Tai, R. H. | Search bioRxiv

bioRxiv2024

Exploring The Association of Student Perceptions of Their Teachers' Science Instruction and Emotional Engagement and The Variance between Grade Level

AbstractPrevious research has consistently shown students have higher learning interests attitudes, and motivation toward science in elementary compared to middle and high school. However, the key predictors behind the observed differences in engagement levels across grade levels remain unclear. This study aims to identify the association between students perceptions of science instruction and their emotional engagement, as well as examine the differences across various educational stages. A multilevel random effect model was employed to investigate this association. In addition, the analysis examined whether the associations vary between grade levels. A sample of 6465 students from 25 schools participated in the study. This study shows that students in higher grades have significantly lower emotional engagement compared to third-grade students. Similarly, students in higher grades have significantly lower values on perceptions of science instruction of interesting science and understandable science decrease compared to third grade. Findings reveal significant associations between students perceptions of science instruction, both in perceiving interesting science instruction and understandable science instruction, and their emotional engagement in science learning. The interaction term (Perception of Interesting Science Instruction x Grade) of Grades four, six through eight, and grade 12 are statistically significantly related to emotional engagement, indicating that more positive slopes correspond to high-grade level than third-grade students. The increase in students perception of interesting science has the steepest slope of improvement in students emotional engagement in 6th grade. We find no statistically significant difference between grades in students perception of understandable science and emotional engagement. Interesting and understandable science classes have positive correlations with students emotional engagement in science learning. For future studies, it is worth exploring science instruction during transition grades to better address and mitigate the observed difference in emotional engagement.

scientific communication and education↗

bioRxiv2023

Use of Large Language Models to Aid Analysis of Textual Data

The increasing use of machine learning and Large Language Models (LLMs) opens up opportunities to use these artificially intelligent algorithms in novel ways. This article proposes a methodology using LLMs to support traditional deductive coding in qualitative research. We began our analysis with three different sample texts taken from existing interviews. Next, we created a codebook and inputted the sample text and codebook into an LLM. We asked the LLM to determine if the codes were present in a sample text provided and requested evidence to support the coding. The sample texts were inputted 160 times to record changes between iterations of the LLM response. Each iteration was analogous to a new coder deductively analyzing the text with the codebook information. In our results, we present the outputs for these recursive analyses, along with a comparison of the LLM coding to evaluations made by human coders using traditional coding methods. We argue that LLM analysis can aid qualitative researchers by deductively coding transcripts, providing a systematic and reliable platform for code identification, and offering a means of avoiding analysis misalignment. Implications of using LLM in research praxis are discussed, along with current limitations.

scientific communication and education↗