posters

Natalia Spitha & Rüdiger Tiemann: Simulationsbasierte Lernaktivitäten für Chemiestudierende

• Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Princeton Hall.
• Heublein, U., Schmelzer, R., & Sommer, D. (2008). Die Entwicklung der Studienabbruchquoten an den deutschen Hochschulen. Report. Hannover.
• Hosbein, K. N., & Barbera, J. (2020). Development and evaluation of novel science and chemistry identity measures. Chemistry Education Research and Practice, 21(3), 852-877
• Schwedler, S., & Kaldewey, M. (2020). Linking the submicroscopic and symbolic level in physical chemistry: how voluntary simulation-based learning activities foster first-year university students’ conceptual understanding. Chemistry Education Research and Practice, 21(4), 1132-1147.
• Thiry, H., Laursen, S. L., & Hunter, A. B. (2011). What experiences help students become scientists? A comparative study of research and other sources of personal and professional gains for STEM undergraduates. The Journal of Higher Education, 82(4), 357-388.

Lisa Bering & Rüdiger Tiemann: Model-Eliciting-Activities (MEA´s) im Chemieunterricht

• Zawojewski, J., Capobianco, B., & Hjalmarson, M. (2004). Model Eliciting Activities: An In Class Approach To Improving Interest And Persistence Of Women In Engineering. In 2004 Annual Conference (pp. 9-919). 
• Lesh, R., Hoover, M., Hole, B., Kelly, A., Post, T., (2000) Principles for Developing Thought-Revealing Activities forStudents and Teachers. In A. Kelly, R. Lesh (Eds.), Research Design in Mathematics and Science Education. (pp. 591-646). Lawrence Erlbaum Associates, Mahwah, New Jersey.
• Vogel, F., Fischer, F. (2020). Computerunterstütztes kollaboratives Lernen. In: H. Niegemann, A. Weinberger (Eds.), Handbuch Bildungstechnologie. Springer, Berlin, Heidelberg.

Christian Dictus-Christoph & Rüdiger Tiemann: MINT-Town - eine Lernumgebung zur Förderung von kritischem Denken im Chemieunterricht

• Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An Empirical Evaluation of the System Usability Scale. International Journal of Human–Computer Interaction, 24(6), 574-594. https://doi.org/https://doi.org/10.1080/10447310802205776
• Brooke, J. (1996). SUS: A 'quick and dirty' usability scale. In P. W. Jordan, B. Thomas, I. L. McClelland, & B. Weerdmeester (Eds.), Usability evaluation in industry (pp. 189-194). Taylor & Francis. 
• Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From Game Design Elements to Gamefulness: Defining "Gamification". Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, Tampere, Finland.
• Dictus, C., & Tiemann, R. (2021). Fostering Critical Thinking by a Gamification Approach. 11th International Conference – The  Future of Education (Virtual Edition), Firenze, Italy.
• Ennis, R. H. (2011). Critical Thinking: Reflection and Perspective - Part I. Inquiry - Critical Thinking Across the Disciplines, 26(1), 4-18. 
• EU. (2019). Key competencies for lifelong learning. In European Commission (Ed.), Education and Training (pp. 1-20). Luxembourg. 
• Iosup, A., & Epema, D. (2014). An experience report on using gamification in technical higher education. 45th ACM technical symposium on Computer science education, Atlanta, Georgia, USA. 
• Kim, J. T., & Lee, W. H. (2013). Dynamical model for gamification of learning (DMGL). Multimedia Tools and Applications (August 2013), 1–11. https://doi.org/10.1007/s11042-013-1612-8 
• Kim, S., Song, K., Lockee, B., & Burton, J. (2018). Gamification in Learning and Education. Springer. https://doi.org/19.1007/978-3-319-47283-6 
• OECD. (2018). The Future of Education and Skills - Education 2030. OECD Publishing.

