dr. B. Bredeweg (Bert)

Lector Didactiek van de Bètavakken

Profiel

Bert Bredeweg is sinds 1 oktober 2018 lector Didactiek van de Bètavakken aan de Hogeschool van Amsterdam. Bert Bredeweg is sinds 1986 werkzaam bij de Universiteit van Amsterdam waar hij in 1992 promoveerde op het proefschrift Expertise in Qualitative Prediction of Behaviour (promotor: prof. dr. B.J. Wielinga). Van 1992 tot 2003 was hij als universitair docent verbonden aan de toenmalige Faculteit der Psychologie. Sinds 2003 is Bert Bredeweg als hoofddocent werkzaam bij het Instituut voor Informatica, waar hij ook na zijn overstap naar de HvA een dag per week aan verbonden blijft.

Als hoofddocent leidde hij van 2003 tot 2012 de Qualitative Reasoning Group. Deze onderzoeksgroep genoot internationale bekendheid door haar werk op het terrein van Kwalitatief Redeneren, een techniek ontwikkeld binnen de Kunstmatige Intelligentie, en hoe deze in te zetten rond complexe vraagstukken zoals duurzaamheid (Sustainable Development). Bert Bredeweg heeft ruime ervaring met toegepast onderzoek en valorisatie, ook in het voortgezet en hoger onderwijs. Hij werkt daarbij aan onderwerpen zoals leren door Conceptueel Modelleren, in het bijzonder oorzaak-gevolg denken, Gamification en Learning Analytics.

Publicaties

IguideME: Supporting Self-Regulated Learning and Academic Achievement with Personalized Peer-Comparison Feedback in Higher Education
Fleur, D. S., Marshall, M., Pieters, M., Brouwer, N., Oomens, G., Konstantinidis, A., Winnips, K., Moes, S., van den Bos, W., Bredeweg, B., & van Vliet, E. A. (2023). IguideME: Supporting Self-Regulated Learning and Academic Achievement with Personalized Peer-Comparison Feedback in Higher Education. Journal of Learning Analytics, 10(2), 100-114. https://doi.org/10.18608/jla.2023.7853
Implicaties van grote taal modellen voor het onderwijs
Bredeweg, B. (Performer). (2023). Implicaties van grote taal modellen voor het onderwijs. Digital or Visual Products https://youtu.be/YXvkfyswEfE
Learning About Circular Motion of Celestial Bodies with Interactive Qualitative Representations
Kragten, M., & Bredeweg, B. (2023). Learning About Circular Motion of Celestial Bodies with Interactive Qualitative Representations. In N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda, & O. C. Santos (Eds.), Artificial Intelligence in Education: Communications in Computer and Information Science (Vol. 1831, pp. 314-320). https://doi.org/10.1007/978-3-031-36336-8
STEM-netwerk: leren van een landelijk lerend netwerk
Van der Zee, S., Bredeweg, B., Embrechts, A., Hotze, A., Jonker, V., Keijzer, R., Kruiskamp, M., Nieveen, N., Muller-van der Molen, A., Pennink, D. H. M., Pijls, M. H. J., & Poortman, C. (2023). STEM-netwerk: leren van een landelijk lerend netwerk. Tijdschrift voor Lerarenopleiders, 44(2), 21.
Stargazing Live! Inspiring with Real Data in a Mobile Planetarium and LearningThrough Conceptual Modelling
Bredeweg, B., Kragten, M., Holt, J., Vaendel, D., Hanse, J., & Bloemen, S. (2023). Stargazing Live! Inspiring with Real Data in a Mobile Planetarium and LearningThrough Conceptual Modelling. In C. Frasson, P. Mylonas, & C. Troussas (Eds.), Augmented Intelligence and Intelligent Tutoring Systems (Vol. 13891, pp. 257-269). (Lecture Notes in Computer Science). Springer, Cham. https://doi.org/10.1007/978-3-031-32883-1_22
The Dutch Black Hole Consortium
Bouisaghouane, I., Holt, J., Bredeweg, B., & Russo, P. (2023). The Dutch Black Hole Consortium: How can modern science support concept learning in primary education to motivate and engage learners from all backgrounds?. Poster session presented at Astronomy Education 2023, Toronto, Canada.
Learning with Interactive Knowledge Representations
Bredeweg, B., Kragten, M., Holt, J., Kruit, P. M., van Eijck, T. J. W., Pijls, M. H. J., Bouwer, A. J., Sprinkhuizen, M. J., Jaspar, E. J. O. A., & de Boer, M. E. (2023). Learning with Interactive Knowledge Representations. Applied Sciences, 13(9), [5256]. https://doi.org/10.3390/app13095256
Social comparison in learning analytics dashboard supporting motivation and academic achievement
Fleur, D. S., van den Bos, W., & Bredeweg, B. (2023). Social comparison in learning analytics dashboard supporting motivation and academic achievement. Computers and Education Open, 4, 100130. https://doi.org/10.1016/j.caeo.2023.100130
Stargazing Live! Inspiring with semi-live astronomy data; teaching curriculum topics using smart education tools.
