A learning scientist approach to modeling human cognition in individual and collaborative problem solving tasks
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The document discusses the modeling of human cognition in problem-solving tasks, focusing on individual and collaborative approaches within the context of educational robotics. It includes references to various studies and projects related to learning sciences, creativity assessment, and technologies for problem-solving. The content emphasizes the importance of prior knowledge, activity analysis, and research programs in understanding how learning occurs and how problems are solved.
A learning scientist approach to modeling human cognition in individual and collaborative problem solving tasks
- 1. A learning scientist approach to modeling human cognition in individual and collaborative problem solving tasks @margaridaromero Margarida.Romero@unice.fr Dir. Laboratoire d’Innovation et Numérique pour l’Education (LINE) 12 février 2021. Mini-cours
- 2. What is your current mood ? 1 2 4 3 5 6 7 8 other moods...
- 3. Some references in relation to this workshop Romero, M., Alexandre, F., Roux, L., Giraudon, G., & Viéville, T. (2020). From computational neuroscience to computational learning science : Modeling the brain of the learner and the context of the learning activity. SophIA-summit 2020. https://hal.inria.fr/hal-03018617 Romero, M., Viéville, T. & Heiser, L. (accepted). Analyse d'activités d'apprentissage médiatisées en robotique pédagogique. Dans Alberto, B., Thievenaz, J. (in press). Traité de méthodologie de la recherche en Sciences de l’Éducation et de la Formation. https://www.researchgate.net/publication/344151929_Analyse_d'activites_d'apprentissage_mediatisees_en_robotique_pedagogique Leroy, A., Romero, M., & Cassone, L. (2021). Interactivity and materiality matter in creativity: educational robotics for the assessment of divergent thinking. Interactive Learning Environments, 1-12. Kalmpourtzis, G., & Romero, M. (2020, December). Artifactual Affordances in Playful Robotics. In International Conference on Games and Learning Alliance (pp. 316-325). Springer, Cham. Roux, L., Romero, M., Alexandre, F., Viéville, T., & Mercier, C. (2020). Développement d’une ontologie pour l’analyse d’observables de l’apprenant dans le contexte d’une tâche avec des robots modulaire. Inria. 2020, 48. https://hal.archives-ouvertes.fr/LINE/hal-03013685v1 Romero, M., & Chevré, A. M. (2020). ANR-18-CE38-0001-OpenAccess-20201201-Rapport Intermédiaire. https://www.researchgate.net/publication/346629855_ANR-18-CE38-0001-OpenAccess-20201201-Rapport_Intermediaire Cassone, L., Romero, M., & Basiri, S. (2020). Group processes and creative components in a problem-solving task with modular robotics, Journal of Computing in Education. https://doi.org/10.1007/s40692-020-00172-7 Romero, M. (2019, July). Analyzing Cognitive Flexibility in Older Adults Through Playing with Robotic Cubes. In International Conference on Human-Computer Interaction (pp. 545-553). Springer, Cham. Romero, M., DeBlois, L., & Pavel, A. (2018). Créacube, comparaison de la résolution créative de problèmes, chez des enfants et des adultes, par le biais d’une tâche de robotique modulaire. MathémaTICE (61). Romero, M., David, D., & Lille, B. (2018, December). CreaCube, a Playful Activity with Modular Robotics. In International Conference on Games and Learning Alliance (pp. 397-405). Springer, Cham. Romero, M., & Loos, E. F. (2018). Playing with robotic cubes: age matters. Intergenerationality in a digital world: Proposals of activities. Publisher: Edições Universitárias Lusófonas. Romero, M. (2017). CreaCube, analyse de la résolution créative de problèmes par le biais d’une tâche de robotique modulaire. Journées Nationales de la Recherche en Robotique, JNRR 2017. https://jnrr2017.sciencesconf.org/167023/document ANR CreaMaker website https://creamaker.wordpress.com/
- 4. Plan 1. Prior knowledge and expectancies 2. How learning happens ? How we solve problems ? 3. Analysis of activities 4. Play ! 5. Analysis of the activity 6. Research program
- 5. European PhD (cotutelle) UAB-UT2 (Extraordinary Thesis Award at UAB) Multimedia Project Management Graduate studies Master in Computer Sciences (Knowledge engineering) Master in Psychology of Education Master studies 2009-2013 Associate professor on psychology of education 2013-2017 Associate professor on educational technology (tenured in 2015) Since 2017 Full professor on learning sciences Academic career Laboratoire d’Innovation et Numérique pour l’Education director
- 6. “Epistemological pluralism recognizes that, in any given research context, there may be several valuable ways of knowing, and that accommodating this plurality can lead to more successful integrated study” (Miller et al. 2008) “methodological tribalism” vs “pluralistic coexistence” as a way to foster dialogue and '”innovative methodological cross-fertilization'” in the spirit of “openness” and “constructive criticism'” (Lamont & Swidler 2014) Lamont, M., & Swidler, A. (2014). Methodological pluralism and the possibilities and limits of interviewing. Qualitative Sociology, 37(2), 153-171. Miller, T. R., Baird, T. D., Littlefield, C. M., Kofinas, G., Chapin III, F. S., & Redman, C. L. (2008). Epistemological pluralism: reorganizing interdisciplinary research. Ecology and Society, 13(2).
