A Framework to Personalise Open Learning Environments by Adapting to Learning Styles

Fasihuddin, Heba; Skinner, Geoff; Athauda, Rukshan

doi:10.5220/0005443502960305

Cited by 12 publications

(12 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the universal moderators, the SEN (sensing) learning style is the mutual coordination learning style. In general, the sensory learners prefer learning by doing [39,89], and they need more practice and well-established examples [46,89]. This characteristic matches with the nature of the computer programming subject.…”

Section: Evaluation Of the Moderation Analysismentioning

confidence: 98%

“…Those studies show that learning styles correlate with computer programming performance and some learning styles are more suitable for learning computer programming subject [44]. The interesting insights from those analyses have been utilized in the adaptive engine in various ways [45,46], such as providing the adaptive learning contexts, adaptive learning resource format, and adaptive teaching strategies.…”

Section: Learning Style In Adaptive Learning Systemmentioning

confidence: 99%

“…This characteristic matches with the nature of the computer programming subject. To encourage the learners, the lecturers should provide details of programming syntax and facts that need to be learned based on activities found in real life [46,89,90]. Moreover, the question-and-answer method, the problem-solving teaching strategy, and the extra example or exercise should be included [91].…”

Section: Evaluation Of the Moderation Analysismentioning

confidence: 99%

See 2 more Smart Citations

Unified Model for Learning Style Recommendation

Ninrutsirikun

Pal

Arpnikanondtand

et al. 2021

JWE

View full text Add to dashboard Cite

Studying computer programming requires not only an understanding of theories and concepts but also coding adeptness. Success in studying or conducting such a course is definitely a challenge. This paper proposes a systematic learning style recommendation. The model is designed to evaluate students’ attributes and ongoing or formative learning outcomes for suggesting the effective style-fit strategy that facilitates learners to enhance their learning performances in terms of knowledge and skill. A two-stage association analysis was designed and conducted on a dataset collected from IT major students who enrolled in the Introduction to Computer Programming course. The first stage of association rules is to analyze and discover important relationships amongst learning styles, students’ attribute, and learning performance. The second stage of moderation analysis is then applied to probe the moderation effect of the different learning preferences on the relationship between student attributes and learning achievement. Experiments expose many insights, for example, mathematics and logical thinking are powerful assets of success in computer programming study. Association rules can effectively identify associations of learning styles and the learning performance in terms of knowledge or skills. By moderation analysis, students in the “Excellent” cluster have a broad learning style than other students. Two types of significant moderators, the universal and specific, exemplify how lecturers can flexibly post style-fit teaching strategies for a class-wide and specific group, respectively.

show abstract

Section: Evaluation Of the Moderation Analysismentioning

confidence: 98%

Section: Learning Style In Adaptive Learning Systemmentioning

confidence: 99%

Section: Evaluation Of the Moderation Analysismentioning

confidence: 99%

See 1 more Smart Citation

Unified Model for Learning Style Recommendation

Ninrutsirikun

Pal

Arpnikanondtand

et al. 2021

JWE

View full text Add to dashboard Cite

show abstract

“…The monolithic distinction between completers and dropout is inadequate for describing the diversity of learners' engagement patterns (Clow, 2013;. Concerning different ways of learning in MOOCs, Fasihuddin et al (2015) propose a framework to personalise open learning environments based on the theory of learning styles, particularly on Felder and Silverman's (1988) learning style model. The framework provides adaptive navigational support through sorting and hiding the learning materials based on learners' learning styles and the involved preferences.…”

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Participants’ Experiences fromthe First International MOOC Offered at the University of Oslo

Singh

Mørch

2018

NJDL

View full text Add to dashboard Cite

This paper explores pedagogical practices and participants' engagement in learning activities during the first international Massive Open Online Course (MOOC) offered by the University of Oslo through the FutureLearn platform in 2015. The data were collected using pre-and post-course surveys and participant observations. We used the acquisition and participation metaphors of learning proposed by Sfard (1998) as a conceptual framework to inform our analyses and discussions. The data indicated that new pedagogical practices are in the making for online learning, involving elements of existing practices and radically new ones. The instructors had sole authority in developing and curating course contents, thus following the acquisition metaphor of teaching and learning. In addition, the data indicated that, overall, the learners had a positive experience of learning by participating in the MOOC. The learners engaged in online discussion forums, interacting asynchronously with fellow learners and mentors. The discussion forums promoted knowledge sharing and collaborative learning activities among diverse groups (joiners, surveyors, and social learners). The apparent contradiction between teaching according to the acquisition metaphor and the learners' preferences for the participation metaphor was resolved by some of the learners through self-organised scaffolding. The teachers did not interact enough with the learners and so, to compensate, some learners took on facilitating roles. We discuss our findings in terms of the related work and contemporary trends in online learning and higher education research, including learning analytics, formative assessment, personalization, collaboration support, and lifelong learning.

show abstract

“…In our work we also use the patterns introduced by (Fasihuddin et al, 2014) to identify the type of learning resources to be provided to the learner based on his answers to the ILS questionnaire. For example, sensing learners prefer to get more examples and exercises (Fasihuddin et al, 2014). We model the learning style by using the term S learner .…”

Section: Definition 2 (Performance Degree Of Learner)mentioning

confidence: 99%

A System to Recommend Open Educational Resources during an Online Course

Hajri

Bourda

Popineau

2018

Proceedings of the 10th International Conference on Computer Supported Education

View full text Add to dashboard Cite

Recently, personalization in Technology Enhanced Learning (TEL) has been researched extensively. With the spreading of online learning environments (OLE) as MOOCs and LMSs, a large number of learners with different characteristics and backgrounds can follow online courses. To support personalization, recommender systems can be used to provide each learner with learning objects helping him to reach his learning objectives. These recommendations are more specific to compute than usual recommendations (like consumer products). Furthermore, if they are included in a course, they depend not only on the learner's profile but also on the content of the course, because they need to fit with the course format at any point. At the same time, there is a growing number of open educational resources (OER) available to usefully enrich the content of online courses. To facilitate their reuse some of these OERs are described with metadata schemas following Linked Open Data principles (LOD). In this paper, we introduce a MOOC-based OER recommender system (MORS) that can be plugged in an OLE to provide recommendations of OERs to learners based on their profiles, the course profile and a process for calculating recommendations based on OERs metadata. This paper presents our approach that has been implemented in a MOOC platform: Open edX. However the proposed approach could be implemented in any OLE by using the same process to calculate recommendations, as long as the learner and the course profiles can be extracted.

show abstract

A Framework to Personalise Open Learning Environments by Adapting to Learning Styles

Cited by 12 publications

References 17 publications

Unified Model for Learning Style Recommendation

Unified Model for Learning Style Recommendation

An Analysis of Participants’ Experiences fromthe First International MOOC Offered at the University of Oslo

A System to Recommend Open Educational Resources during an Online Course

Contact Info

Product

Resources

About