Holt, J., Hanse, J., Vaendel, D., Kragten, M., Bredeweg, B., Bloemen, S., Baan, M., & Groot, P. (2022). Stargazing Live! Inspiring with semi-live astronomy data; teaching curriculum topics using smart education tools. In Proceedings for the 4th Shaw-IAU Workshop on Astronomy for Education: Leveraging the potential of astronomy in formal education (pp. 190-192) https://owncloud.gwdg.de/index.php/s/YRLUQ0nkUWomHmN
Pilot Maakonderwijs STAIJ – Mokum Maakcoalitie
Pijls, M. H. J., & Bredeweg, B. (2022). Pilot Maakonderwijs STAIJ – Mokum Maakcoalitie: Resultaten van het onderzoek.
Representation-Driven Mixed Initiative in Computer Supported Collaborative Learning in Secondary Education
Kragten, M., Pijls, M. H. J., Jaspar, E. J. O. A., Sprinkhuizen, M. J., & Bredeweg, B. (2022). Representation-Driven Mixed Initiative in Computer Supported Collaborative Learning in Secondary Education. In Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption: 17th European Conference on Technology Enhanced Learning (Vol. LNCS 13450, pp. 159). (Proceedings 17th European Conference on Technology Enhanced Learning). Springer, Cham. https://doi.org/10.1007/978-3-031-16290-9_12
Requirements and challenges for hybrid intelligence: A case-study in education
Bredeweg, B., & Kragten, M. (2022). Requirements and challenges for hybrid intelligence: A case-study in education. Frontiers in Artificial Intelligence, 5(891630). https://doi.org/10.3389/frai.2022.891630
Learning Domain Knowledge and Systems Thinking using Qualitative Representations in Secondary Education (grade 10-12)
Kragten, M., Jaspar, E. J. O. A., & Bredeweg, B. (2022). Learning Domain Knowledge and Systems Thinking using Qualitative Representations in Secondary Education (grade 10-12). 13-22. Paper presented at International Conference on Artificial Intelligence (IJCAI’22) -
35th International Workshop on Qualitative Reasoning, Vienna, Austria.
Designing for Self-awareness: Supporting Students’ Reflexive Interactions Based on Photos
ten Brink, M., Witschge, T., Bredeweg, B., & Schouten, B. (2022). Designing for Self-awareness: Supporting Students’ Reflexive Interactions Based on Photos. In Proceedings of the 14th Creativity and Cognition 2022 (pp. 161-170). (ACM Creativity and Cognition Conference Series). Association for Computing Machinery. https://doi.org/10.1145/3527927.3532807
Hoe te komen tot bewijsgestuurd onderwijsontwerp?
Doornenbal, J. W. F., Dekker, I., & Bredeweg, B. (2022). Hoe te komen tot bewijsgestuurd onderwijsontwerp?. Paper presented at D2L Connection Event Benelux, Nijmegen, Netherlands.
Food modelling strategies and approaches for knowledge transfer
Kansou, K., Laurier, W., Charalambides, M. N., Della-Valle, G., Djekic, I., Feyissa, A. H., Marra, F., Thomopoulos, R., & Bredeweg, B. (2022). Food modelling strategies and approaches for knowledge transfer. Trends in Food Science & Technology, 120, 363-373. https://doi.org/10.1016/j.tifs.2022.01.021
Smart Education
Bouwer, A., Dekker, I., & Bredeweg, B. (2022). Smart Education: Derde projectjaar. Hogeschool van Amsterdam, Faculteit Onderwijs en Opvoeding. https://www.hva.nl/binaries/content/assets/subsites/kc-oo/smart-education/smart-education-hva_2021.pdf
Activities and Experiences of Children and Makerspace Coaches During After-School and School Programs in a Public Library Makerspace
Pijls, M. H. J., van Eijck, T. J. W., Kragten, M., & Bredeweg, B. (2022). Activities and Experiences of Children and Makerspace Coaches During After-School and School Programs in a Public Library Makerspace. Journal for STEM education research, 5(2), 163-186. https://doi.org/10.1007/s41979-022-00070-w
Metacognition
Fleur, D. S., Bredeweg, B., & van den Bos, W. (2021). Metacognition: ideas and insights from neuro- and educational sciences. npj Science of Learning, 6(1), [13]. https://doi.org/10.1038/s41539-021-00089-5
Students’ attitude towards technology enhanced photovoice to support critical reflection
