Fuzzy-logic based learning style prediction in e-learning using web interface information

Deborah, L. Jegatha; Sathiyaseelan, R; Audithan, S.; Vijayakumar, Pandi

doi:10.1007/s12046-015-0334-1

Cited by 36 publications

(14 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Educators assessed and changed their pedagogies to accommodate students' personality types to increase learning outcomes. Deborah et al (2015) reported that there was no difference in performance between MBTI personality dichotomies in either learning environments (face-to-face versus Online). However, they observed that Sensors/iNtuitive dichotomies were dissatisfied with the amount of interaction in Online environments.…”

Section: Implication Of Personality Types In Educationmentioning

confidence: 96%

“…Some models are more popular and applicable compared to others based on disciplines. Lakhal, Sévigny, and Frenette (2015) identified and summarized nine such theories with their models as follows: (Felder & Silverman,1988) Even though Deborah, Sathiyaseelan, Audithan, and Vijayakumar (2015) (Cline et al, 2015). Felder emphasized that educators who strive for excellence must be aware of student learning styles and recommended the integration of diverse strategies to address student needs (Ashraf, Fendler, & Shrikhand, 2013;Felder & Brent, 2005).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Student Preferences and Performance in Online and Face-to-Face Classes Using Myers-Briggs Indicator: A Longitudinal Quasi-Experimental Study_

Boghikian-Whitby¹,

Mortagy²

2016

InSITE Conference

View full text Add to dashboard Cite

This longitudinal, quasi-experimental study investigated students' cognitive personality type using the Myers-Briggs personality Type Indicator (BMTI) in Internet-based Online and Face-toFace (F2F) modalities. A total of 1154 students enrolled in 28 Online and concurrent 32 F2F sections taught over a period of fourteen years. The study measured whether the sample is similar to the national average percentage frequency of all 16 different personality types; whether specific personality type students preferred a specific modality of instructions and if this preference changed over time; whether learning occurred in both class modalities; and whether specific personality type students learned more from a specific modality. Data was analyzed using regression, t-test, frequency, and Chi-Squared. The study concluded that data used in the study was similar to the national statistics; that no major differences in preference occurred over time; and that learning did occur in all modalities, with more statistically significant learning found in the Online modality versus F2F for Sensing, Thinking, and Perceiving types. Finally, Sensing and Thinking (ST) and Sensing and Perceiving (SP) group types learned significantly more in Online modality versus F2F.

show abstract

Section: Implication Of Personality Types In Educationmentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 99%

Student Preferences and Performance in Online and Face-to-Face Classes Using Myers-Briggs Indicator: A Longitudinal Quasi-Experimental Study_

Boghikian-Whitby¹,

Mortagy²

2016

InSITE Conference

View full text Add to dashboard Cite

show abstract

“…Deborah et al [9] have identified FSLSM as a suitable model for learning style prediction, especially in web environments. The authors have proposed to use Fuzzy rules to handle the uncertainty in the learning style predictions.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System

Kolekar

Pai

2017

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Abstract-The major requirement of e-learning system is to provide a personalized interface with personalized contents which adapts to the learning styles of the learners. This is possible if the learning styles of the learner is known. In this paper, it is proposed to identify the learning styles of the learner, by capturing the learning behavior of the learner in the e-learning portal using Web Log Mining. The learning styles are then mapped to Felder-Silverman Learning Style Model (FSLSM) categories. Each category of the learner is provided with the contents and interface which are apt for that category. Fuzzy C Means (FCM) algorithm is used to cluster the captured learning behavioral data into FSLSM categories. The learning styles of a learner get changed over a period of time hence the system has to adapt to the changes and accordingly provide the necessary interface and contents. For this, the Gravitational Search based Back Propagation Neural Network (GSBPNN) algorithm is used to predict the learning styles of the learner in real-time. This algorithm is a modification of basic Back Propagation Neural Network (BPNN) algorithm which calculates the weights using Gravitation Search Algorithm (GSA). The algorithm is validated on the captured data and compared using various metrics with the basic BPNN algorithm. The result shows that the performance of GSBPNN algorithm is better than BPNN.

show abstract

“…The learning style model has identified in reference [13] that proposed learning model called Felder-Silverman with fuzzy rules and to evaluate the result Gaussian membership function has been used.…”

Section: Related Workmentioning

confidence: 99%

Skill‐based education through fuzzy knowledge modeling for e‐learning

Gomathi

Rajamani

2017

Comp Applic In Engineering

View full text Add to dashboard Cite

Nowadays outcome‐based methods are adapted in e‐learning system to meet the need of the novel development of e‐learning systems for improved web‐based retrieval results. Typically, knowledge retrieval process is denoted by the production rule, frame, semantic network, fuzzy logic, predicate logic, and group of skill concept. To get the optimized result in proposed skill‐based e‐learning, the fuzzy knowledge model is applied. In knowledge retrieval, the fuzzy membership value of the knowledge and the combinations of framed rules are used to acquire the knowledge. The fuzzy techniques are adapted for analyzing the retrieved knowledge concept of individual skills like Inadequate (Ia), Adequate (A), Value added adequate (Vaa), and Integrated skill (I) and in fuzzy inference system in skill‐based education provides a decision about the learner community skill set and it promotes the excellence skill through the delivery of the suitable courses to the learners instead of supplying common courses to different skilled persons. In the existing knowledge modeling methods known as the Knowledge Capturing and Modeling, concept map‐based knowledge modeling confirm the learners to have known or unknown domain concept. Further, many of the researchers present on the analysis of the learner performances, behavior, learning environment, etc. The proposed paper investigate the individual skill abilities and it is suggested a set of courses in adaptive curriculum and syllabus to the learner and also it adapts andragogy in skill‐based education, to model the fuzzy knowledge, the fuzzy membership function and rules are used.

show abstract

Fuzzy-logic based learning style prediction in e-learning using web interface information

Cited by 36 publications

References 29 publications

Student Preferences and Performance in Online and Face-to-Face Classes Using Myers-Briggs Indicator: A Longitudinal Quasi-Experimental Study_

Student Preferences and Performance in Online and Face-to-Face Classes Using Myers-Briggs Indicator: A Longitudinal Quasi-Experimental Study_

Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System

Skill‐based education through fuzzy knowledge modeling for e‐learning

Contact Info

Product

Resources

About