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Leveraging Learning Analytics for Student-Centered Education

Explore how technology and learning analytics can enhance group work, personalized learning, and 21st-century skills in higher education. Introduce students to meaningful assessments aligning with their interests and promoting deep learning outcomes. Engage students through innovative pedagogy and advance their research and critical thinking abilities.

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Leveraging Learning Analytics for Student-Centered Education

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  1. Learning analytics to promote deep learning: framing information literacy assessment around student interests in enabling education Jennifer Stokes, University of South Australiaand Jenny McDonald, University of Auckland Themes: New Technology Outside the Classroom and Within FABENZ Wellington 2018

  2. 21st Century Skills • Enterprise skills including • Problem-solving • Team work • Critical thinking • Research • Digital literacy • Creativity • (FYA 2017, p. 9).

  3. How can technology be implemented to better negotiate meaningful student-centred topics, enhance group work and lead to greater learning outcomes?

  4. Learning analytics 'The most widely acknowledged definition of Learning Analytics is “the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environment in which it occurs”’ (SoLAR n.d., para. 3 cited in Reyes 2015, p. 76). Why learning analytics? Personalised learning  Timely feedback  Early intervention (Reyes 2015) And many other benefits…

  5. Enabling opportunities The promise of carefully implemented learning analytics is that educators can effectively connect with student interests and promote deep learning outcomes (Corrin, Kennedy & Mulder 2013). Students in enabling programs are characterised by ‘extreme heterogeneity’ (Hodges et al. 2013, p. 32). New technology provides opportunities: to extend enabling pedagogy (Stokes 2014)  further connect with the rich life-worlds of students to engagethem with university education through meaningful learning experiences (Stokes 2018). 

  6. The study Further embedding learning analytics in Future Ideas: Information and the Internet: Specifically Quantext Supported by Learning Catalytics Goals: To better understand student interests and concerns. To design more meaningful assessments, particularly team work. To support students to build transferable 21st Century skills.

  7. Introducing students to analytics Quantext questions delivered in weeks 1 and 2 in lecture and online. Responses analysed and themes generated. Team-based assessment in tutes on weekly readings in weeks 1 to 4.

  8. Team-based Assessment using Learning Catalytics

  9. Where to begin? What problem to solve? Q1 http://bit.ly/challengeshumanity Q2 http://bit.ly/technologysolutions Q3 http://bit.ly/explorechallenges

  10. Wyten & Garcia Lawther & Snelling Lawther & Snelling Trebilcock

  11. Assessments A1 individual quiz and search design. Search based on student’s selected topic from themes gathered through Quantext. A2 group investigative report. Focus on collaborative idea generation and problem-solving through research.Findings given in 20-30 minute presentation. A3 individual advanced investigation. Deep analysis and narrow scope; submitted as a research infographic or report on advanced topic.

  12. Assignment Development 1. 2. 3. Original topic: The relationship between social media and fake news.

  13. Assignment Development 1. 2. 3. Original topic: Global ConcernsSustainable food production for a growing population whilst limiting the negative impact on the environment.

  14. Peer Review of other presentations Meeting minutes shared with tutor via the cloud.

  15. Advanced Rubrics Detailed criteria for each level Clear feedback with constructivecomments for all criteria Aids in consistency of marking and clarifying expectations

  16. Student reflections I enjoyed the freedom … in the final two assessments. I chose a path that interested me, and I feel I did better as a result. It allowed and aided us to explore the different ways of learning. For assessment two, being creative with lego was a different representation on our problems. Enjoyed the learning analytics and researching the different topics. Yes enjoyed the approach towards analysing the topics especially working in groups. All assessments flowed from one to the other, allow for a scaffolding to form and aid us to finishing with grades we wanted. That work that is well done can be put to work within the uni, as my group project could allow for a better study space within the common room, meaning that I can have made a voice for other students. Iliked when we looked at the impact ICT would have on our careers and what they would take over. it allowed the chance to see what would be different, which allows for planning. Future Ideas Survey 2018

  17. Outcomes Ability to capture and respond to student interests. Deep learning encouraged (Ramsden 2003), advanced themes emerge and commitment to research evident. Extension of dialogic approach (Shor & Freire 1987). Creative and collaborative approaches, peer modelling, higher submission rate and grades for assignment two (compared to SP5 2017), stronger bonds with peers.  21stCentury skills development. Further analysis needed once course grades finalised.

  18. References Aguilar, SJ 2018, ‘Learning Analytics: at the Nexus of Big Data, Digital Innovation, and Social Justice in Education’, TechTrends, vol. 62, no. 1, pp. 37-45. Binkley, M., Erstad, O., Hermna, J. Raizen, S., Ripley, M., Miller-Ricci, M. & Rumble M. 2012, ‘Defining Twenty-first Century Skills’ in Griffin, P., Care, E. & McGaw, B.  Assessment and Teaching of 21st Century Skills, Dordrecht, Springer.  ​Foundation for Young Australians 2017, The new work smarts, FYA, viewed 19 September 2018 <https://www.fya.org.au/report/the-new-work-smarts/ >.​ Corrin, L, Kennedy, G & Mulder, R 2013, ‘Enhancing learning analytics by understanding the needs of teachers’ in H Carter, M Gosper & J Hedberg (eds.), Electric Dreams. Proceedings ASCILITE 2013 Sydney, pp. 201-205.  Gašević, D, Dawson, S & Siemens, G 2015, ‘Let’s not forget: Learning analytics are about learning’ TechTrends, vol. 59, no. 1, pp. 64-71. Hodges, B, Bedford, T, Hartley, J, Klinger, C, Murray, N, O’Rourke, J, & Schofield, N 2013, Enabling retention: Processes and strategies for improving student retention in university-based enabling programs, Office for Learning and Teaching, Commonwealth of Australia, Sydney, Australia.   McDonald, J, & Moskal, ACM 2017, ‘Quantext: analysing student responses to short-answer questions’, in H Partridge (ed.), Me, Us, IT! Proceedings ASCILITE2017: 34th International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education. Retrieved from <http://2017conference.ascilite.org/wp-content/uploads/2017/11/Concise-MCDONALD.pdf>.Ramsden, P 2003, Learning to teach in higher education, 2nd edn, Routledge Falmer, London.  Reyes, JA 2015, ‘The skinny on big data in education: Learning analytics simplified’, TechTrends, vol. 59, no. 2, pp. 75-79.  Shor, I & Freire, P 1987, ‘What is the 'Dialogical Method' of teaching?’, Journal of education, vol. 169, no. 3, pp. 11-31.   Stokes, J 2014, ‘Student perspectives on transition to University in South Australia’, The International Journal of Learning in Higher Education, vol. 20, no. 4, pp. 1–8.
 Stokes J 2018, ‘Students on the Threshold: Commencing Student Perspectives and Enabling Pedagogy’ in C Agosti & E Bernat (eds), University Pathway Programs: Local Responses within a Growing Global Trend, Springer, Cham. Questions? Jennifer.stokes@unisa.edu.auJenny@quantext.co.nz .

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