ten Brink, M., Bredeweg, B., & Schouten, B. (2021). Students’ attitude towards technology enhanced photovoice to support critical reflection. In F. De la Prieta, R. Gennari, M. Temperini, T. Di Mascio, P. Vittorini, Z. Kubincova, E. Popescu, D. Rua Carneiro, L. Lancia, & A. Addone (Eds.), Methodologies and intelligent systems for technology enhanced Learning, 11th International Conference (pp. 32-41). (Lecture Notes in Networks and Systems; Vol. 326). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-86618-1_4
Learning Domain Knowledge and Systems Thinking using Qualitative Representations in Secondary Education (grade 8-9)
Spitz, L., Kragten, M., & Bredeweg, B. (2021). Learning Domain Knowledge and Systems Thinking using Qualitative Representations in Secondary Education (grade 8-9). Paper presented at 34th International Workshop on Qualitative Reasoning, Montreal, Canada. https://www.qrg.northwestern.edu/qr2021/program.html
Learning Domain Knowledge and Systems Thinking using Qualitative Representations in Secondary Education (grade 9-10)
Kragten, M., Spitz, L., & Bredeweg, B. (2021). Learning Domain Knowledge and Systems Thinking using Qualitative Representations in Secondary Education (grade 9-10). Paper presented at 34th International Workshop on Qualitative Reasoning, Montreal, Canada. https://www.qrg.northwestern.edu/qr2021/program.html
Qualitative Representations for Systems Thinking in Secondary Education
Bredeweg, B., Kragten, M., & Spitz, L. (2021). Qualitative Representations for Systems Thinking in Secondary Education. Paper presented at 34th International Workshop on Qualitative Reasoning, Montreal, Canada. https://www.qrg.northwestern.edu/qr2021/program.html
Exploring the working and effectiveness of norm-model feedback in conceptual modelling
Spitz, L., Kragten, M., & Bredeweg, B. (2021). Exploring the working and effectiveness of norm-model feedback in conceptual modelling: a preliminary report. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021 : proceedings (II ed., Vol. LNCS 12749, pp. 325-330). (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-78270-2_58
Learning about systems by developing interactive diagrams
Bredeweg, B., Kragten, M., & Spitz, L. (2021). Learning about systems by developing interactive diagrams. In L. Gómez Chova, A. López Martínez, & I. Candel Torres (Eds.), 13th International Conference on Education and New Learning Technologies, 5th-6th July, 2021: online proceedings (pp. 6119-6123). (13th International Conference on Education and New Learning Technologies). International Association of Technology, Education and Development. https://doi.org/10.21125/edulearn.2021.1232
Makers! Leren in de Amsterdamse maakplaatsen
Pijls, M., van Eijck, T., Kragten, M., & Bredeweg, B. (2021). Makers! Leren in de Amsterdamse maakplaatsen. Paper presented at VELON2021, Netherlands.
Smart Education (website)
Bredeweg, B. (Photographer), & Bouwer, A. J. (Photographer). (2021). Smart Education (website). Web publication or website, Hogeschool van Amsterdam, Faculteit Onderwijs en Opvoeding. https://www.hva.nl/smarteducation
Smart Education
Bouwer, A., & Bredeweg, B. (2021). Smart Education: Tweede projectjaar. Hogeschool van Amsterdam, Faculteit Onderwijs en Opvoeding. https://www.hva.nl/binaries/content/assets/subsites/kc-oo/smart-education/smart-education_hva_projectjaar_2020.pdf
Self-construction and interactive simulations to support the learning of drawing graphs and reasoning in mathematics
Palha, S., Bouwer, A., Bredeweg, B., & Keulen, S. (2020). Self-construction and interactive simulations to support the learning of drawing graphs and reasoning in mathematics. In V. Kumar, & C. Troussas (Eds.), Intelligent Tutoring Systems - 16th International Conference, ITS 2020, Proceedings (pp. 364-370). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12149 LNCS). Springer. https://doi.org/10.1007/978-3-030-49663-0_43
Interactieve Concept Cartoons
Bredeweg, B. (Developer), & Kruit, P. (Developer). (2020). Interactieve Concept Cartoons. Web publication or website, Hogeschool van Amsterdam. https://conceptcartoons.nl
Smart Education
Bredeweg, B. (Author), & Bouwer, A. (Author). (2020). Smart Education. Web publication or website, Hogeschool van Amsterdam, Faculteit Onderwijs en Opvoeding. https://www.hva.nl//smarteducation
Ein konzeptuelles Maintenance-Modell – reaktiv oder proaktiv?
