Implementation of Competency Enhancement Program for Science Teachers Assisted by Artificial Intelligence in Designing HOTS-based Integrated Science Learning

Kadek Dwi Hendratma Gunawan, Liliasari Liliasari, Ida Kaniawati, Wawan Setiawan

Abstract


Education currently supported by technological developments such as artificial intelligence (AI) and robotics. Science learning based on higher-order thinking skills (HOTS) is needed as the main goal in learning. AI in teacher professional developments are the new way to be implemented. The purpose of this research was to describe the competency enhancement program for science teacher that assisted by AI in designing HOTS-based integrated science learning. 29 science teachers in West Java participated in this program. Descriptive analysis was used to analyze the data from all participants. The results revealed that the program held in blended learning with face-to-face sessions about higher order thinking skills in science learning, use of multiple representations in science learning, construct of science learnings’ theme, integrated science learning, and continued by online learning by web based-AI. Science teachers perceived helpfulness to integrate various science content, followed courses, held discussion, answer the tests, and validating lesson plan products. Teachers has new experiences for the implementation program.


Keywords


Integrated Science Learning, Artificial Intelligence, HOTS

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References


Bidarra, J & Rusman, E 2017, ‘Key Pedagogical and Technological Factors for Effective Blended Learning Design Introduction’, The Envisioning Report for Empowering Universities, pp 20–2.

Care, E, Griffin, P & Wilson, M 2014, ‘Assessment and teaching of 21st century skills’, Switzerland: Springer

Castro-Schez, JJ, Glez-Morcillo, C, Albusac, J, & Vallejo, D 2021, ‘An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning. Information Sciences, 544, 446–68.

Cigdemoglu, C, & Köseoğlu, F 2019, ‘Improving Science Teachers’ Views about Scientific Inquiry’, Science & Education, vol 28.

deNoyelles, A, & Reyes-Foster, B 2015, ‘Using word clouds in online discussions to support critical thinking and engagement’, Journal of Asynchronous Learning Network, vol 19 no 4.

Desouza, K C, Dawson, G S, & Chenok, D 2020, ‘Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector’, Business Horizons, vol 63, no 2, pp. 205–13.

Duffy, M C, & Azevedo, R 2015, ‘Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system’, Computers in Human Behavior, vol 52, pp338–48.

Elfeky, A I M, Masadeh, T S Y, & Elbyaly, M Y H 2020 ‘Advance organizers in flipped classroom via e-learning management system and the promotion of integrated science process skills’, Thinking Skills and Creativity, vol 35 no 100622.

Fung Choy, J L & Quek, C L 2016, ‘Modelling relationships between students’ academic achievement and community of inquiry in an online learning environment for a blended course’, Australasian Journal of Educational Technology, vol 32, no 4, pp 106–24.

Goodsett, M 2020, ‘Best practices for teaching and assessing critical thinking in information literacy online learning objects’, The Journal of Academic Librarianship, vol 64 no 5, pp. 1–7.

Gunawan, K D H, Liliasari S, Kaniawati, I, Setiawan, W, Rochintaniawati, D, & Sinaga, P 2021, ‘Profile of teachers’ integrated science curricula that support by intelligent tutoring systems’ Journal of Physics: Conference Series, vol 1806, no 1, pp 1-7.

Gunawan, K D H, Liliasari, S, & Kaniawati, I 2019, ‘Investigation of integrated science course process and the opportunities to implement CSCL learning environments Investigation of integrated science course process and the opportunities to implement CSCL learning environments’, Journal of Physics: Conference Series, vol 1157, no 2, pp 71–77.

Gunawan, K D H, Liliasari, S, Kaniawati, I, & Setiawan, W 2020, ‘Exploring Science Teachers’ Lesson Plans by the Implementation of Intelligent Tutoring Systems in Blended Learning Environments’, Universal Journal of Education Research, vol 8 no 10, pp 4776–83.

