Control of Variables Strategy Across Phases of Inquiry in Virtual Labs

Perez, Sarah; Massey-Allard, Jonathan; Ives, Joss; Butler, Deborah L.; Bonn, D. A.; Bale, J. S.; Roll, Ido

doi:10.1007/978-3-319-93846-2_50

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…This study context and methodological approach allowed us first to examine the impact of different inquiry strategies usage on students' conceptual learning, both individually and in combination (RQ1). Our results expand on previous studies, which predominantly concentrated on the CVS strategy [9,19,23], by highlighting that combining strategies is more effective for conceptual learning than utilizing them individually. Specifically, we found four distinct student profiles based on the combination of strategy use.…”

Section: Discussionsupporting

confidence: 83%

“…Similarly, the high number of students belonging to the Low Range cluster may be attributed to the fact that they often did not record any data points. Such an approach might not be sufficient for a quantitative understanding of variable relationships [23]. This behavior correlated with a high percentage of range coverage but low or no data points recorded potentially connected to the simulation task, which encouraged experimentation in exploratory format but did not emphasize the importance of revealing mathematical relationships.…”

Section: Discussionmentioning

confidence: 98%

“…The data collection phase involves gathering data of sufficiently high quality to draw inferences from the data [8,21]. During the data interpretation phase, students examine the data to draw conclusions about the relationships between the variables, by examining graphical representations or even performing quantitative analysis [20,23]. The use of the above-mentioned inquiry strategies plays a crucial role in both of these phases: the strategies help to gather new data when there is no or not sufficient data to begin with, or to select an appropriate subset of the collected data to draw valid conclusions on the relationships between the variables.…”

Section: Teaching Inquiry Strategiesmentioning

confidence: 99%

“…Existing literature primarily focuses on the Control of Variables Strategy (CVS) [9,19,21], which involves conducting consecutive experiments where only one variable changes at a time [2]. For instance, [23] measured CVS usage by counting labeled actions, revealing an association between strategy application and quantitative understanding of scientific phenomena. However, relying solely on CVS does not fully capture the comprehensive experimentation process [13,31].…”

Section: Introductionmentioning

confidence: 99%

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Teaching and Measuring Multidimensional Inquiry Skills using Interactive Simulations

Shved,

Bumbacher,

Mejia-Domenzain

et al. 2024

Preprint

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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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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 98%

Section: Teaching Inquiry Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Teaching and Measuring Multidimensional Inquiry Skills using Interactive Simulations

Shved,

Bumbacher,

Mejia-Domenzain

et al. 2024

Preprint

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“…In [4], the authors trained binary classifiers and logistic regression models on log data from virtual environments to categorise students by their science inquiry skills. Similarly, [27] used student log data as input for a linear regression model to predict the conceptual understanding students acquired after using physics and chemistry simulations. Latent Class Analyses models were able to identify different profiles in inquiry performance in two PISA science assessments involving interactive simulations [30].…”

Section: Introductionmentioning

confidence: 99%

Generalisable Methods for Early Prediction in Interactive Simulations for Education

Cock¹,

Marras²,

Giang³

et al. 2022

Preprint

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Interactive simulations allow students to discover the underlying principles of a scientific phenomenon through their own exploration. Unfortunately, students often struggle to learn effectively in these environments. Classifying students' interaction data in the simulations based on their expected performance has the potential to enable adaptive guidance and consequently improve students' learning. Previous research in this field has mainly focused on a-posteriori analyses or investigations limited to one specific predictive model and simulation. In this paper, we investigate the quality and generalisability of models for an early prediction of conceptual understanding based on clickstream data of students across interactive simulations. We first measure the students' conceptual understanding through their in-task performance. Then, we suggest a novel type of features that, starting from clickstream data, encodes both the state of the simulation and the action performed by the student. We finally propose to feed these features into GRU-based models, with and without attention, for prediction. Experiments on two different simulations and with two different populations show that our proposed models outperform shallow learning baselines and better generalise to different learning environments and populations. The inclusion of attention into the model increases interpretability in terms of effective inquiry. The source code is available on Github 1 .

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What Inquiry with Virtual Labs Can Learn from Productive Failure: A Theory-Driven Study of Students’ Reflections

Brand

Massey-Allard

Perez

et al. 2019

Lecture Notes in Computer Science

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Control of Variables Strategy Across Phases of Inquiry in Virtual Labs

Cited by 5 publications

References 10 publications

Teaching and Measuring Multidimensional Inquiry Skills using Interactive Simulations

Teaching and Measuring Multidimensional Inquiry Skills using Interactive Simulations

Generalisable Methods for Early Prediction in Interactive Simulations for Education

What Inquiry with Virtual Labs Can Learn from Productive Failure: A Theory-Driven Study of Students’ Reflections

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