Sage Journals: Discover world-class research

Abstract

This study explored the impact of MetaClassroom, a virtual immersive programming learning environment designed based on the three-dimensional learning progression (3DLP) concept, on students’ multidimensional development. Utilizing a quasi-experimental research design, this study compared students’ programming learning achievements (PLA), self-regulated learning (SRL) skills, learning beliefs, and learning motivation in MetaClassroom with those in traditional classroom settings. The findings revealed that MetaClassroom significantly enhanced students’ PLA and SRL skills, particularly in subdimensions including Metacognitive Skills, Persistence, and Seeking Help. Additionally, MetaClassroom positively impacted students’ learning beliefs and learning motivation, demonstrating its potential in optimizing knowledge acquisition, application processes, and fostering students’ higher-order thinking skills. By integrating the 3DLP concept into targeted programming learning environments, MetaClassroom created an innovative immersive learning ecosystem, that bridged theory and practice, offering students a comprehensive and engaging platform to develop both foundational knowledge and practical skills. This study not only validated the effectiveness of MetaClassroom in improving students’ programming learning performance and learning experience, but also introduced a new paradigm that integrated teaching, learning, and assessment. The paradigm offers new insights and directions for both programming education and broader educational practices, paving the way for future developments in educational technology.

Keywords

three-dimensional learning progression programming education self-regulated learning learning belief learning motivation metaclassroom

Get full access to this article

View all access options for this article.

References

Abdüsselam

M. S.

Turan-Güntepe

Durukan

Ü. G.

(2022). Programming education in the frameworks of reverse engineering and theory of didactical situations. Education and Information Technologies, 27(5), 6513–6532. https://doi.org/10.1007/s10639-021-10883-8

Adair

J. G.

Sharpe

Huynh

C. L.

(1989). Hawthorne control procedures in educational experiments: A reconsideration of their use and effectiveness. Review of Educational Research, 59(2), 215–228. https://doi.org/10.3102/00346543059002215

Al-Ajmi

Ambusaidi

(2022). Scientific argumentation the level of scientific argumentation skills in chemistry subject among grade 11th students: The role of logical thinking. Science Education International, 33(1), 66–74. https://doi.org/10.33828/sei.v33.i1.7

Alonzo

A. C.

Gotwals

A. W.

(Eds.), (2012). Learning progressions in science: Current challenges and future directions. Springer Science & Business Media.

Anindyaputri

N. A.

Yuana

R. A.

Hatta

(2020). Enhancing students’ ability in learning process of programming language using adaptive learning systems: A literature review. Open Engineering, 10(1), 820–829. https://doi.org/10.1515/eng-2020-0092

Bandura

(1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359–373. https://doi.org/10.1521/jscp.1986.4.3.359

Birdman

Wiek

Lang

D. J.

(2022). Developing key competencies in sustainability through project-based learning in graduate sustainability programs. International Journal of Sustainability in Higher Education, 23(5), 1139–1157. https://doi.org/10.1108/IJSHE-12-2020-0506

Capel

M. I.

Tomeu

A. J.

Salguero

A. G.

(2017). Teaching concurrent and parallel programming by patterns: An interactive ICT approach. Journal of Parallel and Distributed Computing, 105(1), 42–52. https://doi.org/10.1016/j.jpdc.2017.01.010

Çetin

Demircan

H. Ö.

(2020). Empowering technology and engineering for STEM education through programming robots: A systematic literature review. Early Child Development and Care, 190(9), 1323–1335. https://doi.org/10.1080/03004430.2018.1534844

10.

Chan

C. K. Y.

(2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8

11.

Chau

Wong

Wang

Lai

Chan

K. W. Y.

T. M. H.

Chu

Chan

I. K. W.

Sung

(2013). Using 3D virtual environments to facilitate students in constructivist learning. Decision Support Systems, 56(1), 115–121. https://doi.org/10.1016/j.dss.2013.05.009

12.

Chen

Xiao

Chen

Song

Sun

(2024a). ChatScratch: An AI-augmented system toward autonomous visual programming learning for children aged 6-12. In Proceedings of the CHI conference on human factors in computing systems (pp. 1–19). Retrieved from: https://doi.org/10.1145/3613904.3642229

13.

