Enhancing critical learning through an adaptive web application: A case study of lower secondary school students studying artificial intelligence technology

Main Article Content

Siwanit Autthawuttikul
Sitthichai Laisema
Pornpimon Rodkroh

Abstract

This research aims to develop and study the effects of an adaptive web application on critical learning of artificial intelligence technology in students under the Suphan Buri and Nakhon Pathom Secondary Educational Service Area Office. The sample was divided into two groups: 4,264 lower secondary school teachers that were surveyed to create a needs analysis on media use, teaching management and readiness to use technology and a second group of 709 lower secondary school students who volunteered to use the adaptive web application. The research instruments consisted of 1) an opinion and guidelines survey for developing the adaptive web application, 2) an adaptive web application to enhance learning of artificial intelligence, 3) a learning style assessment, 4) a critical learning test on artificial intelligence technology, and 5) a student opinion questionnaire. Data were analysed using content analysis, frequency, percentage, mean, standard deviation, need assessment, one-way ANOVA, and hypothesis tests by paired sample t-test. The results showed that 1) the conditions for media use, teaching management and readiness to use technology were at a high level; 2) there was no significant difference between pre-test and post-test scores at 0.05 significance; 3) there was no significant difference between pre-test and post-test scores among the student learning styles at 0.05 significance; 4) there was no significant difference in the frequency of media use classified by learning style at 0.05 significance; and 5) the students’ opinions toward learning activities via an adaptive web application was at the highest level (M = 4.59, SD = 0.49).

Downloads

Download data is not yet available.

Article Details

Section
Research Articles

References

Ajisoko, P. (2020). The use of duolingo apps to improve english vocabulary learning. International Journal of Emerging Technologies in Learning, 15(7), 149–155. https://doi.org/10.3991/ijet.v15i07.13229

Alemayehu, L., & Chen, H.-L. (2023). The influence of motivation on learning engagement: The mediating role of learning self-efficacy and self-monitoring in online learning environments. Interactive Learning Environments, 31(7), 4605–4618. https://doi.org/10.1080/10494820.2021.1977962

Aljraiwi, S. S. (2017). The effect of classroom web applications on teaching, learning and academic performance among college of education female students. Journal of Education and Learning, 6(2), 132–145. https://doi.org/10.5539/jel.v6n2p132

Alrwele, N. S. (2017). Effects of infographics on student achievement and students' perceptions of the impacts of infographics. Journal of Education and Human Development, 6(3), 104–117. https://doi.org/10.15640/jehd.v6n3a12

Biçer, D. (2014). The effect of students’ and instructors’ learning styles on achievement of foreign language preparatory school students. Procedia - Social and Behavioral Sciences, 141, 382–386. https://doi.org/10.1016/j.sbspro.2014.05.067

Bravo, E., Amante, B., Simo, P., Enache, M., & Fernandez, V. (2011, April 4–6). Video as a new teaching tool to increase student motivation [Paper presentation]. 2011 IEEE Global Engineering Education Conference (EDUCON), Amman, Jordan. https://doi.org/10.1109/EDUCON.2011.5773205

Chen, C.-M., & Sun, Y.-C. (2012). Assessing the effects of different multimedia materials on emotions and learning performance for visual and verbal style learners. Computers & Education, 59(4), 1273–1285. https://doi.org/10.1016/j.compedu.2012.05.006

Czerkawski, B. C., & Lyman, E. W. (2016). An instructional design framework for fostering student engagement in online learning environments. TechTrends, 60(6), 532–539. https://doi.org/10.1007/s11528-016-0110-z

Digital Economy Promotion Agency. (n.d.). Tech series: Artificial intelligence (AI). https://www.depa.or.th/th/article-view/tech-series-artificial-intelligence-ai [in Thai]

Dixson, M. D. (2010). Creating effective student engagement in online courses: What do students find engaging? Journal of the Scholarship of Teaching and Learning, 10(2), 1–13. https://scholarworks.iu.edu/journals/index.php/josotl/article/view/1744

El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students' engagement. International Journal of Educational Technology in Higher Education, 18, Article 53. https://doi.org/10.1186/s41239-021-00289-4

Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43–71. https://doi.org/10.1002/piq.21143

Guerrero-Roldán, A.-E., Rodríguez-González, M. E., Bañeres, D., Elasri-Ejjaberi, A., & Cortadas, P. (2021). Experiences in the use of an adaptive intelligent system to enhance online learners' performance: A case study in economics and business courses. International Journal of Educational Technology in Higher Education, 18, Article 36. https://doi.org/10.1186/s41239-021-00271-0

Jung, L., & Ok-Choon, P. (2007). Adaptive instructional systems. In D. Jonassen, M. J. Spector, M. Driscoll, M. D. Merrill, J. van Merrienboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 634–664). Routledge.

