Interactive recommender systems and dashboards for learning
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The document summarizes Katrien Verbert's research interests which include interactive recommender systems, learning analytics dashboards, and intelligent user interfaces. Some key points: - Her team at KU Leuven studies how to visualize learner data to help students explore connections between effort and outcomes. - Their research also looks at designing dashboards to promote balanced participation in classroom discussions and support advisor-student dialogues. - Interactive recommender systems that allow users to provide feedback and explore recommendations are another focus to improve recommendations and increase user trust. - Future work may explore applying these areas to reskilling employees and using augmented/virtual reality in education.
Interactive recommender systems and dashboards for learning
- 1. Interactive recommender systems and dashboards for learning LALA symposia July 1, 2019, KU Leuven, Belgium Katrien Verbert Augment/HCI - KU Leuven @katrien_v
- 2. Human-Computer Interaction group recommender systems – visualization – intelligent user interfaces Learning analytics Media consumption Research Information Systems Wellness & health Augment prof. Katrien Verbert ARIA prof. Adalberto Simeone Computer Graphics prof. Phil Dutré Language Intelligence & Information Retrieval prof. Sien Moens
- 3. Augment/HCI team Robin De Croon Postdoc researcher Katrien Verbert Associate Professor Francisco Gutiérrez PhD researcher Tom Broos PhD researcher Martijn Millecamp PhD researcher Nyi Nyi Htun Postdoc researcher Houda Lamqaddam PhD researcher Yucheng Jin PhD researcher Oscar Alvarado PhD researcher http://augment.cs.kuleuven.be/ Diego Rojo Carcia PhD researcher Tinne De Laet Associate Professor
- 4. Learning analytics Src: Steve Schoettler
- 6. Learning analytics Src: Steve Schoettler
- 7. Collaborative filtering – Content-based filtering Knowledge-based filtering - Hybrid Recommendation techniques
- 9. Verbert, Katrien; Manouselis, Nikos; Ochoa, Xavier;Wolpers, Martin; Drachsler, Hendrik; Bosnic, Ivana; Duval, Erik. Context- aware recommender systems for learning: a survey and future challenges, IEEETrans. on Learning Technologies, 18 p. (2012) 9
- 11. Learning analytics Src: Steve Schoettler
- 13. Sten Govaerts, Katrien Verbert, Aberlardo Pardo, Erik Duval. The student activity meter for awareness and self-reflection. CHI'12 Extended Abstracts on Human Factors in Computing Systems. ACM, 2012. abundance of data - effort - outcome www.role-project.eu Creating effective learning analytics dashboards blended learning
- 14. blended learning Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2013). Learning dashboards: an overview and future research opportunities. Personal and Ubiquitous Computing, 1-16. Creating effective learning analytics dashboards
- 15. RQ1: How should we visualise learner data to support students to explore the path from effort to outcomes? RQ2: How can we promote students, inside and outside the classroom, to actively explore this effort to outcomes path? blended learning abundance of data - effort - outcome Creating effective learning analytics dashboards
- 16. TwitterBlogs 16
- 19. Charleer, S., Klerkx, J., Santos, J. L., & Duval, E. Improving awareness and reflection through collaborative, interactive visualizations of badges. In Proceedings of the 3rd Workshop on Awareness and Reflection in Technology-Enhanced Learning, pages 69-81. CEUR Workshop Proceedings, 2013 ARTEL 2014 . Graz, Austria ARTEL 2013
- 20. Evaluations 20
- 21. Abstract the LA data Provide access to the artefacts Augment the abstracted data Provide access to teacher and peer feedback Results 21 RQ1: What are relevant learning traces, and how should we visualise these data to support students to explore the path from effort to outcomes?