Yico Ying & Rüdiger Tiemann: Assessing collaborative problem-solving (CPS) skills in chemistry education research

• Avry, S., Chanel, G., Bétrancourt, M., & Molinari, G. (2020). Achievement appraisals, emotions and socio-cognitive processes: How they interplay in collaborative problem-solving?. Computers in Human Behavior, 107, 106267.
• Chang, C. J., Chang, M. H., Liu, C. C., Chiu, B. C., Fan Chiang, S. H., Wen, C. T., ... & Chai, C. S. (2017). An analysis of collaborative problem‐solving activities mediated by individual‐based and collaborative computer simulations. Journal of Computer Assisted Learning, 33(6), 649-662.
• Griffin, P., & Care, E. (Eds.). (2014). Assessment and teaching of 21st century skills: Methods and approach. Springer.
• Heller, K. A., & Perleth, C. (2000). Kognitiver Fähigkeitstest für 4. bis 12. Klassen, Revision: KFT 4-12+ R. Beltz-Test.
• Hendarwati, E., Nurlaela, L., Bachri, B., & Sa'ida, N. (2021). Collaborative Problem based learning integrated with online learning. International Journal of Emerging Technologies in Learning (iJET), 16(13), 29-39.
• Krell, M. (2015). Evaluating an instrument to measure mental load and mental effort using Item Response Theory.
• Kuo, B. C., Liao, C. H., Pai, K. C., Shih, S. C., Li, C. H., & Mok, M. M. C. (2020). Computer-based collaborative problem-solving assessment in Taiwan. Educational Psychology, 40(9), 1164-1185.
• Minkley, N., Kärner, T., Jojart, A., Nobbe, L., & Krell, M. (2018). Students' mental load, stress, and performance when working with symbolic or symbolic–textual molecular representations. Journal of Research in Science Teaching, 55(8), 1162-1187.
• OECD. (2017). PISA 2015 collaborative problem-solving framework. Retrieved from https://www.oecd.org/pisa/pisaproducts/Draft%20PISA%202015%20Collaborative%20Problem%20Solving%20Framework%20.pdf.
• O'Neil, H. F., Chuang, S. H., & Chung, G. K. (2003). Issues in the computer-based assessment of collaborative problem solving. Assessment in Education: Principles, Policy & Practice, 10(3), 361-373.
• Rosen, Y. (2015). Computer-based assessment of collaborative problem solving: Exploring the feasibility of human-to-agent approach. International Journal of Artificial Intelligence in Education, 25(3), 380-406.
• Rost, M. (2021). Modelle als Mittel der Erkenntnisgewinnung im Chemieunterricht der Sekundarstufe I: Entwicklung und quantitative Dimensionalitätsanalyse eines Testinstruments aus epistemologischer Perspektive. Logos Verlag Berlin.
• Rummel, N., & Spada, H. (2005). Learning to collaborate: An instructional approach to promoting collaborative problem solving in computer-mediated settings. The journal of the Learning Sciences, 14(2), 201-241.
• Slotta, J. D., & Linn, M. C. (2000). The knowledge integration environment: Helping students use the Internet effectively. Innovations in science and mathematics education: Advanced designs for technologies of learning, 193-226.
• Turcotte, S. (2012). Computer-supported collaborative inquiry on buoyancy: A discourse analysis supporting the “pieces” position on conceptual change. Journal of Science Education and Technology, 21(6), 808-825.

Joachim Kranz & Rüdiger Tiemann: Modell des inklusive Chemieunterrichts - drei Schritte zur inklusiven Bildung

