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Entlasten oder anregen? Effekte von Augmented Reality auf die kognitive Belastung

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Abstract

Ein in der Literatur häufig beschriebenes Potential beim (naturwissenschaftlichen) Lernen mit Augmented Reality (AR) ist die Reduktion der kognitiven Belastung – Cognitive Load. So wird etwa angenommen, dass das Einblenden digitaler Informationen während der Durchführung eines Experiments Kapazitäten des Arbeitsgedächtnisses freisetzt, sodass in der Folge mehr und effektiver gelernt wird. Die Studienlage zu dieser Annahme ist jedoch widersprüchlich, da, zum Beispiel, AR-angereicherte Lernumgebungen von Lernenden als kognitiv überfordernd wahrgenommen wurden. Basierend auf den Ergebnissen einer Systematic Review werden in diesem Beitrag Ursachen für diese gegensätzlichen Befunde benannt und Designkriterien für die Gestaltung von AR-Lernumgebungen vorgestellt. Zudem wird demonstriert, dass die bisherige Forschung zu AR und Cognitive Load auf das Potential Entlastung fokussierte. Dem Potential von AR, Lernprozesse zu verstärken, im Sinne einer Anregung, wurde bislang noch wenig Aufmerksamkeit zuteil. Wie Forschung zu dieser Thematik aussehen kann und welche forschungsmethodischen Aspekte in zukünftigen Forschungsprojekten zum Lernen mit AR berücksichtigt werden sollten, wird diskutiert.
Josef Buchner & Michael Kerres
Jahrestagung der Gesellschaft für Didaktik der Chemie und Physik
RWTH Aachen, 12.09.2022
Entlasten oder anregen?
Effekte von Augmented Reality auf die kognitive Belastung
Inhalt
1. Annahme 1 Entlasten
2. Annahme 2 Anregen
3. Perspektive Zukünftige Forschung
àBuchner et al., 2021, 2022; Buchner &
Kerres, under review
Annahme 1 Entlasten
§Split-attention effect (CLT)
§Spatial and temporal contiguity principle
(CTML)
§Signaling principle (CTML)
§Modality Principle (CLT & CTML)
Bild von zedinteractive auf Pixabay
(e.g. Garzón et al., 2020; Santos et al., 2016; Sommerauer & Müller, 2014)
Annahme 1 Entlasten
§JA!
§Procedural knowledge:
§Montageaufgaben
§Flugtraining
§Chirurgie
§Programmieraufgaben
Bild aus Alves et al. (2019)
Sample: 54 Veröffentlichungen mit 58 Studien; 2007 bis 2019
Annahme 1 Entlasten
§JA!
§Procedural knowledge:
§Montageaufgaben
§Flugtraining
§Chirurgie
§Programmieraufgaben
Bild aus Alves et al. (2019)
Sample: 54 Veröffentlichungen mit 58 Studien; 2007 bis 2019
Didaktisches Element:
Just-in-time Information + Guidance
e.g., Kester & van Merriënboer (2021)
Annahme 1 Entlasten
§Diskussionswürdig
§Declarative knowledge:
§„Klassische“ Bildungsinhalte
§„Magic Books“
§Faktenwissen
§3D soll entlasten…
Bild aus Buchner & Jeghiazaryan (2020)
Sample: 54 Veröffentlichungen mit 58 Studien; 2007 bis 2019
Annahme 1 Entlasten
§Diskussionswürdig
§Declarative knowledge:
§„Klassische“ Bildungsinhalte
§„Magic Books“
§Faktenwissen
§3D soll entlasten…
Bild aus Buchner & Jeghiazaryan (2020)
Sample: 54 Veröffentlichungen mit 58 Studien; 2007 bis 2019
Medienvergleichsstudien!