Chen

X. J.

Z. B.

Wang

C. L.

(2024b). Empowering education development through aigc: A systematic literature review. Education and Information Technologies, 29(13), 17485–17537. https://doi.org/10.1007/s10639-024-12549-7

14.

Chen

Herskovitz

Matute

April

Yi W.

Sang

Y. L.

Lasecki

W. S.

Oney

(2020). EdCode: Towards personalized support at scale for remote assistance in CS education. In 2020 IEEE symposium on visual languages and human-centric computing. Retrieved from: https://doi.org/10.1109/vl/hcc50065.2020.9127260

15.

Cheng

Poon

L. K.

Lau

W. W.

Zhou

R. C.

(2019). Exploring the relationship between self-regulated learning strategies and computer programming achievement in higher education. Proceedings of the International Conference on Education, 5(1), 67–74. https://doi.org/10.17501/24246700.2019.5108

16.

Cloete

A. L.

(2017). Technology and education: Challenges and opportunities. HTS Teologiese Studies / Theological Studies, 73(4), 1. https://doi.org/10.4102/hts.v73i4.4589

17.

Coşkunserçe

(2023). Comparing the use of block based and robot programming in introductory programming education: Effects on perceptions of programming self efficacy. Computer Applications in Engineering Education, 31(5), 1234–1255. https://doi.org/10.1002/cae.22637

18.

Deci

E. L.

Ryan

R. M.

(2013). Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media.

19.

Deci

E. L.

Vallerand

R. J.

Pelletier

L. G.

Ryan

R. M.

(1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325–346. https://doi.org/10.1080/00461520.1991.9653137

20.

Department for Education . (2013). National curriculum in England: Framework document. Retrieved from: https://www.gov.uk/government/publications/national-curriculum-in-england-framework-for-key-stages-1-to-4

21.

Diaper

(1990). The Hawthorne effect: A fresh examination. Educational Studies, 16(3), 261–267. https://doi.org/10.1080/0305569900160305

22.

Duncan

T. G.

McKeachie

W. J.

(2005). The making of the motivated strategies for learning questionnaire. Educational Psychologist, 40(2), 117–128. https://doi.org/10.1207/s15326985ep4002_6

23.

EDUCAUSE . (2023). 2023 EDUCAUSE horizon report: Teaching and learning edition. Retrieved from: https://library.educause.edu/resources/2023/5/2023-educause-horizon-report-teaching-and-learning-edition

24.

Engineers Australia . (2018). National STEM strategy 2019-2023: Create tomorrows engineers’ strategic priority. Retrieved from: https://www.engineersaustralia.org.au/publications/national-stem-strategy-2019-2023

25.

Gottfried

A. E.

Marcoulides

G. A.

Gottfried

A. W.

Oliver

P. H.

Guerin

D. W.

(2007). Multivariate latent change modeling of developmental decline in academic intrinsic math motivation and achievement: Childhood through adolescence. International Journal of Behavioral Development, 31(4), 317–327. https://doi.org/10.1177/0165025407077752

26.

Hamutoglu

N. B.

Gemikonakli

Duman

Kirksekiz

Kiyici

(2020). Evaluating students experiences using a virtual learning environment: Satisfaction and preferences. Educational Technology Research & Development, 68(1), 437–462. https://doi.org/10.1007/s11423-019-09705-z

27.

Haynes

(2013). Benjamini–hochberg method. In Dubitzky

Wolkenhauer

Cho

K. H.

Yokota

(Eds.), Encyclopedia of systems biology. Springer. Retrieved from: https://doi.org/10.1007/978-1-4419-9863-7_1215

28.

Shin

Kaldaras

Krajcik

(2024). Integrating artificial intelligence into learning progression-based learning systems to support student knowledge-in-use: Opportunities and challenges. In Jin

Yan

Krajcik

(Eds.), Handbook of research on science learning progressions. Routledge.

29.

