[
Böcherer-Linder, K., & Eichler, A. (2017). The impact of visualizing nested sets. An empirical study on tree diagrams and unit squares. Frontiers in psychology 7, 241.
]Search in Google Scholar
[
Cohen, A. L., & Staub, A. (2015). Within-subject consistency and between-subject variability in Bayesian reasoning strategies. Cognitive Psychology, 81, 26–47.10.1016/j.cogpsych.2015.08.001
]Search in Google Scholar
[
De Finetti, B. (2017). Theory of Probability: A Critical Introductory Treatment. New York, NY: John Wiley & Sons.10.1002/9781119286387
]Search in Google Scholar
[
Edwards, W. (1968). Conservatism in human information processing. In B. Kleinmuntz (Ed.), Formal Representation of Human Judgment (pp. 17-52). New York, NY: Wiley.
]Search in Google Scholar
[
Gallistel, C.R. (2011). Mental magnitudes. In S. Dehaene, E.M. Brannon (Eds.), Space, time and number in the brain: Searching for the foundations of mathematical thought (pp. 3-12). London: Elsevier.
]Search in Google Scholar
[
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological Review, 103(4), 650.10.1037/0033-295X.103.4.650
]Search in Google Scholar
[
Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological. Review, 102, 684–704.10.1037/0033-295X.102.4.684
]Search in Google Scholar
[
Heitzman, N., Seidel, T., Opitz, A., Hetmanek, A., Wecker, C., Fischer, M., & Fischer, F. (2019). Facilitating Diagnostic Competences in Simulations: A Conceptual Framework and a Research Agenda for Medical and Teacher Education. Frontline Learning Research, 7(4), 1–24.10.14786/flr.v7i4.384
]Search in Google Scholar
[
Herppich, S., Praetorius, K., Förster, N., Glogger-Frey, I., Karst, K., Leutner, D., Behrmann, L., Böhmer, M., Ufer, S., Klug, J., Hetmanek, A., Ohle, A., Böhmer, I., Karing, C., Kaiser, J., &
]Search in Google Scholar
[
Herppich, S., Wittwer, J., Nückles, M., & Renkl, A. (2016). Expertise amiss: Interactivity fosters learning but expert tutors are less interactive than novice tutors. Instructional Science, 44, 205–219.10.1007/s11251-015-9363-8
]Search in Google Scholar
[
Hoppe, T., Renkl, A., & Rieß, W. (2020). Förderung von unterrichtsbegleitendem Diagnostizieren von Schülervorstellungen durch Video und Textvignetten. Unterrichtswissenschaft, 48, 573–597.10.1007/s42010-020-00075-7
]Search in Google Scholar
[
Johnson-Laird, P. N. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness. Harvard University Press.
]Search in Google Scholar
[
Jøsang, A. (2016). Generalising Bayes’ theorem in subjective logic. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (pp.462–469). Baden-Baden.10.1109/MFI.2016.7849531
]Search in Google Scholar
[
Juslin, P., Lindskog, M., & Mayerhofer, B. (2015). Is there some-thing special with probabilities? Insight vs. computational ability in multiple risk combination. Cognition, 136, 282–303.10.1016/j.cognition.2014.11.041
]Search in Google Scholar
[
Juslin, P., Nilsson, H., & Winman, A. (2009). Probability theory, not the very guide of life. Psychological Review, 116(4), 856–874.10.1037/a0016979
]Search in Google Scholar
[
Kahneman, D., & Tversky, A. (1996). On the reality of cognitive illusions. Psychological Review, 103(3), 582–591.10.1037/0033-295X.103.3.582
]Search in Google Scholar
[
Khemlani, S. S., Lotstein, M., & Johnson‐Laird, P. N. (2015). Naive probability: Model‐based estimates of unique events. Cognitive Science, 39(6), 1216–1258.10.1111/cogs.12193
]Search in Google Scholar
[
Krolak-Schwerdt, S., Pitten Cate, I. M., & Hörstermann, T. (2018). Teachers’ judgments and decision-making: Studies concerning the transition from primary to secondary education and their implications for teacher education. In Assessment of Learning Outcomes in Higher Education (pp. 73–101). Springer, Cham.10.1007/978-3-319-74338-7_5
]Search in Google Scholar
[
Leibovich, T., Katzin, N., Harel, M., & Henik, A. (2017). From ‘sense of number’ to ‘sense of magnitude’ – the role of continuous magnitudes in numerical cognition. Behavioral and Brain Sciences, 40, E164.10.1017/S0140525X16000960
]Search in Google Scholar
[
Leuders, T., & Loibl, K. (2020). Processing probability information in non-numerical settings – teachers’ bayesian and non-bayesian strategies during diagnostic judgment. Frontiers in Psychology, 11, 678.10.3389/fpsyg.2020.00678
]Search in Google Scholar
[
Loibl, K., & Leuders, L. (2020). “Take the middle” – Averaging prior and evidence as effective heuristic in bayesian reasoning. In S. Denison., M. Mack, Y. Xu, & B.C. Armstrong (Eds.), Proceedings of the 42nd Annual Conference of the Cognitive Science Society (pp. 1764–1770). Cognitive Science Society.
