Adaptive multimedia: Using gaze-contingent instructional guidance to provide personalized processing support

Scheiter, Katharina; Schubert, Carina; Schüler, Anne; Schmidt, Holger; Zimmermann, Gottfried; Wassermann, Benjamin; Krebs, M.; Eder, Thérése

doi:10.1016/j.compedu.2019.05.005

Cited by 47 publications

(27 citation statements)

References 48 publications

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“…It does not focus on dynamic LS but on emotion extraction [21]. Scheiter et al [18] proposed a framework for detecting learners' emotions using Eye Tracking sensor. The approach focuses on learners' cognitive process and is designed to support learners in full multimedia environment.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The framework relies on Euclidean distance proximity measures of the learners' eye movements but focuses mostly on identification of area of interest. The approach is still at preliminary stage but paves a way for the future studies on AEHS to use Eye Tracking for physiopsycho data acquisition [18]. Mwambe, Othmar, and Kamioka [22] proposed a Brain-Computer Interface (BCI)-based approach namely e-learning Prior Knowledge Assessment System (ePKAS) that can detect learners' prior knowledge profiles level and their matching score with multimedia content [22].…”

Section: Related Workmentioning

confidence: 99%

“…Even though for over a decade intelligent systems have adapted learners' learning styles for the specification of Learning Objects (LOs) and sustained learning content adaptation in AEHS, real-time dynamic adaptation of LOs has been a challenge due to a failure to install themselves in Learning Objects (LOs) [16,17] and unable to handle fully multimedia content [18]. Thus, navigation links granularity results into time imposition and work overload due to the granularity issue [19].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, almost all the existing AEHS are formulated merely on the basis of two common pedagogical learning theories, namely constructivism and cognitivism, which do not fully support such learners' cognitive processes [8][9][10]. However, very few recent studies on AEHS have started exploring other pedagogical learning theories so as to adapt learners' cognitive processes [18][19][20][21] and metacognitive skills [22] into e-learning platforms. Such cognitive processes play a crucial role in predicting learners' performance, attention level [23,24], and cognitive load [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

2020

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Adaptive Educational Hypermedia Systems (AEHS) play a crucial role in supporting adaptive learning and immensely outperform learner-control based systems. AEHS' page indexing and hyperspace rely mostly on navigation supports which provide the learners with a user-friendly interactive learning environment. Such AEHS features provide the systems with a unique ability to adapt learners' preferences. However, obtaining timely and accurate information for their adaptive decision-making process is still a challenge due to the dynamic understanding of individual learner. This causes a spontaneous changing of learners' learning styles that makes hard for system developers to integrate learning objects with learning styles on real-time basis. Thus, in previous research studies, multiple levels navigation supports have been applied to solve this problem. However, this approach destroys their learning motivation because of imposing time and work overload on learners. To address such a challenge, this study proposes a bioinformatics-based adaptive navigation support that was initiated by the alternation of learners' motivation states on a real-time basis. EyeTracking sensor and adaptive time-locked Learning Objects (LOs) were used. Hence, learners' pupil size dilation and reading and reaction time were used for the adaption process and evaluation. The results show that the proposed approach improved the AEHS adaptive process and increased learners' performance up to 78%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

2020

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Adaptive Instructional Systems. Design and Evaluation

2021

Lecture Notes in Computer Science

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Adoption of e-learning for those with special needs lags that for mainstream learners. Not much is known about barriers and facilitators that drive this disparity. The present study used focus groups and interviews to collect the views of 21 teachers taking part in preliminary evaluations of an adaptive learning system based on multimodal affect recognition for students with learning disabilities and autism. The system uses multimodal detection of affective state and scoring of performance to drive its adaptive selection of learning material. Five themes captured the teachers' views of the system's potential impact, especially regarding learning and engagement but also on factors that might influence adoption. These were: the potential of the system to transform their teaching practice; the ability of the system to impact on learning outcomes; the potential impact on teacher-student/peer to peer relationships; usability issues; and organisational challenges. Despite being highly motivated as volunteer testers, teachers highlighted barriers to adoption, which will need addressing. This finding underscores the importance of involving teachers and students in the design and development process.

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Teachers’ Perspectives on the Adoption of an Adaptive Learning System Based on Multimodal Affect Recognition for Students with Learning Disabilities and Autism

Standen

Brown

Kwiatkowska

et al. 2021

Adaptive Instructional Systems. Design and Evaluation

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Adaptive multimedia: Using gaze-contingent instructional guidance to provide personalized processing support

Cited by 47 publications

References 48 publications

Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

Adaptive Instructional Systems. Design and Evaluation

Teachers’ Perspectives on the Adoption of an Adaptive Learning System Based on Multimodal Affect Recognition for Students with Learning Disabilities and Autism

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