Schabasser, C., & Bredeweg, B. (2020). Ein konzeptuelles Maintenance-Modell – reaktiv oder proaktiv? ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, 115(1-2), 61-64. https://doi.org/10.3139/104.112230
Smart Education
Bouwer, A., & Bredeweg, B. (2020). Smart Education. Hogeschool van Amsterdam, Faculteit Onderwijs en Opvoeding. https://www.hva.nl/kc-onderwijs-opvoeding/gedeelde-content/contentgroep/smart-education/home
Exploring the Design and Impact of Online Exercises for Teacher Training About Dynamic Models in Mathematics
ter Horst, C., Kubbe, L., van de Rotten, B., Peters, K., Bouwer, A., & Bredeweg, B. (2020). Exploring the Design and Impact of Online Exercises for Teacher Training About Dynamic Models in Mathematics. In C. Alario-Hoyos, M. J. Rodríguez-Triana, M. Scheffel, I. Arnedillo-Sánchez, & S. M. Dennerlein (Eds.), Addressing Global Challenges and Quality Education: 15th European Conference on Technology Enhanced Learning, EC-TEL 2020 Heidelberg, Germany, September 14–18, 2020 Proceedings (pp. 398-403). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12315 LNCS). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-57717-9_34
Interactive concept cartoons
Kruit, P., & Bredeweg, B. (2020). Interactive concept cartoons: exploring an instrument for developing scientific literacy. In C. Alario-Hoyos, M. J. Rodríguez-Triana, M. Scheffel, I. Arnedillo-Sánchez, & S. M. Dennerlein (Eds.), Addressing global challenges and quality education: 15th European Conference on Technology Enhanced Learning, EC-TEL 2020 (pp. 404-409). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12315 LNCS). Springer. https://doi.org/10.1007/978-3-030-57717-9_35
Learning analytics dashboard for motivation and performance
Fleur, D. S., van den Bos, W., & Bredeweg, B. (2020). Learning analytics dashboard for motivation and performance. In V. Kumar, & C. Troussas (Eds.), Intelligent Tutoring Systems (pp. 411-419). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-030-49663-0_51
Kunstmatige Intelligentie in het onderwijs
Bredeweg, B. (2019). Kunstmatige Intelligentie in het onderwijs: leren met interactieve kennisrepresentaties. Hogeschool van Amsterdam.
Ein konzeptionelles Modell des Online-Lernens
Schabasser, C., & Bredeweg, B. (2019). Ein konzeptionelles Modell des Online-Lernens. Information-Wissenschaft und Praxis, 70(4), 192-198. https://doi.org/10.1515/iwp-2019-2027
Analysing visitor flow using a bluetooth positioning system
Van Den Ham, P., & Bredeweg, B. (2019). Analysing visitor flow using a bluetooth positioning system. Paper presented at 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019, Brussels, Belgium. http://ceur-ws.org/Vol-2491/abstract118.pdf
StudyGotchi
Hellings, J., Leek, P., & Bredeweg, B. (2019). StudyGotchi: Tamagotchi-like game-mechanics to motivate students during a programming course. In M. Scheffel, J. Broisin, V. Pammer-Schindler, A. Ioannou, & J. Schneider (Eds.), Transforming learning with meaningful technologies: 14th European Conference on Technology Enhanced Learning, EC-TEL 2019 Delft, The Netherlands, September 16–19, 2019 : Proceedings (pp. 622-625). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-030-29736-7_53
Automating Reference-based Feedback for Learning Conceptual Knowledge in Secondary Education
Kada, Y., & Bredeweg, B. (2018). Automating Reference-based Feedback for Learning Conceptual Knowledge in Secondary Education. In Benelux Conference on Artificial Intelligence, Den Bosch, Netherlands (pp. 95-96). (Belgian/Netherlands Artificial Intelligence Conference).
Testing scientific models using Qualitative Reasoning: Application to cellulose hydrolysis
Kansou, K., Rémond, C., Paës, G., Bonnin, E., Tayeb, J., & Bredeweg, B. (2017). Testing scientific models using Qualitative Reasoning: Application to cellulose hydrolysis. Scientific Reports, 7. https://doi.org/10.1038/s41598-017-14281-4
Can learning by qualitative modelling be deployed as an effective method for learning Subject-Specific Content?
Schlatter, E., Bredeweg, B., van Drie, J., & de Jong, P. (2017). Can learning by qualitative modelling be deployed as an effective method for learning Subject-Specific Content? In É. Lavoué, H. Drachsler, K. Verbert, J. Broisin, & M. Pérez-Sanagustín (Eds.), Data Driven Approaches in Digital Education (pp. 479-485). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-66610-5_46
Performance indicators for online secondary education
van Diepen, P., & Bredeweg, B. (2017). Performance indicators for online secondary education: A case study. In B. Bredeweg, & T. Bosse (Eds.), BNAIC 2016: Artificial Intelligence - 28th Benelux Conference on Artificial Intelligence, Revised Selected Papers (pp. 169-177). (Communications in Computer and Information Science; Vol. 765). Springer Verlag. https://doi.org/10.1007/978-3-319-67468-1_12
Preface
Bosse, T., & Bredeweg, B. (2017). Preface. In T. Bosse, & B. Bredeweg (Eds.), BNAIC 2016: Artificial Intelligence (Vol. 765, pp. V-VI). (Communications in Computer and Information Science). Springer. https://doi.org/10.1007/978-3-319-67468-1
Eindrapport VC3BI commissie
Sandberg, J., Belleman, R., Bredeweg, B., Visser, A., Endriss, U., Roelofsen, F., Schaffner, C., van Schoot, T., Sluijter, B., & al., E. (2016). Eindrapport VC3BI commissie. UvA, FNWI.
