Artificial Intelligence and Data Analytics in Education: the case of exploratory learning
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The document discusses the application of artificial intelligence (AI) and learning analytics (LA) in educational settings, particularly focusing on exploratory learning environments. It highlights the potential of AI to augment teaching by providing adaptive feedback and support tailored to individual student needs while addressing challenges such as data management and classroom orchestration. Additionally, it emphasizes the importance of actionable insights for teachers and the integration of intelligent tutoring systems to enhance learning outcomes.
Artificial Intelligence and Data Analytics in Education: the case of exploratory learning
- 1. Artificial Intelligence and Data Analytics in Education The case of exploratory learning Dr Manolis Mavrikis UCL Knowledge Lab @mavrikis @uclknowledgelab#VJornadasIHC
- 2. About me @mavrikis @uclknowledgelab 2 http://bit.ly/ucl-edtech19
- 3. About me @mavrikis @uclknowledgelab 3 http://bit.ly/bjedtech
- 5. How is Artificial Intelligence applied in Education
- 14. Types of AI in Education Intelligent Tutoring Systems (ITS)
- 15. Types of AI in Education Intelligent Tutoring Systems (ITS)
- 17. 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
- 19. Pedagogy matters See November 2019 issue
- 20. Pedagogy matters
- 22. Types of AI in Education Dialogue-based or Conversational Agents
- 23. • Exploratory Learning Environments • Betty’s Brain • Crystal Island • ECHOES • Fractions Lab Types of AI in Education
- 24. Outline • Why Exploratory learning • AI to support student learning • LA to support teacher orchestration @mavrikis @uclknowledgelab 24
- 25. AI LA @mavrikis @uclknowledgelab 25 AI LA
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- 32. 32 @mc2project Karkalas, S., Mavrikis, M. (2016) Feedback Authoring for Exploratory Learning Objects: AuthELO. CSEDU (1) 144-153 https://doi.org/10.5220/0005810701440153
- 34. 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
- 35. iRead games • GPC • Syllabification • Prefixes • Suffixes • Orthography-visual processing 35
- 38. 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
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- 41. Objective: ensure productive interaction and achievement of learning goals through feedback and task sequencing One-to-one support 41
- 42. But what about in a classroom? 42
- 43. Or a bigger classroom? 43
- 44. Or a MOOC? 44
- 45. 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
- 47. Fractions Lab – example of feedback message 47
- 49. 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
- 50. Technical Challenges - Too much data - Unstructured data - Many priorities 50
- 52. 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
- 53. 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
- 54. 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
- 55. 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
- 56. 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
- 57. 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
- 58. 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
- 59. 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
- 60. Limitations • Domain- and Task-specific • Costly – what about scaling up or genaralising? • Inherent ‘limits’ of AI @mavrikis @uclknowledgelab 60
- 61. Black box
- 62. • 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.
- 63. • Could we design tools to assist teachers in their role as facilitators in classrooms with exploratory environments? Our challenge
- 64. 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
- 65. The problem in LA design 65 @mavrikis @uclknowledgelab
- 66. 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
- 67. 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
- 70. 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
- 71. 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
- 72. Emerging technology • Wearable support tools • Cross physical – digital • Multimodal LA • OLM for students • Configurable summaries Holstein et al (2017) LAK 2017 72 @mavrikis#VJornadasIHC
- 74. Summary AI LA 74
- 75. 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
- 81. Recommended books Holmes, Balik, Fadel (2018) Luckin (2018)
- 82. 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
- 83. 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
- 84. 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
- 85. • 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
- 86. 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
- 87. 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
Editor's Notes
- #3: UCL Education and Technology Masters http://bit.ly/ucl-edtech19
- #4: Editor of British Journal of Educational Technology http://bit.ly/bjedtech
- #5: Discuss cases where you’ve seen (or can imagine) AI applied in Education
- #6: Terminology clarifications. Not talking about VR/AR (though can be enabled by AI). Or teaching of AI (though that’s important especially these days)
- #7: While anything is possible, not everything is the right thing we should be spending our time
- #8: Popular AI
- #9: So AI is becoming more and more popular through movies and the same applies for data analysis. Movies like ex machine and transcendence may be actually damaging the perceiption of field with their technocentric portayal of AI. When I talk to teachers about AI in education they usually say that we are building Minory Report to predict students failure but I think we aare more closer to Money Ball that revolves around a coach using data and computer analytics to judge and acquire players rather than on mere biases. Showing that is more about asking the right questions than just having the data.
- #11: A vision of AIED 1992 - cyberpunk dystopian victorial times (post AI) Primer is designed to react to the owners environment teach them what they need to know to survive. Something like a Mentor (reference back to Telemachus)
- #12: Lack of agency
- #13: Critique useful but ignores research
- #14: I ‘ll make a case that it is important for these to come together **led** by teaching and learning needs
- #18: The critics claim that the learner has little agency, is forced along a particular, even if individualised, path This gives no opportunity to interact with the system about what is being learned. They are often positioned as well as being possible to replace the teaacher And being the low hanging fruit
- #19: But it is actually the argument that is the low hanging fruit as it weak
- #22: Still particular type of intelligence People learn in other ways too.
- #28: So I mentioned one goal in the field is to design EEL
- #29: I use the term rather loosley
- #31: Whizz through some examples
- #32: 3d logo
- #33: Some of you may know Dynamic geometry
- #36: Domain language model areas Game knows where to place children within the domain model – stores child’s performance in a learner model – adapts content and activity based on this performance
- #42: 41
- #48: messages that approximate (NOT REPLACE!) human behaviour
- #49: To tap into students’ inner thoughts we introduced speech recognition technology
- #51: And what a better application for AI? A complex world of unstractued data to make sense of !
- #52: We address these challenges party with this architercture
- #53: 06:00
- #54: 06:00 perceived task difficulty classifier (PTDC) which uses prosodic cues in the student’s speech to predict the level of challenge for the current student, and the output from speech recognition software which identifies words in the student’s speech.
- #55: 06:00
- #56: his layer contains an affective state reasoner, implemented as a Bayesian network which draws on information from the learner model, in particular the student’s affective state, to decide what type of feedback should be provided to the student. The resulting feedback type is then stored in the learner model and provided to the feedback generation layer.
- #57: his layer contains an affective state reasoner, implemented as a Bayesian network which draws on information from the learner model, in particular the student’s affective state, to decide what type of feedback should be provided to the student. The resulting feedback type is then stored in the learner model and provided to the feedback generation layer.
- #58: I will not go into details here. But
- #59: 25-30 minus here
- #60: Max 30 minutes here
- #61: So all nice but limitations ! AI Singularity is not close - We know there are places where students will get stuck in new ways where AI fails - And when that happens we know that AI will be perceived again as Black Box
- #63: 62
- #65: But this is where my case for better integration of AI and LA comes
- #66: Most often than not the case is as in this cartoon that helps us remember that
- #68: 67
- #69: 68
- #72: And ok we may not be able to give them cloining but we can simualate eyes in back or whether students are
- #73: So I think so far we are only scratching the surface and some of the work that is emerging is quite powerful
- #86: And we designed different types of feedback that could be applied ranging from talk aloud to remind them to talk to targeting their affect to reflective prompts