Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Artificial Intelligence and Data Analytics in Education: the case of exploratory learning

21 views

Published on

Keynote for V Jornadas Iberoamericanas de Interacción Humano-Computador 2019

http://mexilab.com/V_Jornadas_HCI/#1#schedule


Drawing on examples from academic research in the field of artificial intelligence in education (AIED) and learning analytics (LA), I will cut through the current hype and make a case for carefully designed systems for a wide range of pedagogies.

As an introduction to the field, the talk will first share how the growing concerns about the role of AI in society, big data and big companies are entering education.

Using the case of exploratory learning, I will then offer possible responses challenging designers, developers and educators, to seize the opportunities afforded by the emerging technological context around data, analytics and AI, while carefully considering design choices when it comes to practical implementation.

Published in: Education
  • Be the first to comment

  • Be the first to like this

Artificial Intelligence and Data Analytics in Education: the case of exploratory learning

  1. 1. Artificial Intelligence and Data Analytics in Education The case of exploratory learning Dr Manolis Mavrikis UCL Knowledge Lab @mavrikis @uclknowledgelab#VJornadasIHC
  2. 2. About me @mavrikis @uclknowledgelab 2 http://bit.ly/ucl-edtech19
  3. 3. About me @mavrikis @uclknowledgelab 3 http://bit.ly/bjedtech
  4. 4. About you @mavrikis @uclknowledgelab 4
  5. 5. How is Artificial Intelligence applied in Education
  6. 6. Popular AI 8@mavrikis @uclknowledgelab
  7. 7. Augmenting Intelligence @mavrikis#VJornadasIHC
  8. 8. Research communities
  9. 9. AI LA @mavrikis @uclknowledgelab 13
  10. 10. Types of AI in Education Intelligent Tutoring Systems (ITS)
  11. 11. Types of AI in Education Intelligent Tutoring Systems (ITS)
  12. 12. @mavrikis#VJornadasIHC
  13. 13. Types of AI in Education Intelligent Tutoring Systems (ITS) ∙ Break problems into steps ∙ Provide scaffolding and feedback during problem-solving ∙ Adapt content and personalise the experience of learners Examples online
  14. 14. Pedagogy matters See November 2019 issue
  15. 15. Pedagogy matters
  16. 16. Types of AI in Education Dialogue-based or Conversational Agents
  17. 17. • Exploratory Learning Environments • Betty’s Brain • Crystal Island • ECHOES • Fractions Lab Types of AI in Education
  18. 18. Outline • Why Exploratory learning • AI to support student learning • LA to support teacher orchestration @mavrikis @uclknowledgelab 24
  19. 19. AI LA @mavrikis @uclknowledgelab 25 AI LA
  20. 20. Exploratory learning 26
  21. 21. 27
  22. 22. 28
  23. 23. Exploratory Learning Environments 29
  24. 24. Exploratory Learning Environments 30
  25. 25. Exploratory Learning Environments 31
  26. 26. 32 @mc2project Karkalas, S., Mavrikis, M. (2016) Feedback Authoring for Exploratory Learning Objects: AuthELO. CSEDU (1) 144-153 https://doi.org/10.5220/0005810701440153
  27. 27. @mavrikis @uclknowledgelab 33
  28. 28. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731724. http://iread-project.eu iRead Project @iread_project
  29. 29. iRead games • GPC • Syllabification • Prefixes • Suffixes • Orthography-visual processing 35
  30. 30. @mavrikis#VJornadasIHC
  31. 31. @mavrikis#VJornadasIHC
  32. 32. Exploratory learning • Targeting mostly conceptual learning • Criticised for lack of efficiency (Mayer, Kirschner et al. etc.) • Not the only approach c.f. Rummel et al. ICLS 2016 • Adoption issues due to ‘orchestration’ difficulties (Dillenbourg, 2010) Rummel, N., Mavrikis, M., Wiedmann, M., Loibl, K., Mazziotti, C., Holmes, W., & Hansen, A. (2016). Combining Exploratory Learning with Structured Practice to Foster Conceptual and Procedural Fractions Knowledge. In ICLS 2016 Dillenbourg, P., & Jermann, P. (2010). Technology for classroom orchestration. In New science of learning (pp. 525-552). Springer New York. 38
  33. 33. 39
  34. 34. 40
  35. 35. Objective: ensure productive interaction and achievement of learning goals through feedback and task sequencing One-to-one support 41
  36. 36. But what about in a classroom? 42
  37. 37. Or a bigger classroom? 43
  38. 38. Or a MOOC? 44
  39. 39. iTalk2Learn o European-funded (3 year) research project (FP7). o 4 universities Artificial Intelligence, Computer Science, Technology-Enhanced Learning in Mathematics, Educational Psychology o 3 commercial partners