Hamed, M A & Samy 2017, ‘An intelligent tutoring system for teaching the 7 characteristics for living things’, International Journal of Advanced Research and Development, vol 2, no 1, pp 31–5.

Holstein, K, McLaren, B M, & Aleven, V 2017, ‘Intelligent tutors as teachers’ aides: Exploring teacher needs for real-time analytics in blended classrooms’, ACM International Conference Proceeding Series, pp 257–66.

Huda, M, Maseleno, A, Shahrill, M., Jasmi, K A, Mustari, I, & Basiron, B 2017, ‘Exploring adaptive teaching competencies in big data era’, International Journal of Emerging Technologies in Learning, vol 12 no 3, pp. 68–83.

Jenny, C S, & Sebastian, A S 2012, ‘Artificial education: Expert systems used to assist and support 21st century education’, Journal on Computing, vol 2 no 3, pp. 1–4.

Jiménez, S, Juárez-Ramírez, R, Castillo, V H, & Armenta, J J T 2018, ‘Affective Feedback in Intelligent Tutoring System A Practical Approach’. Cham: Springer International Publishing.

Lee, Y H 2015, ’Facilitating critical thinking using the C-QRAC collaboration script: Enhancing science reading literacy in a computer-supported collaborative learning environment’, Computers and Education, vol 88, pp. 182–91.

Ma’ruf, M, Handayani, Y, Marisda, D H., & Riskawati, R 2020, ‘The needs analysis of basic physics learning devices based on hybrid learning’, Journal of Physics: Conference Series, vol 1422 no 012029, pp 1–5.

Mamun, M A A, Lawrie, G, & Wright, T 2020, ‘Instructional design of scaffolded online learning modules for self-directed and inquiry-based learning environments’, Computers and Education, vol 144, pp. 1-17.

Mohamed, H, & Lamia, M 2018, ‘Implementing flipped classroom that used an intelligent tutoring system into learning process’, Computers & Education, vol 124, pp. 62–76.

Muskita, M, Subali, B, & Djukri 2020, ‘Effects of worksheets base the levels of inquiry in improving critical and creative thinking’, International Journal of Instruction, vol 13 no 2, pp. 519–32.

Nappi, C & Cuocolo, A 2018 ‘The machine learning approach: Artificial intelligence is coming to support critical clinical thinking’, Journal of Nuclear Cardiology, pp 8–10.

National Research Council 2009, Committee on Learning Science in Informal Environments, Washington, DC: The National Academic Press.

National Research Council 2012, A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. Washington, DC: The National Academies Press.

Noroozi, O. Weinberger, A, Biemans, H J A, Mulder, M, & Chizari, M 2013, ‘Facilitating argumentative knowledge construction through a transactive discussion script in CSCL’, Computers and Education, vol 61 no 1, pp. 59–76

Olsen, J K, Belenky, D M, Aleven, V, & Rummel, N 2014, ‘Using an intelligent tutoring system to support collaborative as well as individual learning’, Springer International Publishing Switzerland, pp. 134–43.

Onsee, P, & Nuangchalerm, P 2019, ‘Developing Critical Thinking of Grade 10 Students through Inquiry-Based STEM Learning’, Jurnal Penelitian Dan Pembelajaran IPA, vol 5 no. 2, pp. 132-41.

Peterson, A T, & Roseth, C J 2015, ‘Effects of four CSCL strategies for enhancing online discussion forums: Social interdependence, summarizing, scripts, and synchronicity’, International Journal of Educational Research, vol 76, pp. 147–61.

Popenici, S A D, & Kerr, S 2017, ’Exploring the impact of artificial intelligence on teaching and learning in higher education’, Research and Practice in Technology Enhanced Learning, vol 12 no 1, pp. 1-13

Roll, I, & Wylie, R 2016, ‘Evolution and Revolution in Artificial Intelligence in Education’, International Journal of Artificial Intelligence in Education, vol 26 no 2, pp 582–99.