- 7. Romero, M., & Belhassein, D. (2019). Interdisciplinarité et usages co-créatifs du numérique en éducation. Dans Darbellay, F. (Ed.). L'interdisciplinarité à l'école: succès, résistance, diversité. Alphil.
- 8. Plan 1. Prior knowledge and expectancies 2. How learning happens ? How we solve problems ? 3. Analysis of activities 4. Play ! 5. Analysis of the activity 6. Research program
- 9. 1. Prior knowledge and expectations https://s.42l.fr/NeuroModT1
- 10. Plan 1. Prior knowledge and expectancies 2. How learning happens ? How we solve problems ? 3. Analysis of activities 4. Play ! 5. Analysis of the activity 6. Research program
- 11. How humans learn ?
- 12. Learning sciences How we learn ?
- 13. Learning sciences How we learn ? Learning process as contextual, multilevel (intrapsychological, interpsychological) in interaction with agents and artefacts
- 14. What we learn ?
- 16. Tricot (2015)
- 17. In primary education, what are the most important aspects to learn in mathematics ? What we can do for improving mathematics education ? Performances en mathématiques, classe de CM1. Etude TIMSS 2019. (TIMSS / DEPP)
- 19. Boaler, J. (2015). Mathematical mindsets: Unleashing students' potential through creative math, inspiring messages and innovative teaching. John Wiley & Sons.
- 21. Efklides (2011)
- 23. How we solve problems ?
- 25. Team activity : creation of a problem solving model https://docs.google.com/presentation/d/1RVaYtYNxkHjubOwEchqyadLNAaDF6zT9C As9iBwOXwA/edit?usp=sharing
- 27. Problem solver (Newell & Simon 1972, p. 289)
- 29. Plan 1. Prior knowledge and expectancies 2. How learning happens ? How we solve problems ? 3. Analysis of activities 4. Play ! 5. Analysis of the activity 6. Research program
- 30. Learning activity as a unit of analysis For Conole and Fill (2005), the notion of a learning activity (LA) is composed of three elements: ● The context of the activity: e.g. subject, level of difficulty, intended learning outcomes and the environment within which the activity takes place. ● The learning and teaching approaches: including theories and models. ● The learning tasks: This includes type of task, techniques used, associated tools and resources, interaction and roles of those involved and learner assessment.
- 32. Diversity of learning activities
- 33. Plan 1. Prior knowledge and expectancies 2. How learning happens ? How we solve problems ? 3. Analysis of activities 4. Play ! 5. Analysis of the activity 6. Research program
- 34. Plan 1. Prior knowledge and expectancies 2. How learning happens ? How we solve problems ? 3. Analysis of activities 4. Play ! 5. Analysis of the activity 6. Research program
- 35. Creativity assessment through the CreaCube task
- 36. Problem “Build a vehicle moving from a red point to a black point” CreACubE @ aIde How we solve problems with technology ? Material to solve the problem Solution ?