Hidayatullah

Csíkos

(2024). The role of students’ beliefs, parents' educational level, and the mediating role of attitude and motivation in students’ mathematics achievement. The Asia-Pacific Education Researcher, 33(2), 253–262. https://doi.org/10.1007/s40299-023-00724-2

30.

Hwang

W.-Y.

Wang

C.-Y.

Hwang

G.-J.

Huang

Y.-M.

Huang

(2008). A web-based programming learning environment to support cognitive development. Interacting with Computers, 20(6), 524–534. https://doi.org/10.1016/j.intcom.2008.07.002

31.

Jansen

R. S.

Van Leeuwen

Janssen

Kester

Kalz

(2017). Validation of the self-regulated online learning questionnaire. Journal of Computing in Higher Education, 29(1), 6–27. https://doi.org/10.1007/s12528-016-9125-x

32.

Jin

Sun

Pan

Lin

C. H.

(2025). High heels, compass, spider-man, or drug? Metaphor analysis of generative artificial intelligence in academic writing. Computers & Education, 228(1), 105248. https://doi.org/10.1016/j.compedu.2025.105248

33.

Jing

Y. H.

Dai

Wang

C. L.

Shen

Shadiev

(2024a). Unleashing the power of virtual learning environment: Exploring the impact on learning outcomes through a meta-analysis. Interactive Learning Environments, 2(1), 1–18. https://doi.org/10.1080/10494820.2024.2335481

34.

Jing

Y. H.

Wang

C. L.

Chen

Z. Y.

Shen

S. S.

Shadiev

(2024b). A bibliometric analysis of studies on technology-supported learning environments: Hot topics and frontier evolution. Journal of Computer Assisted Learning, 40(3), 1185–1200. https://doi.org/10.1111/jcal.12934

35.

Jing

Y. H.

Wang

H. M.

Chen

X. J.

Wang

C. L.

(2024c). What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study. Humanities & Social Sciences Communications, 11(1), 319. https://doi.org/10.1057/s41599-024-02751-w

36.

Kaldaras

Akaeze

Krajcik

(2021). Developing and validating Next Generation Science Standards‐aligned learning progression to track three‐dimensional learning of electrical interactions in high school physical science. Journal of Research in Science Teaching, 58(4), 589–618. https://doi.org/10.1002/tea.21672

37.

Kaldaras

Akaeze

H. O.

Krajcik

(2023). Developing and validating an Next Generation Science Standards aligned construct map for chemical bonding from the energy and force perspective. Journal of Research in Science Teaching, 61(7), 1689–1726. https://doi.org/10.1002/tea.21906

38.

Kao

Matlen

Weintrop

(2022). From one language to the next: Applications of analogical transfer for programming education. ACM Transactions on Computing Education, 22(4), 1–21. https://doi.org/10.1145/3487051

39.

Kazemitabaar

Wang

Henley

A. Z.

Denny

Craig

Grossman

(2024). Codeaid: Evaluating a classroom deployment of an llm-based programming assistant that balances student and educator needs. In Proceedings of the CHI conference on human factors in computing systems (pp. 1–20). Retrieved from: https://doi.org/10.1145/3613904.3642773

40.

Kong

S. C.

Chiu

M. M.

Lai

(2018). A study of primary school students' interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127(1), 178–189. https://doi.org/10.1016/j.compedu.2018.08.026

41.

Kryeziu

S. A.

Avdiu

T. A.

Avdiu

(2021). Examining the teachers, administrators and parents’ view on drawbacks of technology use in education. Ilköğretim Online, 20(2), 1.

42.

Kulesza

DeHondt II

Nezlek

(2011). More technology, less learning? Information Systems Electronic Journal, 9(7), 4. https://isedj.org/2011-9/N7/ISEDJv9n7p4.html

43.

Lee

H. Y.

Lin

C. J.

Wang

W. S.

Chang

W. C.

Huang

Y. M.

(2023b). Precision education via timely intervention in K-12 computer programming course to enhance programming skill and affective-domain learning objectives. International Journal of STEM Education, 10(1), 52. https://doi.org/10.1186/s40594-023-00444-5

44.

