Learning with Interactive Knowledge Representations
Source abstract
Abstract
Computers are promising tools for providing educational experiences that meet individual learning needs. However, delivering this promise in practice is challenging, particularly when automated feedback is essential and the learning extends beyond using traditional methods such as writing and solving mathematics problems. We hypothesize that interactive knowledge representations can be deployed to address this challenge. Knowledge representations differ markedly from concept maps. Where the latter uses nodes (concepts) and arcs (links between concepts), a knowledge representation is based on an ontology that facilitates automated reasoning. By adjusting this reasoning towards interacting with learners for the benefit of learning, a new class of educational instruments emerges. In this contribution, we present three projects that use an interactive knowledge representation as their foundation. DynaLearn supports learners in acquiring system thinking skills. Minds-On helps learners to deepen their understanding of phenomena while performing experiments. Interactive Concept Cartoons engage learners in a science-based discussion about controversial topics. Each of these approaches has been developed iteratively in collaboration with teachers and tested in real classrooms, resulting in a suite of lessons available online. Evaluation studies involving pre-/post-tests and action-log data show that learners are easily capable of working with these educational instruments and that the instruments thus enable a semi-automated approach to constructive learning.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Innovative Teaching and Learning Methods · Advanced Text Analysis Techniques · Educational Tools and Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Education, Amsterdam University of Applied Sciences, 1091 GM Amsterdam, The Netherlands
Informatics Institute, Faculty of Science, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
Funding
Regieorgaan SIA
References (56)
Show source-provided references
1. Harmelen, F.V., Lifschitz, V., and Porter, B. (2008). Handbook of Knowledge Representation, Elsevier.
2. What is a knowledge representation?
3. Computer science as empirical inquiry: Symbols and search
4. Representation of knowledge in digital educational environments: A systematic review of literature
5. Metarepresentation: Native competence and targets for instruction10.1207/s1532690xci2203_2 ↗
6. Gilbert, J.K., Reiner, M., and Nakhleh, M. (2008). Visualization: Theory and Practice in Science Education, Springer.10.1007/978-1-4020-5267-5 ↗
7. Understanding of relation structures of graphical models by lower secondary students10.1007/s11165-015-9474-x ↗
8. Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles10.1002/sce.20164 ↗
9. Representation construction, externalised cognition and individual differences10.1016/S0959-4752(98)00051-6 ↗
10. Learning through Constructing Representations in Science: A framework of representational construction affordances10.1080/09500693.2011.626462 ↗
11. Why a diagram is (sometimes) worth ten thousand words10.1111/j.1551-6708.1987.tb00863.x ↗
12. Dual coding theory and education10.1007/BF01320076 ↗
13. The development of a collaborative problem solving environment that integrates a scaffolding mind tool and simulation-based learning: An analysis of learners’ performance and their cognitive process in discussion10.1080/10494820.2020.1719163 ↗
14. What recent research on diagrams suggests about learning with rather than learning from visual representations in science10.1080/09500693.2016.1158435 ↗
15. DeFT: A conceptual framework for considering learning with multiple representations10.1016/j.learninstruc.2006.03.001 ↗
16. Twenty Years on: Reflections on Supporting the Use of External Representations in Problem Solving10.1007/s40593-015-0054-z ↗
17. Mindtools: Affording multiple knowledge representations for learning10.1201/9781315045337-8 ↗
18. The promise of multimedia learning: Using the same instructional design methods across different media10.1016/S0959-4752(02)00016-6 ↗
19. Sharma, L., and Garg, P.K. (2021). Artificial Intelligence—Technologies, Applications, and Challenges, Chapman and Hall.10.1201/9781003140351 ↗
