Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
102 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Visualizing Intelligent Tutor Interactions for Responsive Pedagogy (2404.12944v1)

Published 19 Apr 2024 in cs.HC

Abstract: Intelligent tutoring systems leverage AI models of expert learning and student knowledge to deliver personalized tutoring to students. While these intelligent tutors have demonstrated improved student learning outcomes, it is still unclear how teachers might integrate them into curriculum and course planning to support responsive pedagogy. In this paper, we conducted a design study with five teachers who have deployed Apprentice Tutors, an intelligent tutoring platform, in their classes. We characterized their challenges around analyzing student interaction data from intelligent tutoring systems and built VisTA (Visualizations for Tutor Analytics), a visual analytics system that shows detailed provenance data across multiple coordinated views. We evaluated VisTA with the same five teachers, and found that the visualizations helped them better interpret intelligent tutor data, gain insights into student problem-solving provenance, and decide on necessary follow-up actions - such as providing students with further support or reviewing skills in the classroom. Finally, we discuss potential extensions of VisTA into sequence query and detection, as well as the potential for the visualizations to be useful for encouraging self-directed learning in students.

View on arXiv
Definition Search Book Streamline Icon: https://streamlinehq.com
References (51)
  1. A dashboard to support teachers during students’ self-paced AI-supported problem-solving practice. In European Conference on Technology Enhanced Learning. Springer, 16–30.
  2. A new paradigm for intelligent tutoring systems: Example-tracing tutors. International Journal of Artificial Intelligence in Education 19, 2 (2009), 105–154.
  3. Developing a teacher dashboard for use with intelligent tutoring systems. technology 34 (2010), 44.
  4. Leilani Battle and Jeffrey Heer. 2019. Characterizing exploratory visual analysis: A literature review and evaluation of analytic provenance in tableau. In Computer graphics forum, Vol. 38. Wiley Online Library, 145–159.
  5. D33{}^{3}start_FLOATSUPERSCRIPT 3 end_FLOATSUPERSCRIPT data-driven documents. IEEE transactions on visualization and computer graphics 17, 12 (2011), 2301–2309.
  6. Richard Brath and Craig Hagerman. 2021. Automated Insights on Visualizations with Natural Language Generation. In 2021 25th International Conference Information Visualisation (IV). IEEE, 278–284.
  7. Finding waldo: Learning about users from their interactions. IEEE Transactions on visualization and computer graphics 20, 12 (2014), 1663–1672.
  8. Click2annotate: Automated insight externalization with rich semantics. In 2010 IEEE symposium on visual analytics science and technology. IEEE, 155–162.
  9. Albert T Corbett and John R Anderson. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction 4 (1994), 253–278.
  10. Foresight: Recommending visual insights. arXiv preprint arXiv:1707.03877 (2017).
  11. A review on visualization of educational data in online learning. In Learning Technologies and Systems: 19th International Conference on Web-Based Learning, ICWL 2020, and 5th International Symposium on Emerging Technologies for Education, SETE 2020, Ningbo, China, October 22–24, 2020, Proceedings 5. Springer, 15–24.
  12. Recovering reasoning processes from user interactions. IEEE computer graphics and applications 29, 3 (2009), 52–61.
  13. MOOC visual analytics: Empowering students, teachers, researchers, and platform developers of massively open online courses. Journal of the Association for Information Science and Technology 68, 10 (2017), 2350–2363.
  14. D Randy Garrison. 1997. Self-directed learning: Toward a comprehensive model. Adult education quarterly 48, 1 (1997), 18–33.
  15. Visualizing learner engagement, performance, and trajectories to evaluate and optimize online course design. PloS one 14, 5 (2019), e0215964.
  16. Steven Gomez and David Laidlaw. 2012. Modeling task performance for a crowd of users from interaction histories. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2465–2468.
  17. David Gotz and Harry Stavropoulos. 2014. Decisionflow: Visual analytics for high-dimensional temporal event sequence data. IEEE transactions on visualization and computer graphics 20, 12 (2014), 1783–1792.
  18. CCVis: Visual analytics of student online learning behaviors using course clickstream data. Electronic Imaging 31 (2019), 1–12.
  19. Visual progression analysis of event sequence data. IEEE transactions on visualization and computer graphics 25, 1 (2018), 417–426.
  20. Eventthread: Visual summarization and stage analysis of event sequence data. IEEE transactions on visualization and computer graphics 24, 1 (2017), 56–65.
  21. Luna: a dashboard for teachers using intelligent tutoring systems. Education 60, 1 (2010), 159–171.
  22. Design a Dashboard for Secondary School Learners to Support Mastery Learning in a Gamified Learning Environment. In European Conference on Technology Enhanced Learning. Springer, 542–549.
  23. Kenneth R. Koedinger and John R. Anderson. 1997. Intelligent Tutoring Goes To School in the Big City. International Journal of Artificial Intelligence in Education 8 (1997), 30–43.