Assessing learner-constructed conceptual models and simulations of dynamic systems
Bredeweg, B., Liem, J., & Nicolaou, C. (2016). Assessing learner-constructed conceptual models and simulations of dynamic systems. In K. Verbert, M. Sharples, & T. Klobučar (Eds.), Adaptive and Adaptable Learning (pp. 357-362). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-45153-4_27
Learning Analytics Pilot with Coach2 - Searching for effective mirroring
Brouwer, N., Bredeweg, B., Latour, S., Berg, A., & van der Huizen, G. (2016). Learning Analytics Pilot with Coach2 - Searching for effective mirroring. In K. Verbert, M. Sharples, & T. Klobučar (Eds.), Adaptive and Adaptable Learning (pp. 363-369). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-45153-4_28
Qualitative Representations for Education
Bredeweg, B., & Forbus, K. D. (2016). Qualitative Representations for Education. In R. A. Sottilare, A. C. Graesser, X. Hu, A. M. Olney, B. D. Nye, & A. M. Sinatra (Eds.), Design Recommendations for Intelligent Tutoring Systems (Vol. 4, pp. 55-68). (A Book in the Adaptive Tutoring Series). US Army Research Laboratory.
Testing scientific models using a QR model: Application to cellulose biodegradation
Bredeweg, B., & Kansou, K. (2016). Testing scientific models using a QR model: Application to cellulose biodegradation. In 29th International workshop on Qualitative Reasoning
Hypothesis assessment with qualitative reasoning: Modelling the Fontestorbes fountain
Kansou, K., & Bredeweg, B. (2014). Hypothesis assessment with qualitative reasoning: Modelling the Fontestorbes fountain. Ecological Informatics, 19, 71-89. https://doi.org/10.1016/j.ecoinf.2013.10.007
Towards Assessing and Grading Learner Created Conceptual Models
Bredeweg, B., Nicolaou, C. T., Liem, J., & Constantinou, C. P. (2014). Towards Assessing and Grading Learner Created Conceptual Models. In S. Trausan-Matu, C. E. Boyer, M. Crosby, & K. Panourgia (Eds.), Intelligent Tutoring Systems (pp. 442-447). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-07221-0_55
Support options provided and required for modeling with DynaLearn - A case study
Or-Bach, R., & Bredeweg, B. (2013). Support options provided and required for modeling with DynaLearn - A case study. Education and Information Technologies, 18(4), 621-639. https://doi.org/10.1007/s10639-012-9194-z
DynaLearn - An Intelligent Learning Environment for Learning Conceptual Knowledge
Bredeweg, B., Liem, J., Beek, W., Linnebank, F., Gracia, J., Lozano, E., Wißner, M., Bühling, R., Salles, P., Noble, R., Zitek, A., Borisova, P., & Mioduser, D. (2013). DynaLearn - An Intelligent Learning Environment for Learning Conceptual Knowledge. The AI Magazine, 34(4), 46-65.
Guest Editorial: Special Section on Learning Systems for Science and Technology Education
Bredeweg, B., McLaren, B. M., & Biswas, B. (2013). Guest Editorial: Special Section on Learning Systems for Science and Technology Education. IEEE Transactions on Learning Technologies, 6(3), 194-196. https://doi.org/10.1109/TLT.2013.30
How plants changed the world: Using qualitative reasoning to explain plant macroevolution's effect on the long-term carbon cycle
Kansou, K., Nuttle, T., Farnsworth, K., & Bredeweg, B. (2013). How plants changed the world: Using qualitative reasoning to explain plant macroevolution's effect on the long-term carbon cycle. Ecological Informatics, 17, 117-142. https://doi.org/10.1016/j.ecoinf.2013.02.004
Learning by Conceptual Modeling - Changes in Knowledge Structure and Content
Zitek, A., Poppe, M., Stelzhammer, M., Muhar, S., & Bredeweg, B. (2013). Learning by Conceptual Modeling - Changes in Knowledge Structure and Content. IEEE Transactions on Learning Technologies, 6(3), 217-227. https://doi.org/10.1109/TLT.2013.7
Question Generation and Adaptation Using a Bayesian Network of the Learner’s Achievements
Wißner, M., Linnebank, F., Liem, J., Bredeweg, B., & André, E. (2013). Question Generation and Adaptation Using a Bayesian Network of the Learner’s Achievements. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education (pp. 729-732). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-642-39112-5_99
Scripting the Collaboration for Exploiting the Learning Affordances of a Modeling Environment
Or-Bach, R., & Bredeweg, B. (2013). Scripting the Collaboration for Exploiting the Learning Affordances of a Modeling Environment. Issues in Informing Science and Information Technology, 10, 401-411.