20. Forbus, K.D. (2018). How People Reason and Learn about the Continuous World, MIT Press.
21. Qualitative Modeling in Education
22. Why don’t well-educated adults understand accumulation? A challenge to researchers, educators, and citizens10.1016/j.obhdp.2008.03.003 ↗
23. Understanding and control of a simple dynamic system10.1002/sdr.267 ↗
24. Transfer of Learning and Teaching: A Review of Transfer Theories and Effective Instructional Practices10.22492/ije.7.1.06 ↗
25. Okada, A.L.P., Buckingham Shum, S.J., and Sherborne, T. (2014). Knowledge Cartography: Software Tools and Mapping Techniques, Springer.10.1007/978-1-4471-6470-8 ↗
26. Graphical means for inspecting qualitative models of system behaviour10.1007/s11251-008-9083-4 ↗
27. Garp3—Workbench for qualitative modelling and simulation10.1016/j.ecoinf.2009.09.009 ↗
28. Learning spaces as representational scaffolds for learning conceptual knowledge of system behaviour
29. Novak, J.D., and Gowin, D.B. (1984). Learning how to Learn, Cambridge University Press.10.1017/CBO9781139173469 ↗
30. Bredeweg, B., Kragten, M., and Spitz, L. (2021, January 19). Qualitative Representations for Systems Thinking in Secondary Education. Proceedings of the 34th International Workshop on Qualitative Reasoning, Montreal, Canada.
31. Kragten, M., Loek Spitz, L., and Bredeweg, B. (2021, January 19). Learning Domain Knowledge and Systems Thinking using Qualitative Representations in Secondary Education (grade 9–10). Proceedings of the 34th International workshop on Qualitative Reasoning, Montreal, QC, Canada.
32. Exploring the working and effectiveness of norm-model feedback in conceptual modelling—A pre-liminary report
33. The Difficult Process of Scientific Modelling: An analysis of novices’ reasoning during computer-based modelling10.1080/09500690500206408 ↗
34. Requirements and challenges for hybrid intelligence: A case-study in education10.3389/frai.2022.891630 ↗
35. Designing for complementarity: Teacher and student needs for orchestration support in AI-enhanced classrooms
36. Meelissen, M., and Punter, A. (2016). Ontwikkelingen in Leerling Prestaties in de Exacte Vakken in het Basisonderwijs 1995–2015, Universiteit Twente.
37. Primary teachers’ attitudes toward science: A new theoretical framework10.1002/sce.20467 ↗
38. Struggling or succeeding in science and technology education: Elementary school students’ individual differences during inquiry and design-based learning10.3389/feduc.2022.842537 ↗
39. Teaching scientific practices: Meeting the challenge of change10.1007/s10972-014-9384-1 ↗
40. Elementary science teaching
41. Spaan, W., Oostdam, R., Schuitema, J., and Pijls, M. (2022). Analysing teacher behaviour in synthesizing hands-on and minds-on during practical work. Res. Sci. Technol. Educ., 1–18.10.1080/02635143.2022.2098265 ↗
42. Forsthuber, B., Motiejunaite, A., and de Almeida Coutinho, A.S. (2011). Education, Audiovisual and Culture Executive Agency: European Commission, EU Bookshop.
43. Bredeweg, B. (2023, February 19). Kunstmatige Intelligentie in Het Onderwijs: Leren Met Interactieve Kennisrepresentaties. Hogeschool van Amsterdam: Amsterdam. Available online: https://research.hva.nl/en/publications/kunstmatige-intelligentie-in-het-onderwijs-leren-met-interactieve.
44. Developing higher-order thinking skills with concept mapping: A case of pedagogic frailty
45. Louman, E., and van Eijck, T. (2023, February 19). Leren redeneren. Didactief. Available online: https://didactiefonline.nl/artikel/leren-redeneren.
46. National Research Council (2012). Committee Conceptual Framework New K-12 Science Education Standards, The National Academies Press.
47. Will public trust in science survive the pandemic?
48. Research on teaching and learning of nature of science
49. Two instructional designs for dialogic citizenship education: An effect study10.1348/978185408X393852 ↗
50. Guiding discussions in the class about ethnic diversity10.1080/14675986.2010.521378 ↗
51. Teaching with and about nature of science, and science teacher knowledge domains10.1007/s11191-012-9520-2 ↗
52. Argumentation and primary science10.1007/s11165-005-9002-5 ↗
53. Interactive concept cartoons: Exploring an instrument for developing scientific literacy
54. Promoting science learning in game-based learning with question prompts and feedback10.1016/j.compedu.2016.10.005 ↗
55. Why do humans reason? Arguments for an argumentative theory10.1017/S0140525X10000968 ↗
56. From design to implementation to practice a learning by teaching system: Betty’s Brain10.1007/s40593-015-0057-9 ↗