Skip to main content
SpringerLink
Log in

Information undergraduate and non-information undergraduate on an artificial intelligence learning platform: an artificial intelligence assessment model using PLS-SEM analysis

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Artificial intelligence (AI) education is becoming an advanced learning trend in programming education. However, AI subjects can be difficult to understand because they require high programming skills and complex knowledge. This makes it challenging to determine how different departments of students are affected by them. This study draws on research in programming education and STEM education to explore the different factors that affect students in AI learning. Therefore, the purpose of this study is to investigate the impact of AI learning platforms on information undergraduate and non-information undergraduate by using a research model. The course was implemented for 65 students in the information undergraduate group and 39 students in the non-information undergraduate group. The findings showed that the two groups had different learning effects under different variables. Students with different cognitive styles may use different skills to positively influence self-regulated learning. This study provides important evidence to understand the learning impact of artificial intelligence among university students from different disciplines.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Abbreviations

AI:

Artificial Intelligence

STEM:

Science, Technology, Engineering, and Mathematics

References

  • Abdullah, M. N. L. Y. (2016). Interaction Effects of Gender and Motivational Beliefs on Self-Regulated Learning: A Study at ICT-Integrated Schools. Malaysian Journal of Learning and Instruction, 13(1), 25–41.

    Article  Google Scholar 

  • Bishop-Clark, C. (1995). Cognitive style, personality, and computer programming. Computers in Human Behavior, 11(2), 241–260.

    Article  Google Scholar 

  • Bouckenooghe, D., Cools, E., De Clercq, D., Vanderheyden, K., & Fatima, T. (2016). Exploring the impact of cognitive style profiles on different learning approaches: Empirical evidence for adopting a person-centered perspective. Learning and Individual Differences, 51, 299–306. https://doi.org/10.1016/j.lindif.2016.08.043

    Article  Google Scholar 

  • Chang, J.-H., Chiu, P.-S., & Lai, C.-F. (2023). Implementation and evaluation of cloud-based e-learning in agricultural course. Interactive Learning Environments, 31(2), 908–923. https://doi.org/10.1080/10494820.2020.1815217

  • Chen, C., Sonnert, G., Sadler, P. M., & Malan, D. J. (2020). Computational thinking and assignment resubmission predict persistence in a computer science MOOC. Journal of Computer Assisted Learning, 36(5), 581–594.

    Article  Google Scholar 

  • Chen, L.-H. (2010). Web-based learning programs: Use by learners with various cognitive styles. Computers & Education, 54(4), 1028–1035. https://doi.org/10.1016/j.compedu.2009.10.008

    Article  Google Scholar 

  • Chen, Y.-C. (2019). Effect of mobile augmented reality on learning performance, motivation, and math anxiety in a math course. Journal of Educational Computing Research, 57(7), 1695–1722.

    Article  Google Scholar 

  • Chen, Y.-T., Liou, S., & Chen, L.-F. (2019). The relationships among gender, cognitive styles, learning strategies, and learning performance in the flipped classroom. International Journal of Human-Computer Interaction, 35(4–5), 395–403.

    Article  CAS  Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.

    Google Scholar 

  • Chu, P. C., & Spires, E. E. (1991). Validating the computer anxiety rating scale: Effects of cognitive style and computer courses on computer anxiety. Computers in Human Behavior, 7(1), 7–21. https://doi.org/10.1016/0747-5632(91)90025-V

    Article  Google Scholar 

  • Cools, E., & Van den Broeck, H. (2007). Development and validation of the Cognitive Style Indicator. The Journal of Psychology, 141(4), 359–387.

    Article  PubMed  Google Scholar 

  • Cools, E., & Van Den Broeck, H. (2008). Cognitive Styles and Managerial Behaviour: A Qualitative Study. Education & Training, 50(2), 103–114.

    Article  Google Scholar 

  • Deng, W., Pi, Z., Lei, W., Zhou, Q., & Zhang, W. (2020). Pencil Code improves learners’ computational thinking and computer learning attitude. Computer Applications in Engineering Education, 28(1), 90–104.

