Enhancing Self-Efficacy for Learning and Performance in High School: a Simulation-Enhanced Predict-Observe-Explain Intervention

EasyChair Preprint no. 13388

Abstract

To foster student motivation, it is crucial to infuse learning with enjoyment during the educational process. The advent of information and communication technologies has made access to digital tools increasingly convenient. Teachers frequently employ free digital resources, such as simulations, pictures, videos, and games, in their teaching-learning process. By incorporating simulations in the Predict-Observe-Explain (POE) method, the POE method can positively impact student success, particularly in bolstering students' be-lief in their ability to learn and perform well in chemistry courses. This study aimed to examine the impact of a simulation-supported POE intervention on high school students' self-efficacy for learning and performance. The study randomly selected two groups of students as the experimental and control groups. While the experimental group received lessons using the POE meth-od supported by simulations, the control group was taught using the teacher's conventional method. The Self-Efficacy for Learning and Performance scale, one of the subscales of the Motivated Strategies for Learning Questionnaire (MSLQ), was used to assess the self-efficacy levels of high school students. The findings indicated that simulation-supported POE led to a significant increase in student self-efficacy. The results of this study suggest that employing the POE method with digital tools such as simulations in teaching-learning environments can enhance teachers' effectiveness in teaching chemistry compared to the control group.

Keyphrases: Chemistry, high school, Predict-Observe-Explain, self-efficacy, simulation

@Booklet{EasyChair:13388,
  author = {Berrak Alan and Sezgi Ayna and Senol Şen},
  title = {Enhancing Self-Efficacy for Learning and Performance in High School: a Simulation-Enhanced Predict-Observe-Explain Intervention},
  howpublished = {EasyChair Preprint no. 13388},

  year = {EasyChair, 2024}}