• Abels, S. & Markic, S. (2013). Umgang mit Vielfalt – Neue Perspektiven im Chemieunterricht. Naturwissenschaften im Unterricht – Chemie, S. 2–6.
• Frohn, J., Brodesser, E., Moser, V. & Pech, D. (2019). Inklusives Lehren und Lernen. Allgemein- und fachdidaktische Grundlagen. Bad Heilbrunn: Verlag Julius Klinkhardt, 209 S.
• Heimlich, U. & Kahlert, J. (2014). Inklusion in Schule und Unterricht. Stuttgart: Kohlhammer-Verlag
• Huwer, J. & Eilks, I. (2017). Multitouch Learning Books für schulische und außerschulische Bildung. In J. Messinger-Koppelt, S. Schanze & J.·Gross (Hg.), Lernprozesse mit digitalen Werkzeugen unterstützen. Perspektiven aus der Didaktik naturwissenschaftlicher Fächer (S. 81−94). Hamburg: J. Herz Stiftung.
• Kranz, J. & Tiemann, R. (2020). Inklusion und Problemlösen im Chemieunterricht − ein Modellansatz. In S. Habig (Hg.), Naturwissenschaftliche Kompetenzen in der Gesellschaft von morgen (S. 796−799). Gesellschaft für Didaktik der Chemie und Physik.
• Kranz, J. & Tiemann, R. (2021). Multitouch-Learning-Books im Chemieunterricht, MNU-Journal. 03.2021, 240-245. Neuss: Verlag Klaus Seeberger.
• Prediger, S. & Aufschnaiter, C. v. (2017). Umgang mit heterogenen Lernvoraussetzungen aus fachdidaktischer Perspektive. In Bohl, T., Budde, J. & Rieger-Ladich, M. (Hrsg.), Umgang mit Heterogenität in Schule und Unterricht. Bad Heilbrunn: Klinkhardt, 291-307.
• Puentedura, Ruben R. (2015): SAMR - A Brief Introduction. Abgerufen von: http://hippasus.com/rrpweblog/archives/2015/10/SAMR_ABriefIntro.pdf, (09.11.2019)
• Ramseger, J. & Anders, Y. (2013). Wissenschaftliche Untersuchungen zur Arbeit der Stiftung „Haus der kleinen Forscher“. Schaffhausen: SCHUBI Lernmedien AG, Bd. 5.
• Rose, D. & Meyer, A. (2013). Universal Design for Learning: Theory and Practice. Wakefield: Cast Publishing.
• Reiners, C. & Groß, K. (2017). Aktuelle Herausforderungen im Chemieunterricht. Heidelberg: Springer-Verlag.
• Seitz, S. (2018). Forschung zu inklusivem Sachunterricht – Bestandsaufnahme und Perspektiven. In: Pech, D., Schomaker, C. & Simon, T. (Hrsg.): Sachunterrichtsdidaktik & Inklusion. Ein Beitrag zur Entwicklung. Baltmannsweiler: Schneider, S. 96-111.
• Stäudel, L. (2009). Differenzieren im Chemieunterricht - Eine Herausforderung für Lehrkräfte, Lernende und das Selbstverständnis von Schule – In: Unterricht Chemie, Differenzieren - Heft 111/112, 20. Jg. S. 8-12.
• Stäudel, L. (2009). Aufgaben mit gestuften Hilfen – In: Unterricht Chemie, Differenzieren - Heft 111/112, 20. Jahrgang, S. 72-78.
• UNESCO (1994). Salamanca-Framework. World conference on special need education: acces and quality. In: http://www.unesco.org/education/pdf/SALAMA_E.PDF. (25.08.2019).
• Wodzinski, R., Hänze, M. & Stäudel, L. (2005) Lernen von Physik und Chemie durch Aufgaben mit gestuften Lernhilfen. In: A. Pitton, Lehren und Lernen mit neuen Medien. Kassel: GdCP. Universität Kassel. 

Christian Kressmann & Rüdiger Tiemann: Kreatives Denken und Problemlöser im Chemieunterricht

• Amabile, T. M. (1982). Social Psychology of Creativity: A Consensual Assessment Technique. Journal of Personality and Social Psychology, (43), 997–1013.
• Arnold, K., Eberle, A., Fleischer, H., Hein, A., Kronabel, C., Lüttgens, U. et al. (2017). Fokus Chemie - 9./10. Schuljahr (Berlin, Brandenburg,1. Auflage, [Neubearbeitung]. Berlin: Cornelsen.
• Runco, M. A. (2014). Creativity. Theories and Themes: Research, Development, and Practice (2nd ed.). San Diego: Elsevier Science & Technology. Retrieved from https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=1641933
• Treffinger, D. J., Selby, E. C. & Isaksen, S. G. (2008). Understanding individual problem-solving style: A key to learning and applying creative problem solving. Learning and Individual Differences, 18(4), 390–401. https://doi.org/10.1016/j.lindif.2007.11.007

Adrian Schmidt, Rüdiger Tiemann & Gunnar Friege: Conditions of effective problem solving

