Simona Daguati


Last Name

Daguati

First Name

Simona

ORCID

0009-0001-4706-2909

Organisational unit

02871 - Didaktische Ausbildung

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2020 - 202510
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Publications 1 - 10 of 10
  • Optimizing the Learning Process in Higher Education: The Six Process Features of Learning
    Item type: Journal Article
    Thurn, Christian Maximilian; Daguati, Simona (2025)
    Zeitschrift für Hochschulentwicklung
    Human learning is characterised by six process features: learning is active, constructive, emotional, self-regulated, situated, and social. However, teaching often fails to adequately honour these features, particularly within higher education. To address this issue, we explain the six process features of learning and present examples of how to integrate them in class. As this article is intended for lecturers teaching at higher education institutions, we offer methodological guidance by providing additional material including visualizations, a didactic self-assessment tool, and posters that can be applied in diverse classroom settings, aiming to enhance teaching practices across disciplines.
  • Students’ opinions on a digital tool for constructing and analyzing concept maps
    Item type: Other Conference Item
    Daguati, Simona; Thurn, Christian Maximilian (2024)
    Abstracts Congress of the German and Austrian Psychological Society, Vienna 2024
    Concept maps organize knowledge elements visually: They visualize concepts and their relations via nodes and links, while all relationships need to be justified by the learner. Concept maps stimulate constructive learning processes and capture knowledge gaps. They can therefore be utilised to support learning as well as to assess knowledge structures. A big challenge, however, lies in the analysis of concept maps. They are difficult to interpret and time-consuming to evaluate. Network analysis provides an objective and scalable approach to analyse concept maps. It addresses questions such as: Which concepts are well-connected and central? How does the aggregated concept map across learners look like? How do learners’ maps correspond to an expert map? To benefit from network analysis with concept maps in teaching, we developed the web-based R Shiny dashboard ConceptMappR, which will be freely available. We used the dashboard in three different courses at a Swiss university. To further refine the tool, we asked students to fill in a survey on its usability. Our research question was: How do students perceive the tool and which suggestions for improvement do they have? Answers from 25 participants showed that students viewed the tool as intuitive and powerful. A System Usability Score of 72.32 indicated an acceptable usability. A mean Net Promoter Score of 6.1 on a scale from 0 to 10 suggested that students would likely recommend the tool to colleagues or friends, although there is room for improvement.
  • Promoting Learning of Mathematical Functions by Cognitively Activating Instructions
    Item type: Conference Poster
    Daguati, Simona; Braas, Thomas; Hungerbühler, Norbert; et al. (2025)
  • Problem‑solving before instruction for learning linearalgebra in university mathematics
    Item type: Journal Article
    Baumgartner, Vera; Daguati, Simona; Trninic, Dragan; et al. (2025)
    Instructional Science
    Problem-solving before instruction has been shown to be a more effective learning design than traditional tell-and-practice for several mathematical concepts at the secondary school level. In particular, the more a problem-solving before instruction design follows the productive failure principles, such as comparing and contrasting student-generated solutions, the higher the effect on students’ conceptual understanding and transfer. University mathematics education poses several inherent constraints that complicate the implementation of these principles. In the present study, we implemented a problem-solving before instruction design in a university linear algebra course adhering to the productive failure principles as closely as possible. Participation in the preparatory problems was voluntary. We investigated the effect on students’ learning over four one-year iterations in a design-based research approach. Compared to the baseline (aggregate of cohorts prior to the intervention), we observed a significant increase in final exam performance for all four cohorts with effect sizes between Cohen’s d = 0.28 and d = 0.59. For students who agreed to further analyses, our results show that up to 16% of the variance in students’ performance can be explained by variance in their participation in the problem-solving before instruction design. As our design did not include a control group, we refrain from conclusions regarding any design components that might have caused these effects. However, these results are promising, given that our implementation involved only minor changes to the original course structure and required little extra time for students.
  • Mathematisches Problemlösen auf gymnasialer Stufe: Anforderungen an das Arbeitsgedächtnis
    Item type: Other Conference Item
    Daguati, Simona; Stern, Elsbeth (2020)
    Beiträge zum Mathematikunterricht 2020: 54. Jahrestagung der Gesellschaft für Didaktik der Mathematik
  • Describing Conceptual Knowledge in Concept Maps by Network Analysis
