Background and motivation

  • Researches on conceptual change [1] has shown in a variety of scientific fields that learners come up with alternative frameworks and identifiable preconceptions that resist to instructional remediation.
  • Academic students score low marks in conceptual diagnostic tests of electromagnetism. Even studens who obtain the best marks at final exams show erroneus understanding of basic concepts [2].
  • Students confuse mechanisms involved at a microscopic level with their sensitive experience at macroscopic level, routinely use definitions and mathematics formulas without conceptual understanding[3].

Challenge: Make meaningful connections between abstraction and sensitive experiences, build up mental models as close as possible to institutionalised knowledge in order to interpret natural phenomena.

Device

Design Principles


  • Features
  • Color traking
  • touch interface
  • 30 to 60 frames per second display
  • 5 mm accuracy

  • Hardware
  • Orbecc Astra depth camera
  • 720p short throw projector
  • Mini PC - intel NUC
  • 3D printed supports

  • Software
  • Free and open source development
  • Processing language
  • PapARt [4] open library for calibration

Design Principles

  • Authentic and natural interaction

Manipulate authentic materials (magnets, compas needles, circuits, matters). We can play with true macroscopic phenomena

  • Multiple representations

Deape Learn allows us to switch from one type of representation to another (vector field, intensity map, field lines, formula) to facilitate knowledge building.

  • Transparent interface

Minimal interface and natural interactions tend to extrinsic reduce cognitive load. Minimal interface allow students to forget the technical device and focus on learning contents.

  • Physics simulation

We interact with real phenomena, Deape Learn doesn't replace it but explain it through rich and interactive multiple visualisations

Research paradigms

Augmented reality [4]

Enactive interface allowing spatial, temporal and semantic superimposition of multiple representations of a same physic phenomena.

/!\ Not only a educational guidance principle but a more complexe interaction between internal (mental) and external (semiotic) representations of the world.

KNOWLEDGE INTEGRATION FRAMEWORK [5]

Educational activities based on learners'conceptual difficulties.

/!\ Deep Learn helps students in distinguishing between abstract ideas or in linking scientific models to authentic contexts

Coherence Formation when Learning with MRs [6]

Cognitive process consisting in creating links between relevant entities and structures in different representations

/!\ Not automatic, students need to be supported and encouraged in this process [7], [8].

References

  1. Vosniadou, S. (2013). International handbook of research on conceptual change (2nd ed.). New York: Routledge/Taylor & Francis Group.
  2. Törnkvist, S. (1993). Confusion by representation: On student’s comprehension of the electric field concept. American Journal of Physics, 61(4), 335.
  3. Finkelstein, N. (2005). Learning Physics in Context: A study of student learning about electricity and magnetism. Int J Sci Educ, 27(10), 1187–1209.
  4. Laviole, J., & Hachet, M. (2012). Spatial augmented reality for physical drawing. In Adjunct proceedings of the 25th annual ACM symposium on User interface software and technology - UIST Adjunct Proceedings ’12 (p. 9). New York, New York, USA: ACM Press.
  5. Bottecchia, S. (2010). Système TAC : Télé-Assistance Collaborative. Réalité augmentée et NTIC au service des opérateurs et des experts dans le cadre d’une tâche de maintenance industrielle supervisée.
  6. Linn, M. C., & Clark, D. B. (2013). The knowledge integration perspective - connections across research and education. In International handbook of research on conceptual change (2nd ed., pp. 520–538). New York: Routledge/Taylor & Francis Group.
  7. Ainsworth, S. (2008). The Educational Value of Multiple-representations when Learning Complex Scientific Concepts. In J. K. Gilbert, M. Reiner, & M. Nakhleh (Eds.), Visualization: Theory and Practice in Science Education (pp. 191–208). Dordrecht: Springer Netherlands.
  8. Suwa, M., & Tversky, B. (2002). External Repre-sentations Contribute to the Dynamic Construction of Ideas. In International Conference on Theory and Application (Vol. 2317, pp. 149–160). Springer Berlin
  9. Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and Instruction, 13(2), 227–237.