Researcher

Dr Lorenzo Vigentini

Field of Research (FoR)

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Biography

Research Interests

My background is in Psychology, Learning and teaching in the Higher Education sector and have a lot of experience in IT/e-learning. My expertise is into learning processes at the crossing between cognitive psychology, differential psychology, education and human-computer interaction. My main interest is about technology, its use, its evolution, its interaction with learning and the interface between human and machines (also...view more

Research Interests

My background is in Psychology, Learning and teaching in the Higher Education sector and have a lot of experience in IT/e-learning. My expertise is into learning processes at the crossing between cognitive psychology, differential psychology, education and human-computer interaction. My main interest is about technology, its use, its evolution, its interaction with learning and the interface between human and machines (also physical using computer vision and brain activity monitoring).

In the educational context this means a keen interest for the student experience considering specifically how learning technology and by technological innovation can support teaching excellence, augment Quality Assurance processes and aid Quality Enhancement. 

However, to study the impact of technological innovation, I  am a strong supporter of data-driven approaches to understand patterns and relations. In recent years this has been termed Learning Analytics or Educational Data Mining.  Such an approach is essential to inform Institutional Research and evidence-driven practice providing a stronger perspective than traditional educational discourse. I am interested in MOOCs development and evaluation and the use of new 'smart' technologies as tools to support and enhance teaching and learning.

In the past few years I led the development of the Learning Analytics and Educational Data Science research group at UNSW.

I am verse with both quantitative and qualitative methods and I am always keen to learn about new methodologies from interdisciplinary cross-insemination.

Educational data mining is one of such examples I have been working with to explore emergent patterns in students' types from cognitive/learning styles, academic performance and interaction with learning technology. Sentiment analysis is another application of student generated data which the Universities gather but do not use much.

I am also interested in the cognitive, emotional and social determinants of performance under undue stress (e.g. students’ first year undergrad experience, work environment in highly competitive or critical situations, decision making processes or dysfunctional team work).

One more area attracting my interest is how individual differences shape team interaction and drive entrepreneurial behaviour and whicj sychometric markers are most important in determining entrepreneurial success.

 

Recent Publications

Vigentini, L., Wang, Y., Paquette, L., & León Urrutia, M. (2017). MOOC analytics: live dashboards, post-hoc analytics and the long-term effects. Joint MOOCs workshops from the Learning analytics and Knowledge Conference 2017 (online, Vol. 1967). CEUR-WS.org. Retrieved from http://ceur-ws.org/Vol-1967/
Mirriahi N; Vigentini L, 2016, 'Videos in the curriculum: Analytics to understand learner use, engagement, and learning', in Siemens G; Lang C (ed.), Handbook of Learning Analytics & Educational Data Mining, ROS ID: 810327
Vigentini, L., Clayphan, A., Zhang, X., & Chitsaz, M. (2017). Overcoming the MOOC Data Deluge with Learning Analytic Dashboards. In Learning Analytics: Fundaments, Applications, and Trends (pp. 171–198). Springer International Publishing.
Vigentini, L., León Urrutia, M., & Fields, B. (2017). FutureLearn data: what we currently have, what we are learning and how it is demonstrating learning in MOOCs. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 512–513). ACM.

 

Vigentini L; Mirriahi N; Kligyte G, 2016, 'From reflective practitioner to active researcher: Towards a role for learning analytics in higher education scholarship', in Spector M; Lockee BB; Childress MD (ed.), Learning, Design, and Technology. An International Compendium of Theory, Research, Practice, and Policy, Springer International Publishing, pp. 1 - 29, http://dx.doi.org/10.1007/978-3-319-17727-4_6-1, ROS ID: 810326

Vigentini L; McIntyre S; Mirriahi N; Alonzo D, 2016, 'Exploring the real flexibility of learning sequences: Does course design constrain students behaviours or do students shape their own learning?', in ElAtia S; Zaïane O; Ipperciel D (ed.), Data Mining and Learning Analytics in Educational Research, Wiley and Blackwell, ROS ID: 511538

Chitsaz, M., Vigentini, L., & Clayphan, A. (2016). Toward the development of a dynamic dashboard for FutureLearn MOOCs: insights and directions. In 33rd International Conference of Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education (p. 116).

Ford, R., Vigentini, L., Vulic, J., Chitsaz, M., & Prusty, Bg. (2016). Through engineers’ eyes: A MOOC experiment. In 27th Annual Conference of the Australasian Association for Engineering Education: AAEE 2016 (p. 654). Southern Cross University.
Vigentini, L., McIntyre, S., Mirriahi, N., & Alonzo, D. (2016). Exploring the real flexibility of learning sequences: Does course design constrain students behaviours or do students shape their own learning? In Data Mining and Learning Analytics: Applications in Educational Research (p. 175). Wiley and Blackwell.
Vigentini, L., Mirriahi, N., & Kligyte, G. (2016). From reflective practitioner to active researcher: Towards a role for learning analytics in higher education scholarship. Learning, Design, and Technology: An International Compendium of Theory, Research, Practice, and Policy, 1–29.
Vigentini, L., & Zhao, C. (2016). Evaluating the’Student’Experience in MOOCs. In Proceedings of the Third (2016) ACM Conference on Learning@ Scale (pp. 161–164). ACM.

 

Grants

  • 2016 – OLT SP16-5264 (Strategic Priority Projects) 'Scaling the provision of personalised learning support actions for large student cohorts', Co-investigator  - $350.000
  • 2015 – UNSW Learning and Teaching Unit ‘Skunkworks’ fund (project designed to develop sprints of development in Learning analytics and Educational Data Science in partnership with students) – $120.000
  • 2013 – The Division of the DVC Academic, Student Voice Project (An evaluation and redesign of the Course And Teaching Evaluation Instrument), Principal Investigator -  $20.000
  • 2012 Principal Investigator for the Evaluation of the Postgraduate Research Experience survey – The Higher Education Academy, UK.
  • 2004-06 Co-researcher for Psychology in the STEER Project - The University of Edinburgh Principal E-learning Fund

 

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Location

Library Stage 1

Contact

02 9385 6226