Reflections on the research and limitations of the approach

There are well defined guides to the nature of summative assessment in networked learning (Goodyear, Jones, Asensio, Hodgson, & Steeples, 2001), typically emphasising a constructive approach where students have a greater determination in the nature of the assessment and peer feedback is used to feedback on the initial plan, the draft and the marking scheme (McConnell, 2006). The nature of the tutor and the feedback processes in these communities has also been thoroughly researched; the community of practice model defines the nature of the expert and the relationship to novices; the community of inquiry model demonstrates best practices and allows an analysis of the interactions between these groups.

What has been missing is an exploration of the practical mechanisms of formative feedback in the NL context. The different form of e-Portfolio shown here provides further evidence that richer formative representations are possible compared to summative measures (Yorke, 2005) and has proved to be an effective way of encouraging these responses in networked learning. The key characteristic of formative feedback is that of a path, where students become owners of their own learning and forward directions are influenced by

interactions with peers and the tutor (Wiliam, 2011). Wenger’s conception of artifacts enables a rich depiction of this path, where the negotiation - participation process fosters reflection and progression (Romer, 2002). Allowing participants flexibility in artifact creation after the equifinality community model (Pedler, 1981) allows for an individual’s ‘continuum of learning’ to be visible to the tutor in real time, enabling the learning progression to be influenced by informed action. The positioning of where the learner is and where they are going is a key aspect in formative assessment (Black & Wiliam, 2009b), and the e- Portfolio makes this ‘location’ visible to both the participants and the tutor. Formative feedback is typically categorised into verification and elaboration, where verification is a simple comment on the validity of the work (Shute, 2008). The popularity of an artifact provides implicit verification from the community as a whole; comments inside the e-Portfolio fall into the elaboration category with rapid and frequent replies providing multi-layered responses and guidance, suggesting validity through improved action (Harlen & James, 1997), discussing errors, providing guidance and promoting connections. The tutor feedback role can be shared out amongst participants by using links and directions to other artifacts if facilitated through a structured induction process.

Participants engage with other forms of feedback inside the e-Portfolio community with the visible reification process amongst the peers suggesting levels of appropriate activity; the nature of the artifacts indicates the overall types of activities that are being followed by the group as a whole; participants tend to align their learning by seeing the nature of the work being created by the community, even if it is out of sequence with the tutor driven interactions (figure 7.3, p. 179).

This research focusses on a narrow field of study, where there are opportunities for practical skills and theoretical topics to be blended in the learning design and through that into the artifact construction process. The standard critiques of action research apply here; there are inherent issues in the lecturer researching their own practice as discussed in the research design chapter. It is hoped that there is an appropriate level of detail here to support Heikkinen’s quality indicators (2007) such as an evocative account and workable practices.

Aligning action research with an open source philosophy can work, but only in particular domains where there are particular technical skill sets in evidence.

There can be differences between code generated in an educational environment and production code; code which is readable in class and suitable for teaching and learning may not be scalable and safe to use in a production environment. The rapid creation and implementation required to adjust the software as the cycle progresses can also be challenging, particularly as changes are made live to the software in use.

It is also possible that processes or activities that seem desirable, may take a long time to implement and then fail in practice, for example the graphical taxonomic representations. It is the nature of open source software for features to be developed that subsequently fail due to lack of demand, but this can be difficult where there are limited resources and the tutor is simultaneously developer. The recommendation system is used to create connections between the learners and the electronic resources (artifacts), supporting the networked learning philosophy. There are issues with such systems; there exists the possibility that only artifacts from a limited set are returned, reflecting artifacts from a narrow selection that match and reinforce the participant’s own views and work similar to the ‘filter bubble’ that has been identified as an issue in news sites and search engines. To counter this, it is important to add a degree of randomness to recommendation results, acknowledging the advantages of designed serendipity (Acosta, 2012; Saadatmand & Kumpulainen, 2013).

A more recent, potentially unethical experiment was conducted by Facebook, where the behaviours of participants were influenced by returning positive or negative news stories to separate classes of users (Kramer, Guillory, & Hancock, 2014). Facebook does not allow users to see the algorithm that suggests stories, so this manipulation was only discovered when it was announced in an academic journal. To counter possibilities of this, recommendation systems should be examinable in place, that is, it should be possible to query the mechanics of why an item was recommended.

Despite the fact that the use of analytics was directly integrated and used to reflect work back to the students, it was only in the post reflective period that students expressed surprise over the degree of tracking possible, reflecting a wider trend of general public disinterest. It is only when directly confronted with the evidence of what is recorded that participants acknowledged the level of activity detail possible. It is not widely understood that every interaction in online educational systems are being stored and are available for analysis,

perhaps because the results from these interactions are not currently analysed or used inside most academic institutions.

Analytical information has to be used cautiously, as without a rich picture of participant activity, incorrect conclusions could be drawn. De Laat’s (2006b) original mixed methods framework reflects the complex nature of praxis that exists in NL. Similarly, the use of analytics to create sociograms for real-time community monitoring is valid, but a richer picture about the depth of individual participation requires it to be mixed with detailed content from actual interactions.