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  4. Teaching and Measuring Multidimensional Inquiry Skills Using Interactive Simulations
 
conference paper

Teaching and Measuring Multidimensional Inquiry Skills Using Interactive Simulations

Shved, Ekaterina  
•
Bumbacher, Engin Walter  
•
Mejia-Domenzain, Paola  
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2024
Artificial Intelligence in Education
25th Conference on Artificial Intelligence in Education (AIED)

Interactive simulations play a significant role in science education, serving as a platform for inquiry-based learning and fostering the development of scientific knowledge and skills. However, teaching and quantitatively measuring inquiry strategies has proven to be challenging due to their complex and inherently multidimensional nature. Our study goes beyond the prevalent focus on the Control of Variables Strategy (CVS) in prior work by incorporating additional relevant inquiry strategies in both teaching and measurement: exploring the variable range and conducting experiments under optimal conditions. We tested two different instructional approaches to jointly teach the three strategies by focusing either on data collection or on data interpretation. 161 chemistry apprentices were randomly assigned to one of the two instructional conditions or a control group without instruction and engaged in experimentation using an interactive simulation. In order to analyze joint strategy use, we applied a multi-step clustering method to students' log data that helped identify multidimensional student profiles of inquiry strategies. We found four profiles that related differently to conceptual learning, suggesting that combining strategies is more effective for conceptual learning than utilizing them individually. We also found that students instructed on data collection increased the use of strategy combinations with an emphasis on CVS. This suggests a potential avenue for assessing instruction efficacy, indicating that the impact may be strategy-specific. Source code and materials are released at https://github.com/epfl-ml4ed/inquiry-skills.

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Type
conference paper
DOI
10.1007/978-3-031-64302-6_34
Author(s)
Shved, Ekaterina  

EPFL

Bumbacher, Engin Walter  

Haute École Pédagogique du Canton de Vaud

Mejia-Domenzain, Paola  

EPFL

Kapur, Manu

ETH Zurich

Käser, Tanja  

EPFL

Date Issued

2024

Publisher

Springer Nature Switzerland

Publisher place

Cham, Switzerland

Published in
Artificial Intelligence in Education
ISBN of the book

978-3-031-64301-9

Total of pages

501

Book part number

1

ISSN (of the series)

0302-9743

Published in
Lecture Notes in Computer Science
Volume

14829

Start page

482

End page

496

Subjects

Inquiry-based Learning

•

Experimental Strategies

•

Clustering

•

Trace Data Analysis

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
ML4ED  
AVP-E-LEARN  
Event nameEvent acronymEvent placeEvent date
25th Conference on Artificial Intelligence in Education (AIED)

AIED 2024

Recife, Brazil

2024-07-08 - 2024-07-12

Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/241546
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