Category: Week 23

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.

Week 23 – Activity 15: Citation networks

Timing: 4 hours

This activity focuses on an article that you are likely to find difficult to understand unless you have previous experience of network analytics. It was written for one of the Learning Analytics and Knowledge (LAK) conferences by some of the leaders in the field.

  • Now turn to the practical implications of the paper (Section 4.3). Read this, and expand your notes in the form of a bullet-point list. Note which findings are linked to a practical implication. At this stage, you will have gained a sense of what the paper is about, although you have only read a couple of sections.
    • the development of curriculum in the growing number of academic programs that include learning analytics as a topic; (practical – expansion of the application of LA)
    • promotion of under-represented groups and research methods to the learning community; (practical – expanding the research types and sister fields)
    • fostering the development of empirical work and decreased reliance on founding, overview and conceptual papers; and
    • improved connection to sister organisation such as the International Educational Data Mining Society. (practical – combining experience)
  • Note what the authors aimed to do in the paper, and what their main findings were.
    • An evaluation of the current state the field of learning analytics through analysis of articles and citations occurring in the LAK conferences.
    • A citation analysis and structured mapping aimed to identify the emergence of trends and disciplinary hierarchies that are influencing the development of the field to date.
    • An evaluation of the current state of learning analytics. 
  • Begin by looking at the abstract of this paper:
  • Note whether you think this is an example of learning analytics, according to the definition you produced in Week 21 of this module, or whether it is simply about learning analytics.
    • My definition: Learning analytics concerns the process of measuring and collecting learning data from a variety of student activity, with the aim of using said data to improve and/or alter the student learning process.
    • Noting the bolded section, this would fit more as being about learning analytics, as it is research based and no student based. 
  • Look at the figures and tables in the paper. Some require specialist knowledge, but others are clearer to non-experts. Figure 4 shows the disciplinary background of learning analytics researchers. Table 1 identifies the ten most-cited papers in the field.
  • In the forum, suggest why it is that these most-cited papers have similar numbers of citations in the learning analytics literature, but very different citation rates in a wider context (the Google Scholar citation counts shown on the right of Table 1).
    • Possibly due to the papers cross applicability. For example, if a paper has implications for other research areas, or provides a useful summary of an issue, it will be picked up by those in other fields in referencing and discussion. Whilst, within the LAK community, the paper might only provide a few cases of important information for their work. 
  • Now read carefully through Sections 1 and 2 of the paper, and use all you have read to produce a two-page summary of the article.

    the strategic application of analytics [16] to inform practice has not been extensive within the education sector.

    The outcome of limited systemic analytics activity is a predominance of research that is founded on the extraction of readily available data such as those drawn from learning management systems (LMS), student information systems (SIS), and basic demographics and student grades.

    However, these commonly bivariate analyses are t h e “low hanging fruit” in terms of the overall potential for analytics to redefine and shape education praxis.

    For example, while it is helpful to note that students who regularly log into a LMS may perform better than their less active peers, this information is not suitable for developing a focused response to poor performing students.

    LMS or SIS data can be a useful proxy for seeing a part of a problem, but it is insufficient to serve as a model for intervention that is based on the current state of learning sciences.

    Learning analytics to date has served to identify a condition, but has not advanced to deal with t h e learning challenges in a more nuanced and integrated manner.

    For instance, the field draws on assorted theory and methodologies from disciplines as diverse as education, psychology, philosophy, sociology, linguistics, learning sciences, statistics, machine learning/artificial intelligence and computer science.

    An evaluation of the current state of learning analytics provides numerous benefits for the development of the field, including:

    •  a foundation for future research through the acknowledgement of past research activities;
    •  assistance for grant-making agencies by identifying promising research areas that align with regional and national education goals;
    •  identify disciplines that are under-represented and require more strategic and targeted support and funding opportunities;
    •  identify gaps in research for researchers and students; and
    •  improve the integration between theory and practice by identifying connections between researchers and papers.
    • To address these questions this paper explores through the lens of structured mapping and citation networks the research domains and relationships that have extensively contributed to the field to date.

     

    To address these questions this paper explores through the lens of structured mapping and citation networks the research domains and relationships that have extensively contributed to the field to date.

    In undertaking a mapping and review of the collaborations that have evolved in the field, researchers and practitioners can identify the cliques and sub-culture that define the broader learning analytics community.

    Citation profiles and analyses are central measures adopted in these practices in order to determine research impact.

    As Waltman et al., [20] argued, highly cited papers are not always indicative of impactful research. However, as the authors further noted, on average this premise does tend to hold true. As such, it is reasonable to assume that high citation rates do reflect a certain level of quality [23].

    Current citation databases such as Elsevier’s Scopus, Google Scholar, web of science and others have made the extraction of co-citations and identification of co-author networks more accessible.

    The aim for this paper is to provide a mapping of the learning analytics research community to identify the:

    •  prominent papers referred to in the research;
    •  dominant disciplines and methodologies adopted in learning analytics; and
    • diversity of research paper genres that comprise learning analytics (e.g., opinion papers, reviews, conceptual, empirical research, etc.).

  • Write an entry in your learning journal or blog about your experience of reading a paper in this way. What do you think you gained or lost by missing out some sections?

I found that the general context was lost. I was picking up the surface, but not really able to think critically about what was being said.

