
Lean Manufacturing ◽  
Teaching Method ◽  
Learning Preferences ◽  
Teaching Approaches ◽  
Content Type ◽  
Learning And Teaching

PurposeThis article identifies the association between two methods for teaching lean manufacturing (LM): problem-based learning (PBL) and classroom lectures, and students' learning styles of a postgraduate course.Design/methodology/approachData were collected from graduate students LM courses that present different teaching approaches. Thus, students' learning preferences were gathered through the application of the Index of Learning Style questionnaire, and their performance assessed after each course.FindingsResults indicate that learning styles are indeed associated with LM teaching approaches, and comprehending interaction effects between learning style dimensions is essential for properly adapting the teaching method. However, these interactions have different extensions.Originality/valueAlthough teaching LM has significantly evolved over the past decades, the single application of traditional teaching methods jeopardizes learning effectiveness of graduate students because of the practical nature of LM. This study provides evidence to better understand the effect of complementary teaching methods and their relationship with students' preferences, empirically examining that there is not one best approach for understanding LM.

Knowledge Mining for Adaptive Multimedia Web-Based Educational Platform

2008 ◽  
pp. 205-257 ◽  
Author(s):  
Leyla Zhuhadar ◽  
Olfa Nasraoui ◽  
Robert Wyatt
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Intelligent System ◽  
Learning Material ◽  
Online Information ◽  
Learning Sequence ◽  
Online Students ◽  
User Models ◽  
Web Based ◽  
The Web

This chapter introduces an Adaptive Web-Based Educational platform that maximizes the usefulness of the online information that online students retrieve from the Web. It shows in a data driven format that information has to be personalized and adapted to the needs of individual students; therefore, educational materials need to be tailored to fit these needs: learning styles, prior knowledge of individual students, and recommendations. This approach offers several techniques to present the learning material for different types of learners and for different learning styles. User models (user profiles) are created using a combination of clustering techniques and association rules mining. These models represent the learning technique, learning style, and learning sequence, which can help improve the learning experience on the Web site for new users. Furthermore, the user models can be used to create an intelligent system that provides recommendations for future online students whose profile matches one of the mined profiles that represents the discovered user models.

SRL/SDL and Technology-Enhanced Learning

2010 ◽  
pp. 396-412
Author(s):  
Jane Pilling-Cormick
Keyword(s):  
Learner Control ◽  
Technology Enhanced Learning ◽  
Self Directed Learning ◽  
Comprehensive Overview ◽  
Web Based ◽  
Research Framework ◽  
Self Regulated Learning ◽  
Directed Learning ◽  
One Step ◽  
Enhanced Learning

When exploring the central role control plays in implementing technology-enhanced learning initiatives, it is essential to take into consideration self-regulated learning (SRL) and self-directed learning (SDL). Pilling-Cormick & Garrison’s (2007) work provides a research framework which includes a comprehensive overview of how SRL and SDL are integrally related. In this chapter, the connection is taken one step further by using the framework to explore SRL/SDL Technology-Enhanced learning. Implications for practice are derived from three exploratory studies using technology-enhanced learning (handheld, web-based, and online) with a focus on learner control. Solutions and recommendations arise, including considerations for designing instruction with a focus on learner control as it relates to technology.

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