Open Access

Mathematics teachers’ motivational and emotional orientations regarding digital tools in mathematics classrooms are key aspects influencing whether and how technology is used to teach mathematics—making the support of those characteristics one central goal for teacher education. In this article we investigated if and how a workshop-based in-service teacher training can foster teachers’ perceived value of digital media in mathematics education, their self-efficacy, and their anxiety towards teaching mathematics with digital tools. In an intervention study with N = 83 in-service teachers with varying teaching experience, we used cluster analysis based on their experience, value, self-efficacy, and anxiety before the intervention to determine three different teacher orientations regarding teaching mathematics with digital tools. Paired sample t-tests with pretest and posttest data revealed that for two of three clusters these beliefs, motivation, and emotions changed in a positive way during the intervention while for the third no change was found. Our study sheds light on the role of motivational and emotional orientations for the implementation of digital tools in mathematics education: it shows that these orientations can be utilized to cluster teachers on this topic and illustrates that these orientations can be successfully fostered—while individual differences may exist in the effect and success of interventions.

Tasks in which learners are asked to compare two data sets using box plots and decide which distribution contains more observations above a given threshold have already been investigated in research. There are indications that these tasks are solved schema-based and that different (correct and erroneous) schemas are used depending on the arrangement of the quartiles around the threshold. Erroneous schemas can cause systematic errors and are often based on typical misconceptions. For example, if learners did not complete the conceptual change and assume that in box plots – like in most other statistical representations (e.g., bar or circle diagrams) - more (box) area also represents more observations, they decide the task according to which box plot shows more box area above the threshold. However, this can lead to incorrect answers, as the box area does not represent frequency but the range of the middle half of the data (interquartile range) and thus a measure of variability. So far, these schema-based reasoning processes have mainly been investigated via differences in solution rates of congruent and incongruent items. The present study investigates whether eye-tracking data can help to better understand which information is processed in the different schemas. Our research interest is based on hypotheses specifying which box plot components are significantly involved in the different schemas. We assume that the gaze patterns of learners using different schemas differ both regarding the number and duration of fixations on the relevant box plot components (areas of interest) and in terms of the number of transitions between them. We asked N = 14 participants to solve congruent and incongruent items and simultaneously collected eye movement data. In the analysis, we first used the solution rates to assign the schemas most likely used. Subsequently, the eye-tracking data were analyzed regarding differences in line with our hypotheses. We found hypothesis-compliant effects in all schemas regarding the number of fixations and transitions, but not regarding fixation duration. These results not only validate the schemas identified in previous studies, but also indicate that the schemas differ primarily in terms of which quartile is focused.

Abstract Mathematical word problem solving is influenced by various characteristics of the task and the person solving it. Yet, previous research has rarely related these characteristics to holistically answer which word problem requires which set of individual cognitive skills. In the present study, we conducted a secondary data analysis on a dataset of N = 1282 undergraduate students solving six mathematical word problems from the Programme for International Student Assessment (PISA). Previous results had indicated substantial variability in the contribution of individual cognitive skills to the correct solution of the different tasks. Here, we exploratively reanalyzed the data to investigate which task characteristics may account for this variability, considering verbal, arithmetic, spatial, and general reasoning skills simultaneously. Results indicate that verbal skills were the most consistent predictor of successful word problem solving in these tasks, arithmetic skills only predicted the correct solution of word problems containing calculations, spatial skills predicted solution rates in the presence of a visual representation, and general reasoning skills were more relevant in simpler problems that could be easily solved using heuristics. We discuss possible implications, emphasizing how word problems may differ with regard to the cognitive skills required to solve them correctly.

Abstract The integration of dynamic visualisations, feedback formats and digital tools is characteristic of state-of-the-art digital mathematics textbooks. Although there already is evidence that students can benefit from these technology-based features in their learning, the direct comparison between the use of a comparable digital and printed resource has not yet been sufficiently investigated. We address this research gap by contrasting the use of an enriched digital textbook that includes these features and comparable printed materials without them. To do so, we investigate the achievement of 314 students in a pretest-posttest control group design in a five-hour series of lessons on conditional probability. Using the Rasch model and mixed ANOVA, the results indicate that students can benefit from digital textbook features, especially compared to the use of comparable printed materials. In line with other studies on mathematical achievement and the use of digital resources, our study also shows differences between boys and girls. It seems that particularly girls benefit from the use of the digital textbook, whereas, for the boys, it does not seem to make a difference what kind of resources they use. The group and gender differences are discussed against the background of other studies considering that, especially in Bayesian situations, the way statistical situations are visualised can be decisive for a student’s performance.

Research on fraction comparison shows that students often follow biased comparison strategies, in particular such strategies that build on their knowledge of natural numbers. On the other hand they also apply successful comparison strategies such as benchmarking or fraction magnitude processing. Which strategies are applied or even combined depends on the students’ knowledge and on the task type. To investigate these complex relationships, we developed a balanced 2 × 2-dimensional itemset (congruent vs. incongruent items; benchmarking vs. non-benchmarking items) and a Bayesian classification of individual students’ performance (solution patters, response time, and individual distance effect), which we applied to an assessment of N = 350 sixth graders. We could show that the classification of the students with respect to possible solution strategies matched our hypotheses: We could replicate existing patterns and found additional composite strategies such as ‘benchmarking or bias‘ with a bias only in solution rates of non-benchmark items. In further analyses we found ‘benchmarking or suppressed bias-strategies (i.e., a bias in problem solving time of non-benchmarking items). Our study extends previous knowledge on individual strategies in fraction comparison and proposes a new person-centered approach to classify individual student profiles even with small profile sizes.