Collaborative Reflection activities with DynaLearn
Or-Bach, R., & Bredeweg, B. (2012). Collaborative Reflection activities with DynaLearn. In Proceedings of the:12th IEEE International Conference on Advanced Learning Technologies : ICALT 2012 (pp. 134-137). IEEE Computer Society. https://doi.org/10.1109/ICALT.2012.78
Context-dependent help for novices acquiring conceptual systems knowledge in DynaLearn
Beek, W., & Bredeweg, B. (2012). Context-dependent help for novices acquiring conceptual systems knowledge in DynaLearn. In S. A. Cherri, W. J. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Intelligent Tutoring Systems (pp. 292-297). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-642-30950-2_38
Increasing Learners’ Motivation through Pedagogical Agents: The Cast of Virtual Characters in the DynaLearn ILE
Wißner, M., Beek, W., Lozano, E., Mehlmann, G., Linnebank, F., Liem, J., Häring, M., Bühling, R., Gracia, J., Bredeweg, B., & André, E. (2012). Increasing Learners’ Motivation through Pedagogical Agents: The Cast of Virtual Characters in the DynaLearn ILE. In M. Beer, C. Brom, F. Dignum, & V-W. Soo (Eds.), Agents for Educational Games and Simulations (pp. 151-165). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-642-32326-3_10
Providing Feedback for common problems in learning by conceptual modeling using expectation-driven consistency maintenance
Beek, W. G. J., & Bredeweg, B. (2012). Providing Feedback for common problems in learning by conceptual modeling using expectation-driven consistency maintenance. In 26th International Workshop on Qualitative Reasoning (QR’26), Playa del Rey, CA, USA
Qualitative model patterns: a toolkit for learning by modelling
Salles, P., Bredeweg, B., Noble, R., Zitek, A., & Souza, A. (2012). Qualitative model patterns: a toolkit for learning by modelling. In 26th International Workshop on Qualitative Reasoning (QR’26), Playa del Rey, CA, USA
Simulation preferences - Means towards usable QR engines
Bredeweg, B., & Linnebank, F. E. (2012). Simulation preferences - Means towards usable QR engines. In 26th International Workshop on Qualitative Reasoning (QR’26), Playa del Rey, CA, USA
Character roles and interaction in the DynaLearn Intelligent Learning Environment
Wißner, M., Beek, W., Lozano, E., Mehlmann, G., Linnebank, F., Liem, J., Häring, M., Bühling, R., Gracia, J., Bredeweg, B., & André, E. (2011). Character roles and interaction in the DynaLearn Intelligent Learning Environment. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Artificial Intelligence in Education (pp. 585-587). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-642-21869-9_109
Context-Dependent Help for the DynaLearn Modelling and Simulation Workbench
Beek, W., Bredeweg, B., & Latour, S. (2011). Context-Dependent Help for the DynaLearn Modelling and Simulation Workbench. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Artificial Intelligence in Education (pp. 420-422). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-642-21869-9_56
Evaluating the effects of a new qualitative simulation software (DynaLearn) on learning behavior, factual and causal understanding
Zitek, A., Poppe, M., Stelzhammer, M., Muhar, S., & Bredeweg, B. (2011). Evaluating the effects of a new qualitative simulation software (DynaLearn) on learning behavior, factual and causal understanding. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Artificial Intelligence in Education (pp. 594-596). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-642-21869-9_112
International workshop on learning by modelling in science education
Bredeweg, B., & Salles, P. (2011). International workshop on learning by modelling in science education. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Artificial Intelligence in Education (pp. 633). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-642-21869-9_133
Knowledgeable Feedback via a Cast of Virtual Characters with Different Competences
Beek, W., Liem, J., Linnebank, F., Bühling, R., Wißner, M., Lozano, E., Gracia del Río, J., & Bredeweg, B. (2011). Knowledgeable Feedback via a Cast of Virtual Characters with Different Competences. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Artificial Intelligence in Education (pp. 620). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-642-21869-9_121
Pair modeling with DynaLearn - Students' attitudes and actual effects
Or-Bach, R., & Bredeweg, B. (2011). Pair modeling with DynaLearn - Students' attitudes and actual effects. Interdisciplinary Journal of Information, Knowledge, and Management, 6, 119-135. https://doi.org/10.28945/1371
Semantic feedback for the enrichment of conceptual models
Lozano, E., Gracia, J., Liem, J., Gómez-Pérez, A., & Bredeweg, B. (2011). Semantic feedback for the enrichment of conceptual models. In KCAP 2011 (pp. 187-188). Association for Computing Machinery. https://doi.org/10.1145/1999676.1999718
Use of the DynaLearn learning environment by naïve student modelers: Implications for automated support
Noble, R., & Bredeweg, B. (2011). Use of the DynaLearn learning environment by naïve student modelers: Implications for automated support. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Artificial Intelligence in Education (pp. 519-521). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-642-21869-9_88
How can we cope with the complexity of the environment? A "Learning by modelling" approach using qualitative reasoning for developing causal models and simulations with focus on Sustainable River Catchment Management
Poppe, M., Zitek, A., Salles, P., Bredeweg, B., & Muhar, S. (2010). How can we cope with the complexity of the environment? A "Learning by modelling" approach using qualitative reasoning for developing causal models and simulations with focus on Sustainable River Catchment Management. Geophysical Research Abstracts, 12.