    Item type: Other Conference Item
    Daguati, Simona; Rütsche, Bruno; Thurn, Christian Maximilian (2024)
    Conceptual Change in the Era of Digital Transformation: 13th International Conference on Conceptual Change - Programm & Abstracts
    Whereas conceptual change research has identified how concepts can impede or support learning, there is no consensus on a standardized method for evaluating the quality of knowledge structures. Identifying indices of the quality of knowledge networks would help to derive a more general theory of how knowledge structures hinder or facilitate learning. One method to assess learners’ knowledge structures is concept mapping. As concept maps represent networks of concepts and their relations, they can be analyzed via network analysis. To this end, we developed a R Shiny dashboard that allows students to draw and analyze their maps. To judge the validity of different network indices, we accompanied the use of this dashboard in three university courses by the Learning Strategies of University Students questionnaire. From a convergent validity perspective, we will evaluate which network indices are indicative of elaborate learning strategies, and thus meaningful learning.
  • The Six Process Features of Learning
    Item type: Educational Material
    Thurn, Christian Maximilian; Daguati, Simona; Gattlen, Estelle (2025)
  • ConceptMappR: Ein digitales Tool zum Erstellen und zur Analyse von Concept Maps
    Item type: Other Conference Item
    Thurn, Christian Maximilian; Daguati, Simona; Rütsche, Bruno (2024)
    6. Tagung Fachdidaktiken: Fachdidaktiken als vernetzende Wissenschaften. Dokumentation
    Concept Mapping ist eine Methode zur visuellen Organisation von Wissen, die sich zur Abbildung fachspezifischer Konzeptzusammenhänge eignet und in verschiedensten Fächern gewinnbringend eingesetzt werden kann. Concept Maps regen einerseits tiefe, konstruktive Lernprozesse an und erfassen andererseits das Wissen und die Fehlvorstellungen der Lernenden (Novak, 1990; Wallace & Mintzes, 1990). Concept Maps visualisieren Konzepte und ihre Beziehungen ähnlich wie Mind Maps, mit dem Unterschied, dass alle Beziehungen begründet werden müssen und mehrere zentrale Knotenpunkte existieren können. Als Visualisierungsmethode regen Concept Maps zu vernetztem Denken an (Buzan, 2006; Stähli, 2014) und helfen, komplexe Informationen zu organisieren. Zudem fördern sie kritisches Denken (Khajeloo & Siegel, 2022). Schliesslich bieten Concept Maps Lehrenden die Möglichkeit, vernetztes anstatt isoliertes Wissen zu prüfen. Die Nutzung von Concept Maps ist jedoch herausfordernd für Lehrende, weil Concept Maps im Vergleich zu anderen Formative Assessment Methoden schwierig zu interpretieren und aufwändig in der Auswertung sind (Kinchin, 2001). Bei der gegenwärtigen Interpretation und Analyse von Concept Maps wird typischerweise heuristisch nach bestimmten Strukturen in der Map gesucht, wie z. B. Begriffsketten oder zentralen Knotenpunkten, oder es wird lediglich ein subjektiver, intuitiver Gesamteindruck der Kohärenz gebildet. Diese kaum objektivierbare Herangehensweise ist aufgrund des erforderlichen Zeitaufwandes zudem nicht skalierbar für eine grossen Anzahl Lernende. Netzwerkanalysen bieten sowohl die Möglichkeit, Concept Maps objektiv auszuwerten, als auch die Option, verschiedene Concept Maps zu aggregieren. Mit dieser Art der Analyse können unter anderem folgende Aspekte beurteilt werden: Welche Konzepte sind in der Concept Map gut vernetzt und damit zentral? Welche Konzepte wurden von den Lernenden ausgelassen? Wie sieht die aggregierte Concept Map über alle Lernenden hinweg aus? Welche Unterschiede weisen die Concept Maps der Lernenden im Vergleich zu Referenz-Concept Maps von Expert:innen auf? Diese Analysen liefern nicht nur wertvolle Informationen für Lehrende, sondern können auch genutzt werden, um Lernenden gezieltes Feedback zu geben. Um solche Netzwerkanalysen von Concept Maps in der Lehre gewinnbringend einsetzen zu können, wurde im vorliegenden Projekt das webbasierte Dashboard «ConceptMappR» mit R Shiny entwickelt. In ConceptMappR können Concept Maps sowohl erstellt als auch mittels Netzwerkanalyse analysiert werden. Bei der Erstellung können Wissenselemente interaktiv hinzugefügt, gelöscht und umgeordnet sowie Verknüpfungen zwischen Elementen beschriftet werden. Die finale Concept Map kann daraufhin exportiert werden. Zur Auswertung können Concept Maps in vielfältigen Formaten importiert und durch verschiedene Netzwerkanalyseverfahren objektiv ausgewertet werden. Es können wichtige Verbindungen, die Zentralität von Konzepten sowie Unterschiede zwischen verschiedenen Concept Maps dargestellt und untersucht werden. Neben der Analyse von einzelnen Concept Maps ist es auch möglich, mehrere Concept Maps, z. B. von verschiedenen Lernenden zu aggregieren, und Vergleiche mit einer Referenz-Concept Map durchzuführen. Das Dashboard wird in einem ersten Schritt in vier Lehrveranstaltungen einer Schweizer Universität mit Dozierenden aus unterschiedlichen Fachgebieten, nämlich Biologie, Erziehungswissenschaften, Informatik und Physik, eingesetzt. Die Erfahrungen und Perspektiven der Lernenden und Lehrenden werden begleitend evaluiert und zur Weiterentwicklung des Dashboards genutzt. In der Präsentation werden das Dashboard sowie die Ergebnisse dieser Evaluation vorgestellt. Damit ConceptMappR zu einem späteren Zeitpunkt allen Interessierten zur Verfügung steht, ist eine