- 37. Problem “Build a vehicle moving from a red point to a black point” CreACubE @ aIde How we solve problems with technology ? Material to solve the problem Solution ? Norman (1986) designates as the gulf of execution, the distance between the user's goals and the means of achieving them through the system. What we can do ? Explore the material Be creative (association). Use the material in an alternative way (AUT, Guilfort, 1967) Learn about the material features and exploit it Evaluate solutions before recombining in order to inhibit unsuccessful ideas
- 38. CreACubE @ aIde How we solve problems with technology ? Information inputs Instructions State of the system (unitary and system level) Mental model of the situation Mental model of the solution Behaviors (grasp, turn, explore…) Goals : performance play/explore
- 39. On divergent thinking, subjects generate new ideas (configurations of the cubes)
- 41. Having new ideas is not enough; creativity as a means of finding different useful solutions (divergent + convergent + critical thinking) for a given problem-situation. F07 F06 F08 F01 F02 F11
- 42. Plan 1. Prior knowledge and expectancies 2. How learning happens ? How we solve problems ? 3. Analysis of activities 4. Play ! 5. Analysis of the activity : modeling the activity 6. Research program
- 43. Learning task Analysis of the activity Task model Ontology and data model Learner model Learning activity Traces of interaction (learning analytics) generated in the technological environments Data from coding schemas (video, observations, etc.) Memory Goals Technological environment Stimulii Affordances Actions
- 44. CreACubE @ aIde How we solve problems with technology ? Which observables (and grammar) for analysing exploratory and exploitation analysis ?
- 45. Objects-to-think-with Creative exploration as a means of analyzing the problem situation and the objects to be engaged in the solution.
- 47. Creative problem solving with interactive robotic cubes CreACubE @ aIde How we solve problems with technology ? Exploration Explotation At the unitary level (cubes) At the system level (figure)
- 48. Creative problem solving with interactive robotic cubes CreACubE @ aIde How we solve problems with technology ?
- 49. Creative problem solving with interactive robotic cubes CreACubE @ aIde How we solve problems with technology ?
- 50. Creative problem solving with interactive robotic cubes CreACubE @ aIde How we solve problems with technology ?
- 51. CreACubE @ aIde How we solve problems with technology ? ● Exploration (at the unitary level, at the figure level) and exploitation (testing specific features at the unitary level, testing certain figures) should be combined. ● Which combination of exploratory and exploitation behavior happens in the problem solving task ? ● Which states can be defined within the task ? ● Which observables (and grammar) for analysing exploratory and exploitation analysis ?
- 52. Aide : Artificial Intelligence Devoted to Education (AIDE)
- 53. Older adults EHPAD Action exploratoire Artificial Intelligence Devoted to Education (AIDE) Lifelong learning ⇒ ANR #CreaMaker 7 to 107 years Critical thinking Transformative agency Computational thinking Learning sciences research lab INRIA project-team in computational neurosciences Adults Children
- 54. A learning scientist approach to modeling human cognition in individual and collaborative problem solving tasks @margaridaromero Margarida.Romero@unice.fr Dir. Laboratoire d’Innovation et Numérique pour l’Education (LINE) 12 février 2021. Mini-cours
- 55. Annexes
- 56. Assessment of creativity ‘Ideation’ VS ‘creation’
- 58. ● The creativity results of AUT and CréaCube do not display the same creativity results
- 61. AIDE tasks Learning in specific tasks CreACubE @ aIde How we solve problems with technology ? Problem solving tasks engaging technological knowledge (computational thinking)
- 62. CreaCube Component 1: Organize & model the situation Component 2: Identify problems Component 5: Devise a solution Component 6: Adopt an iterative process Component 3: Hone formal systems (e.g. coding, maths, logic) Component 4: Integrate physical systems COMPO1 Understanding the problem-situation Concept of autonomous vehicle COMPO2 Imagining the use of the cubes for meeting the task objectives COMPO3 Importance of the order of a sequence (system behaviour defined by the order of the cubes) COMPO4 Magnets Sensors Actuatuors Electric circuit Cubes assembled as a system COMPO5 Creating a solution by assembling by inverting the distance sensor signal COMPO6 Solution anlysis for improvement through a new figure
- 63. Model of the task (ontologie) Modèles neurosciences computationnelles (Mnémosyne) CreACubE @ aIde How we solve problems with technology ?