Tagged: W23A16

Week 23 – Activity 16: Socialised learning analytics

Timing: 2 hours

Very good and information paper. Lots of notes made.

  • Finish reading the article and make notes on the different types of socialised learning analytic and how they might be implemented.

All information below is from the paper. Much of it is copied directly.

Social Learning Network analytics: Social network analysis investigates ties, relations, roles, and network formations. Social learning network analysis is concerned with how these are developed and maintained to support learning. Particular uses include: As social network analysis is developed and refined in the context of social learning, it has the potential to be combined with other types of social learning analytic in order to define what counts as a learning tie and thus to identify interactions which promote the learning process. It also has the potential to be extended in order take more account of socio-material networks, identifying and, where appropriate, strengthening and developing indirect relationships between people which are characterised by the ways in which they interact with the same ‘objects of knowledge’ [20].

Discourse analytics: The ties between learners in a network are typically established or strengthened by their use of dialogue. These interactions can be studied using the various forms of discourse analysis that offer ways of understanding the large amounts of text generated in online courses and conferences. Educational success and failure have been related to the quality of learners’ educational dialogue [24]. Social learning discourse analytics can be employed to analyse, and potentially to influence, dialogue quality. Particular uses include: A related approach to social learning discourse analytics employs a structured deliberation/argument mapping platform to study what learners are paying attention to, what they focus on, which viewpoints they take up, how learning topics are distributed amongst participants, how learners are linked by semantic relationships such as support and challenge, and how learners react to different ideas and contributions

Content analytics: The various methods used to examine, index, and filter online media assets for learners. The analytics may be used to provide recommendations of resources tailored to the needs of an individual or a group of learners. Particular uses include: through tags, and possible other technology to extract details from images and other content to create links; iSpot.

Disposition analytics: These dispositions can be used to render visible the complex mixture of experience, motivation, and intelligences that make up an individual’s capacity for lifelong learning and influence responses to learning opportunities. Particular uses include: Drawing learners’ attention to the importance of relationships and interdependence as one of the seven key learning dispositions. Secondly, they can be used to suppose learners as they reflect on their ways of perceiving, processing and reacting to learning interactions. Finally, they play a central role in an extended mentoring relationship.

Context analytics: Students may be learning alone, in a network, in an affinity group, in communities of inquiry, communities of interest, or communities of practice. Context analytics are the analytic tools that expose, make use of, or seek to understand these context. Particular uses include: (an example extract) Rebecca is on the edge of a community, and dispositions analysis shows that she is currently working on her collaboration skills, then a context-focused recommendation might suggest that she could join a teamwork skills group and use analytics visualizations to monitor her position within the group. Several weeks later, she might be prompted to reflect on her collaboration skills and to rate the group. She might receive this prompt directly from the system, or the system could recommend her teacher, mentor or group leader to engage with her and to make the recommendation.

 

  • Consider the mock-ups of different learning analytics that are presented in Figures 1–5 of the paper. If you had to prioritise the development of one of these for use on the H817 module website, which would it be, and why?

Social learning network analytics. Especially at a masters level, and indeed at PhD level, it would be very useful to know of others who are doing similar work, and whom I could contact for additional information of collaboration. This could include a degree of content analysis as well, to define what I wish to investigate and offer relationships. 

  • Share your opinion in the discussion forum and comment on your colleagues’ opinions.