Journal of Baltic Science Education

177-178

Authors: Editorial Board

Journal of Baltic Science Education, Vol. 23, No. 2, 2024 ISSN 1648-3898 /Print/, ISSN 2538-7138 /Online/

180-186

Authors: Georgios Tsaparlis

According to Alex H. Johnstone, “most of the concepts we develop in everyday living (such as cars, trees, houses, dogs, and flowers) are based on tangible things; things we can touch, see, smell and taste. … However, concepts such as element, enzyme, atom, gas, resistance, molarity, entropy, and many others cannot be constructed directly by our senses. Some are one or even two steps removed from direct sensory perception” (Johnstone, 2007, p. 8). The famous Johnstone triangle for the physical sciences (Johnstone, 1982 ─ see Figure 1) has at one of its corners tangible things, constituting the “Macroscopic” or “Macro Level”. The two other levels that facilitate further understanding are the “Symbolic and Mathematical Level”, which involves symbols, mathematical and chemical equations, as well as graphs and calculations, and the “Submicroscopic” or “Submicro Level”, which refers to entities such as atoms and molecules, that are so tiny that we cannot observe them either directly or even with the help of a strong microscope; these entitles are objects of thought and we draw conclusions about them through logical thinking and inferences. The emphasis among the three levels varies between the physical sciences, physics and chemistry.

208-220

Authors: Pei-Yu Chen, Yuan-Chen Liu

This study explored the integration of neural networks and artificial intelligence in image recognition for object identification. The aim was to enhance students’ 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’ comprehension of the "cell division" concept and boosting motivation to learn science.

240-259

Authors: Nthoesele Hlaela, Loyiso C. Jita

Pedagogical content knowledge (PCK) is considered an important ingredient in shaping classroom practice. PCK requires valid measurement at domain-specific level on different components excluding the teaching context. While the research focuses on measuring the PCK of unqualified teachers and pre-service teachers, the description of qualified teachers’ domain-specific PCK is often overlooked. The purpose of this study was to measure quantitatively the domain-specific PCK of qualified physics teachers focusing on the five PCK components: content knowledge, pedagogical knowledge, assessment knowledge, knowledge of students and curricular knowledge. Data were collected through the paper-and pencil PCK test that was responded to by 87 Physics teachers teaching the last two years of secondary school. Data were analyzed using the Extended Rasch Model and descriptive statistics. The results revealed that Physics teachers have a low PCK in general and low levels of PCK components, the lowest being content knowledge. The study recommends regular refresher workshops for qualified teachers focused on developing different PCK components, with more emphasis on content knowledge.

280-291

Authors: Hunkoog Jho, Minsu Ha

This study aimed at examining the performance of generative artificial intelligence to extract argumentation elements from text. Thus, the researchers developed a web-based framework to provide automated assessment and feedback relying on a large language model, ChatGPT. The results produced by ChatGPT were compared to human experts across scientific and non-scientific contexts. The findings revealed marked discrepancies in the performance of AI for extracting argument components, with a significant variance between issues of a scientific nature and those that are not. Higher accuracy was noted in identifying claims, data, and qualifiers, as opposed to rebuttals, backing, and warrants. The study illuminated AI's promise for educational applications but also its shortcomings, such as the increased frequency of erroneous element identification when accuracy was low. This highlights the essential need for more in-depth comparative research on models and the further development of AI to enhance its role in supporting argumentation training.

315-330

Authors: Soyeon Kim, Jung Bog Kim

Despite increased research interest in improving students' scientific inquiry abilities, how to conduct scientific inquiry using science textbooks that are easily accessible to primary school students remains understudied. This study developed and verified an inquiry activity model (IAM) to improve the performance of primary school students in science textbook inquiry activities. Data were collected from sixth-grade primary students (n = 167) to analyze the difficulty level of textbook inquiry activities. An analysis tool was developed, focusing on inquiry skills and process flow. Expert analysis increased the tool’s validity. The analysis revealed that students possessed low integrated inquiry skills, were partially aware of the inquiry process flow, and could not design experiments. Hence, the IAM was developed to enhance students’ ability to perform textbook inquiry activities and understand the activity phases. It emphasized the flow and representation of the inquiry process for students to easily recall the contents as they learn the interconnectivity between phases. The post-test of the experimental group and the inquiry process flow chart showed significant improvement in all areas of inquiry ability. The scores for “connections” and “interconnectivity” in the inquiry process flow were high, reflecting the model’s effectiveness in showing the interconnectedness of all stages.

352-376

Authors: Uladzimir K. Slabin

Scholars as exemplars and their linked eponyms play a significant role in education. Since the discovery of the periodic law in 1869, perceptions of Mendeleev and his eponyms may have shifted. With the aim of assessing the historical and current portrayal of Mendeleev and his eponyms in chemistry textbooks and on the Internet, content analysis of textbooks, including Soviet and contemporary Russian (11) and Ukrainian (16), was conducted. Additionally, an Internet search in 7 languages was conducted to assess the prevalence of Mendeleevian eponyms online. Primary Mendeleevian eponyms are predominantly utilized in the Russian segment of the Internet. While Mendeleev’s portrayal held significance in mid-20th-century Soviet chemistry textbooks, his presence has dwindled in current Russian and Ukrainian textbooks. This decline is attributed to the obsolescence of associated chemical realities, the disputed priority of Mendeleev, and an ambiguous attitude towards him. The implications of this decline include loss of historical context, reduced memorization aids, impact on critical thinking, disruption of continuity, and reduced humanism in education. The research underscores the importance of a balanced and inclusive approach to science education, recognizing contributions from scientists of diverse backgrounds and perspectives, and highlights the interconnected nature of science education and international relations.

390-410

Authors: Jian-Bo Zhu, Yu-Zhou Luo

Science identity, encompassing perceptions of competence, interest, and recognition in science, alongside learning self-efficacy reflecting confidence to master science skills, are key drivers of outcomes. However, developmental patterns likely vary across contexts. Participants were 512 Chinese students spanning grades 1-3 who completed the Science Identity Scale and Science Learning Self-Efficacy Scale, with physics, chemistry, and biology achievement scores gathered. A Partial Least Squares Structural Equation Model assessed relationships. Results substantiated psychometrics for motivational measures. The model indirectly predicting achievement via first-order discipline-specific paths explained more variance than direct second-order effects. Effects significantly varied across groups stratified by grade and region. Interest and conceptual knowledge drove physics and chemistry success, while higher-order skills enhanced biology achievement overall. However, relationships differed within subgroups, suggesting personalized motivational support needs—self-belief/competence foundations for struggling learners, conceptual development for those with high prior achievement, and intrinsic enrichment for disinterested students. Results detail complex motivational profiles underlying science achievement requiring tailored identity safety and self-efficacy scaffolding alongside conceptual and skill-building for excellence across scientific disciplines. Motivational support systems may spur more equitable and optimal science outcomes among diverse adolescent learners.