Integration into mathematics classrooms of an adaptive and intelligent individualized e-learning environment: Implementation and evaluation of UZWEBMAT

Özyurt, Özcan; Baki, Adnan; Güven, Bülent

doi:10.1016/j.chb.2012.11.013

Cited by 50 publications

(29 citation statements)

References 19 publications

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“…Researches indicated that students have the positive attitude to the learning experience in smart classroom, for the smart technologies or smart devices give the students possibility to interact, communicate with learning content and learning object under the real and virtual combined learning context, and give the students possibility to be individually concerned (Özyurt et al 2013;Yang et al 2013). In the smart classroom students can get the unique learning experience, their learning interest and learning motivation are engaged by the open and authentic smart learning environment.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of the smart classroom inventory

Kong

Guang

2015

Smart Learn. Environ.

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This paper discusses the development and validation of the Smart Classroom Inventory (SCI). The SCI is derived from existing technology integration learning environment instruments, including TROFLEI, TICI and CCEI (ergonomic). To accurately describe the features of smart classroom, the factors of flexibility, learning data, learning experience are added to SCI, and the description of information technology usage of some items are also altered to fit the smart classroom environment. More than 640 11 to 15 years old students validated the instrument, revealing ten scales: Physical Design, Flexibility, Technology Usage, Learning Data, Differentiation, Investigation, Cooperation, Students Cohesiveness, Equity, and Learning Experience. Cronbach's alpha coefficient is 0.92 and 0.94 for the entire questionnaire in Actual and Preferred forms respectively. The results of EFA and CFA prove SCI to be a parsimonious instrument for assessing the smart classroom learning environment. In assessing Actual and Preferred learning environments, SCI results indicated that there are significant differences in all of the ten factors. Students' perception of factors of Differentiation, Flexibility, Technology Usage, and Learning Data are the largest four actual-preferred discrepancies within the smart classroom learning environment. These suggest the importance of innovative application of the current information equipment, and schools should pay more attention to satisfying the students' personalized learning demands. The differences between the gender and between the school settings indicate that smart classroom should integrate both the technology and the humanities factors into the design and application of smart classroom, as well as design multi-level learning activities to satisfy the choices of different age students.

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Section: Discussionmentioning

confidence: 99%

Development and validation of the smart classroom inventory

Kong

Guang

2015

Smart Learn. Environ.

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“…Bakiet et al (2010) mainly focused on the role of ALS in high school mathematics classrooms [21]. Özyurt et al (2013) designed an adaptive and intelligent individualized e-learning system named UZWEBMAT based on learning style for secondary school mathematics curricula [22]. There have been almost no studies that have focused on ALS for basic education in China So design and evaluation of an adaptive and intelligent ALS is what students need in China.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Learning System based on Cloud Computing: Implementation and Evaluation of BDS

Liao¹,

Huang²,

Zhou³

et al. 2016

IJGDC

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“…Therefore, many other authors have focused their studies in the field of teaching mathematics using E-Learning (see for example [3], [4] and [5]). …”

Section: T He International Journal Of Interactive Multimedia and Artmentioning

confidence: 99%

“…In the last years, many authors are concerned with this problem and are putting all efforts in desgining new Websites related to Mathematics and their teaching (see [1]), investigating how math skills are acquired in different virtual environments (see [2]) or even developing different E-Learning environments which allow teachers evaluate their students in a compact and computerized way (see [3]). Therefore, many other authors have focused their studies in the field of teaching mathematics using E-Learning (see for example [3], [4] and [5]).…”

Section: T He International Journal Of Interactive Multimedia and Artmentioning

confidence: 99%

IJIMAI Editor's Note - Vol. 3 Issue 4

Magreñán¹

2015

IJIMAI

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Integration into mathematics classrooms of an adaptive and intelligent individualized e-learning environment: Implementation and evaluation of UZWEBMAT

Cited by 50 publications

References 19 publications

Development and validation of the smart classroom inventory

Development and validation of the smart classroom inventory

An Adaptive Learning System based on Cloud Computing: Implementation and Evaluation of BDS

IJIMAI Editor's Note - Vol. 3 Issue 4

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