  40. 40. iTalk2learn 46@mavrikis @uclknowledgelab
  41. 41. Fractions Lab – example of feedback message 47
  42. 42. @mavrikis @uclknowledgelab 48
  43. 43. Pedagogic strategies for student support • Supporting processes of exploration • Supporting students to set and work towards explicit goals. • Directing students’ attention. • Helping students organise their working environment. • Provoking cognitive conflict. • Encouraging alternative solutions. • Supporting reflection • Promoting motivation • Supporting collaboration Mavrikis, M et al. (2008) "Revisiting pedagogic strategies for supporting students’ learning in mathematical microworlds." Proceedings of the International Workshop on Intelligent Support for Exploratory Environments at EC-TEL. http://ceur-ws.org/Vol-381/paper04.pdf 49 @mavrikis#VJornadasIHC
  44. 44. Technical Challenges - Too much data - Unstructured data - Many priorities 50
  45. 45. Intelligent Support 51@mavrikis @uclknowledgelab
  46. 46. FRAME - Separation of concerns Gutierrez-Santos S, Mavrikis M, Magoulas G (2012) A separation of concerns for engineering intelligent support for exploratory learning environments. Journal of Research and Practice in Information Technology 44(3):347–360 52 Microworld/Model & Events Analysis Reasoning Feedback
  47. 47. Intelligent Support Janning, R., Schatten, C., Schmidt-Thieme, L.: Perceived task-difficulty recognition from log-file infor- mation for the use in adaptive intelligent tutoring systems. Int. J. Artif. Intell. Educ. 26(3), 855–876 (2016) Analysis Reasoning Feedback
  48. 48. Analysis Grawemeyer, B., Mavrikis, M., Hansen, A., Mazziotti, C., Gutierrez-Santos, S. (2014) Employing Speech to Contribute to Modelling and Adapting to Students' Affective States. EC-TEL 2014. 54 Feedback Reasoning
  49. 49. Reasoning 55 Reasoning Feedback Current affect Feedback followed Feedback type Enhanced affective state Grawemeyer, B., Mavrikis, M., Holmes, W. et al. Affective learning: improving engagement and enhancing learning with affect-aware feedback. UMUAI (2017) 27: 119. doi:10.1007/s11257-017-9188-z http://bit.ly/affect-italk2learn
  50. 50. Feedback 56 Reasoning Feedback Grawemeyer, B., Mavrikis, M., Holmes, W. et al. Affective learning: improving engagement and enhancing learning with affect-aware feedback. UMUAI (2017) 27: 119. doi:10.1007/s11257-017-9188-z http://bit.ly/affect-italk2learn
  51. 51. Student Needs Analysis • Tailor the next exercise to a student based on their: • Previous task and representations • Performance on current task • Level of challenge • Affective state @mavrikis @uclknowledgelab 57
  52. 52. AI as assistance to human intelligence • Delegate responsibility in support • Domain-specific and affect-based feedback • But by no means aimed to replace teachers ! @mavrikis @uclknowledgelab 58
  53. 53. Promising results • Meta-analyses show impact of intelligent tutoring systems (VanLehn, 2011; du Boulay, 2016) • Combination of exploratory and structure - Rummel et al. (2016) • Affect-aware support contributes to reducing boredom and off-task behavior, and may have an effect on learning (UMUAI, 2017) Grawemeyer, B., Mavrikis, M., Holmes, W. et al. Affective learning: improving engagement and enhancing learning with affect-aware feedback. UMUAI (2017) 27: 119. doi:10.1007/s11257-017-9188-z http://bit.ly/affect-italk2learn 59
  54. 54. Limitations • Domain- and Task-specific • Costly – what about scaling up or genaralising? • Inherent ‘limits’ of AI @mavrikis @uclknowledgelab 60
  55. 55. Black box
  56. 56. • Lack of awareness & ‘control’ of the classroom ELE Orchestration Mavrikis, M., Gutierrez-Santos, S., Geraniou, E., Noss, R., & Poulovassilis, A. (2013). Iterative Context Engineering to Inform the Design of Intelligent Exploratory Learning Environments for the Classroom. In R. Luckin, S. Puntambekar, P. Goodyear, B. L. Grabowski, J. Underwood, N. Winters (Eds.), Handbook of Design in Educational Technology (pp. 80-92). Routledge.
  57. 57. • Could we design tools to assist teachers in their role as facilitators in classrooms with exploratory environments? Our challenge
  58. 58. Learning Analytics as an answer to AI limits • Design should be based on analysis of teacher needs (in the context of AIED systems) • Where are the ‘actionable insights’ in LA? @mavrikis @uclknowledgelab 64
  59. 59. The problem in LA design 65 @mavrikis @uclknowledgelab