    Article  Google Scholar 

  • Durak, H. Y., & Saritepeci, M. (2018). Analysis of the relation between computational thinking skills and various variables with the structural equation model. Computers & Education, 116, 191–202. https://doi.org/10.1016/j.compedu.2017.09.004

    Article  Google Scholar 

  • Engin, A., & Vetschera, R. (2017). Information representation in decision making: The impact of cognitive style and depletion effects. Decision Support Systems, 103, 94–103. https://doi.org/10.1016/j.dss.2017.09.007

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Ho, L.-A., & Kuo, T.-H. (2010). How can one amplify the effect of e-learning? An examination of high-tech employees’ computer attitude and flow experience. Computers in Human Behavior, 26(1), 23–31. https://doi.org/10.1016/j.chb.2009.07.007

    Article  Google Scholar 

  • Hooshyar, D., Malva, L., Yang, Y., Pedaste, M., Wang, M., & Lim, H. (2021). An adaptive educational computer game: Effects on students’ knowledge and learning attitude in computational thinking. Computers in human behavior, 114, 106575. https://doi.org/10.1016/j.chb.2020.106575

    Article  Google Scholar 

  • Hu, Y., Chen, C.-H., & Su, C.-Y. (2020). Exploring the Effectiveness and Moderators of Block-Based Visual Programming on Student Learning: A Meta-Analysis. Journal of Educational Computing Research, 58(8), 1467–1493.

    Article  Google Scholar 

  • Hwang, G.-J., & Chang, H.-F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers & Education, 56(4), 1023–1031.

    Article  Google Scholar 

  • Ilishkina, D. I., de Bruin, A., Podolskiy, A. I., Volk, M. I., & van Merriënboer, J. J. G. (2022). Understanding self-regulated learning through the lens of motivation: Motivational regulation strategies vary with students’ motives. International Journal of Educational Research, 113, 101956. https://doi.org/10.1016/j.ijer.2022.101956

    Article  Google Scholar 

  • Järvelä, S., Järvenoja, H., Malmberg, J., Isohätälä, J., & Sobocinski, M. (2016). How do types of interaction and phases of self-regulated learning set a stage for collaborative engagement? Learning and Instruction, 43, 39–51. https://doi.org/10.1016/j.learninstruc.2016.01.005

    Article  Google Scholar 

  • Kong, S.-C., Man-Yin Cheung, W., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026. https://doi.org/10.1016/j.caeai.2021.100026

    Article  Google Scholar 

  • Kozhevnikov, M., Evans, C., & Kosslyn, S. M. (2014). Cognitive style as environmentally sensitive individual differences in cognition: A modern synthesis and applications in education, business, and management. Psychological Science in the Public Interest, 15(1), 3–33.

    Article  PubMed  Google Scholar 

  • Kpolovie, P. J., Joe, A. I., & Okoto, T. (2014). Academic achievement prediction: Role of interest in learning and attitude towards school. International Journal of Humanities Social Sciences and Education (IJHSSE), 1(11), 73–100.

    Google Scholar 

  • León, J., Núñez, J. L., & Liew, J. (2015). Self-determination and STEM education: Effects of autonomy, motivation, and self-regulated learning on high school math achievement. Learning and Individual Differences, 43, 156–163. https://doi.org/10.1016/j.lindif.2015.08.017

    Article  Google Scholar 

  • Macher, D., Paechter, M., Papousek, I., & Ruggeri, K. (2012). Statistics anxiety, trait anxiety, learning behavior, and academic performance. European Journal of Psychology of Education, 27(4), 483–498.

    Article  Google Scholar 

  • Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

    Article  Google Scholar 

  • Ng, D. T. K., Luo, W., Chan, H. M. Y., & Chu, S. K. W. (2022). Using digital story writing as a pedagogy to develop AI literacy among primary students. Computers and Education: Artificial Intelligence, 3, 100054. https://doi.org/10.1016/j.caeai.2022.100054

    Article  Google Scholar 

  • Papanikolaou, K. A., Mabbott, A., Bull, S., & Grigoriadou, M. (2006). Designing learner-controlled educational interactions based on learning/cognitive style and learner behaviour. Interacting with Computers, 18(3), 356–384. https://doi.org/10.1016/j.intcom.2005.11.003

    Article  Google Scholar 

  • Parayitam, S., Desai, K. J., Desai, M. S., & Eason, M. K. (2010). Computer attitude as a moderator in the relationship between computer anxiety, satisfaction, and stress. Computers in Human Behavior, 26(3), 345–352. https://doi.org/10.1016/j.chb.2009.11.005

    Article  Google Scholar 

  • Peters-Burton, E. E., Cleary, T. J., & Kitsantas, A. (2015). The Development of Computational Thinking in the Context of Science and Engineering Practices: A Self-Regulated Learning Approach. International Association for Development of the Information Society.

    Google Scholar 

  • Peters-Burton, E. E., Cleary, T. J., & Kitsantas, A. (2018). Computational Thinking in the Context of Science and Engineering Practices: A Self-Regulated Learning Approach. In D. Sampson, D. Ifenthaler, J. M. Spector, & P. Isaías (Eds.), Digital Technologies: Sustainable Innovations for Improving Teaching and Learning (pp. 223–240). Springer International Publishing.

