"CHEMISTRY AROUND US" LEARNING MANAGEMENT INNOVATION: CONTEXT-BASED LEARNING INTEGRATED WITH ARTIFICIAL INTELLIGENCE FOR DEVELOPING CHEMISTRY CONCEPTS OF PRE-SERVICE TEACHERS

Main Article Content

Surasak Maotheuak

Abstract

      The key challenge in learning chemistry at the basic education level lies in the abstract nature of the content, which often leads to conceptual misunderstandings among learners. In addition, memorization-based teaching does not effectively enable students to recognize the relevance and real-life applications of chemistry. Therefore, it is necessary to develop instructional approaches that address these issues and enhance meaningful learning. This research aimed to develop the chemistry concepts of pre-service teachers who received instruction through the Chemistry Around Us" Learning Management Innovation : Context-Based Learning Integrated with Artificial Intelligence. The target group consisted of 21 students majoring in Chemistry at the Faculty of Education, Chiang Mai University, who were enrolled in the course 064232 “Chemistry Concepts in the Basic Education Curriculum 2” during the first semester of the academic year 2024. The research instruments included: The “Chemistry Around Us” learning Management Innovation, consisting of 13 activities. An activity quality assessment form for the “Chemistry Around Us” learning Management Innovation. A test on chemistry concepts in the “Chemistry Concepts in the Basic Education Curriculum 2” course. Data were analyzed using frequency, percentage, mean, and standard deviation.


      The results revealed that students’ concepts of the properties of gases, applications of gas properties in daily life and industry, and reaction rates had an average score of 80.71. Among them, 42.86% scored between 81.00–100.00, and 38.10% scored between 71.00–80.00. Regarding students’ concepts of chemical equilibrium, acid–base properties, and acid–base reactions, the average score was 81.19, with 42.86% scoring between 81.00–100.00 and 42.86% scoring between 71.00–80.00.

Article Details

How to Cite
Maotheuak , S. (2025). "CHEMISTRY AROUND US" LEARNING MANAGEMENT INNOVATION: CONTEXT-BASED LEARNING INTEGRATED WITH ARTIFICIAL INTELLIGENCE FOR DEVELOPING CHEMISTRY CONCEPTS OF PRE-SERVICE TEACHERS. Journal of Research and Innovation for Sustainability (JRIS), 2(6), 46–64. retrieved from https://so17.tci-thaijo.org/index.php/JRIS/article/view/1441
Section
Research article

References

Bennett, J., & Lubben, F. (2006). Context‐based Chemistry: The Salters approach. International Journal of Science Education, 28(9), 999–1015. https://doi.org/10.1080/09500690600702496

Brown, J. S., Collins, A., & Duguid, P. (1989). Situated Cognition and the Culture of Learning. Educational Researcher, 18(1), 32-42. https://doi.org/10.3102/0013189x018001032

Gilbert, J. K. (2006). On the Nature of “Context” in Chemical Education. International Journal of Science Education, 28(9), 957–976. https://doi.org/10.1080/09500690600702470

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education Promises and Implications for Teaching and Learning (1st ed.). Center for Curriculum Redesign.

Johnstone, A. H. (1991). Why is science difficult to learn? Things are not what they seem. Journal of Computer Assisted Learning, 7(2), 75–83. https://doi.org/10.1111/j.1365-2729.1991.tb00230.x

Kadsosot, K. (2023). The Development an Instructional Package Using The Project Based Learning with Design Thinking Process to Innovators via Artificial Intelligence Technology [Master of Education thesis, Naresuan University]. Naresuan University.

Lemke, J. L. (1990). Talking Science: Language, Learning and Values. Ablex.

Matti Tedre, Vartiainen, T., & Vihavainen, S. (2021). Teaching machine learning concepts using Teachable Machine for K-12 students. In Proceedings of the 2021 IEEE Global Engineering Education Conference (EDUCON) (pp. 1656-1660). IEEE.

Novak, J. D. (1998). Learning, creating, and using knowledge: Concept maps as facilitative tools in schools and corporations. Lawrence Erlbaum Associates, Inc.

OECD. (2018). Effective teacher policies: Insights from PISA. OECD Publishing.

Prain, V., & Tytler, R. (2012). Learning Through Constructing Representations in Science: A framework of representational construction affordances. International Journal of Science Education, 34(17), 2751–2773. https://doi.org/10.1080/09500693.2011.626462

Sa-nguansak, P. (2019). Effects of context-based learning on chemical literacy of upper secondary students [Master of Education thesis, Chulalongkorn University]. Chulalongkorn University.

Taber, K. (2002). Chemical misconceptions - Prevention, diagnosis and cure. Royal Society of Chemistry.

Tondeur, J., van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the Relationship between Teachers’ Pedagogical Beliefs and Technology Use in Education: A Systematic Review of Qualitative Evidence. Educational Technology Research and Development, 65, 555-575. https://doi.org/10.1007/s11423-016-9481-2

Treagust, D., Chittleborough, G., & Mamiala, T. (2003). The role of submicroscopic and symbolic representations in chemical explanations. International Journal of Science Education, 25(11), 1353–1368. https://doi.org/10.1080/0950069032000070306

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic Review of Research on Artificial Intelligence Applications in Higher Education—Where Are the Educators? International Journal of Educational Technology in Higher Education, 16, Article No. 39. https://doi.org/10.1186/s41239-019-0171-0