• [1] Loibl, Katharina; Roll, Ido; Rummel, Nikol (2017): Towards a Theory of When and How Problem Solving Followed by Instruction Supports Learning. In: Educ Psychol Rev 29 (4), S. 693–715. DOI: 10.1007/s10648-016-9379-x.
• [2] Taconis, R.; Ferguson-Hessler, M.G.M.; Broekkamp, H. (2001): Teaching science problem solving: An overview of experimental work. In: J. Res. Sci. Teach. 38 (4), S. 442–468. DOI: 10.1002/tea.1013.
• [3] Hattie, John; Beywl, Wolfgang; Zierer, Klaus (2013): Lernen sichtbar machen: Schneider-Verl. Hohengehren.
• [4] Fischer, Frank; Kollar, Ingo; Ufer, Stefan; Sodian, Beate; Hussmann, Heinrich; Pekrun, Reinhard et al. (2014): Scientific Reasoning and Argumentation: Advancing an Interdisciplinary Research Agenda in Education. In: Frontline Learning Research 2 (3), 28-45. https://eric.ed.gov/?id=EJ1090940.
• [5] Heindl, Manuela (2019): Inquiry-Based Learning and the Pre-Requisite for Its Use in Science at School: A Meta-Analysis 3 (2), 52-61 (10 Seiten). https://eric.ed.gov/?id=ED597954.
• [6] Page, M. J.; McKenzie, J. E.; Bossuyt, P. M.; Boutron, I.; Hoffmann, T. C.; Mulrow, C. D. et al. (2021): The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. In: The BMJ 372. DOI: 10.1136/bmj.n71.

Christian Dictus-Christoph & Rüdiger Tiemann: Critical Thinking spielerisch lernen

• Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011, 28.-30.09). From Game Design Elements to Gamefulness: Defining "Gamification". Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, Tampere, Finland.
• Dictus, C., & Tiemann, R. (2021, 01.-02.07.). Fostering Critical Thinking by a Gamification Approach. 11th International Conference – The  Future of Education (Virtual Edition), Firenze, Italy.
• Ennis, R. H. (2011). Critical Thinking: Reflection and Perspective - Part I. Inquiry - Critical Thinking Across the Disciplines, 26(1), 4-18. 
• Ennis, R. H. & Millman J. (2005). Cornell Critical Thinking Test Level X, (5. Ed.). The Critical Thinking Co., 1991 Sherman Ave., Suite 200, North Bend. ISBN 978-0-89455-286-1
• EU. (2019). Key competencies for lifelong learning. In European Commission (Ed.), Education and Training (pp. 1-20). Luxembourg. 
• Heller, K. A. & Perleth, C. (2000). Kognitiver Fähigkeitstest für 4. bis 12. Klassen, Revision. Göttingen: Beltz.
• Krell, M. (2015). Evaluating an instrument to measure mental load and mental effort using Item Response Theory. Science Education Review Letters, Volume 2015, 1-6. https://doi.org/10.18452/8212
• OECD. (2018). The Future of Education and Skills - Education 2030. OECD Publishing.

Lisa Bering & Rüdiger Tiemann: Förderung der Modellierungskompetenz durch MEA´s

• Diefes-Dux, H., Follman, D., Imbrie, P. K., Zawojewski, J., Capobianco, B., & Hjalmarson, M. (2004). Model Eliciting Activities: An In Class Approach To Improving Interest And Persistence Of Women In Engineering. In 2004 Annual Conference (S. 9.919.1-9.919.15).
• Lesh, R., Hoover, M., Hole, B., Kelly, A., Post, T., (2000) Principles for Developing Thought-Revealing Activities forStudents and Teachers. In A. Kelly, R. Lesh (Hrsg.), Research Design in Mathematics and Science Education. (S. 591-646). Lawrence Erlbaum Associates, Mahwah, New Jersey.
• Koch, S., Krell, M., & Krüger, D. (2015). Förderung von Modellkompetenz durch den Einsatz einer Blackbox. Erkenntnisweg Biologiedidaktik, 93–108.
• Fleige, J., Seegers, A., Upmeier zu Belzen, A. & Krüger, D. (2012a). Förderung von Modellkompetenz im Biologieunterricht. In: MNU 65(1), 19–28

