Enhancing Teachers' Visual Analysis Skills for Learning Progress Data: A Training Evaluation

Steiner, Theresa Maria; Bosch, Jannis; Lüke, Timo; Wilbert, Jürgen

Abstract

The growing importance of diagnostic methods for monitoring learning progress in special education emphasizes the need for evidence based tools to evaluate pedagogical interventions or to document learning progress (Maggin et al., 2018). Similarly, in the context of learning disabilities, there is an increasing use of Single Case Designs, CBM , and DBI (Data-Based Individualization) that can be observed in scientific studies (e.g., Derek et al., 2024; Snyder et al., 2020). Visual inspection, commonly used to evaluate progress data (e.g., Fälth et al., 2024), often relies on intuitive assessments, making it prone to errors, particularly in cases involving continuous data trends (Zeuch et al., 2017; Wilbert et al., 2021). Studies suggest that targeted training can enhance the validity of judgments in visually analyzing learning progress graphs (Lane et al., 2021; Wolfe & Slocum, 2015). Building on these findings, we developed a training program to improve visual analysis skills, focusing on methodological concepts (e.g., interventioneffects, , datatrends,, measurement error). Our research interest focuses on the influence of an existing datatrend on judgment accuracy and whether a methodological training improves judgment accuracy. Additionally, participants' confidence in their evaluations was assessed. A total of N = 129 teacher students participated in a Randomized Control study . Participants analyzed 40 graphs before and after receiving either our video-based training or a control video. Results indicate that datatrends significantly reduced participants’ judgment accuracy while after the training, experimental group participants were less likely to misjudge graphs with trend effects, demonstrating that visual analysis skills can be effectively trained.