Stage-Based Generative Learning Object Model to Support Automatic Content Generation and Adaptation

tuikys, Vytautas; Burbaitė, Renata; Bespalova, Kristina; Drąsutė, Vida; Ziberkas, Giedrius; Venčkauskas, Algimantas

doi:10.1109/compsac.2016.197

Cited by 4 publications

(2 citation statements)

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“…Also, it does not include full multimedia features [36]. Štuikys et al [37] also proposed a stage based Generative Learning Object (GLO) model to specify learning content and tackling LO inability to add new element. This approach relies on a stage-based process algorithm that automates LO generative process using an Arduino robotics environment.…”

Section: Related Workmentioning

confidence: 99%

“…The robotic approach performed better than an existing traditional basis one and it provided a wide coverage process with the easy implementation. However, their GLO automation requires precise domain level and predefined context parameter values which are not convenient for both real-time adaptive process and dynamic adaptation [37]. Both described approaches do not focus on learners' cognitive processes.…”

Section: Related Workmentioning

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%