Towards an adaptive model to personalise open learning environments using learning styles

Fasihuddin, Heba; Skinner, Geoff; Athauda, Rukshan

doi:10.1109/icts.2014.7010580

Cited by 22 publications

(10 citation statements)

References 21 publications

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“…Table 6 shows the year wise summary of the papers, their types, and publishers. [55] Journal Science Direct [56,57] Conference IEEE [58] Conference SIMBig 2014 [59] Conference ACM [60,61] Conference EDM [62][63][64][65][66][67] Conference IEEE [68] Journal Elsevier [69] Conference INTED2015 [70] Conference ICRTEST [71][72][73][74] Conference IEEE [75] Conference EDM [76] Journal RISTI [77,78] Conference ACM [79] During this search, we have identified journals that support this domain, these are shown in. Table 7.…”

Section: Identification Of Primary Studiesmentioning

confidence: 99%

“…We found studies using learning style analytics to achieve LO recommendations. Fasihuddin et al [56] exploited learners interaction patterns with open learning environment to classify users based on their learning styles and generated recommendations based on their learning styles. Dai [75] used latent dirichlet allocation to predict the distribution of the course contents in the knowledge domain and predicted knowledge covered in an unknown syllabus.…”

Section: Rq1 How Many Studies Supported Their Claim With Experiments and Which Datasets Were Used In The Studies?mentioning

confidence: 99%

“…We found the use of Learning analytics in the literature for recommendation. Fasihuddin et al [56] proposed an idea for an adaptive model to personalized the open learning environment based on the Felder & Silverman learning style model [11]. Li and Mitros [63] showed how learners could collaborate by improving resources for remediation.…”

Section: Knowledge-based Filtering (Kbf)mentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

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Online learning environments (OLE) are gaining popularity, including learning management systems (LMS) and massive open online courses (MOOCs), which are the best modern alternate solutions available for education in the current era. The luxury to learn irrespective of geographical and temporal restrictions makes it an attractive resource. At the start of 2020, the global pandemic enforced social distance practice worldwide, changing the work environment dynamics, leaving the people with options like online trading, work from home, and online education. The online learning environments gained particular attention in the educational sector, where users could access the online learning resources to fulfil their academic requirements during the lockdown. From massively available content such as MOOC, the learners are overwhelmed with the available choices. In this scenario, recommender systems (RS) come to the rescue to help the learner make appropriate choices for completing the enrolled course. There is tremendous scope and a multitude of opportunities available for researchers to focus on this domain. An exhaustive analysis is required to spotlight the opportunities in this realm. Various studies have been performed to provide such solutions in multiple areas of the MOOC recommendation systems (MOOCRS) such as course recommendation, learner peer recommendation, resource recommendations, to name a few. This is a compendious study into the research conducted in this area, identifying 670 articles out of 116 selected for analysis published from 2013 to 2021. It also highlights multiple areas in MOOC, where the recommendation is required, as well as technologies used by other researchers to provide solutions over time.

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Section: Identification Of Primary Studiesmentioning

confidence: 99%

Section: Rq1 How Many Studies Supported Their Claim With Experiments and Which Datasets Were Used In The Studies?mentioning

confidence: 99%

Section: Knowledge-based Filtering (Kbf)mentioning

confidence: 99%

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A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

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“…Learning style can be defined as the individual characteristic in the way of their perceived and received information (Noraziah et al, 2011). Therefore, different learner has a different learning style (Fasihuddin et al, 2014). There have many of learning style modelshas been proposed by the researcher.…”

Section: Learning Stylementioning

confidence: 99%

Alsdoc: Adaptive Learning Style for Document Retrieval

Normadhi1¹,

Shuib²,

Nasir³

2018

IJISE

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Learning can be enhanced when its learning process aligned with learner learning styles. Individual has different learning preferences. The consideration of learner preferences in finding document can enable a learner to obtain knowledge easily and fast where it constantly changes and shift according to learning situation and environment. Thus, the incorporation of Learning Style (LS) with adaptive learning environment is one of a method to assist novice learner in finding a suitable document and enhance the e-learning system; while it can overcome the one-size-fitsfor-all problem. The LS identification is an important component to identify the learner learning style. There are two conventional techniques to identify learners learning style, which are questionnaires and computer-based detection technique. The questionnaire involves the learner to fill up a survey explicitly. However, existing of LS identification techniques facing some major drawbacks can lead the LS tool to become static nature. The questionnaire technique d facing issues such as questionnaire too long and took long time that can make learner lack of motivation to answer the question. Moreover, the result from the questionnaire technique is fixed because it only prompts a learner to do it only one at the time. Therefore, a few researchers employed computer-based detection techniques, which use a numerous computer algorithm to determined LS. However, these techniques suffer on cold start problem, required a large amount of training data and the existing computer-based detection were not focused document as learning material. Hence, there is a need to develop an adaptive learning style identification that uses document as a learning material. This paper presents the architecture for ALSDoc, an adaptive LS identification for document retrieval. In particular, this study explained how LS is adapt to the learner that allows the retrieval the suitable document that match their LS.

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“…The learning studies, these days are dealing with a new trend where learning styles detection in web-based learning systems need to be done automatically [6].…”

Section: Learning Stylementioning

confidence: 99%

Designing and Implementing of Dynamic Technique for Detecting Learning Style Using Literature Based Approach

Ateia¹,

Hamtini²

2016

IJDTA

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This paper is a part of ongoing research that tries to approve our hypothesis about adaptive personalization in web-based learning systems; we are debating that building online learning environments with the ability to detect learners learning style dynamically by observing their behaviour and then presenting learning material based on the detected learning style is more effective than using ILS questionnaire. In an earlier work, we provided a dynamic technique that identifies learners VAK learning styles according to their behaviour in the learning environment influenced by literature approach, in this paper we have modified our technique and re-proposed it. First, we connected behavioural patterns (time, visits and answer patterns) to the features (contents, outlines, group forums, examples, case studies, exercises and assessments), then we defined the effect each VAK learning style will have on each pattern. Next, we described three general rules and three algorithms that should detect the learning style. Now we are in the process of building two online learning environments to test and confirm the differences between dynamic learning style detection _ based on our technique _ and the traditional ILS questionnaire environments; we choose a course called 'Computer Skills for medicine faculty students', which is provided from the faculty of Information Technology at the University of Jordan; the results of the planed experiment will be saved and analysed using SPSS.

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Towards an adaptive model to personalise open learning environments using learning styles

Cited by 22 publications

References 21 publications

A Systematic Mapping Review on MOOC Recommender Systems

A Systematic Mapping Review on MOOC Recommender Systems

Alsdoc: Adaptive Learning Style for Document Retrieval

Designing and Implementing of Dynamic Technique for Detecting Learning Style Using Literature Based Approach

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