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 activi...
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, opens in a new window or Educational Data Mining, opens in a new window. 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, opens in a new window 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., Swibel, B. & Hasler, G. (in publication). A comparison of LA dashboards in Schools and Higher Education: a focus on goal setting and support processes. In Ifenthaler, Muhittin (Eds.) Visualisations and dashboards for learning analytics.
Arthars, N., Dollinger, M., Vigentini, L., Liu, D., Kondo, E., & King, D. (2019). Empowering teachers to personalize learning support. In Ifenthaler, Mah, Yau (Eds.) Utilizing Learning Analytics to Support Study Success. https://www.springer.com/gp/book/9783319647913
Swibel, B., Hasler, G. & Vigentini, L. (under review). Developing a growth learning data mindset: a secondary school approach to creating a culture of data driven improvement. Journal of Learning Analytics, Special issue ‘Learning analytics in schools’
Vigentini, L., Liu, D. Y. T., Arthars, N., & Dollinger, M. (2020). Evaluating the scaling of a LA tool through the lens of the SHEILA framework: A comparison of two cases from tinkerers to institutional adoption. The Internet and Higher Education, 45, 100728. https://doi.org/10/ggm39d
Ford, R., Vigentini, L., Vulic, J., Chitsaz, M., Prusty, B.G., (2019) A Massive Open Online Course (MOOC) on Engineering Mechanics: Data Analytics Informing Learning Design and Improvement. Australian Journal of Mechanical Engineering https://doi.org/10.1080/14484846.2019.1596049 ,
Pardo, A., Bartimote, K., Shum, S. B., Dawson, S., Gao, J., Gašević, D., … Vigentini, L. (2018). OnTask: Delivering Data-Informed, Personalized Learning Support Actions. Journal of Learning Analytics, 5(3), 235–249–235–249. https://doi.org/10.18608/jla.2018.53.15
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:
810327Vigentini, 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.
