Introduction
mLearning is driven by technological evolution and an increase in mobile device users. Ryan and Finn observe that field-workers are prime candidates for mLearning. A context-aware learning system provides valuable solutions for field-workers, and for novice field-workers in particular (e.g. geologist, mechanic, archaeologist, journalist). The user-friendliness of any system is of significant importance. Systems should not be implemented purely for the sake of using technology, Ryan and Finn state, but be based on the requirements of an activity that embeds the use of technology.
Approach
Ryan and Finn interviewed experts in an unstructured and semi-structured format. The experts were identified as being highly experienced field-based workers or trainers. Those interested in mLearning were invited to follow up workshops. Ryan and Finn:
- established the new system is required through relevant literature; and
- confirmed the real-world requirements within specific domains.
Deeper levels of understanding were communicated with user profiling, task analysis and context definition "to fill in the layers of knowledge necessary" (pg. 1).
Results and findings
Accumulated knowledge comes from the domains of:
- telecommunications engineers (three types);
- probationary police officers;
- electrical network technicians; and
- news journalists.
In total, more than four thousand field-based workers were represented. Analysis was conducted to prepare generic results. The diversity of professions provided relatively high standard deviations.
Ryan and Finn noted that experience levels in field-based workers left some unable to justify the need for mLearning. Those who attended the workshops detailed specific "just-in-context learning scenarios" (pg. 2). Ryan and Finn suggest that there is a unique learning need in field-based professions, but indicate that it may not apply to all domains where target platforms are laptops instead of handheld devices. The prototype should follow a sequence where learning activities require "participatory design and training" and are followed up with "collaboration and assessment" (pg. 2).
Ryan and Finn use current expert opinion to analyse scenarios where generic, viable mLearning as it relates to competent field-based workers is employed:
- situations where field workers are introduced to new technology, process, procedure, equipment or knowledge and require remote assistance;
- changes to policy and procedure that changes practice can be delivered to the field worker without recalling staff for training updates;
- situations where field workers are introduced to rare or old technology and require remote assistance;
- components or influences that exist in the real-world are not covered during class, or are different from theory and remote assistance is required; and
- the learning need must be satisfied before the task can be completed.
Future work and conclusions
Generic characteristics gathered from the user profiles, task scenarios and contextual factors are being tested against other domains to establish validity. Progressively generic characteristics "elicit specific instances with each new candidate" (pg. 2), indicating that using real-world scenarios in mLearning informs an initial design and specific prototype evaluations. Ryan and Finn have revealed the significance of the users needs and requirements. Their approach to designing a new concept software system to enable mLearning was based on valid responses from domain-based experts when asked "Who wants what", rather than designing a concept software and asking "Who wants this?" (pg. 2).
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