Annahme 2 Anregen
§Komplexität von CL berücksichtigen
§Germane CL àIntrinsic CL (CLT)
§Generative processing (CTML)
§Lernaktivitäten mithilfe von AR initiieren!
z.B. AR Escape Game (Paraschivoiu et al., 2021) oder
Real-World Annotationen (Lim & Lim, 2020)
(Mayer, 2020; Sweller et al., 2019)
Annahme 2 Anregen
§Offen
§GCL / GP nicht gemessen
§Ausnahme: Strzys et al. (2019)
àn.s., aber Tendenz
§Keller et al. (2021) noch nicht im Sample
Annahme 2 Anregen
§Didaktische Elemente
§Visuelle Cues / Hilfen
(e.g. Murauer et al., 2018)
§Lernen am Modell menschlicher Avatar
(e.g. Lampen et al., 2019)
§Generative Lernaktivitäten
(e.g. Ferdous et al., 2019)
§3D Darstellung
(e.g. Cheung et al., 2015a, b)
Bild aus Buchner & Jeghiazaryan (2020)
Perspektive zukünftige Forschung
§Schwierigkeitsgrad
variieren
§Zielgruppen untersuchen
§Spezifische Lernziele
adressieren
§Value-added & LTI
Forschungsdesigns
§Langfristige Effekte –
Designstudien
(e.g. Deshpande & Kim, 2018;
Habig, 2019)
Entlasten
Perspektive zukünftige Forschung
§Schwierigkeitsgrad
variieren
§Zielgruppen untersuchen
§Spezifische Lernziele
adressieren
§Value-added & LTI
Forschungsdesigns
§Langfristige Effekte –
Designstudien
(e.g. Deshpande & Kim, 2018;
Habig, 2019)
§AR-Anwendungen neu
denken
§„Wie“ in den Fokus stellen
§Value-added & LTI
Forschungsdesigns
§Langfristige Effekte –
Designstudien
(e.g. Lim & Lim, 2020; Strzys et
al., 2019; Wu et al., 2018)
Entlasten Anregen
Perspektive zukünftige Forschung
§Schwierigkeitsgrad
variieren
§Zielgruppen untersuchen
§Spezifische Lernziele
adressieren
§Value-added & LTI
Forschungsdesigns
§Langfristige Effekte –
Designstudien
(e.g. Deshpande & Kim, 2018;
Habig, 2019)
§AR-Anwendungen neu
denken
§„Wie“ in den Fokus stellen
§Value-added & LTI
Forschungsdesigns
§Langfristige Effekte –
Designstudien
(e.g. Lim & Lim, 2020; Strzys et
al., 2019; Wu et al., 2018)
§How to solve educational problems with AR
statt
§thing-oriented research
§Medienvergleichsstudien
§Value-added Studien
§LTI Studien
§Designstudien
(e.g. Hodges, Curry, et al., 2020; Hodges, Moore, et al.,
2020; Kerres, 2020; Mayer, 2020; McKenney & Reeves,
2021; Reeves & Lin, 2020; Reeves & Reeves, 2015)
Entlasten GenerellAnregen
Perspektive zukünftige Forschung
§Schwierigkeitsgrad
variieren
§Zielgruppen untersuchen
§Spezifische Lernziele
adressieren
§Value-added & LTI
Forschungsdesigns
§Langfristige Effekte
Designstudien
(e.g. Deshpande & Kim, 2018;
Habig, 2019)
§AR-Anwendungen neu
denken
§„Wie“ in den Fokus stellen
§Value-added & LTI
Forschungsdesigns
§Langfristige Effekte
Designstudien
(e.g. Lim & Lim, 2020; Strzys et
al., 2019; Wu et al., 2018)
§How to solve educational problems with AR
statt
§thing-oriented research
§Medienvergleichsstudien
§Value-added Studien
§LTI Studien
§Designstudien
(e.g. Hodges, Curry, et al., 2020; Hodges, Moore, et al.,
2020; Kerres, 2020; Mayer, 2020; McKenney & Reeves,
2021; Reeves & Lin, 2020; Reeves & Reeves, 2015)
Entlasten GenerellAnregen
Literatur
Alves, J., Marques, B., Oliveira, M., Araujo, T., Dias, P., & Santos, B. S. (2019). Comparing Spatial and Mobile Augmented Reality for Guiding Assembling Procedures with Task Validation. 2019 IEEE International Conference on Autonomous Robo t Systems and Competitions (ICARSC), 1–6. https://doi.org/10.1109/ICARSC.2019.8733642
Buchner, J., Buntins, K., & Kerres, M. (2021). A systematic map of research characteristics in studies on augmented reality and cognitive load. Computers and Education Open, 2, 100036. https://doi.org/10.1016/j.caeo.2021.100036
Buchner, J., Buntins, K., & Kerres, M. (2022). The impact of augmented reality on cognitive load and performance: A systematic review. Journal of Computer Assisted Learning, 38(1), 285–303. https://doi.org/10.1111/jcal.12617
Buchner, J., & Jeghiazaryan, A. (2020). Work-in-Progress–The ARI2VE Model for Augmented Reality Books. 6th Internationa l Conference of the Immersive Learning Research Network (ILRN 202 0), 287–290.
