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Suleyman Demirel University bulletin: pedagogy and teaching methods.
ISSN
2709-264X
Издатель
Suleyman Demirel University
3 результаты
Показано: 1 - 3 из 3
- Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Evaluation of an interdisciplinary Project-based STEM model in biotechnology education(Suleyman Demirel University, 2026-09-30); ; ; The integration of STEM education into biotechnology is essential for developing research competence, interdisciplinary thinking, and practical problem-solving skills in future biology teachers. However, the implementation of interdisciplinary STEM models supported by experimental research and artificial intelligence remains limited in biotechnology education. This study aimed to develop and evaluate an interdisciplinary STEM project integrating biotechnology, ecology, chemistry, engineering, mathematics, and artificial intelligence within biology teacher education. A mixed-methods approach combining laboratory experimentation, project-based learning, questionnaires, interviews, and statistical analysis was employed. The STEM project was based on investigating the effects of copper, zinc, and cadmium on the growth of Phaseolus vulgaris, while students applied artificial intelligence tools, including ChatGPT, Claude AI, Python, and OneCompiler, for data processing, visualization, and scientific interpretation. Educational outcomes showed that 83.3% of participants reported mastering the stages of experimental planning, data collection, and data analysis, while 91.7% reported increased interest in biotechnology after completing the project. Participants also reported improved research competence, interdisciplinary understanding, and confidence in applying scientific methods to environmental problems.The scientific novelty of this study lies in the development of an interdisciplinary STEM model that integrates experimental biotechnology, project-based learning, and artificial intelligence into a unified educational framework. The proposed model contributes to strengthening research-oriented biotechnology education and provides a practical approach for preparing future biology teachers capable of addressing interdisciplinary scientific challenges. - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, 8-сыныпта зар заман поэзиясын оқытуда генеративті ЖИ қолдану: пилоттық экспериментThe article examines the possibilities and risks of using generative artificial intelligence (ChatGPT GPT-4o) in teaching one of the most complex sections of the Kazakh literature school curriculum – "Zar Zaman" poetry (Shortanbai Kanaiuly, Dulat Babataiuly, Murat Monkeuly) to 8th-grade students. The aim of the study is to empirically evaluate the didactic effectiveness and potential threats of using AI in national literature lessons. A pilot study with a quasi-experimental design was conducted in a secondary school in Almaty over 6 weeks (12 lessons). In the experimental group (n=27), traditional teaching was supplemented with ChatGPT elements, while the control group (n=25) was taught using only the traditional methodology. The students' learning quality was assessed using an author-designed 10-point rubric covering 6 criteria. Statistical analysis was performed using the Mann-Whitney U test. Results revealed a statistically significant improvement in the experimental group for the skills of "understanding the historical context" (+39%) and "comparative analysis" (+48%) (p 0.05), which is consistent with the issue of "academic dependency" discussed in international science. Furthermore, the study identified cases of AI hallucinations (confabulation) due to a lack of understanding of national-historical and cultural codes. In conclusion, generative AI can be an effective supplementary tool, but it requires strict teacher moderation and mandatory fact-checking by students. To reduce AI dependency, the article proposes specific prompt templates and the "own opinion first – then AI" strategy. - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Engineering students’ perceptions of mobile-assisted language learning for English terminological competence in KazakhstanThis study investigates engineering students’ perceptions of the contribution of Mobile-Assisted Language Learning (MALL) tools to English terminological competence in Kazakhstan. The findings are interpreted through the Technology Acceptance Model (TAM), Self-Regulated Learning (SRL) Theory, and Mobile Learning Theory as theoretical frameworks. A quantitative survey design with supplementary open-ended responses was employed. The final analytic sample comprised 132 adult first- and second-year engineering students studying at higher education institutions in Kazakhstan after all responses from participants younger than 18 were excluded. Quantitative data were analysed using descriptive statistics, while qualitative responses were examined through thematic analysis. The findings indicate that students generally perceive MALL tools as beneficial for learning technical English terminology. Nearly half of the respondents reported daily use of mobile learning applications, with ChatGPT, Quizlet, Grammarly, and Google Classroom among the most frequently used tools. Students reported perceived benefits for understanding technical definitions, using engineering terminology accurately, and supporting academic writing. However, several challenges were identified, including the linguistic complexity of AI-generated explanations, limited access to digital resources, and insufficient pedagogical guidance. Students also described planning and monitoring behaviours consistent with aspects of self-regulated learning, whereas evidence of reflective evaluation was less consistent. The study highlights the importance of integrating mobile learning tools into English for Specific Purposes (ESP) curricula and providing students with structured AI literacy training. As the study relies on self-report data and descriptive analysis, its findings concern perceived contribution rather than objectively measured development of terminological competence.