]Search in Google Scholar
[
Loibl, K., Leuders, T., & Dörfler, T. (2020). A framework for explaining teachers’ diagnostic judgements by cognitive modeling (DiaCoM). Teaching and Teacher Education, 91, 103059.10.1016/j.tate.2020.103059
]Search in Google Scholar
[
Lopes, L. L. (1985). Averaging rules and adjustment processes in Bayesian inference. Bulletin of the Psychonomic Society, 23, 509–512.10.3758/BF03329868
]Search in Google Scholar
[
Mandel, D. R. (2014). The psychology of bayesian reasoning. Frontiers in Psychology, 5, 1144.10.3389/fpsyg.2014.01144
]Search in Google Scholar
[
Martignon, L., Vitouch, O., Takezawa, M., & Forster, M. R. (2003). Naive and yet enlightened: From natural frequencies to fast and frugal decision trees. In D. Hardman & L. Macchi (Eds.), Thinking: Psychological perspective on reasoning, judgment, and decision making (pp. 189–211). Chichester, UK: Wiley.
]Search in Google Scholar
[
Nickerson, R. S. (1999). How we know—and sometimes misjudge—what others know: Imputing one’s own knowledge to others. Psychological Bulletin, 125, 737–759.10.1037/0033-2909.125.6.737
]Search in Google Scholar
[
Phelps-Gregory, C. M., & Spitzer, S. M. (2018). Developing prospective teachers’ ability by classroom intervention: Replicating a classroom intervention. In T. Leuders, J. Leuders, & K. Philipp (Eds.), Diagnostic Competence of Mathematics Teachers: Unpacking a complex construct in teacher education and teacher practice (pp. 223–240). New York: Springer.
]Search in Google Scholar
[
Richter-Gebert, J., & Kortenkamp, U. H. (2000). User manual for the interactive geometry software cinderella. Springer Science & Business Media.10.1007/978-3-642-58318-6
]Search in Google Scholar
[
Shanteau, J. (1975). Averaging versus multiplying combination rules of inference judgement. Acta Psychologica, 39, 83–89.10.1016/0001-6918(75)90023-2
]Search in Google Scholar
[
Simon, H. A. (1995). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.10.2307/1884852
]Search in Google Scholar
[
Südkamp, A. (2018). Teachers’ assessment competence: Integrating knowledge-, process-, and product-oriented approaches into a competence-oriented conceptual model. Teaching and Teacher Education, 76, 181–193.10.1016/j.tate.2017.12.001
]Search in Google Scholar
[
Südkamp, A., Kaiser, J., & Möller, J. (2012). Accuracy of teachers’ judgments of students’ academic achievement: A meta-analysis. Journal of educational psychology, 104(3), 743.10.1037/a0027627
]Search in Google Scholar
[
Sundh, J. (2019). The Cognitive Basis of Joint Probability Judgments. Processes, Ecology, and Adaption. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences, 166. Uppsala: Acta Universitatis Upsaliensis.
]Search in Google Scholar
[
Villejoubert, G., & Mandel, D. R. (2002). The inverse fallacy: an account of deviations from Bayes’ theorem and the additivity principle. Memory & Cognition, 30(2), 171–178.10.3758/BF03195278
]Search in Google Scholar
[
Weber, P., Binder, K., & Krauss, S. (2018). Why can only 24% solve Bayesian reasoning problems in natural frequencies: Frequency phobia in spite of probability blindness. Frontiers in Psychology, 9, 1833.10.3389/fpsyg.2018.01833
]Search in Google Scholar
[
Witzigmann, St., Sachse, St. (2021). Diagnostic competencies of prospective teachers of French as a foreign language: judgement of oral language samples. RISTAL, 4, 70–86.
]Search in Google Scholar
[
Zhu, L., & Gigerenzer, G. (2006). Children can solve Bayesian problems: The role of representation in mental computation. Cognition, 98, 287–308.10.1016/j.cognition.2004.12.003
]Search in Google Scholar
