gms | German Medical Science

14. Workshop der gmds-Arbeitsgruppe "Computerunterstützte Lehr- und Lernsysteme in der Medizin (CBT)" und des GMA-Ausschusses "Neue Medien"

Institut für Didaktik & Bildungsforschung im Gesundheitswesen (IDBG),
Private Universität Witten/Herdecke

16.04. - 17.04.2010, Witten

Recognition of anatomical structures in CT images – relevance of cognitive load and spatial ability for the e-learning instructional design of dynamic and static visualizations

Analyse anatomischer Strukturen in CT-Bildern – Bedeutung des Cognitive Load und des räumlichen Vorstellungsvermögens für das e-learning Design dynamischer und statischer Visualisierungen

Meeting Abstract

  • corresponding author Britta Fricke - Institut für Diagnostische Radiologie, Interventionelle Radiologie, Neuroradiologie und Nuklearmedizin, Knappschaftskrankenhaus Bochum-Langendreer Universitätsklinik, Ruhr-Universität, Bochum, Deutschland
  • author Ferdinand Stebner - Lehrstuhl für Lehr-Lernforschung, Ruhr-Universität, Bochum, Deutschland
  • author Joachim Wirth - Lehrstuhl für Lehr-Lernforschung, Ruhr-Universität, Bochum, Deutschland
  • author Thorsten Schäfer - Medizinische Fakultät, Studiendekanat, Ruhr-Universität, Bochum, Deutschland
  • author Martin Fischer - Institut für Didaktik und Bildungsforschung im Gesundheitswesen, Witten-Herdecke, Deutschland
  • author Rolf Dermietzel - Abteilung für Neuroanatomie und Molekulare Hirnforschung, Institut für Anatomie, Ruhr-Universität, Bochum, Deutschland

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Gesellschaft für Medizinische Ausbildung. 14. Workshop der gmds-Arbeitsgruppe "Computerunterstützte Lehr- und Lernsysteme in der Medizin (CBT)" und des GMA-Ausschusses "Neue Medien". Witten, 16.-17.04.2010. Düsseldorf: German Medical Science GMS Publishing House; 2010. Doc10cbt31

DOI: 10.3205/10cbt31, URN: urn:nbn:de:0183-10cbt313

Veröffentlicht: 13. April 2010

© 2010 Fricke et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Background: Learning cross-sectional anatomy on CT (computed tomography) images is a complex task for students in gross anatomy computer-based training. In current research there is evidence, that for the evaluation of e-learning instructional designs more criteria are relevant than the measurement of the test performance. Up to now few studies exist, that take learner specific factors as learning strategies and cognitive processing into consideration.

Aim: Focus of this research was to analyze, if learner specific factors as the spatial ability and the cognitive load play a role in the learning process.

Method: 233 undergraduate medical students participated in the training of CT images of the human heart in an e-learning setting. In a 2x2 factorial design the effects of visualization (static versus dynamic images) and practical training with a 3D-model of the heart were analyzed. Data collection included: (1) students performance (knowledge); (2) spatial ability evaluated with the Logical Ordering Test (LOT), the Paper Folding Test (PFT) and Closure Flexibility (CF); (3) mental effort and mental efficiency.

Results: Spatial ability (LOT, PF, CF) significantly correlated with the students performance. The LOT showed a high potential in differentiating between static and dynamic visualizations. Analyzing mental efficiency and mental effort aptitude-treatment interactions were found for visualization method (static-dynamic) and spatial ability. Students with high LOT levels had an advantage in learning with static visualizations, whereas for students with low LOT levels static visualizations induced high levels of mental effort. Their individual learning was supported by dynamic visualization (high mental efficiency and low mental effort). For the practical training (3D-heart model) the test for CF was more relevant than LOT and PFT. Students with low CF levels showed more efficient learning with dynamic whereas those with high CF level with static visualizations.

Conclusions: Learner specific factors as spatial ability and cognitive load interfere in an e-learning scenario and should be measured in research on the effectiveness of learning with visualizations. In addition to the test performance these factors play an important role in the evaluation of instructional designs. In complex tasks like CT image analysis demands on learners are so complex that more than one factor of spatial ability are crucial for learning. Specific spatial ability tests should be selected dependend on the instructional design of the e-learning environment.


References

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