    Chapter  Google Scholar 

  • Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31(6), 459–470. https://doi.org/10.1016/S0883-0355(99)00015-4

    Article  Google Scholar 

  • Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. Handbook of self-regulation (pp. 451–502). Elsevier.

    Chapter  Google Scholar 

  • Pintrich, P. R. (2003). A Motivational Science Perspective on the Role of Student Motivation in Learning and Teaching Contexts. Journal of Educational Psychology, 95(4), 667–686.

    Article  Google Scholar 

  • Pintrich, P. R., & De Groot, E. V. (1990). Motivational and Self-Regulated Learning Components of Classroom Academic Performance. Journal of Educational Psychology, 82(1), 33–40.

    Article  Google Scholar 

  • Rodríguez, J. D., León, J. M., González, M. R., & Robles, G. (2020). LearningML: A Tool to Foster Computational Thinking Skills Through Practical Artificial Intelligence Projects. RED: Revista de Educación a Distancia, 20(63), 7.

    Google Scholar 

  • Schunk, D. H. (2005). Self-regulated learning: The educational legacy of Paul R. Pintrich. Educational Psychologist, 40(2), 85–94.

    Article  Google Scholar 

  • Shi, C. (2011). A Study of the Relationship between Cognitive Styles and Learning Strategies. Higher Education Studies, 1(1), 20–26.

    Article  CAS  Google Scholar 

  • Sing, C. C., Teo, T., Huang, F., & Chiu, T. K. (2022). Secondary school students’ intentions to learn AI: testing moderation effects of readiness, social good and optimism. Educational technology research and development, 70(3), 765–782.

    Article  Google Scholar 

  • Sırakaya, M., Alsancak Sırakaya, D., & Korkmaz, Ö. (2020). The Impact of STEM Attitude and Thinking Style on Computational Thinking Determined via Structural Equation Modeling. Journal of Science Education and Technology, 29(4), 561–572. https://doi.org/10.1007/s10956-020-09836-6

    Article  ADS  Google Scholar 

  • Smarandache, I. G., Maricutoiu, L. P., Ilie, M. D., Iancu, D. E., & Mladenovici, V. (2022). Students’ approach to learning: Evidence regarding the importance of the interest-to-effort ratio. Higher Education Research & Development, 41(2), 546–561.

    Article  Google Scholar 

  • Song, D., Hong, H., & Oh, E. Y. (2021). Applying computational analysis of novice learners’ computer programming patterns to reveal self-regulated learning, computational thinking, and learning performance. Computers in human behavior, 120, 106746. https://doi.org/10.1016/j.chb.2021.106746

    Article  Google Scholar 

  • Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence, 3, 100065. https://doi.org/10.1016/j.caeai.2022.100065

    Article  Google Scholar 

  • Sun, J.C.-Y., Syu, Y.-R., & Lin, Y.-Y. (2017). Effects of conformity and learning anxiety on intrinsic and extrinsic motivation: The case of Facebook course groups. Universal Access in the Information Society, 16(2), 273–288. https://doi.org/10.1007/s10209-016-0456-1

    Article  Google Scholar 

  • Sun, L., Hu, L., Yang, W., Zhou, D., & Wang, X. (2020). STEM learning attitude predicts computational thinking skills among primary school students. Journal of computer assisted learning, 37(2), 346–358. https://doi.org/10.1111/jcal.12493

    Article  Google Scholar 

  • Sun, L., Hu, L., & Zhou, D. (2022). Programming attitudes predict computational thinking: Analysis of differences in gender and programming experience. Computers & Education, 181, 104457.

    Article  Google Scholar 

  • Tsai, C.-W. (2010). Do students need teacher’s initiation in online collaborative learning? Computers & Education, 54(4), 1137–1144.

    Article  Google Scholar 

  • Tsai, C.-W. (2020). Applying online competency-based learning and design-based learning to enhance the development of students’ skills in using PowerPoint and Word, self-directed learning readiness, and experience of online learning. Universal Access in the Information Society, 19(2), 283–294.

    Article  Google Scholar 

  • Tsai, M.-J., Liang, J.-C., & Hsu, C.-Y. (2021). The Computational Thinking Scale for Computer Literacy Education. Journal of Educational Computing Research, 59(4), 579–602.

    Article  Google Scholar 

  • Tseng, K.-H., Chang, C.-C., Lou, S.-J., & Chen, W.-P. (2013). Attitudes towards science, technology, engineering and mathematics (STEM) in a project-based learning (PjBL) environment. International Journal of Technology and Design Education, 23(1), 87–102.

