Lecture on Tuesday, November 10, 2026, at 4:00 p.m. sharp, Mathematics Lab, Building I, Ground Floor, Room I 1.08, Directions

Prof. Dr. Timo Leuders, Freiburg University of Education

How Do Teachers Make Diagnostic Judgments? – A Report from the DiaKoM Research Program

Diagnostic competence is a central prerequisite for adaptive teaching and encompasses professional dispositions, situation-specific processes of perception, interpretation, and decision-making, as well as diagnostic performance (Blömeke et al., 2015; Leuders et al., 2018). Accordingly, this field of research focuses on various perspectives, such as judgment accuracy, professional noticing, diagnostic decision-making processes, and formative assessment (Leuders et al., 2022).

The DiaKoM research program focuses in particular on the genesis of diagnostic judgments as an information-processing process in which characteristics of the situation and the teacher influence which information is perceived, interpreted, and integrated into a judgment (Loibl et al., 2020; Leuders et al., 2020). This presentation reports on new research developments within this perspective.

Studies on task diagnosis show how subject-specific pedagogical knowledge supports the identification and integration of task features that generate difficulty, and how situational conditions such as time pressure influence these processes (Rieu et al., 2020, 2022). Other studies examine how mindsets and the handling of ambiguity shape information processing (Özel et al., 2024, 2025) and how reorganizing knowledge can make professional knowledge more readily available for diagnostic processes (Schaffert, 2022).

New developments concern the use of external diagnostic tools. Teachers must interpret and evaluate diagnostic information from a subject-matter perspective and integrate it with their own observations. This increasingly applies to AI-supported diagnostic systems as well, whose conclusions must be reviewed and integrated into their own decisions (Hoppe et al., 2026). Professionally structured diagnostic models and targeted professional development are central to this (Leuders et al., 2019; Schulz et al., 2020).

This broadens the perspective from the genesis of diagnostic judgments to evidence-based diagnostic practice: Diagnostic competence increasingly encompasses the ability to select, evaluate, and integrate various internal and external sources of evidence to inform well-founded pedagogical decisions.

Lecture on Tuesday, December 15, 2026, at 4:00 p.m. sharp, Mathematics Lab, Building I, Ground Floor, Room I 1.08, Directions

Prof. Dr. Karin Binder & Leon Wiemann, University of Paderborn

The Significant Error: Debunking Misconceptions About p-Values and Overcoming Them Through Simulations

A basic understanding of statistical significance tests and p-values is mandatory for students in many departments. In light of media reports such as “Women use artificial intelligence significantly less often than men,” an understanding of statistical significance is also necessary for critically evaluating media claims. Misconceptions about p-values have also been widely documented among researchers, leading to a statement by the American Statistical Association on the use and interpretation of p-values (Wasserstein & Lazar, 2016). While some journals recommend reporting effect sizes, it remains questionable whether simply expanding the statistical information presented is sufficient to reduce misconceptions about p-values. In this presentation, we will introduce a study that investigated this question by systematically varying the statistical information presented in medical abstracts. Building on this, we will present simulation-based approaches—both enactive and computer-assisted—for introducing significance tests and p-values.