Lilly Bliesener & Rüdiger Tiemann: Kontextualisierung im Chemieunterricht

• Heller, K., & Perleth, C. (2000). Kognitiver Fähigkeitstest für 4.-12. Klassen, Revision (KFT 4-12+ R). 
• Krapp, A. (1998). Entwicklung und Förderung von Interessen im Unterricht. Psychologie in Erziehung und Unterricht, 45, 186-203. 
• Krell, M. (2015). Evaluating an instrument to measure mental load and mental effort using Item Response Theory. 
• Kultusministerkonferenz. (2004). Bildungsstandards im Fach Chemie für
• den Mittleren Schulabschluss: Beschluss vom 16.12.2004 [Beschluss]. https://www.kmk.org/fileadmin/veroeffentlichungen_beschluesse/2004/2004_12_16-Bildungsstandards-Chemie.pdf
• LISUM. (2015). Rahmenlehrplan Chemie, Teil C: Sekundarstufe I. https://bildungsserver.berlin-brandenburg.de/fileadmin/bbb/unterricht/rahmenlehrplaene/Rahmenlehrplanprojekt/amtliche_Fassung/Teil_C_Chemie_2015_11_10.pdf
• Mayer, R. E. (2005). Cognitive Theory of Multimedia Learning. In R. Mayer (Ed.), The Cambridge Handbook of Multimedia Learning (pp. 31-48). Cambridge University Press. 
• Moosbrugger, H., Oehlschlägel, J., & Steinwascher, M. (2011). Frankfurter Aufmerksamkeits-Inventar 2: FAIR-2 (2., überarbeitete, ergänzte und normenaktualisierte Auflage des FAIR) Huber. https://www.testzentrale.de/shop/frankfurter-aufmerksamkeits-inventar-2.html 
• Preckel, F. (2016). NFC-Teens: Eine deutsche Need for Cognition Skala für ältere Kinder und Jugendliche GESIS – Leibniz-Institut für Sozialwissenschaften. 
• Sundararajan, N., & Adesope, O. (2020). Keep it Coherent: A Meta-Analysis of the Seductive Details Effect. Educational Psychology Review, 32(3), 707-734. 
• Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive Architecture and Instructional Design: 20 Years Later. Educational Psychology Review, 31(2), 261-292. 
• van Vorst, H., Dorschu, A., Fechner, S., Kauertz, A., Krabbe, H., & Sumfleth, E. (2014). Charakterisierung und Strukturierung von Kontexten im naturwissenschaftlichen Unterricht – Vorschlag einer theoretischen Modellierung. Zeitschrift für Didaktik der Naturwissenschaften, 21, 1-11. 
• Wilhelm, O., Schroeders, U., & Schipolowski, S. (2014). Berliner Test zur Erfassung fluider und kristalliner Intelligenz für die 8. bis 10. Jahrgangsstufe (BEFKI 8-10).  

Arne Petter & Rüdiger Tiemann: Einfluß von Persönlichkeit  und Interesse auf wiss. Denken und Arbeiten

• Ainley, M. (2017). Interest: Knowns, Unknowns, and Basic Processes. In P. A. O'Keefe & J. M. Harackiewicz (Hg.), The Science of Interest (S. 3–24). Springer International Publishing. 
• Arndt, K. (2016). Experimentierkompetenz erfassen. Analyse von Prozessen und Mustern am Beispiel von Lehramtsstudierenden der Chemie. Humboldt-Universität zu Berlin. 
• Borghans, L., Golsteyn, B. H. H., Heckman, J. J., & Humphries, J. E. (2016). What grades and achievement tests measure. PNAS, 113(47), 13354–13359. 
• Hübner, N., Spengler, M., Nagengast, B., Borghans, L., Schils, T., & Trautwein, U. (2022). When academic achievement (also) reflects personality: Using the personality-achievement saturation hypothesis (PASH) to explain differential associations between achievement measures and personality traits. Journal of Educational Psychology, 114. 
• Jansen, M., Lüdtke, O., & Schroeders, U. (2016). Evidence for a positive relation between interest and achievement: Examining between-person and within-person variation in five domains. Contemporary Educational Psychology, 46, 116–127. 
• Krapp, A. (2002). Structural and dynamic aspects of interest development: theoretical considerations from an ontogenetic perspective. Learning and Instruction, 12(4), 383–409. 
• Krapp, A., & Prenzel, M. (2011). Research on Interest in Science: Theories, methods, and findings. International Journal of Science Education, 33(1), 27–50. 
• Kultusministerkonferenz (KMK). (2020). Bildungsstandards im Fach Chemie für die Allgemeine Hochschulreife. Sekretariat der Ständigen Konferenz der Kultusminister der Länder in der Bundesrepublik Deutschland. 
• McCrae, R. R., & Costa, P. T. (1987). Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52. 
• Meyer, J., Fleckenstein, J., Retelsdorf, J., & Köller, O. (2019). The relationship of personality traits and different measures of domain-specific achievement in upper secondary education. Learning and Individual Differences,69. 
• Schiefele, U., Krapp, A., & Winteler, A. (1992). Interest as a predictor of academic achievement: A meta-analysis of research. In K. A. Renninger, S. Hidi, & A. Krapp (Hg.), The role of interest in learning and development (S. 183–212). Erlbaum. 
• Sonnenschein, I. (2019). Naturwissenschaftliche Denk- und Arbeitsprozesse Studierender im Labor Humboldt-Universität zu Berlin. 
• Trautwein, U., Nagengast, B., Roberts, B., & Lüdtke, O. (2019). Predicting Academic Effort: The Conscientiousness × Interest Compensation (CONIC) Model. In K. A. Renninger & S. E. Hidi (Hg.), The Cambridge Handbook of Motivation and Learning (S. 353–372). Cambridge University Press. 
• Urhahne, D., & Wijnia, L. (2023). Theories of Motivation in Education: an Integrative Framework. Educational Psychology Review, 35(2), 45. 
• Ziegler, M., Danay, E., Heene, M., Asendorpf, J., & Bühner, M. (2012). Openness, fluid intelligence, and crystallized intelligence: Toward an integrative model. Journal of Research in Personality, 46(2), 173–183. 