Lee

S. W. Y.

Liang

J. C.

Hsu

C. Y.

Tsai

M. J.

(2023a). Students’ beliefs about computer programming predict their computational thinking and computer programming self-efficacy. Interactive Learning Environments, 32(8), 4088–4108. https://doi.org/10.1080/10494820.2023.2194929

45.

Baker

Warschauer

(2020). Using clickstream data to measure, understand, and support self-regulated learning in online courses. Internet and Higher Education, 45(1), 100727. https://doi.org/10.1016/j.iheduc.2020.100727

46.

Y.-F.

Guan

J.-Q.

Wang

X.-F.

Chen

Hwang

G.-J.

(2024). Examining students' self-regulated learning processes and performance in an immersive virtual environment. Journal of Computer Assisted Learning, 40(6), 2948–2963. https://doi.org/10.1111/jcal.13047

47.

Lin

Niu

(2023). Roles of ambiguity tolerance and learning effectiveness: Structural equation modeling evidence from EFL students’ perceptions of factors influencing peer collaboration. Language Teaching Research, 13621688231216201. https://doi.org/10.1177/13621688231216201

48.

Lin

(2024). Learner perceptions of artificial intelligence-generated pedagogical agents in language learning videos: Embodiment effects on technology acceptance. International Journal of Human-Computer Interaction, 41(2), 1606–1627. https://doi.org/10.1080/10447318.2024.2359222

49.

Liu

(2024). Toward artificial intelligence-human paired programming: A review of the educational applications and research on artificial intelligence code-generation tools. Journal of Educational Computing Research, 62(5), 1385–1415. https://doi.org/10.1177/07356331241240460

50.

Liu

Y. C.

G. R.

Yuan

Zhou

Wang

C. L.

(2024). Assessment as learning: Evidence based on meta-analysis and quantitative ethnography research. Studies In Educational Evaluation, 83(1), 101423. https://doi.org/10.1016/j.stueduc.2024.101423

51.

Loksa

Margulieux

Becker

B. A.

Craig

Denny

Pettit

Prather

(2022). Metacognition and self-regulation in programming education: Theories and exemplars of use. ACM Transactions on Computing Education, 22(4), 1–31. https://doi.org/10.1145/3487050

52.

Lukkarinen

Malmi

Haaranen

(2021). Event-driven programming in programming education: A mapping review. ACM Transactions on Computing Education, 21(1), 1–31. https://doi.org/10.1145/3423956

53.

Lye

S. Y.

Koh

J. H. L.

(2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41(1), 51–61. https://doi.org/10.1016/j.chb.2014.09.012

54.

Qian

Gong

(2023). Promoting programming education of novice programmers in elementary schools: A contrasting cases approach for learning programming. Education and Information Technologies, 28(7), 9211–9234. https://doi.org/10.1007/s10639-022-11565-9

55.

Magen-Nagar

Cohen

(2017). Learning strategies as a mediator for motivation and a sense of achievement among students who study in MOOCs. Education and Information Technologies, 22(3), 1271–1290. https://doi.org/10.1007/s10639-016-9492-y

56.

Messer

Brown

N. C. C.

Kölling

Shi

(2023). Automated grading and feedback tools for programming education: A systematic review. ACM Transactions on Computing Education, 24(1), 1–43. https://doi.org/10.1145/3636515

57.

C. Y.

Wang

C. L.

Dai

Jin

P. Q.

(2022). Video playback speed influence on learning effect from the perspective of personalized adaptive learning: A study based on cognitive load theory. Frontiers in Psychology, 13(1), 839982. https://doi.org/10.3389/fpsyg.2022.839982

58.

Moazami

Bahrampour

Azar

M. R.

Jahedi

Moattari

(2014). Comparing two methods of education (virtual versus traditional) on learning of Iranian dental students: A post-test only design study. BMC Medical Education, 14(1), 45–55. https://doi.org/10.1186/1472-6920-14-45

59.

Morales-Navarro

Giang

M. T.

Fields

D. A.

Kafai

Y. B.

(2023). Connecting beliefs, mindsets, anxiety and self-efficacy in computer science learning: An instrument for capturing secondary school students’ self-beliefs. Computer Science Education, 34(3), 387–413. https://doi.org/10.1080/08993408.2023.2201548

60.