Buchner, J., & Kerres, M. (under review). Media comparison studies dominate comparative rese arch on augmented reality in education.
Deshpande, A., & Kim, I. (2018). The effects of augmented reality on improving spatial problem solving for object assembly. Advanced Engineering Informatics, 38, 760 –775. https://doi.org/10.1016/j.aei.2018.10.004
Garzón, J., Kinshuk, Baldiris, S., Gutiérrez, J., & Pavón, J. (2020). How do pedagogical approaches affect the impact of augmented reality on education? A meta-analysis and research synthesis. Educational Re search Review, 31, 100334. https://doi.org/10.1016/j.edurev.2020.100334
Habig, S. (2019). Who can benefit from augmented reality in chemistry? Sex differences in solving stereochemistry problems using augmented reality. British Journa l of Educational Technology. https://doi.org/10.1111/bjet.12891
Hodges, C., Curry, J., & Grant, M. (2020). Getting started with Educational Technology Research [Presentation]. Curriculum Studies Summer Collaborative. https://digitalcommons.georgiasouthern.edu/cssc/2020/2020/23
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Keller, S., Rumann, S., & Habig, S. (2021). Cognitive Load Implications for Augmented Reality Supported Chemistry Learning. Informati on, 12(3), 96. https://doi.org/10.3390/info12030096
Kerres, M. (2020). Bildung in der digitalen Welt: Über Wirkungsannahmen und die soziale Konstruktion des Digitalen. MedienPädagogik: Zeitschrift für Theorie und Praxis der Medienbil dung, 17 (Jahrbuch Medienpädagogik), 1–32. https://doi.org/10.21240/mpaed/jb17/2020.04.24.X
Kester, L., & van Merriënboer, J. J. G. (2021). Implications of the Four Component Instructional Design Model for Multimedia Learning. In R. E. Mayer & L. Fiorella (Eds.), The Cambridge Handbook of Multimedia Learning (3rd ed., pp. 100–120). Cambridge University Press. https://doi.org/10.1017/9781108894333.011
Lim, K. Y. T., & Lim, R. (2020). Semiotics, memory and augmented reality: History education with learner-generated augmentation. British Journal of Educational Technology, 51(3), 673–691. https://doi.org/10.1111/bjet.12904
Mayer, R. E. (2020). Multimedia Learning (Third Edition). Cambridge University Press. cambridge.org/9781107187504
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Paraschivoiu, I., Buchner, J., Praxmarer, R., & Layer-Wagner, T. (2021). Escape the Fake: Development and Evaluation of an Augmented Reality Escape Room Game for Fighting Fake News. Extended Abstracts of the 2021 Annual Symposium on Compu ter-Human Interaction in Play, 320–325. https://doi.org/10.1145/3450337.3483454
Reeves, T. C., & Lin, L. (2020). The research we have is not the research we need. Educational Technology Research and Development, 68(4), 1991–2001. https://doi.org/10.1007/s11423-020-09811-3
Reeves, T. C., & Reeves, P. M. (2015). Reorienting educational technology research from things to problems. Learning: Research and Practice, 1(1), 91–93. https://doi.org/10.1080/23735082.2015.1008120
Santos, M. E. C., Lübke, A. in W., Taketomi, T., Yamamoto, G., Rodrigo, Ma. M. T., Sandor, C., & Kato, H. (2016). Augmented reality as multimedia: The case for situated vocabulary learning. Research and Practice in Technology Enhanced Learning, 11(1). https://doi.org/10.1186/s41039-016-0028-2
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Wu, P.-H., Hwang, G.-J., Yang, M.-L., & Chen, C.-H. (2018). Impacts of integrating the repertory grid into an augmented reality-based learning design on students’ learning achievements, cognitive load and degree of satisfaction. Interactive Learnin g Environments, 26(2), 221–234. https://doi.org/10.1080/10494820.2017.1294608
Vielen Dank!
Josef Buchner & Michael Kerres
josef.buchner@uni-due.de
learninglab.uni-due.de
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