- 22. Results 22 RQ2: How can we promote students, inside and outside the classroom, to actively explore this effort to outcomes path? Visualise the learner path Integrate LA into the workflow Facilitate collaborative exploration of the LA data
- 23. RQ1: visualise to facilitate exploring Visualise the learning path 23
- 24. RQ1: visualise to facilitate exploring Visualise the learning path 24
- 25. RQ1: visualise to facilitate exploring Visualise the learning path 25
- 26. RQ1: visualise to facilitate exploring26 Visualise the learning path
- 27. Balanced discussion in the classroom 27 F2F Group Work RQ3: What are the design challenges for ambient Learning Dashboards to promote balanced group participation in classrooms, and how can they be met? RQ4: Are ambient Learning Dashboards effective means for creating balanced group participation in classroom settings? over- and under-participation
- 28. K. Bachour, F. Kaplan, and P. Dillenbourg. An interactive table for supporting participation balance in face-to-face collaborative learning. IEEE Trans. Learn. Technol., 3(3):203–213, July 2010. Over- participation:“free -riders” can affect the motivated learner to reduce contributions G. Salomon and T. Globerson. When teams do not function the way they ought to. International Journal of Educational Research, 13(1):89 – 99, 1989.
- 30. case study 1 # participants 12 students deployment 1 3h session with dashboard 1 3h session without dashboard evaluation class discussion, questionnaires (perceived distraction/awareness/usefuln ess), activity/quality loggingcase study 2 # participants 19 students deployment half 3h session without dashboard, half 3h session with dashboard evaluation questionnaires (perceived importance feedback/motivation) activity/quality logging Evaluation setup 30
- 32. Visualise balance in an abstract and neutral way Add the qualitative dimension to the visualisation Create a realistic picture of the classroom situation Results 32 RQ3: What are the design challenges for ambient LDs to promote balanced group participation in classrooms, and how can they be met?
- 33. Ambient dashboards as support for teacher/presenter Ambient dashboards raise awareness of the invisible Ambient feedback information can activate students Results 33 RQ4: Are ambient LDs effective means for creating balanced group participation in classroom settings? Charleer, S., Klerkx, J., Duval, E., De Laet, T. and Verbert, K. (2017) ‘Towards balanced discussions in the classroom using ambient information visualisations’, Int. J. Technology Enhanced Learning, Vol. 9, Nos. 2/3, pp.227–253.
- 34. RQ5: What are the design challenges for creating a Learning Dashboard to support study advice sessions, and how can they be met? RQ6: How does such a Learning Dashboard contribute to the role of the adviser, student, and dialogue? Supporting advisor-student dialogue 34 lack of data-based feedback
- 36. Supporting advisor-student dialogue Charleer, S., Moere, A. V., Klerkx, J., Verbert, K., & De Laet, T. (2018). Learning analytics dashboards to support adviser-student dialogue. IEEE Transactions on Learning Technologies, 11(3), 389-399.
- 37. Academic risk prediction 37 Gutiérrez, F., Seipp, K., Ochoa, X., Chiluiza, K., De Laet, T. and Verbert, K., 2018. LADA: A learning analytics dashboard for academic advising. Computers in Human Behavior.
- 38. “Black box” approach does not work 38 Illustration: Erik Blad for The Intercept
- 39. AHMOSE: Augmented by human model selection 3 9 https://ahmose.ml/
- 40. Interactive recommender systems Core objectives: • Explaining recommendations to increase user trust and acceptance • Enable users to interact with the recommendation process
- 42. Interactive recommender systems ¤ Transparency: explaining the rational of recommendations ¤ User control: closing the gap between browse and search ¤ Diversity – novelty ¤ Cold start ¤ Context-aware interfaces 42 He, C., Parra, D. and Verbert, K., 2016. Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications, 56, pp.9-27.
- 43. 43 Explaining and exploring competence- based recommendations
- 44. 44
- 45. 45 Explaining and exploring competence- based recommendations
- 46. Evaluation setup ¤ Participants: 66 job seekers ¤ Data Collection and measurements: ¤ ResQue questionnaire ¤ Open questions ¤ Logging 46 Francisco Gutierrez, Sven Charleer, Robin De Croon, Nyi Nyi Htun, Gerd Goetschalckx, Katrien Verbert Explaining and exploring job recommendations: a user-driven approach for designing an interactive job recommender system. RecSys 19. pp. 1-10
- 47. Results 47
- 48. Results 48
- 49. Next steps ¤ Learning analytics and interactive recommender systems for reskilling of employees ¤ AR/VR for education ¤ Explaining predictive models 49
- 50. Peter Brusliovsky Nava Tintarev Cristina ConatiDenis Parra Collaborations Bart Knijnenburg Jurgen Ziegler