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Application of 1:1 mobile learning scenarios in computer engineering education

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Date
2016
Author
Juškevičienė, Anita
Jasutė, Eglė
Kurilov, Jevgenij
Mamčenko, Jelena
Metadata
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Abstract
The paper aims at presenting case study on personalised mobile learning scenarios on Computer Engineering and other STEM subjects in Lithuania. Fleming (VARK) learning styles model was used to personalise CCL project’s mobile learning scenarios by establishing suitable learning components for particular students according to their personal needs. Personalised mobile learning scenarios were compared against traditional ‘‘one size fits all’’ learning scenarios. Comparison was performed on the base of the analysis of Lithuanian CCL teachers’ answers on questionnaire during the 2nd cycle of CCL project implementation and previous research results on expert evaluation of these types of learning scenarios. During this cycle, inquiry-based learning (IBL) activities developed in mascil project and a number of mobile applications (apps) were used by Lithuanian teachers while implementing the mobile learning scenarios, and interconnections between personalised IBL sub-activities and mobile apps were established. The authors have also analysed CCL observation visits’ final report in terms of learning personalisation, creativity and innovation in schools. Based on this three-fold research, the authors concluded that (a) pedagogical change is necessary to improve learning outcomes for students, and (b) the main success factors in implementing mobile learning scenarios in Lithuania were: (1) identification of students’ learning styles; (2) identification and application of suitable learning activities, methods, learning objects, tools and mobile apps according to students’ learning styles; and (3) use of proper sets and sequences of learning methods while implementing mobile learning scenarios.
Issue date (year)
2016
URI
https://etalpykla.vilniustech.lt/handle/123456789/115966
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  • Straipsniai Web of Science ir/ar Scopus referuojamuose leidiniuose / Articles in Web of Science and/or Scopus indexed sources [7946]

 

 

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