Markus Thiel & Rüdiger Tiemann: Digitallabor für Mathematik im Chemiestudium

• Albaladejo, J. D. P., Broadway, S., Mamiya, B., Petros, A., Powell, C. B., Shelton, G. R., Walker, D. R., Weber, R., Williamson, V. M., & Mason, D. (2018). ConfChem Conference on Mathematics in Undergraduate Chemistry Instruction: MUST-Know Pilot Study—Math Preparation Study from Texas. Journal of Chemical Education, 95(8), 1428–1429. https://doi.org/10.1021/acs.jchemed.8b00096
• Helmke, A. (2014). Unterrichtsqualität und Lehrerprofessionalität. Diagnose, Evaluation und Verbesserung des Unterrichts (5. überarbeitete Aufl., Schule weiterentwickeln – Unterricht verbessern. Orientierungsband). Seelze: Klett-Kallmeyer.
• Koedinger, K. R., Blaser, M., McLaughlin, E. A., Cheng, H., & Yaron, D. J. (2025). Mathematics matters or maybe not: An astonishing independence between mathematics and the rate of learning in general chemistry. JACS Au, 5(3), 1268–1278. https://doi.org/10.1021/jacsau.4c01126
• Mayring, P. (2000). Qualitative Inhaltsanalyse. Grundlagen und Techniken (7. Aufl.). Weinheim: Deutscher Studien Verlag.
• McCluskey, A. R., Rivera, M., & Mey, A. S. J. S. (2024). Digital skills in chemical education. Nature Chemistry, 16(9), 1383–1384. https://doi.org/10.1038/s41557-024-01613-x
• Potgieter, M., Harding, A., & Engelbrecht, J. (2008). Transfer of algebraic and graphical thinking between mathematics and chemistry. Journal of Research in Science Teaching, 45(2), 197–218. https://doi.org/10.1002/tea.20208
• Reeves, T. C., Herrington, J., & Oliver, R. (2005). Design research: A socially responsible approach to instructional technology research in higher education. Journal of Computing in Higher Education, 16(2), 96–115. https://doi.org/10.1007/BF02961476
• Schmidt, I. & Di Fuccia, D. (2014). Mathematical models in chemistry lessons. In Conference proceedings. New perspectives in science education (p. 313).
• Weiss, C. J. (2017). Scientific computing for chemists: An undergraduate course in simulations, data processing, and visualization. Journal of Chemical Education, 94(5), 592–597. https://doi.org/10.1021/acs.jchemed.7b00078
• Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

Smita Singh & Rüdiger Tiemann: SURAL-AI

• Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007
• Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111
• Kingir, S., Tas, Y., Gok, G., & Vural, S. S. (2013). Relationships among constructivist learning environment perceptions, motivational beliefs, self-regulation and science achievement. Research in Science & Technological Education, 31(3), 205–226. https://doi.org/10.1080/02635143.2013.825594
• Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press. https://doi.org/10.1017/CBO9780511815355
• Rose, D. H., & Meyer, A. (2006). A practical reader in universal design for learning. Harvard Education Press.
• Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100014. https://doi.org/10.1016/j.caeai.2021.100014
• Koolen, R., van Vugt, F., & Dalmijn, M. (2024). LLM-as-a-judge: A user-centric and task-oriented evaluation of LLMs. arXiv. https://arxiv.org/abs/2404.05350
• Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

Veronique Dünkler & Rüdiger Tiemann: Informatives tutorielles Feedback (ITF)