A categorization of KR&R methods for requirement analysis of a query answering knowledge base
Chaudhri, V. K., Bredeweg, B., Fikes, R., McIlraith, S., & Wellman, M. P. (2010). A categorization of KR&R methods for requirement analysis of a query answering knowledge base. Frontiers in Artificial Intelligence and Applications, 209, 158-171. https://doi.org/10.3233/978-1-60750-535-8-158
Acquiring Conceptual Knowledge about How Systems Behave
Liem, J., Bredeweg, B., Linnebank, F., Bühling, R., Wißner, M., Gracia del Río, J., Beek, W., & Gómez Pérez, A. (2010). Acquiring Conceptual Knowledge about How Systems Behave. In V. Aleven, J. Kay, & J. Mostow (Eds.), Intelligent Tutoring Systems (Vol. 2, pp. 448). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-642-13437-1_105
A survey on education and training in ecological informatics
Salles, P., Bredeweg, B., & Pizzigatti Correa, P. L. (2010). A survey on education and training in ecological informatics. 162--164.
Differentiating qualitative representations into learning spaces
Liem, J., Beek, W., & Bredeweg, B. (2010). Differentiating qualitative representations into learning spaces. In J. de Kleer, & K. D. Forbus (Eds.), 24th International Workshop on Qualitative Reasoning (QR’10), Portland, Oregon, USA (pp. 37-46). Qualitative Reasoning Group, Northwestern University.
DynaLearn: architecture and approach for investigating conceptual system knowledge acquisition
Bredeweg, B., Liem, J., Linnebank, F., Bühling, R., Wißner, M., Gracia del Río, J., Salles, P., Beek, W., & Gómez Pérez, A. (2010). DynaLearn: architecture and approach for investigating conceptual system knowledge acquisition. In V. Aleven, J. Kay, & J. Mostow (Eds.), Intelligent Tutoring Systems (Vol. 2, pp. 272-274). (Lecture Notes In Computer Science). Springer. https://doi.org/10.1007/978-3-642-13437-1_42
DynaLearn: Architecture and approach for investigating conceptual system knowledge acquisition
Bredeweg, B., Liem, J., Linnebank, F., Bühling, R., Wißner, M., Del Río, J. G., Salles, P., Beek, W., & Gómez Pérez, A. (2010). DynaLearn: Architecture and approach for investigating conceptual system knowledge acquisition. In Intelligent Tutoring Systems - 10th International Conference, ITS 2010, Proceedings (pp. 272-274). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6095 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-13437-1_42
First evaluations of the effect of the DynaLearn software on conceptual systems understanding of river catchments
Zitek, A., Poppe, M., Stelzhammer, M., Bredeweg, B., & Muhar, S. (2010). First evaluations of the effect of the DynaLearn software on conceptual systems understanding of river catchments. 162--164.
From qualitative reasoning models to Bayesian-based learner modeling
Milošević, U., & Bredeweg, B. (2010). From qualitative reasoning models to Bayesian-based learner modeling. In J. de Kleer, & K. D. Forbus (Eds.), 24th International Workshop on Qualitative Reasoning (QR’10), Portland, Oregon, USA (pp. 132-137)
Graphical means for inspecting qualitative models of system behaviour
Bouwer, A., & Bredeweg, B. (2010). Graphical means for inspecting qualitative models of system behaviour. Instructional Science, 38(2), 173-208. https://doi.org/10.1007/s11251-008-9083-4
Hypothesis assessment with qualitative reasoning: modelling the intermittent Fontestorbes fountain
Kansou, K., & Bredeweg, B. (2010). Hypothesis assessment with qualitative reasoning: modelling the intermittent Fontestorbes fountain. 65--66.
Learning spaces as representational scaffolds for learning conceptual knowledge of system behaviour
Bredeweg, B., Liem, J., Beek, W., Salles, P., & Linnebank, F. (2010). Learning spaces as representational scaffolds for learning conceptual knowledge of system behaviour. In M. Wolpers, P. A. Kirschner, M. Scheffel, S. Lindstaedt, & V. Dimitrova (Eds.), Sustaining TEL: From Innovation to Learning and Practice (pp. 46-61). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-642-16020-2_4
Progressive Knowledge Representations for Learning Conceptual Knowledge of System Behaviour
Bredeweg, B., Liem, A., Beek, W. G. J., Salles, P., & Linnebank, F. (2010). Progressive Knowledge Representations for Learning Conceptual Knowledge of System Behaviour. 142--143.