National Research Council . (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.

61.

Panadero

Jonsson

Pinedo

Fernández-Castilla

(2023). Effects of rubrics on academic performance, self-regulated learning, and self-efficacy: A meta-analytic review. Educational Psychology Review, 35(4), 113. https://doi.org/10.1007/s10648-023-09823-4

62.

Reisoğlu

Topu

Yılmaz

Karakuş Yılmaz

Göktaş

(2017). 3D virtual learning environments in education: A meta-review. Asia Pacific Education Review, 18(1), 81–100. https://doi.org/10.1007/s12564-016-9467-0

63.

Schunk

D. H.

(2008). Metacognition, self-regulation, and self-regulated learning: Research recommendations. Educational Psychology Review, 20(4), 463–467. https://doi.org/10.1007/s10648-008-9086-3

64.

Sedgwick

Greenwood

(2015). Understanding the Hawthorne effect. BMJ, 351(1), h4672. https://doi.org/10.1136/bmj.h4672

65.

Seralidou

Douligeris

(2021). Learning programming by creating games through the use of structured activities in secondary education in Greece. Education and Information Technologies, 26(1), 859–898. https://doi.org/10.1007/s10639-020-10255-8

66.

Sobral

S. R.

(2021). Teaching and learning to program: Umbrella review of introductory programming in higher education. Mathematics, 9(15), 1737. https://doi.org/10.3390/math9151737

67.

Sorva

Karavirta

Malmi

(2013). A review of generic program visualization systems for introductory programming education. ACM Transactions on Computing Education, 13(4), 1–64. https://doi.org/10.1145/2490822

68.

Y. S.

Shao

Zhao

(2022). Effect of mind mapping on creative thinking of children in scratch visual programming education. Journal of Educational Computing Research, 60(4), 906–929. https://doi.org/10.1177/07356331211053383

69.

Sung

C. Y.

Cherng

F. Y.

Chiu

Y. L.

Chen

P. H.

Chen

B. Y.

(2024). 3CPEs: Concrete computational concepts programming environments for elementary computer science education. In Extended abstracts of the CHI conference on human factors in computing systems (pp. 1–7). Retrieved from: https://doi.org/10.1145/3613905.3650833

70.

Tang

W. Y. R.

Tang

W. Y. R.

(2021). Education, whole person, and the sustainability of a society. Transforming Education in Practice: In Search of a Community of Phronimos, 1(1), 119–140. https://doi.org/10.1007/978-981-16-6871-5_7

71.

Tellhed

Björklund

Kallio Strand

(2022). Sure I can code (but do I want to?). Why boys' and girls’ programming beliefs differ and the effects of mandatory programming education. Computers in Human Behavior, 135(1), 107370. https://doi.org/10.1016/j.chb.2022.107370

72.

Tellhed

Björklund

Kallio Strand

Schöttelndreier

(2023). “Programming is not that hard!” When a science center visit increases young women’s programming ability beliefs. Journal for STEM Education Research, 6(2), 1–23. https://doi.org/10.1007/s41979-023-00094-w

73.

The White House . (2017). Expanding access to high quality STEM and computer science education provides more pathways to good jobs. Retrieved from: https://trumpwhitehouse.archives.gov/briefings-statements/expanding-access-high-quality-stem-computer-science-education-provides-pathways-good-jobs/

74.

The White House . (2018). Charting a course for success:America’s strategy for STEM education. Retrieved from: https://www.whitehouse.gov/wp-content/uploads/2018/12/STEM-Education-Strategic-Plan-2018.pdf

75.

TIOBE . (2023). TIOBE index | TIOBE - the software quality company. Retrieved from: https://www.tiobe.com/tiobe-index/

76.

Trontl

(2010). Problem of computer programming education for technical engineers. Computer Applications in Engineering Education, 18(1), 200–202. https://doi.org/10.1002/cae.20187

77.

Troussas

Krouska

Sgouropoulou

(2020). A novel teaching strategy through adaptive learning activities for computer programming. IEEE Transactions on Education, 64(2), 103–109. https://doi.org/10.1109/TE.2020.3012744

78.

