2011
DOI: 10.5121/ijcsit.2011.3204
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Behavior Analysis in a learning Environment to Identify the Suitable Learning Style

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Cited by 19 publications
(10 citation statements)
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“…So we briefly presented some of the valuable case studies in education environments. In (Hamada et al, 2011;Dung and Florea, 2013;Scott et al, 2014), they used an Index of Learning Style (ILS) to determine the suitable learning styles based on behavior analysis in a learning environment. Learning styles characteristic were {active, reflective, sensing, intuitive, visual, verbal, global and sequential}.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…So we briefly presented some of the valuable case studies in education environments. In (Hamada et al, 2011;Dung and Florea, 2013;Scott et al, 2014), they used an Index of Learning Style (ILS) to determine the suitable learning styles based on behavior analysis in a learning environment. Learning styles characteristic were {active, reflective, sensing, intuitive, visual, verbal, global and sequential}.…”
Section: Related Workmentioning
confidence: 99%
“…Learning styles characteristic were {active, reflective, sensing, intuitive, visual, verbal, global and sequential}. Hamada et al (2011), discussed the utilizing of a social bookmarking website, e.g., www.tagme1.com to collect hints about the learners' behavior through learners are browsing/exploring their favorite web pages. Dung and Florea (2013), introduced the domain ontology in elearning environments.…”
Section: Related Workmentioning
confidence: 99%
“…(2010), an automatic identification for affective states and learning styles in web-based learning management systems [11]; (4) Hamada, et. al (2011), behavior analysis in e-Learning environment to identify the suitable learning style [12]; Dung and Florea (2012), an approach for detecting FSLSM learning styles in learning management systems based on students' behaviours [13].…”
Section: Identifying Students' Characteristics In E-learningmentioning
confidence: 99%
“…TAM has two core constructs influencing users' technology acceptance behavior: perceived usefulness (PU) and perceived ease of use (PEOU) [11]. As the best known theory in information systems, TAM has been used extensively for understanding user acceptance of different information technology, e.g., groupware acceptance [12], online games adoption [13], enterprise systems adoption [14], mobile games acceptance [15], e-learning system adoption [16], e-payment system acceptance [17], and web 2.0 acceptance [18].…”
Section: Determinants Of User Acceptancementioning
confidence: 99%