Qualitative models of global warming amplifiers
Milošević, U., & Bredeweg, B. (2010). Qualitative models of global warming amplifiers. In J. de Kleer, & K. D. Forbus (Eds.), 24th International Workshop on Qualitative Reasoning (QR’10), Portland, Oregon, USA (pp. 54-59)
Qualitative Models of Global Warming Amplifiers
Milosevic, U., & Bredeweg, B. (2010). Qualitative Models of Global Warming Amplifiers. 80--81.
Semantic enrichment of models in DynaLearn learning environment
Lozano, A. M., Liem, A., Gracia, J., Gómez-Pérez, A., & Bredeweg, B. (2010). Semantic enrichment of models in DynaLearn learning environment. 154.
Semantic techniques for enabling knowledge reuse in conceptual modelling
Gracia, J., Liem, J., Lozano, E., Corcho, O., Trna, M., Gómez-Pérez, A., & Bredeweg, B. (2010). Semantic techniques for enabling knowledge reuse in conceptual modelling. In 9th International Semantic Web Conference, ISWC 2010 (pp. 82-97). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6497 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-17749-1_6
A qualitative model of limiting factors for a salmon life cycle in the context of river rehabilitation
Noble, R. A. A., Bredeweg, B., Linnebank, F., Salles, P., & Cowx, I. G. (2009). A qualitative model of limiting factors for a salmon life cycle in the context of river rehabilitation. Ecological Informatics, 4(5-6), 299-319. https://doi.org/10.1016/j.ecoinf.2009.09.006
A qualitative model of Riacho Fundo (DF, Brazil) water basin sustainability
Salles, P., & Bredeweg, B. (2009). A qualitative model of Riacho Fundo (DF, Brazil) water basin sustainability. Ecological Informatics, 4(5-6), 320-338. https://doi.org/10.1016/j.ecoinf.2009.09.012
A qualitative model of the salmon life cycle in the context of river rehabilitation
Noble, R. A. A., Bredeweg, B., Linnebank, F., Salles, P., & Cowx, I. G. (2009). A qualitative model of the salmon life cycle in the context of river rehabilitation. In J. Žabkar, & I. Bratko (Eds.), 23rd International Workshop on Qualitative Reasoning (QR’09), Ljubljana, Slovenia (pp. 84-94)
A qualitative reasoning model of algal bloom in the Danube Delta Biosphere Reserve (DDBR)
Cioaca, E., Linnebank, F. E., Bredeweg, B., & Salles, P. (2009). A qualitative reasoning model of algal bloom in the Danube Delta Biosphere Reserve (DDBR). Ecological Informatics, 4(5-6), 282-298. https://doi.org/10.1016/j.ecoinf.2009.09.010
Collaborative capture and reuse of system knowledge
Liem, J., & Bredeweg, B. (2009). Collaborative capture and reuse of system knowledge. Ecological Informatics, 4(5-6), 367-380. https://doi.org/10.1016/j.ecoinf.2009.09.011
DynaLearn - Engaging and informed tools for learning conceptual system knowledge
Bredeweg, B., Gómez-Pérez, A., André, E., & Salles, P. (2009). DynaLearn - Engaging and informed tools for learning conceptual system knowledge. In R. Pirrone, R. Azevedo, & G. Biswas (Eds.), Cognitive and metacognitive educational systems (pp. 46-51). (AAAI Fall Symposium Series Technical Reports). AAAI Press.
Evaluating the potential of qualitative reasoning models to contribute to sustainable catchment management
Zitek, A., Schmutz, S., Preis, S., Salles, P., Bredeweg, B., & Muhar, S. (2009). Evaluating the potential of qualitative reasoning models to contribute to sustainable catchment management. Ecological Informatics, 4(5-6), 381-395. https://doi.org/10.1016/j.ecoinf.2009.09.007
Garp3 — Workbench for qualitative modelling and simulation
Bredeweg, B., Linnebank, F., Bouwer, A., & Liem, J. (2009). Garp3 — Workbench for qualitative modelling and simulation. Ecological Informatics, 4(5-6), 263-281. https://doi.org/10.1016/j.ecoinf.2009.09.009
Mediating conceptual knowledge using qualitative reasoning
Bredeweg, B., & Salles, P. (2009). Mediating conceptual knowledge using qualitative reasoning. In S. E. Jørgensen, T-S. Chon, & F. Recknagel (Eds.), Handbook of ecological modelling and informatics (pp. 351-398). WIT Press.
Model building experiences using Garp3: problems, patterns and debugging
Liem, J., Linnebank, F. E., & Bredeweg, B. (2009). Model building experiences using Garp3: problems, patterns and debugging. In J. Žabkar, & I. Bratko (Eds.), 23rd International Workshop on Qualitative Reasoning (QR’09), Ljubljana, Slovenia (pp. 68-75)

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