Różewski, P, Kieruzel, M, Lipczyński, T, Prys, M, Sicilia, MA, García-Barriocanal, E, Urasd, F 2019, ‘Concept of expert system for creation of personalized digital skills learning pathway’, Procedia Computer Science, vol 159, pp. 2304–12.

Rubini, B, Ardianto, D, & Pursitasari, I D 2019, ‘Teachers’ Perception Regarding Integrated Science Learning and Science Literacy’, Advances in Social Science, Education and Humanities Research, vol 253, pp. 364–366.

Rubini, B, Permanasari, A, & Yuningsih, W 2018, ‘Learning Multimedia Based on Science Literacy on the Lightning Theme’, Jurnal Penelitian dan Pembelajaran IPA, vol 4 no 2, 89. Pp. 89-104

Saprudin, S, Liliasari, L, Setiawan, A, & Prihatmanto, A S 2020, ‘Optical gamification (OG); Serial versus random model to improve pre-service physics teachers’ concept mastery’, International Journal of Emerging Technologies in Learning, vol 15 no 9, pp. 39–9

Shulman, L S 1986, ‘Those Who Understand: Knowledge Growth in Teaching’, Educational Researcher, vol 15 no 2, pp. 4–14.

Sias, C M, Nadelson, L S, Juth, S M, & Seifert, A L 2017, ‘The best laid plans: Educational innovation in elementary teacher generated integrated STEM lesson plans’, Journal of Educational Research, vol 110 no 3, pp. 227–38.

Siburian, J, Corebima, A D, Ibrohim, & Saptasari, M 2019, ‘The correlation between critical and creative thinking skills on cognitive learning results’, Eurasian Journal of Educational Research, vol 81, pp. 99–114.

So, K 2013, ‘Knowledge construction among teachers within a community based on inquiry as stance’, Teaching and Teacher Education, vol 29 no 1, pp. 188–196.

Sun, D, Wang, Z H, Xie, W T, & Boon, C C 2014, ‘Status of Integrated Science Instruction in Junior Secondary Schools of China: An exploratory study’, International Journal of Science Education, vol 36 no 5, pp. 808–38.

Timms, M J 2016, ‘Letting Artificial Intelligence in Education out of the Box: Educational Cobots and Smart Classrooms’, International Journal of Artificial Intelligence in Education, vol 26 no 2, 701–12.

Wallace, C S, & Coffey, D J 2019, ‘Investigating elementary preservice teachers’ designs for integrated science/literacy instruction highlighting similar cognitive processes’, Journal of Science Teacher Education, vol 30 no 5, pp. 507–27.

Wang, X, Kollar, I, & Stegmann, K 2017, ‘Adaptable scripting to foster regulation processes and skills in computer-supported collaborative learning’, International Journal of Computer-Supported Collaborative Learning, vol 12 no 2, pp. 153–172.

Winarno, N, Widodo, A, Rusdiana, D, Rochintaniawati, D, & Afifah, R M A 2019, ‘Pre-service Science Teachers’ Conceptual Understanding of Integrated Science Subject: A Case Study. Journal of Physics: Conference Series, vol 1204, no 1.

Wogu, I A P, Misra, S, Assibong, P A, Olu-Owolabi, E F, Maskeliūnas, R, & Damasevicius, R 2019, ‘Artificial intelligence, smart classrooms and online education in the 21st century: Implications for human development’, Journal of Cases on Information Technology, vol 21 no 3, pp. 66–79.

Xhakaj, F, Aleven, V, & McLaren, B M 2017, ‘Effects of a teacher dashboard for an intelligent tutoring system on teacher knowledge, lesson planning, lessons and student learning’, European Conference on Technology Enhanced Learning, pp. 315–29.

Yang, K K, Lee, L, Hong, Z R, & Lin, H S 2016, ‘Investigation of effective strategies for developing creative science thinking’, International Journal of Science Education, vol 38 no 13, pp. 2133–51.




DOI: http://dx.doi.org/10.30870/jppi.v7i1.8655

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