  60. 60. R. Martinez-Maldonado, A. Pardo, N. Mirriahi, K. Yacef, J. Kay, and A. Clayphan. The LATUX workflow: Designing and deploying awareness tools in technology-enabled learning settings. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, pages 1–10, 2015. Mavrikis, M., Gutierrez-Santos, S., Geraniou, E., Noss, R., & Poulovassilis, A. (2013). Iterative Context Engineering to Inform the Design of Intelligent Exploratory Learning Environments for the Classroom. In R. Luckin, S. Puntambekar, P. Goodyear, B. L. Grabowski, J. Underwood, N. Winters (Eds.), Handbook of Design in Educational Technology (pp. 80-92). Routledge. 66
  61. 61. Classroom Dynamics Gutierrez-Santos, S., Mavrikis, M., Geraniou E., Poulovassilis, A. (2012). Usage Scenarios and Evaluation of Teacher Assistance Tools for Exploratory Learning Environments (Under review) Available at http://www.dcs.bbk.ac.uk/research/techreps/2012/bbkcs-12-02.pdf 67
  62. 62. http://www.migen.org Classroom Dynamics 68
  63. 63. Goal achievement 69
  64. 64. S. Gutierrez-Santos; M. Mavrikis; E. Geraniou; A. Poulovassilis, "Similarity-based Grouping to Support Teachers on Collaborative Activities in an Exploratory Mathematical Microworld," in IEEE Transactions on Emerging Topics in Computing , in press Grouping 70
  65. 65. Common desired ‘superpowers’ Holstein, K., McLaren, B. M., & Aleven, V. (2017). Intelligent tutors as teachers’ aides: Exploring teacher needs for real-time analytics in blended classrooms. 7th International Conference on Learning Analytics and Knowledge, Vancouver, Canada, March 13-17, 2017. • students’ thought processes • which students are really “stuck” • which students are “almost there”, just need a nudge • clone themselves • have “eyes in the back of my head” • know whether a student is actually trying 71
  66. 66. Emerging technology • Wearable support tools • Cross physical – digital • Multimodal LA • OLM for students • Configurable summaries Holstein et al (2017) LAK 2017 72 @mavrikis#VJornadasIHC
  67. 67. https://kenholstein.myportfolio.com/the-lumilo-project Emerging technology
  68. 68. Summary AI LA 74
  69. 69. Summary • AI and LA (perceptions) are changing rapidly • Integration encourages adoption • Focus on: • Delegating teacher responsibility • Actionable insights • Context and user needs 7575 @mavrikis#VJornadasIHC
  70. 70. Augmenting Intelligence
  71. 71. So many questions?
  72. 72. http://bit.ly/tech-pele
  73. 73. Recommended books Holmes, Balik, Fadel (2018) Luckin (2018)
  74. 74. Rose Luckin Mutlu Cukurova, Nikol Rummel Sokratis KarkalasKaska Poryaska-Pomsta FUNDERS & PROJECTS Mina Vasalou Beate Grawemeyer Sergio Gutiérrez-Santos Wayne Holmes @mavrikis #VJornadasIHC @uclknowledgelab 82
  75. 75. Grawemeyer, B., Mavrikis, M., Holmes, W. et al. Affective learning: improving engagement and enhancing learning with affect-aware feedback. UMUAI (2017) 27: 119. doi:10.1007/s11257-017-9188-z http://bit.ly/affect-italk2learn
  76. 76. Grawemeyer, B., Mavrikis, M., Holmes, W. et al. Affective learning: improving engagement and enhancing learning with affect-aware feedback. UMUAI (2017) 27: 119. doi:10.1007/s11257-017-9188-z http://bit.ly/affect-italk2learn
  77. 77. • Talk aloud • “Remember to talk aloud, and tell us what are you thinking” • “What is the task asking you to do?” • “Please think aloud, what are your thoughts or feelings?” • Affect boosts • “It may be hard, but keep trying” • “If you find this easy, check your work and change the task” • Problem solving • “What do you need to do now, to complete the fraction?” • Instructive feedback • “You can’t add fractions with different denominators” • Reflection • “What did you learn from this task?” • “What do you notice about the two fractions?” Feedback types 85
  78. 78. Feedback framework Holmes W., Mavrikis M., Hansen A., Grawemeyer B. (2015) Purpose and Level of Feedback in an Exploratory Learning Environment for Fractions. In: Conati C., Heffernan N., Mitrovic A., Verdejo M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science, vol 9112. Springer. 86
  79. 79. Pedagogic strategies for student support • Supporting processes of exploration • Supporting students to set and work towards explicit goals. • Directing students’ attention. • Helping students organise their working environment. • Provoking cognitive conflict. • Encouraging alternative solutions. • Supporting reflection • Promoting motivation • Supporting collaboration Mavrikis, M et al. (2008) "Revisiting pedagogic strategies for supporting students’ learning in mathematical microworlds." Proceedings of the International Workshop on Intelligent Support for Exploratory Environments at EC-TEL. 87

×