UNESCO . (2023). Generative AI and the future of education. Retrieved from: https://unesdoc.unesco.org/ark:/48223/pf0000385877

79.

Vinnervik

(2023). Programming in school technology education: The shaping of a new subject content. International Journal of Technology and Design Education, 33(4), 1449–1470. https://doi.org/10.1007/s10798-022-09773-y

80.

Magis-Weinberg

Jansen

B. R.

van Atteveldt

Janssen

T. W.

Lee

N. C.

van der Maas

H. L. J.

Raijmakers

M. E. J.

Sachisthal

M. S. M.

Meeter

(2022). Motivation-achievement cycles in learning: A literature review and research agenda. Educational Psychology Review, 34(1), 39–71. https://doi.org/10.1007/s10648-021-09616-7

81.

Wang

A. Y.

Head

Zhang

A. G.

Oney

Brooks

(2023). Colaroid: A literate programming approach for authoring explorable multi-stage tutorials. In Proceedings of the 2023 CHI conference on human factors in computing systems (pp. 1–22). Retrieved from: https://doi.org/10.1145/3544548.3581525

82.

Wang

C. L.

Chen

X. J.

Z. B.

Jin

X. Q.

(2025). Deconstructing university learners’ adoption intention towards aigc technology: A mixed-methods study using ChatGPT as an example. Journal of Computer Assisted Learning, 41(1), e13117. https://doi.org/10.1111/jcal.13117

83.

Wang

C. L.

Chen

X. J.

Liu

Y. D.

Jing

Y. H.

(2024a). Education reform and change driven by digital technology: A bibliometric study from a global perspective. Humanities & Social Sciences Communications, 11(1), 256. https://doi.org/10.1057/s41599-024-02717-y

84.

Wang

C. L.

Wang

H. M.

Y. Y.

Dai

X. Q.

(2024b). Factors influencing university students’ behavioral intention to use generative artificial intelligence: Integrating the theory of planned behavior and AI literacy. International Journal of Human-Computer Interaction, 1(1), 1–23. https://doi.org/10.1080/10447318.2024.2383033

85.

Wei

Wang

Tang

Lei

Peng

(2024). Influence of programming education modalities on the computational thinking in young children: A comprehensive review and meta analysis. Journal of Computer Assisted Learning, 40(5), 2385–2397. https://doi.org/10.1111/jcal.13038

86.

Zhao

Liew

Zhou

Kogut

(2023). Synthesizing research evidence on self-regulated learning and academic achievement in online and blended learning environments: A scoping review. Educational Research Review, 39(1), 100510. https://doi.org/10.1016/j.edurev.2023.100510

87.

Yasmin

Naseem

Masso

I. C.

(2019). Teacher-directed learning to self-directed learning transition barriers in Pakistan. Studies In Educational Evaluation, 61(1), 34–40. https://doi.org/10.1016/j.stueduc.2019.02.003

88.

Yolcu

Demirer

(2023). The effects of educational robotics in programming education on students' programming success, computational thinking, and transfer of learning. Computer Applications in Engineering Education, 31(6), 1633–1647. https://doi.org/10.1002/cae.22664

89.

Zdawczyk

Varma

(2022). Engaging girls in computer science: Gender differences in attitudes and beliefs about learning scratch and python. Computer Science Education, 33(4), 600–620. https://doi.org/10.1080/08993408.2022.2095593

90.

Zhang

Jia

Cheng

Son

J. Y.

Stigler

J. W.

(2025). Empowering novice programmers: Redesigning documentation for effective informal learning. Journal of Educational Computing Research, 1(1), 1–33. https://doi.org/10.1177/07356331241311505

91.

Zhang

Lin

C. H.

(2021). Effects of community of inquiry, learning presence and mentor presence on K-12 online learning outcomes. Journal of Computer Assisted Learning, 37(3), 782–796. https://doi.org/10.1111/jcal.12523

92.

Zimmerman

B. J.

(2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

1.07 MB

MetaClassroom: A New Paradigm and Experience for Programming Education

Abstract

Keywords

Get full access to this article

References

Supplementary Material