Lizenzierung als Open Source Software vorgesehen. Bibliografie | Bibliographie Buzan, T. (2006). The mind map book. Pearson Education. Khajeloo, M., & Siegel, M. A. (2022). Concept map as a tool to assess and enhance students' system thinking skills. Instructional Science, 50(4), 571-597. Kinchin, I. M. (2001). If concept mapping is so helpful to learning biology, why aren't we all doing it?. International Journal of Science Education, 23(12), 1257-1269. Novak, J. D. (1990). Concept mapping: A useful tool for science education. Journal of research in science teaching, 27(10), 937-949. https://doi.org/10.1002/tea.3660271003 Stähli, L. (2014). Thinkmap: Plattformübergreifende Visualisierung interaktiver Wissensnetzwerke. In Keller, S. A., Schneider, R., & Volk, B. (Eds.). Wissensorganisation und -repräsentation mit digitalen Technologien (Vol. 55). de Gruyter. Wallace, J.D.; Mintzes, J.J. (1990) The concept map as a research tool: Exploring conceptual change in biology. Journal of Research in Science Teaching, 27, 1033–1052.
  • Working Memory Requirements for Problem Solving in Advanced School Mathematics
    Item type: Doctoral Thesis
    Daguati, Simona (2022)
    The current study focused on the interplay between general reasoning abilities, working memory functions and math achievement in order to investigate the role of working memory in advanced school mathematics. Based on mathematics test scores and scores on a measure of general reasoning ability (three subtests from the Kognitiver Fähigkeits-Test (KFT), Heller & Perleth, 2000), a sample comprising high-achieving, over-achieving and under-achieving students at advanced placement schools in Switzerland (Swiss Gymnasium) was chosen (N = 120, Mage = 16.3 years). All study participants underwent a working memory test battery arranged by von Bastian and Oberauer (2013), which contains nine different tests targeting all facets of working memory described by the facet model (Oberauer, Süß, Schulze, Wilhelm, & Wittmann, 2000; Oberauer, Süß, Wilhelm, & Wittman, 2003; Süß, Oberauer, Wittmann, Wilhelm, & Schulze, 2002). In addition, they completed a questionnaire on mathematical self-concept and interest in mathematics (the majority of the questionnaire items was utilised in PISA 2000, the items were taken from Gaspard et al., 2015, 2018; Mang et al., 2018; Trautwein, Lüdtke, Marsh, Köller, & Baumert, 2006). Furthermore, two mathematics tests were administered to the study participants, namely a speed test aiming at the knowledge representations of the students and a power test covering mathematical problem solving tasks. For the data analysis, group comparisons between the different groups of students and within the group of high-achievers were carried out. The findings regarding the group comparison between highachieving and over-achieving students suggested that different factors, such as knowledge structures, mathematical self-concept and interest in mathematics could play a role in explaining over-achievement in mathematics. The results related to the group comparison between high-achievers and underachievers identified a poorer mathematical self-concept and a lower performance of the working memory function Storage-Processing as potential root causes of under-achievement in mathematics. Moreover, they showed that in the sample of the current study, under-achievement in mathematics cannot be ascribed to a scarce interest in mathematics. Within the group of high-achievers, the motivational variables of mathematical self-concept and interest in mathematics seemed to be relevant for discriminating between the subgroup of high-achievers with top scores in the Mathematics Power Test and the subgroup of high-achievers with lower scores in the Mathematics Power Test.
  • ConceptMappR: A digital tool for creating and analyzing concept maps
    Item type: Other Conference Item
    Thurn, Christian Maximilian; Daguati, Simona; Rütsche, Bruno (2025)
    Concept mapping is a powerful method for organizing and visualizing relational knowledge. Concept maps stimulate deep, constructive learning processes while capturing learners’ conceptions about a topic. They are thus an important tool in science education. However, using and analysing concept maps is challenging for teachers as concept maps are difficult and time-consuming to analyse. This workshop introduces ConceptMappR, a digital dashboard for drawing and analysing concept maps. The analysis draws on graph theoretical measures (also known as network analysis). These measures allow objective comparisons of concept maps, highlighting similarities and differences. ConceptMappR allows for an efficient analysis of concept maps in contrast to traditional methods which are time-consuming and do not scale well. Also for a larger number of concept maps, ConceptMappR can be valuable by its function of aggregating concept maps. The aim of this workshop is to familiarise lecturers and researchers with ConceptMappR. The aim is that after the workshop, participants will be able to 1) explain the value of concept maps as a formative assessment and support method, 2) use ConceptMappR to draw and analyse concept maps, and 3) generate ideas for applying ConceptMappR in their own teaching and research. Participants from all fields and career stages are welcome to join this interactive workshop. The dashboard is freely available at https://conceptmappr.vlab.ethz.ch/.
Publications 1 - 10 of 10