Inclusive Learner Model for Adaptive Recommendations in Virtual Education

Mejía, Carolina; Gómez, Sergio; Mancera, Laura; Taveneau, Sibylle

doi:10.1109/icalt.2017.101

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…Dirks et al, 2020;Colucci et al, 2017) -VLE, LMS (e.g. Dirks et al, 2020;Mejia et al, 2017) -BYOD classroom (e.g. Adhikari et al, 2016) -emergency remote teaching and learning (during COVID-19 pandemic) (e.g.…”

Section: Digital Educational Settingsmentioning

confidence: 99%

Inclusive Digital Education

2022

Educational Communications and Technology: Issues and Innovations

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Section: Digital Educational Settingsmentioning

confidence: 99%

Inclusive Digital Education

2022

Educational Communications and Technology: Issues and Innovations

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“…They encompassed in their modeling, prior knowledge, affective states, personality traits, cognitive characteristics, personal characteristic and knowledge. Mejia et al (2017) considered people with disabilities in their model and so their setup consisted of demographic data, competencies, reading difficulties, and cognitive traits. Mejia et al (2017) did not consider any learning theory.…”

Section: E-learning Modelsmentioning

confidence: 99%

“…Mejia et al (2017) considered people with disabilities in their model and so their setup consisted of demographic data, competencies, reading difficulties, and cognitive traits. Mejia et al (2017) did not consider any learning theory. Huang et al (2017) placed the learner I a contextual environment and modelled learner's context with regard to social context, cognitive levels, basic information, learners learning style, learner preferences and related interests.…”

Section: E-learning Modelsmentioning

confidence: 99%

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

Anne¹,

Maina²

2021

OJIT

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We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

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“…al. [33] approached the task by estimating learner's cognitive deficit specifically for students with dyslexia or reading difficulties. Although a promising and inclusive architecture, this work does not report specific results, and instead discusses several principles.…”

Section: Introductionmentioning

confidence: 99%

Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models

Twomey¹,

McMullan²,

Elhalal³

et al. 2022

Preprint

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At present, the educational data mining community lacks many tools needed for ensuring equitable ability estimation for Neurodivergent (ND) learners. On one hand, most learner models are susceptible to under-estimating ND ability since confounding contexts cannot be held accountable (e.g. consider dyslexia and text-heavy assessments), and on the other, few (if any) existing datasets are suited for appraising model and data bias in ND contexts. In this paper we attempt to model the relationships between context (delivery and response types) and performance of ND students with zero-inflated learner models. This approach facilitates simulation of several expected ND behavioural traits, provides equitable ability estimates across all student groups from generated datasets, increases interpretability confidence, and can double the number of learning opportunities for ND students in some cases. Our approach consistently out-performs baselines in our experiments and can also be applied to many other learner modelling frameworks.

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Inclusive Learner Model for Adaptive Recommendations in Virtual Education

Cited by 10 publications

References 3 publications

Inclusive Digital Education

Inclusive Digital Education

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models

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