Week 23 – Activity 14: Visualising social networks

Timing: 4 hours

Part 1

  • Read this short paper that provides an introduction to social network visualisations and to the SNAPP tool:
  • In your learning journal, or blog, make a note of the things that can be revealed by a network diagram of students’ discussions. The authors identify six – you may be able to think of more.
  • Use these as headings and, under each one, note how this information could be used to support learning and/or teaching in a group.
  • Also note any potential problems, and how these might be addressed.

Part 2

The figures in this paper show some typical visualisations of forum discussions. The paper discusses what these reveal about the discussions.

  • Try creating your own social network diagram. Take a recent thread in the tutor group forum, which includes six or more postings, and sketch it as a network diagram. Note who appears to be central to the discussion.
  • Now take another discussion thread and add it to the same diagram. Drawing by hand, you probably won’t be able to include any further threads in your diagram, unless they are very short.
  • Reflect on what is represented in this diagram and on what is missing.
    1. Does the diagram change your understanding of the tutor group and of your role within it?
    2. Could you use this diagram to make recommendations that might improve learning in the forum or would you need more information?
    3. In what ways is the diagram misleading?
  • Share your conclusions in the forum. If possible, add a scan or a photograph of your network diagram.
  • Discuss situations in your local educational environment, or in one you know well, in which a social network diagram might be used to improve learning and/or teaching.

Answers:

What can be revealed by a network diagram of students’ discussions:

  • identify disconnected (at risk) students;

SS who do not interact with forums, especially on key topics, could be contacted individually.

  • (continuation from above) monitor lone students

Students who are not receiving interaction from others could be at risk of feeling excluded, and as research has shown (in Dawson et al. ,2010), tend to have higher attrition rates, and also evaluation courses/tutors poorly.

  • identify key information brokers within a class;

Comparing a key information broker’s grades with his/her contributions could be valuable, as he/she could be sharing information that is not accurate if it goes unchallenged. A tutor might need to reply directly to him/her in threads to mediate the discussion.

Furthermore, as from Dawson et al. (2010), we can identify if a ‘waggon wheel’ type discussion is occurring, and evaluate if this is desirable given the timing of the discussion (early discussions in a course tend to take this formation, but it is less desirable generally).

  • identify potentially high and low performing students so teachers can better plan learning interventions;

Tutors/teachers needn’t intervene with high performing students, saving them time to focus on students who do not engage with forum activity.

  • indicate the extent to which a learning community is developing within a class;

Courses normally aim for a class to grow closer together and feel more comfortable with sharing ideas as it progresses. Using SNAPP a tutor could monitor if this is indeed occurring. If not, an intervention to prop up the discussion might help, and forums can be further monitored to see the effect.

  • provide a “before and after” snapshot of the various interactions occurring pre and post learning interventions. (This diagrammatic representation is also a useful indicator of reflective teaching practice e.g. through integration with teaching portfolio artefacts);

Similar to above, though a tutor could also monitor if all interventions were positive or negative, and learn from such. For example, tutor involvement can also result in breaking up a discussion as some students shy away.

  • monitor individual student contributions

A tutor could monitor a particular student’s interactions over time. These could be combined with course grades. If their is an effect such as grades lowering that coincides with lower engagement, the tutor could point out such to the student.

Diagram:

IMG_0251.jpg

  1. Does the diagram change your understanding of the tutor group and of your role within it?
    • Slightly. It shows the layout of the discussion as being more evenly spread than what appeared when looking only at the forum. I feel as though the indentation in the forums made my mind think of those with direct replies as carrying more clout than replies. This should not be the case, which the diagram represents.
    • In both cases, A was the initiator of the discussion, however it is evident that both G and K had equally important roles in the discussion.
  2. Could you use this diagram to make recommendations that might improve learning in the forum or would you need more information?
    • It could do, though the context of the discussion is important. In the case of the two threads used, they were both suggestions from A for group projects. As such, seeing two spinoff discussions but generally a good trend where discussion came back to the thread starter is a positive sign. However, if the purpose of the thread was for general discussion, we might wish to have seen a wider spread in communication between, for example K and G.
  3. In what ways is the diagram misleading?
    • The diagram doesn’t accurately portray the purpose of the thread. This is fundamental to interpreting the discussion.
    • P’s role was only an intervention, not a contribution to encourage more participation.

– Discuss situations in your local educational environment, or in one you know well, in which a social network diagram might be used to improve learning and/or teaching.

  • Evaluation meetings
  • General team meetings
  • Email and communication later communication adherence.

Week 23 – Activity 13: Why do we need social learning analytics?

Timing: 4 hours

Part 1

Part 2

Imagine you work in an educational institution that is considering making more use of learning analytics. Staff already have some understanding of learning analytics, but don’t know anything about social learning analytics. You have been tasked with addressing this lack of knowledge.

  • Prepare slides for a ten-minute presentation that can be used to explain to staff what social learning analytics are, why they are becoming more important and the benefits they may offer. Use suitable software, such as PowerPoint, Keynote, Prezi or something else you know well.
  • In the time available, you will be able to include 5-10 slides, so select points and examples that are key to understanding social analytics.
  • In the forum, share your slides or a link to your presentation, together with a brief description of your target audience.
  • Consider the presentations put together by other learners and discuss why those key points have been selected.

Answer: