@article {1365,
	title = {IMPACT OF AI ROBOT IMAGE RECOGNITION TECHNOLOGY ON IMPROVING STUDENTS{\textquoteright} CONCEPTUAL UNDERSTANDING OF CELL DIVISION AND SCIENCE LEARNING MOTIVATION},
	journal = {Journal of Baltic Science Education},
	volume = {23},
	year = {2024},
	month = {April/2024},
	pages = {Continuous},
	type = {Original article},
	chapter = {208-220},
	abstract = {This study explored the integration of neural networks and artificial intelligence in image recognition for object identification. The aim was to enhance students{\textquoteright} learning experiences through a "Learning by Teaching" approach, in which students act as instructors to train AI robots in recognizing objects. This research specifically focused on the cell division unit in the first grade of lower-secondary school. This study employed a quasi-experimental research design involving four seventh-grade classes in a rural lower-secondary school. The experimental group (41 students) were taught via an AI robot image recognition technology, whereas the control group (40 students) were taught via a more conventional textbook-centered approach. The research followed a pre-test design, with three classes lasting 45 min each, totaling 135 min of teaching time over two weeks. Evaluation tools include the "Cell Division Two Stage Diagnostic Test" and the "Science Learning Motivation Scale." The results indicate that learning through teaching AI robot image recognition technology is more effective than textbook learning in enhancing students{\textquoteright} comprehension of the "cell division" concept and boosting motivation to learn science.},
	keywords = {Artificial Intelligence, cell division, image recognition technology, learning by teaching, science learning motivation},
	issn = {1648-3898},
	doi = {https://doi.org/10.33225/jbse/24.23.208 },
	url = {https://oaji.net/articles/2023/987-1713272661.pdf},
	author = {Pei-yu Chen and Yuan-Chen Liu}
}
@article {693,
	title = {A LONGITUDINAL TRAJECTORY OF SCIENCE LEARNING MOTIVATION IN KOREAN HIGH SCHOOL STUDENTS},
	journal = {Journal of Baltic Science Education},
	volume = {17},
	year = {2018},
	month = {August/2018},
	pages = {Continuous},
	type = {Original article},
	chapter = {674-687},
	abstract = {This research examined longitudinal trajectories of Korean students{\textquoteright} motivation to learn science using group-based trajectory modeling (GBTM). The changes in five motivational constructs were focused in this research: self-efficacy, intrinsic motivation, grade motivation, self-determination, and career motivation. In addition, the relationship between academic track of students and each trajectory group was examined. Using Science Motivation Questionnaire II, data from 255 male high school students were collected during five semesters. Longitudinal data were analyzed by GBTM.. Chi-square analysis was also conducted to examine the relationships between academic track and each trajectory. As a result, it was found that students shared the same trajectory patterns in self-efficacy. Otherwise, two distinct trajectory groups ({\textquoteleft}high{\textquoteright} and {\textquoteleft}low{\textquoteright}) were found in each four constructs. There were significant correlations between academic track and trajectory groups. The findings of this research suggest that Korean science educators need to consider individual student{\textquoteright}s longitudinal trajectory of motivation to provide better science teaching and learning. },
	keywords = {academic track, group based trajectory modeling, high school, longitudinal study, science learning motivation},
	issn = {1648-3898},
	doi = {https://doi.org/10.33225/jbse/18.17.674},
	url = {http://oaji.net/articles/2017/987-1533709152.pdf},
	author = {Sein Shin and Arif Rachmatullah and Minsu Ha and Jun-Ki Lee}
}