    Article  Google Scholar 

  • Urbach, N., Smolnik, S., & Riempp, G. (2010). An empirical investigation of employee portal success. The Journal of Strategic Information Systems, 19(3), 184–206.

    Article  Google Scholar 

  • Wang, X., Wallace, M. P., & Wang, Q. (2017). Rewarded and unrewarded competition in a CSCL environment: A coopetition design with a social cognitive perspective using PLS-SEM analyses. Computers in Human Behavior, 72, 140–151. https://doi.org/10.1016/j.chb.2017.02.045

    Article  Google Scholar 

  • Wang, Y.-Y., & Wang, Y.-S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634.

    Article  MathSciNet  Google Scholar 

  • Xu, J. J., & Babaian, T. (2021). Artificial intelligence in business curriculum: The pedagogy and learning outcomes. The International Journal of Management Education, 19(3), 100550. https://doi.org/10.1016/j.ijme.2021.100550

    Article  Google Scholar 

  • Yang, J., Wong, G. K., & Dawes, C. (2018). An exploratory study on learning attitude in computer programming for the twenty-first century. New media for educational change (pp. 59–70). Springer.

    Chapter  Google Scholar 

  • Yang, W. (2022). Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation. Computers and Education: Artificial Intelligence, 3, 100061. https://doi.org/10.1016/j.caeai.2022.100061

    Article  Google Scholar 

  • Yu, Y., & Chen, Y. (2018). Design and development of high school artificial intelligence textbook based on computational thinking. Open Access Library Journal, 5(9), 1–15.

    MathSciNet  CAS  Google Scholar 

  • Yukselturk, E., & Altiok, S. (2017). An investigation of the effects of programming with Scratch on the preservice IT teachers’ self-efficacy perceptions and attitudes towards computer programming. British Journal of Educational Technology, 48(3), 789–801.

    Article  Google Scholar 

  • 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(1), 1–27.

    Article  Google Scholar 

  • Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (2022). Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: An exploratory study. International Journal of Artificial Intelligence in Education, 1–35. https://doi.org/10.1007/s40593-022-00293-3

  • Zhong, H.-X., Lai, C.-F., Huang, Y.-C., Wu, P.-H., & Chang, J.-H. (2021). Exploring the impact of artificial intelligence learning platforms on interest in and attitudes toward learning. In Innovative technologies and learning: 4th International Conference, ICITL 2021, Virtual Event (vol 13117, pp. 22–29). Springer, Cham. https://doi.org/10.1007/978-3-030-91540-7_3

  • Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17.

    Article  Google Scholar 

  • Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82–91.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the anonymous reviewers for their constructive comments. This research work was supported in part by the Ministry of Science and Technology of Taiwan, ROC. (NSTC 112-2221-E-006-164-, 110-2511-H-006 -011 -MY3, and 112-2917-I-006-018)

Author information

Authors and Affiliations

  1. Department of Engineering Science, National Cheng Kung University 1, University Rd., Tainan, 701, Taiwan, Republic of China

    Hua-Xu Zhong & Chin-Feng Lai

  2. Computer and Network Center, and Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, Republic of China

    Jui-Hung Chang

  3. Department of Computer Science and Information Engineering, National Cheng Kung University 1, University Rd., Tainan, 701, Taiwan, Republic of China

    Pei-Wen Chen & Shang-Hsuan Ku

  4. Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan

    Shih-Yeh Chen

Authors
  1. Hua-Xu Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jui-Hung Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chin-Feng Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pei-Wen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shang-Hsuan Ku
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shih-Yeh Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

HXZ writes manuscripts to assist in the planning and analysis of the research course, and JHC makes a correction to the manuscript work, assist with substantial revision of paper and system design development. CFL assists in the definition of survey questions and statistical analysis. PWC and SHK help build systems and assist with paper revision. SYC writes manuscripts and provides data to tables and writes the results of the analysis. All of the authors have reviewed the final manuscript.

Corresponding author

Correspondence to Shih-Yeh Chen.

Ethics declarations

Ethical Approval

This material has not been published in whole or in part elsewhere, the manuscript is not currently being considered for publication in another journal, authors have been personally and actively involved in substantive work leading to the manuscript, and will hold themselves jointly and individually responsible for its content.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Questionnaire.

Table

Table 5 Examples of questionnaire items and the source of the questionnaire 
Full size table

5

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhong, HX., Chang, JH., Lai, CF. et al. Information undergraduate and non-information undergraduate on an artificial intelligence learning platform: an artificial intelligence assessment model using PLS-SEM analysis. Educ Inf Technol 29, 4371–4400 (2024). https://doi.org/10.1007/s10639-023-11961-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-023-11961-9

Keywords

Search

Navigation