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Using online decision trees to support students' self-efficacy in the laboratory
Sarah McLean1,2, Ken N Meadows3, Austin Heffernan1
1Department of Physiology and Pharmacology, Western University, London, Ontario, Canada.
Advances in Physiology Education
|July 23, 2020
Summary
Online decision trees enhance undergraduate science education by allowing students to practice problem-solving safely. This approach boosts self-efficacy and intrinsic motivation for wet laboratory experiments.
Area of Science:
- Science Education
- Undergraduate Laboratory Learning
- Pedagogical Innovations
Background:
- Undergraduate labs often use fixed protocols, limiting student exploration and problem-solving.
- Facilitating student-led experiments with potential failures is logistically difficult.
- Safe environments are needed for students to develop critical thinking and problem-solving skills through experimental failure.
Purpose of the Study:
- To evaluate the impact of online decision trees on undergraduate students' self-efficacy, metacognition, and motivation.
- To assess how the timing of decision tree use (pre- or post-lab) affects student outcomes.
- To explore students' perceptions of online decision trees as a learning tool.
Main Methods:
- A mixed-methods approach was employed, utilizing three surveys administered throughout an academic term.
- Students interacted with online decision trees, which simulated experimental protocols and provided feedback.
- Surveys measured self-efficacy, metacognition, and motivation before and after laboratory sessions, with decision tree use varied.
Main Results:
- Administering online decision trees *before* the wet laboratory significantly increased students' self-efficacy and intrinsic motivation.
- No significant changes were observed in extrinsic motivation or metacognitive scores.
- Student feedback emphasized the value of visual components within the decision tree simulations.
Conclusions:
- Online decision trees offer a valuable pedagogical tool for science education, particularly for developing problem-solving skills.
- Using decision trees proactively (pre-lab) appears most effective for enhancing student confidence and engagement.
- Future iterations should leverage visual elements to maximize learning impact.
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