Khanhachai, P. (2019). Development of 5E flipped learning model with infographic design process to enhance science process skills and visual literacy of lower secondary school students [Master’s thesis, Chulalongkorn University]. Chulalongkorn University Theses and Dissertations (Chula ETD). https://digital.car.chula.ac.th/chulaetd/8979 [in Thai]

Klabpadung, S. (2017). Adaptive learning using web-based instruction for calculating ability and retention improvement for children with learning disabilities [Master’s thesis, Prince of Songkla University]. PSU Knowledge Bank. https://kb.psu.ac.th/psukb/bitstream/2016/11343/1/420160.pdf [in Thai]

Kolekar, S. V., Pai, R. M., & Pai, M. M. M. (2018). Adaptive user interface for moodle based e-learning system using learning styles. Procedia Computer Science, 135, 606–615. https://doi.org/10.1016/j.procs.2018.08.226

Kuder, G. F., & Richardson, M. W. (1937). The theory of the estimation of test reliability. Psychometrika, 2, 151–160. http://dx.doi.org/10.1007/BF02288391

Lindner, M. A., Eitel, A., Barenthien, J., & Köller, O. (2021). An integrative study on learning and testing with multimedia: Effects on students’ performance and metacognition. Learning and Instruction, 71, Article 101100. https://doi.org/10.1016/j.learninstruc.2018.01.002

Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511811678

Ministry of Digital Economy and Society. (2021, December 15). Thailand internet users behavior 2021. https://www.etda.or.th/getattachment/a3ad051d-e372-48f6-9fbe-9a22de75666c/IUB2021_Slides-V5.pdf.aspx [in Thai]

Namwong, R., Hiranpongsin, S., & Ditcharoen, N. (2018). Development of web application to support project proposal management for research-based learning. Journal of Science and Science Education, 1(1), 1–16. [in Thai]

Na-songkhla, J. (2018). Digital learning design. Chulalongkorn University Press. [in Thai]

Nessessence. (2018, December 15). Panya pradit kue arai [What is artificial intelligence?]. Thai Programmer Association. https://www.thaiprogrammer.org/2018/12/whatisai/ [in Thai]

Office of the Basic Education Commission, Ministry of Education. (2017). Tua chi wat lae sara kan rian ru kaen klang klum sara kan rian ru witthayasart (chabap prapprung 2560 B.E.) tam lak sut kaen klang kan suek sa khan phuen than 2551 B.E. [Indicators and core learning areas, science learning area (Revised Edition 2017), according to the basic education core curriculum A.D. 2008] (1st ed.). The Cooperative Agriculture Federation of Thailand Limited. [in Thai]

Office of the Education Council (OEC). (2017). The national scheme of education B.E. 2560–2579 (2017–2036) (1st ed.). Prikwarn Graphic. [in Thai]

Park, E. E. (2022). Expanding reference through cognitive theory of multimedia learning videos. The Journal of Academic Librarianship, 48(3), Article 102522. https://doi.org/10.1016/j.acalib.2022.102522

Pennsylvania Higher Education Assistance Agency (PHEAA). (n.d.). What's your learning style? 20 questions. EducationPlanner.org. http://www.educationplanner.org/students/self-assessments/learning-styles-quiz.shtml

Salas-Rueda, R.-A. (2020). Design, construction and evaluation of a web application for the teaching-learning process on financial mathematics. International Journal of Emerging Technologies in Learning, 15(08), 100–115. https://doi.org/10.3991/ijet.v15i08.12275

Sanjabi, T., & Montazer, G. A. (2020). Personalization of e-Learning environment using the Kolb's learning style model. 2020 6th International Conference on Web Research (ICWR) (pp. 89–92). IEEE. https://doi.org/10.1109/ICWR49608.2020.9122314

SAS Institute Inc. (n.d.). Panya pradit kue arai lae samkhan yang rai [What is innovation and why is it important?]. https://www.sas.com/th_th/insights/analytics/what-is-artificial-intelligence.html [in Thai]

Smith, A. R., Jr., Cavanaugh, C., Jones, J., Venn, J., & Wilson, W. (2006). Influence of learning style on instructional multimedia effects on graduate student cognitive and psychomotor performance. Journal of Allied Health, 35(3), e182–e203. https://pubmed.ncbi.nlm.nih.gov/19759970/

The National Strategy 2561–2580 B.E. (2021, October 13). Royal Thai Government Gazette. No. 155. Section 82 C. pp. 1–71. [in Thai]

Wilson, D. G., & Wagner, E. E. (1981). The Watson-Glaser critical thinking appraisal as a predictor of performance in a critical thinking course. Educational and Psychological Measurement, 41(4), 1319–1322.

Yorganci, S. (2022). The interactive e-book and video feedback in a multimedia learning environment: Influence on performance, cognitive, and motivational outcomes. Journal of Computer Assisted Learning, 38(4), 1005–1017. https://doi.org/10.1111/jcal.12658