SELF-MANAGED LEARNING IN OUT-OF-SCHOOL CONTEXTS

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[1.1] [1.2] [1.3] [1.4] [1.5] [1.6] [1.7]

1.3 Categorise Resource Elements

Here, we draw on a specific set of data collection framed by the (1.2) Focus of Attention identified earlier.  The data sets illustrated below represent samples of the kinds of data available during the visit: photographs, notes of ad hoc discussions and extracts from researcher field notes. In this next step, various data are categorised through application of the EoR category elements: ENVIRONMENT, KNOWLEDGE, RESOURCES. In contrast to the (1.1) brainstorming step - this involves a more detailed mapping of available Forms of Assistance in the learner's ZAA (Zone of Available Assistance).

As with the brainstorm step, there a range of questions framing the data collected in this phase and examples of such questions are included here:

• what are the attributes of the learners' learning contexts as they move across and between different spaces
• what kinds of resources (Forms of Assistance) can be found in these contexts or setting, how might they be categorised or organised
• what kinds of environment are there (physical, social, conceptual, etc.)
• what technologies are available, what are the learners' perceptions of and towards the technologies they encounter, use and/or own
• what kinds of purposeful uses do or can learners generate through technology use across and between these shifting contexts
• how do learners engage with unanticipated technologies (e.g. those available at the visit site)
• what kinds of resources (e.g. tools, people, spaces, events) are and aren't available at the visit site, which are used, which are not, why
• what, amongst the available Forms of Assistance engages and motivates the learner, how and why, where and when

Data Samples for this stage

Data sample 1.3.1: Photos captured on a visit to the London Planetarium - Focus of Attention - Learners' access to and use of technologies to support learning.

Data Sample 1.3.1 Preliminary analysis grounded in data

This data sample provides an 'at a glance' view of the kinds of resources (and potential filters) that might be adduced from learner activity during their trip to the planetarium. These included notions of access, utility, space (proximity, distance, overcrowding, etc.), rules of use/access, ambiance (light, dark, rainy), tactility of materials, learner motivation for interaction/engagement with their environment and available resources, seating arrangements, comfort and direction of movement and access, subtitling, signing for deaf visitors, use of visual/interactive displays and exhibits, etc.

Data sample 1.3.2: ad hoc discussions with and by students during the trip

Data Sample 1.3.2 Preliminary analysis grounded in data

These data samples are brief unanticipated interactions between researcher and learners. They build on the notion of filters identified in the previous example and those exhibited further in the following example. Filters relating to learner affect and motivation are particularly foregrounded in these examples.

Data sample 1.3.3: field notes from meeting between learner, researcher and learning mentor on day of trip, prior to departure

Data Sample 1.3.3 Preliminary analysis grounded in data

This data sample foregrounds learner perceptions around their technology use and adoption with a particular focus on learner affect, disposition and motivation. They make a clear distinction between personally owned technologies and those owned by the learning centre. The underlying perception appears to be that, when adopting and using technologies across and between contexts to support their learning, for learners the benefit must outweigh the burden.

Following the preliminary analyses grounded in the data (see links above), we can now move towards categorisation of these data using the EoR category elements: Knowledge, Environment, Resources. This process is similar to the generation of the initial ZAA (Zone of Available Assistance) generated at the brainstorm stage, except that this time, it is framed by the EoR Model and Design Framework more explicitly, i.e. the Forms of Assistance identified or derived from the data are explicitly categorised. In this exemplar study, we used a basic table to do this. In this table, the top row contains the Focus of Attention. The second row contains the category elements. The third row shows descriptors derived from the above data samples generated during the learners' trip to the London planetarium. These have been subjected to a preliminary categorisation using the Category Elements (Knowledge, Environment and Resources) of the EoR Model.

What we can see here is how different kinds of category element can be derived from the data. Further, it is clear that the types of data that can be drawn on may be many and varied. The discussion of the PSP in data sample 1.3.3 is, for example, not directly related to the focus of attention but it may nevertheless tell us something about the kinds of technology learners have available which could potentially be used in any adaptation or adjustment of the learner context. Similarly, the third data sample gives us an indication of some of the psychosocial contexts around learner's use and non-use of technologies which may also feed into a future design of the learner's learning context. For example, it appears, here that ownership, ease of use, flexibility and accessibility of technologies are all important to learners and their interactions with technologies, actual or potential, to support learning.

It is now possible to produce a basic EoR Model for this Focus of Attention drawing on the preliminary categorisation of the Category Elements outlined in the table above.

This preliminary EoR Model now becomes the focus for step (1.4) identification of potential filters. For example, those elements highlighted in red in the above diagram represent some of the potential filters of the learner's experience in the social and physical environment of the Planetarium.