A Felder and Silverman Learning Styles Model Based Personalization Approach to Recommend Learning Objects

Çiloğlugil, Birol; İnceoğlu, Mustafa Murat

doi:10.1007/978-3-319-42092-9_30

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…The accuracy of predictions in this study ranges from 38% to 77%, which is comparatively dissatisfying. The low accuracy is acceptable considering the exploratory nature of quasi-experimentation (Ciloglugil and Inceoglu, 2016). The most important is, the accuracy rate indicates that different types of materials have different levels of prediction abilities and there are many factors affecting accuracy rate in the eyetracking experiment.…”

Section: Rq2 How Accurate Are the Predictions Based On Eye Movement Patterns Of Visualisers And Verbalisers?mentioning

confidence: 99%

The use of eye-tracking technology to identify visualisers and verbalisers: accuracy and contributing factors

Luo

O’Steen

Brown

2020

ITSE

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Purpose To build adaptive learning systems for a better learning experience, designers need to identify users’ behaviour patterns and provide adaptive learning materials accordingly. This study involved a quasi-experiment and also this paper aims to investigate the accuracy of eye-tracking technology in identifying visualisers and verbalisers and the contributing factors to diverse levels of accuracy, which lays the foundation for the establishment of adaptive learning systems. Design/methodology/approach The authors prepared eight documents with different image-text combinations with the intention of triggering participants’ natural reading habits. By analysing the eye-movement data, this author categorised the 22 participants as visualisers or verbalisers. The results were compared for accuracy measure with participants’ self-reports in response to the index of learning style questionnaire. Findings The results showed that visualisers and verbalisers presented significantly different eye-movement patterns, which was confirmed by the fixation data from the Tobii eye-tracker with the detection accuracy ranged from 38% to 77%. Various factors contributed to a range of levels of accuracy, including highlighted elements, learning context, complex background, low relevance of images and texts, learner differences, awareness of experimental settings, self-conception and prior knowledge. Originality/value To the authors’ knowledge, this is the first paper investigating the feasibility of eye-tracking technology to identify visualisers and verbalisers for the development of adaptive learning systems.

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Section: Rq2 How Accurate Are the Predictions Based On Eye Movement Patterns Of Visualisers And Verbalisers?mentioning

confidence: 99%

The use of eye-tracking technology to identify visualisers and verbalisers: accuracy and contributing factors

Luo

O’Steen

Brown

2020

ITSE

View full text Add to dashboard Cite

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“…After conspicuous verification of feature vectors and association between things, the accompanying stage is to make cases for rundown out of abilities by considering historical results. This work has framed diverse cases with id, problem, learning style [9], learning objects, test execution and utilized way. This model had considered preparing a dataset for a finding of proper estimation of K. When the finding of estimation of K had done then KNN is prepared for producing similitude list for new user.…”

Section: Neighbourhood Formation Processmentioning

confidence: 99%

Enhanced e-Learning Experience using Case based Reasoning Methodology

Shekapure¹,

Dipti²

2019

IJACSA

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In recent year's improvement in innovation includes new limits for verifying data that will incite essential changes in eLearning. The user can see e-learning material subject to the reference given to them and select the best approach to see the resources. This proposed system addresses retrieval, reuse, revise and retain phases of CBR. For building personalized e-Learning, this work identifies different feature set such as learning style, learning object, knowledge level, and problem list. For constructing this model used case-based reasoning along with a k-nearest neighbour. Role of the Knearest neighbour method is to identify the perfect k factor for better analysis for calculation of accurate retrieval process. There is further addition of new cases based on the simulation of new user history limit to a certain threshold value. This model acquires dynamically incremental dataset for classification. Further, there is time and accuracy comparison on dataset done by K-nearest neighbour, decision tree and support vector machine. Eventually, eLearning spares time, upgrades the learning knowledge and gives scholarly achievement.

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“…Proposed system provides suggestion to learner as per learner learning style [12]. For finding learning style of entire class surveyed of around 100 students has done.…”

Section: Learning Management Systemmentioning

confidence: 99%

Performance Based Adaptive Personalized eLearning System

Shekapure¹,

Patil²

2019

IJRTE

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Step by step eLearning is developing pattern in industry. To the extent, learning technique is concerned it has been seen that conventional learning strategy, for example, instructor and learner as well as chalk and duster swings too inventive learning. Because of innovation in technology each one started learning by utilizing web. If learner is asking for particular learning material sometimes they are not getting relevant result. So there is need to acquire certain data of learner. This data incorporates their learning style, foundation learning, Knowledge level, learning interest, age and so forth. This proposed system tends to use retrieve, reuse, revise and retain phases of CBR. For construction of customized eLearning there has been identification of various list of features. In light of list of features there has been task of assignment of priorities according to need of it. Before retrieval process standardization of features set process is carried out. Job of K-nearest neighbour strategy to recognize impeccable k factor for better examination. Because of dynamically incremental dataset this work identifies which classification algorithm has more suitable for the dataset. Eventually eLearning saves time, enhance learning experience and provides academic success.

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A Felder and Silverman Learning Styles Model Based Personalization Approach to Recommend Learning Objects

Cited by 10 publications

References 5 publications

The use of eye-tracking technology to identify visualisers and verbalisers: accuracy and contributing factors

The use of eye-tracking technology to identify visualisers and verbalisers: accuracy and contributing factors

Enhanced e-Learning Experience using Case based Reasoning Methodology

Performance Based Adaptive Personalized eLearning System

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