A survey of personality and learning styles models applied in virtual environments with emphasis on e-learning environments

Fatahi, Somayeh; Moradi, Hadi; Kashani-Vahid, Leila

doi:10.1007/s10462-016-9469-7

Cited by 39 publications

(18 citation statements)

References 39 publications

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“…This segment describes some of the current reviews. The appropriate suggestions made by the recommender systems help learners in decision-making toward self-regulated learning (Fatahi et al, 2016 ; Aguilar & Riofrio, 2017 ). Klašnja-Mili'cevi'c et al ( 2015 ) conducted an inclusive review of recommender systems in e-learning environments.…”

Section: Related Workmentioning

confidence: 99%

A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

Renumol

2021

J. Comput. Educ.

100

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In personalized learning, each student gets a customized learning plan according to their pace of learning, instructional preferences, learning objects, etc. Hence the content recommender system in Personalized Learning Environment (PLE) should adapt to learner attributes and suggest appropriate learning resources to aid the learning process and improve the learning outcomes. This systematic literature review aims to analyze and summarize the studies on learning content recommenders in adaptive and personalized learning environments from 2015 to 2020. The publications were searched using proper keywords and filtered using the inclusion and exclusion criteria, which resulted in 52 publications. This paper summarizes the recent trends in research on different aspects of the recommender systems, such as learner attributes, recommendation methods, evaluation metrics, and the usability tests used by the researchers. It is observed that cognitive aspects of learners like learning style, preferences, knowledge level, etc., are used by most studies than non-cognitive aspects as social tags or trust. In most cases, recommendation engines are a hybrid of collaborative filtering, content-based filtering, ontological approaches, etc. All models were evaluated for the correctness of the prediction done, and a few studies have also done evaluations based on learner satisfaction or usability.

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Section: Related Workmentioning

confidence: 99%

A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

Renumol

2021

J. Comput. Educ.

100

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“…Constraints for the success of personality tests include Eysenck Model, FFM, 16PF and MBTI (Fatahi et al, 2016) because the execution test process with a questionnaire have issues around thoughtful and honest respondent involvement are constantly significant and are often related to the causes of measurement errors (Gideon, 2012) by the test using a questionnaire, the organization's concerns about prospective workers or respondents pretended when a personality test was carried out during the selection process, namely with the risk of the possibility of participants lying when the personality test was running (Patterson et al, 2016). This condition can affect the organization's decisions accuracy to hire (O'Neill et al, 2017).…”

Section: Study Literaturementioning

confidence: 99%

Confirmation of Personality Types Using Visual Evoked Potential with User Interface Design Stimulus

Perdana¹,

Santosa²,

Setiawan³

et al. 2021

Journal of Computer Science

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After obtaining the consistency of Visual Evoked Potential (VEP) by using a stimulus with data-entry user interface design. This study tries to confirm whether an individual's VEP will be classified according to personality type, which results from the proceeds of conventional personality tests usually carried out by the organization that is the place of this research. The results of this study will be an attempt to complement the needs of particular organizations for the elicitation stage to show user personas by using personality types as something to consider in software development in organizations. The recording of 20 participants whose personality types were known from the results of personality tests at the organization was completed. Each individual has a unique VEP. to confirm personality types and it is necessary to prove the similarity of VEP between individuals of the same type. 93.75% of VEP is classified according to personality type. Therefore it is necessary to consider that personality type can be used as user persona for user classification at the stage of user needs. To identify personality types at the personality test stage, it is still necessary to add more participants involved to get more convincing results.

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“…It is widely believed that matching teaching and LS can positively impact learning process (Marković & Jovanovic, 2012). Although numerous LS models had introduced (Coffield, Moseley, Hall, & Ecclestone, 2004) but among them FSLSM was viewed as most preferred LS due to its simplicity (Fatahi, Moradi, & Kashani-Vahid, 2016). Thus, adaptive learning systems are still underexplored in terms of other LS theories (Truong, 2016).…”

Section: Effective Personalization Parametersmentioning

confidence: 99%

Developing Adaptive E-Learning Environment Using Cognitive and Noncognitive Parameters

Siddique

Durrani

Naqvi

2018

Journal of Educational Computing Research

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The falling learning outcome is one of the major challenges faced by most of the educational systems. Adaptive educational systems (AESs) are viewed as catalyst to reinforce learning. Several AESs have been developed considering only single aspect of learners, for example, learning styles. The impact of learning style-based AESs in terms of improving learning outcomes is still unclear. In this study, an adaptive learning system is being proposed considering combination of multiple sources of personalization such as learning styles, working memory capacity, and prior knowledge. An experiment was conducted using sample size of 184 students to assess the impact of proposed approach in comparison to traditional classroom teaching and learning. The student sample was equally divided into control and experimental groups. Both groups further consisted of subgroups formed on the basis of their cognitive and noncognitive characteristics, identified using standardized tools. The experimental subgroups learned the subject contents with proposed adaptive learning system, whereas control subgroups learned in traditional classroom environment. The results revealed that the participants of experimental subgroups exhibit significantly better learning performance than their counterparts of control subgroups.

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A survey of personality and learning styles models applied in virtual environments with emphasis on e-learning environments

Cited by 39 publications

References 39 publications

A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

Confirmation of Personality Types Using Visual Evoked Potential with User Interface Design Stimulus

Developing Adaptive E-Learning Environment Using Cognitive and Noncognitive Parameters

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