CIC-Around: A co-designed learning and analytics community for postgraduates

Teaching & Learning Grant, 2016: PI: Simon Knight (Research fellow: Connected Intelligence Centre) & Theresa Anderson (Senior lecturer & MDSI course coordinator: Connected Intelligence Centre); with Ollie Coady (Learning technologist: IML); Shawn Wang (Web developer: Connected Intelligence Centre).

Using a participatory design process postgraduate MDSI students and UTS staff will collaboratively develop an online (wordpress-multisite) learning environment and community. This co-developed ‘open environment’ will enable students to creatively explore transdisciplinary and professional connections across their course and to interact and build a community with peers and industry partners, supplementing the capabilities of UTSOnline for community building. Initial implementation will be supported by a ‘community steward’ [9] to foster activity, evaluate student’s experiences and support ongoing adjustments. Learning analytics will be co-designed for student formative-reflection, and academic investigation into the value of this model of engagement for postgraduate education.

There has been concern that learning analytic technologies focus on learning management systems [4, 8] and passive interventions for predictions of `at risk’ students, rather than empowering students to create and use their own learning analytic tools [5]. There is potential to move from individual learner-centred approaches, to richer pedagogical models of social learning analytics, with a focus on learners as producers (for example, through blogs), learning interaction and dialogue, and the motivational and contextual factors around learning behaviours [1, 6, 7]. Earlier research has analysed participatory processes in understanding the learning context [2], but not in the development of the platforms and analytic approaches. This project will adopt a participatory action research approach [3] to iteratively design the online space for collaborative learning, supported by a community steward [9]. This design and curriculum model should have wider application across UTS.

Supporting student learning is at the heart of this project, and is actualised in four specific ways:

1. with core subjects delivered in block-mode, creating an effective, meaningful blended learning environment for engaging with peers and industry connections is fundamental for supporting student learning in the MDSI program. Development of CIC-Around emerged in response to challenges current students reported facing in being active participants in the existing spaces;
2. working with data science students, we will design a learning opportunity enabling data science students to become more minded about their learning (through the analytics they co-design and co-evaluate);
3. this co-design experience will contribute richer understanding to the wider UTS community about analytics students feel help them become more minded about their learning journeys;
4. responding to feedback from MDSI students (eg. to extract meaningful analytics about students online activities in a major ‘quantified self’ project). Analytics developed could become meaningful components of assessment, while concurrently improving evaluation of the analytics generated within CICaround.

The project responds to and builds upon the bedrock of learning.futures, supporting the UTS Model of Learning and graduate attributes by designing a community space for student and peer learning that will also facilitate interaction with industry contacts. Co-production and analysis of student data about and for learning and engagement with shared digital resources and social media will support active formal and informal learning. Sharing of open peer-sourced and produced educational resources and a community-knowledge base; and engagement with industry contacts will enable these experts to become co-participants and mentors (in a defined part of the environment). To showcase students key work and developing graduate attributes through industry and peer engagement, students will be able to build flexible and inviting portfolios. Through structured templates and student feedback authentic varieties of text genres will be framed and produced.

[1]                  Buckingham Shum, S. and Ferguson, R. 2012. Social Learning Analytics. Educational Technology & Society. 15, 3 (2012), 3–26.
[2]                  Hickey, D.T. et al. 2014. Small to Big Before Massive: Scaling Up Participatory Learning Analytics. Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (New York, NY, USA, 2014), 93–97.
[3]                  Kindon, S. et al. 2007. Participatory action research approaches and methods: Connecting people, participation and place. Routledge.
[4]                  Kitto, K. et al. 2015. Learning analytics beyond the LMS: the connected learning analytics toolkit. (2015), 11–15.
[5]                  Kruse, A. and Pongsajapan, R. 2012. Student-centered learning analytics. CNDLS Thought Papers. (2012), 1–9.
[6]                  Lockyer, L. et al. 2013. Informing Pedagogical Action: Aligning Learning Analytics With Learning Design. American Behavioral Scientist. (Mar. 2013), 0002764213479367.
[7]                  Schneider, D. et al. 2012. Requirements for learning scenario and learning process analytics. (2012), 1632–1641.
[8]                  Siemens, G. et al. 2011. Open Learning Analytics: an integrated & modularized platform. Society for Learning Analytics Research (SoLAR).
[9]                  Wenger, E. et al. 2009. Digital habitats: Stewarding technology for communities. CPsquare.