Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.646462
Title: Modelling of the relationships between Mobile Device Technologies (MDTs) and UK educational practices
Author: Olasoji, Motunrayo Olutoyin Remy
ISNI:       0000 0004 5362 5805
Awarding Body: University of East London
Current Institution: University of East London
Date of Award: 2014
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Abstract:
This study investigates the state of the art of the concept and practice of Mobile Learning (ML) and the integration of Mobile Device Technologies (MDTs) in educational processes. Using a combination of techniques from Requirement Engineering (RE) and Agent Oriented Software Engineering (AOSE), the domain is explored and analysed for ongoing effectiveness and sustainability. Impressive advances in MDTs have made them pervasive and entrenched in many cultures, systems and in everyday living. In the last decade, the emergent of mobile / handheld devices, and subsequently, wireless technology standards have given rise to the concept of ML. Although MDT was seen by many early on as part of the solutions for learning transformation, quantifying benefits and placement in teaching and learning, either to achieve learning objectives or enhance the process remain problematic. In spite of efforts in the last decade by researchers and educators, expected potentials for learning mobility and adaptability resulting from their use are largely unfulfilled. Rapid changes in development and manufacture also continue to present additional challenges. Most research studies typically employ the approach of evidencing benefits through usage implementations and experimentation. In the review of this thesis, application of techniques provided in domain neutral RE and AOSE disciplines for specifying goals and requirements for complex systems is proposed. Alignment with teaching and learning strategies as well as institutional goals and strategies is considered essential for successful integration in any learning institution. Consequently, this review advocate strategies for alignment through elicitation and modelling techniques of RE and AOSE disciplines. Requirement elicitation is carried out using a mixed methods of inquiry comprising of four phases in sequential & parallel investigations. Phase I involves literature / citation report analysis / systematic review and quantitative survey. Secondary quantitative data is also sought during this phase. Phase II includes further in-depth quantitative and qualitative study. Questions used during this phase are designed from issues arising in Phase I. Phase III comprises of targeted studies among stakeholders in Higher Educational Institutions (HEIs), allowing for comparison of underpinning policies, cultures and practices; gaining an understanding of the concept and influential factors. Data gathering techniques include surveys, observations, interviews and focus group sessions. Using both sequential and parallel mixed method of enquiry afford opportunities to establish a frame of reference and analyse opinions within the domain among relevant stakeholders: students, academics / educators, those in the role of learning support and governance and IT support personnel. The survey is analysed using descriptive statistical analysis techniques, also involving comparison of responses from all participating groups. Qualitative data is analysed using thematic methods The review of this thesis contributes to the body of knowledge on ML as a concept and practice, evaluating definitions, frameworks and practices as relating to HEIs for the most part. Approaches to integration by selected HEIs are explored and analysed for effectiveness. A series of models is created illustrating the use of RE and AOSE techniques to align ML system requirements with organisational goals and strategies. Outcomes from the review will make it possible to advance research and knowledge forward for the practice of ML and integration of MDTs in educational processes.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.646462  DOI: Not available
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