This dissertation presents, describes and discusses an interdisciplinary study which investigated the design and development of a language learning instructional solution to address the problem of inadequate aural skills acquisition for college ESP (English for Special Purposes) students. Specifically, it focused on the use of mobile technology to expand learning beyond the classroom.
The eighteen-month process of data collection and analysis resulted in a conceptual model and design principles for a Mobile-Enabled Language Learning (MELL) solution. Mobile-Enabled Language Learning Eco-System was thus designed, developed and trialled in the real-life learning context. Through the iterative process of the design, development and evaluation of the MELL system and its components, design principles were also generated. These design recommendations were refined and reformulated in a cyclical fashion with the help of more than 100 students and ten experts. The resulting MELLES design framework encompasses guidelines addressing the essential characteristics of the desired MELL intervention as well as procedures recommended to operationalize those features.
The study also resulted in a better understanding of the broader context of ESP learning using mobile devices and the role of elements of environment, ultimately contributing to real-life praxis of the Ecological Constructivist framework and the complementary approach of Design-Based Research (DBR) methodology. / 2012-06
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEAU.91/17 |
| Date | 08 May 2012 |
| Creators | Palalas, Agnieszka |
| Contributors | Ally, Mohamed (Centre for Distance Education, Athabasca University), Dron, Jon ( School of Computing and Information Systems, Athabasca University), Colpaert, Jozef (Universiteit Antwerpen), Hoven,Debra (Centre for Distance Education, Athabasca University) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Detected Language | English |
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