  24. Cognitive tutors: Technology bringing learning sciences to the classroom. na.
  25. The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning. Cognitive Science 36, 5 (2012), 757–798. https://doi.org/10.1111/j.1551-6709.2012.01245.x
  26. Gloria Ladson-Billings. 2014. Culturally Relevant Pedagogy 2.0: a.k.a. the Remix. Harvard Educational Review 84, 1 (03 2014), 74–84. https://doi.org/10.17763/haer.84.1.p2rj131485484751 arXiv:https://meridian.allenpress.com/her/article-pdf/84/1/74/2112227/haer_84_1_p2rj131485484751.pdf
  27. Temporal search and replace: An interactive tool for the analysis of temporal event sequences. HCIL, University of Maryland, College Park, Maryland, Tech. Rep. HCIL-2013-TBD (2013).
  28. Characterizing automated data insights. In 2020 IEEE Visualization Conference (VIS). IEEE, 171–175.
  29. Karsten Øster Lundqvist and Steven Warburton. 2019. Visualising learning pathways in MOOCs. In 2019 IEEE Learning With MOOCS (LWMOOCS). IEEE, 185–190.
  30. Analytic provenance in practice: The role of provenance in real-world visualization and data analysis environments. IEEE Computer Graphics and Applications 39, 6 (2019), 30–45.
  31. e-Learning, online learning, and distance learning environments: Are they the same? The Internet and higher education 14, 2 (2011), 129–135.
  32. Operationalizing Culturally Relevant Pedagogy: A Synthesis of Classroom-Based Research. Equity & Excellence in Education 41, 4 (2008), 433–452. https://doi.org/10.1080/10665680802400006 arXiv:https://doi.org/10.1080/10665680802400006
  33. John Pane. 1967. Effectiveness of Cognitive Tutor Algebra I at Scale. Angewandte Chemie International Edition, 6(11), 951–952. (1967), 5–24.
  34. Django Paris. 2012. Culturally Sustaining Pedagogy: A Needed Change in Stance, Terminology, and Practice. Educational Researcher 41, 3 (2012), 93–97. https://doi.org/10.3102/0013189X12441244 arXiv:https://doi.org/10.3102/0013189X12441244
  35. Characterizing provenance in visualization and data analysis: an organizational framework of provenance types and purposes. IEEE transactions on visualization and computer graphics 22, 1 (2015), 31–40.
  36. Jennifer D Robinson and Adam M Persky. 2020. Developing self-directed learners. American Journal of Pharmaceutical Education 84, 3 (2020).
  37. “The Coat Traps All Your Body Heat”: Heterogeneity as Fundamental to Learning. Journal of the Learning Sciences 19, 3 (2010), 322–357. https://doi.org/10.1080/10508406.2010.491752 arXiv:https://doi.org/10.1080/10508406.2010.491752
  38. Design study methodology: Reflections from the trenches and the stacks. IEEE transactions on visualization and computer graphics 18, 12 (2012), 2431–2440.
  39. Visualization of student activity patterns within intelligent tutoring systems. In Intelligent Tutoring Systems: 11th International Conference, ITS 2012, Chania, Crete, Greece, June 14-18, 2012. Proceedings 11. Springer, 46–51.
  40. Vandana Singh and Alexander Thurman. 2019. How many ways can we define online learning? A systematic literature review of definitions of online learning (1988-2018). American Journal of Distance Education 33, 4 (2019), 289–306.
  41. Augmenting visualizations with interactive data facts to facilitate interpretation and communication. IEEE transactions on visualization and computer graphics 25, 1 (2018), 672–681.
  42. Knowledgepearls: Provenance-based visualization retrieval. IEEE Transactions on Visualization and Computer Graphics 25, 1 (2018), 120–130.
  43. Learning trajectory based instruction: Toward a theory of teaching. Educational researcher 41, 5 (2012), 147–156.
  44. Kurt VanLehn. 2006. The behavior of tutoring systems. International journal of artificial intelligence in education 16, 3 (2006), 227–265.
  45. Teachers’ uses of a learning trajectory in student-centered instructional practices. Journal of Teacher Education 66, 3 (2015), 227–244.
  46. Krist Wongsuphasawat and David Gotz. 2012. Exploring flow, factors, and outcomes of temporal event sequences with the outflow visualization. IEEE Transactions on Visualization and Computer Graphics 18, 12 (2012), 2659–2668.
  47. Effects of a teacher dashboard for an intelligent tutoring system on teacher knowledge, lesson planning, lessons and student learning. In Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, September 12–15, 2017, Proceedings 12. Springer, 315–329.
  48. Using information visualization to promote students’ reflection on” gaming the system” in online learning. In Proceedings of the Seventh ACM Conference on Learning@ Scale. 37–49.
  49. Survey on the analysis of user interactions and visualization provenance. In Computer Graphics Forum, Vol. 39. Wiley Online Library, 757–783.
  50. (s— qu) eries: Visual regular expressions for querying and exploring event sequences. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2683–2692.
  51. Towards a better understanding of the role of visualization in online learning: A review. Visual Informatics (2022).
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Grace Guo (11 papers)
  2. Aishwarya Mudgal Sunil Kumar (1 paper)
  3. Adit Gupta (6 papers)
  4. Adam Coscia (8 papers)
  5. Chris MacLellan (1 paper)
  6. Alex Endert (40 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets