Reimagining the Master–Apprentice: A Hybrid Physical-Digital Pedagogy Framework for Traditional Shaanxi Heritage-Art Education in Chinese Primary Schools

Authors

Shi FengRui

Faculty of Computing and Meta-Technology, University Pendidikan Sultan Idris (UPSI), Tanjung Malim, Perak 35900 (Malaysia)

Chee Ken Nee

Faculty of Computing and Meta-Technology, University Pendidikan Sultan Idris (UPSI), Tanjung Malim, Perak 35900 (Malaysia)

Article Information

DOI: 10.47772/IJRISS.2026.100800675

Subject Category: Education

Volume/Issue: 10/8 | Page No: 10008-10028

Publication Timeline

Submitted: 2026-09-02

Accepted: 2026-09-07

Published: 2026-09-15

Abstract

Traditional Shaanxi artisan practices such as Ansai paper-cutting, Fuping folk embroidery, Qinqiang facial painting, and loess landscape painting are disappearing as youth interest declines and the population of mas-ter artisans ages, while uneven resourcing places rural learners at a further disadvantage. This study designed, implemented, and evaluated a hybrid physical-digital pedagogy integrating segmented multimedia heritage modules, immersive virtual reality, an adaptive artificial-intelligence tutor, and a cross-regional collaborative platform. A sequential explanatory mixed-methods quasi-experiment was conducted over sixteen weeks with 60 upper-primary pupils recruited in equal numbers from three Shaanxi schools of differing resource level, with intact art classes assigned to condition (n = 30 per condition). The design was grounded in cognitive load theory, the cognitive theory of multimedia learning, and dual coding theory. The experimental group outperformed the control group on all six learning-outcome dimensions, with large between-group effects for technical skill (d = 2.20, 95% CI [1.56, 2.84]) and cultural appreciation (d = 2.14, 95% CI [1.50, 2.77]); anal-yses of covariance confirmed these advantages after adjustment for pre-test scores. Because pupils were nested within intact classes and schools, a design-effect sensitivity analysis was conducted; all differences remained significant at intraclass correlations up to .20. Daily learning duration rose by 42.3%, while test anxiety (d = -1.30) and cognitive distraction (d = -1.39) declined. Reflexive thematic analysis produced three positive and two challenge themes, from which two constructs are proposed as hypotheses for confirmatory testing rather than as established findings: algorithmic folk-art correction bias and the equity paradox in her-itage educational technology. A third contribution, the Hybrid Physical-Digital Pedagogy Framework, speci-fies a theoretically derived five-phase progression from digitally mediated understanding to authentic embod-ied practice.

Keywords

heritage-art education, multimedia learning, artificial intelligence in education, virtual reality

Downloads

References

1. Adesope, O. O., & Nesbit, J. C. (2012). Verbal redundancy in multimedia learning environments: A meta-analysis. Journal of Educational Psychology, 104(1), 250–263. [Google Scholar] [Crossref]

2. Baddeley, A. D. (1992). Working memory. Science, 255(5044), 556–559. [Google Scholar] [Crossref]

3. Bartolini, R., Antonio, P., & Concas, G. (2017). Digital heritage and intangible cultural transmission. Digital Scholarship in the Humanities, 32(3), 451–469. [Google Scholar] [Crossref]

4. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psy-chology, 3(2), 77–101. [Google Scholar] [Crossref]

5. Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. [Google Scholar] [Crossref]

6. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 1–15. [Google Scholar] [Crossref]

7. Chen, Y. (2018). The reform of traditional Chinese art education in the digital era. Frontiers in Educa-tion, 13(2), 245–262. [Google Scholar] [Crossref]

8. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum. [Google Scholar] [Crossref]

9. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage. [Google Scholar] [Crossref]

10. Ericsson, K. A. (2008). Deliberate practice and acquisition of expert performance. Academic Emer-gency Medicine, 15(11), 988–994. [Google Scholar] [Crossref]

11. Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3. Behavior Research Methods, 39(2), 175–191. [Google Scholar] [Crossref]

12. Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage. [Google Scholar] [Crossref]

13. Glenberg, A. M., Witt, J. K., & Metcalfe, J. (2013). From the revolution to embodiment. Perspectives on Psychological Science, 8(5), 573–585. [Google Scholar] [Crossref]

14. Hamal, O., El Faddouli, N. E., Harouni, M. H. A., & Lu, J. (2022). Artificial intelligence in education. Sustainability, 14(5), 2862. [Google Scholar] [Crossref]

15. Hannum, E., Behrman, J., Wang, M., & Liu, J. (2008). Education in the reform era. In L. Brandt & T. G. Rawski (Eds.), China’s great economic transformation (pp. 215–249). Cambridge University Press. [Google Scholar] [Crossref]

16. Hattie, J. (2009). Visible learning. Routledge. [Google Scholar] [Crossref]

17. Hayes, A. F., & Coutts, J. J. (2020). Use omega rather than Cronbach’s alpha for estimating reliability. Communication Methods and Measures, 14(1), 1–24. [Google Scholar] [Crossref]

18. Kalay, Y. E., Kvan, T., & Affleck, J. (Eds.). (2008). New heritage: New media and cultural heritage. Routledge. [Google Scholar] [Crossref]

19. Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Edu-cational Psychology Review, 19(4), 509–539. [Google Scholar] [Crossref]

20. Kilteni, K., Groten, R., & Slater, M. (2012). The sense of embodiment in virtual reality. Presence, 21(4), 373–387. [Google Scholar] [Crossref]

21. Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford. [Google Scholar] [Crossref]

22. Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems. Review of Educa-tional Research, 86(1), 42–78. [Google Scholar] [Crossref]

23. Liu, X., Zhang, H., & Wang, M. (2021). Tertiary art education and heritage transmission in northwest China. Asia Pacific Journal of Education, 41(3), 567–584. [Google Scholar] [Crossref]

24. Maria, A., Francisco, J. P., & Carmen, R. (2014). Multimedia in art education. International Journal of Art & Design Education, 33(3), 367–382. [Google Scholar] [Crossref]

25. Mayer, R. E. (2014). The Cambridge handbook of multimedia learning (2nd ed.). Cambridge Univer-sity Press. [Google Scholar] [Crossref]

26. Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of evidence-based practices in online learning. U.S. Department of Education. [Google Scholar] [Crossref]

27. Merchant, Z., Goetz, E. T., Cifuentes, L., Keeney-Kennicutt, W., & Davis, T. J. (2014). Effectiveness of virtual reality-based instruction. Computers & Education, 70, 29–40. [Google Scholar] [Crossref]

28. Ministry of Culture and Tourism of the People’s Republic of China. (2021). National intangible cul-tural heritage protection plan. [Google Scholar] [Crossref]

29. Morgan, D. L. (2014). Pragmatism as a paradigm for social research. Qualitative Inquiry, 20(8), 1045–1053. [Google Scholar] [Crossref]

30. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. [Google Scholar] [Crossref]

31. Paivio, A. (1986). Mental representations: A dual coding approach. Oxford University Press. [Google Scholar] [Crossref]

32. Parong, J., & Mayer, R. E. (2018). Learning science in immersive virtual reality. Journal of Educa-tional Psychology, 110(6), 785–797. [Google Scholar] [Crossref]

33. Plass, J. L., Heidig, S., Hayward, E. O., Homer, B. D., & Um, E. (2014). Emotional design in multi-media learning. Learning and Instruction, 29, 128–140. [Google Scholar] [Crossref]

34. Polanyi, M. (1966). The tacit dimension. Routledge. [Google Scholar] [Crossref]

35. Polit, D. F., & Beck, C. T. (2006). The content validity index. Research in Nursing & Health, 29(5), 489–497. [Google Scholar] [Crossref]

36. Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immer-sive virtual reality applications for higher education. Computers & Education, 147, 103778. [Google Scholar] [Crossref]

37. Roege, G. B., & Kim, K. H. (2013). Why we need arts education. Empirical Studies of the Arts, 31(2), 121–130. [Google Scholar] [Crossref]

38. Schroeder, N. L., & Cenkci, A. T. (2018). Spatial contiguity and spatial split-attention effects. Educa-tional Psychology Review, 30(3), 679–701. [Google Scholar] [Crossref]

39. Selwyn, N. (2019). Should robots replace teachers? Polity. [Google Scholar] [Crossref]

40. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs. Houghton Mifflin. [Google Scholar] [Crossref]

41. Slater, M., & Sanchez-Vives, M. V. (2016). Enhancing our lives with immersive virtual reality. Fron-tiers in Robotics and AI, 3, 74. [Google Scholar] [Crossref]

42. Sun, M., Liu, J., & Zhang, F. (2022). Reform pathways for traditional Chinese art education. Journal of Chinese Higher Education, 47(2), 89–104. [Google Scholar] [Crossref]

43. Sweller, J. (2011). Cognitive load theory. Psychology of Learning and Motivation, 55, 37–76. [Google Scholar] [Crossref]

44. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer. [Google Scholar] [Crossref]

45. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson. [Google Scholar] [Crossref]

46. Taylor, M. K., Kass, R. E., & Lemelin, K. (2021). Artificial intelligence and the visual arts curricu-lum. Studies in Art Education, 62(4), 314–328. [Google Scholar] [Crossref]

47. UNESCO. (2003). Convention for the safeguarding of the intangible cultural heritage. UNESCO. [Google Scholar] [Crossref]

48. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. [Google Scholar] [Crossref]

49. Wang, L., & Zhao, Y. (2022). The aging crisis in China’s heritage art transmission. Asian Ethnology, 81(1), 79–102. [Google Scholar] [Crossref]

50. Wenger, E. (1998). Communities of practice. Cambridge University Press. [Google Scholar] [Crossref]

51. Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. [Google Scholar] [Crossref]

52. Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625–636. [Google Scholar] [Crossref]

53. Wittrock, M. C. (1989). Generative processes of comprehension. Educational Psychologist, 24(4), 345–376. [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles