Effects of Pair Learning on Girls’ Learning Performance in Robotics Education

Abstract

Robotics Education (RE) is viewed as a tool to narrow the gender gap in STEM fields, especially for promoting girls’ learning. As an emerging and potential model in RE, Pair Learning (PL) is different from Individual Learning (IL), which is impacted by many factors. Among them, social factors need to be further explored. Therefore, this study emphasized gender as a key social factor and focused on girls’ learning performance in different learning models (IL and PL) and gender pairing patterns (boy-girl and girl-girl), as well as exploring the effective pedagogy for enhancing girls’ learning performance in RE. To this end, we conducted a comparison experiment in two classes from the fifth grade in a Chinese primary school. Results indicated that: (a) PL outperforms IL in enhancing girls’ learning engagement; (b) PL and IL have the same effect on the girls’ learning attitude and robotics works; (c) Mixed-gender pairing is more beneficial for girls to complete robotics works than single-gender pairing; and (d) Mixed-gender pairing and single-gender pairing have the same effects on girls’ learning attitudes and engagement. The key findings, possible reasons, and implications for practice are also discussed.

Keywords

pair learning individual learning gender difference girls robotics education K-12 education

Get full access to this article

View all access options for this article.

References

Abbiss

(2008). Rethinking the ‘‘problem’’ of gender and IT schooling: Discourses in literature. Gender and Education, 20(2), 153–165. https://doi.org/10.1080/09540250701805839

Arisholm

Gallis

Dyba

Sjoberg

D. I.

(2007). Evaluating pair programming with respect to system complexity and programmer expertise. IEEE Transactions on Software Engineering, 33(2), 65–86. https://doi.org/10.1109/tse.2007.17

Atmatzidou

Demetriadis

(2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661–670. https://doi.org/10.1016/j.robot.2015.10.008

Avsec

Rihtarsic

Kocijancic

(2016). The impact of robotics-enhanced approach on students’ satisfaction in open learning environment. International Journal of Engineering Education, 32(2), 804–817. https://www.ijee.ie/contents/c320216A.html

Barker

B. S.

Nugent

Grandgenett

N. F.

(2014). Examining fidelity of program implementation in a STEM‐oriented out‐of‐school setting. International Journal of Technology and Design Education, 24(1), 39–52. https://doi.org/10.1007/s10798-013-9245-9

Barron

(2003). When smart groups fail. The Journal of the Learning Sciences, 12(3), 307–359. https://doi.org/10.1207/s15327809jls1203_1

Beer

R. D.

Chiel

H. J.

Drushel

R. F.

(1999). Using autonomous robotics to teach science and engineering. Communications of the ACM, 42(6), 85–92. https://doi.org/10.1145/303849.303866

Bennett

Hogarth

Lubben

Campbell

Robinson

(2010). Talking science: The research evidence on the use of small group discussions in science teaching. International Journal of Science Education, 32(1), 69–95. https://doi.org/10.1080/09500690802713507

Bishop-Clark

Courte

Howard

E. V.

(2006). Programming in pairs with alice to improve confidence, enjoyment, and achievement. Journal of Educational Computing Research, 34(2), 213–228. https://doi.org/10.2190/cfkf-uggc-jg1q-7t40

10.

Brandts

Garofalo

(2012). Gender pairings and accountability effects. Journal of Economic Behavior and Organization, 83(1), 31–41. https://doi.org/10.1016/j.jebo.2011.06.023

11.

Çal

Can

(2020). The influence of pair programming on secondary school students’ confidence and achievement in computer programming. Trakya Eğitim Dergisi, 10(1), 221–237. https://doi.org/10.24315/tred.575098

12.

Castro

Cecchi

Valente

Buselli

Salvini

Dario

(2018). Can educational robotics introduce young children to robotics and how can we measure it? Journal of Computer Assisted Learning, 34(6), 970–977. https://doi.org/10.1111/jcal.12304

13.

Cheng

C. C.

Huang

P. L.

Huang

K. H.

(2013). Cooperative learning in Lego robotics projects: Exploring the impacts of group formation on interaction and achievement. Journal of Networks, 8(7), 1529–1535. https://doi.org/10.4304/jnw.8.7.1529-1535

14.

Chen

Shen

Barth-Cohen

Jiang

Huang

Eltoukhy

(2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers and Education, 109, 162–175. https://doi.org/10.1016/j.compedu.2017.03.001

15.

Choi

K. S.

(2015). A comparative analysis of different gender pair combinations in pair programming. Behaviour and Information Technology, 34(8), Article e825–e837. https://doi.org/10.1080/0144929x.2014.937460

16.

Chong

Hurlbutt

(2007, May). The social dynamics of pair programming. In 29th International Conference on Software Engineering (ICSE’07) (pp. 354–363). IEEE.

17.

Correll

Rus

(2010). Peer-to-peer learning in robotics education: Lessons from a challenge project class. Computers in Education Journal, 1(3), 60–66. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.475.2597&rep=rep1&type=pdf

18.

Correll

S. J.

Seymour

Hewitt

N. M.

(1997). Talking about leaving: Why undergraduates leave the sciences. Contemporary Sociology, 26(5), 644. https://doi.org/10.2307/2655673

19.

Crombie

Pyke

S. W.

Silverthorn

Jones

Piccinin

(2003). Students’ perceptions of their classroom participation and instructor as a function of gender and context. The Journal of Higher Education, 74(1), 51–76. https://doi.org/10.1353/jhe.2003.0001

20.

Denner

Werner

Campe

Ortiz

(2014). Pair programming: Under what conditions is it advantageous for middle school students? Journal of Research on Technology in Education, 46(3), 277–296. https://doi.org/10.1080/15391523.2014.888272

21.

European institute of gender equality (EIGE) . (2019). Gender equality and youth: Opportunities and risks of digitalisation - main report. https://doi.org/10.2839/148393

22.

Fitzpatrick

Hardman

(2000). Mediated activity in the primary classroom: girls, boys and computers. Learning and Instruction, 10(5), 431–446. https://doi.org/10.1016/s0959-4752(00)00007-4

23.

Flowerday

Schraw

Stevens

(2004). The role of choice and interest in reader engagement. Journal of Experimental Education, 72(2), 93–114. https://doi.org/10.3200/jexe.72.2.93-114

24.

Fredricks

J. A.

Blumenfeld

Friedel

Paris

(2005). School engagement. In Moore

K. A.

Lippman

(Eds.), What do children need to flourish? conceptualizing and measuring indicators of positive development (pp. 305–321). Springer.

25.

Fredricks

J. A.

Blumenfeld

P. C.

Paris

A. H.

(2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059

26.

Gaspard

Häfner

Parrisius

Trautwein

Nagengast

(2017). Assessing task values in five subjects during secondary school: Measurement structure and mean level differences across grade level, gender, and academic subject. Contemporary Educational Psychology, 48, 67–84. https://doi.org/10.1016/j.cedpsych.2016.09.003

27.

Gomoll

Hmelo-Silver

C. E.

Šabanović

Francisco

(2016). Dragons, ladybugs, and softballs: Girls’ STEM engagement with human-centered robotics. Journal of Science Education and Technology, 25(6), 899–914. https://doi.org/10.1007/s10956-016-9647-z

28.

Gottfried

M. A.

Plasman

J. S.

(2018). From secondary to postsecondary: Charting an engineering career and technical education pathway. Journal of Engineering Education, 107(4), 531–555. https://doi.org/10.1002/jee.20236

29.

Grandell

Peltomäki

Back

R. J.

Salakoski

(2006). Why complicate things? Introducing programming in high school using python. In: Proceedings of the 8th Australasian Conference on Computing Education (52, pp. 71–80). ACM Press. https://dl.acm.org/doi/10.5555/1151869.1151880

30.

Guo

Wang

M.-T.

Ketonen

E. E.

Eccles

J. S.

Salmela-Aro

(2018). Joint trajectories of task value in multiple subject domains: From both variable- and pattern-centered perspectives. Contemporary Educational Psychology, 55, 139–154. https://doi.org/10.1016/j.cedpsych.2018.10.004

31.

Guzzetti

B. J.

Williams

W. O.

(1996). Gender, text, and discussion: Examining intellectual safety in the science classroom. Journal of Research in Science Teaching, 33(1), 5–20. https://doi.org/10.1002/(sici)1098-2736(199601)33:1<5::aid-tea1>3.0.co;2-z

32.

AAUW . (2010). Women and girls in science, technology, engineering, and mathematics. In Hill

Corbett

Rose

A. St.

(Eds.), Why so few women in science, technology, engineering, and mathematics. AAUW.

33.

Howe

K. R.

(1997). Understanding equal educational opportunity: Social justice, democracy, and schooling. Teachers College Press.

34.

Howe

Tolmie

(2002). Productive interaction in the context of computer-supported collaborative learning in science. In Light

Littleton

(Eds.), Learning with computers (pp. 38–59). Routledge.

35.

Hwang

W.-Y.

S.-Y.

(2014). A case study of collaboration with multi-robots and its effect on children’s interaction. Interactive Learning Environments, 22(4), 429–443. https://doi.org/10.1080/10494820.2012.680968

36.

Inkpen

Ho-Ching

W. L.

Kuederle

Scott

S. D.

Shoemaker

G. B.

(1999). This is fun! We’re all best friends and we’re all playing: Supporting children’s synchronous collaboration. In: Proceedings of the 1999 Conference on Computer Support For Collaborative Learning (pp. 252–259). ACM Press. https://dl.acm.org/doi/10.5555/1150240.1150271

37.

Iskrenovic-Momcilovic

(2019). Pair programming with scratch. Education and Information Technologies, 24(5), 2943–2952. https://doi.org/10.1007/s10639-019-09905-3

38.

Jeong

Lee

(2008). The effects of active versus reflective learning style on the processes of critical discourse in computer-supported collaborative argumentation. British Journal of Educational Technology, 39(4), 651–665. https://doi.org/10.1111/j.1467-8535.2007.00762.x

39.

Karau

S. J.

Williams

K. D.

(1994). Social loafing: A meta-analytic review and theoretical integration. Journal of Personality and Social Psychology, 65(4), 681–706. http://doi.org/10.1037/0022-3514.65.4.681

40.

Kersey

A. J.

Braham

E. J.

Csumitta

K. D.

Libertus

M. E.

Cantlon

J. F.

(2018). No intrinsic gender differences in children’s earliest numerical abilities. NPJ Science of Learning, 3(1), 12. https://doi.org/10.1038/s41539-018-0028-7

41.

Kucuk

Sisman

(2020). Students’ attitudes towards robotics and STEM: Differences based on gender and robotics experience. International Journal of Child-Computer Interaction, 23-24, 100167. https://doi.org/10.1016/j.ijcci.2020.100167

42.

Kuttal

S. K.

Gerstner

Bejarano

(2019, October). Remote pair programming in online CS education: Investigating through a gender lens. In Proceedings of the 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (pp. 75–85). IEEE.

43.

Leonard

Buss

Gamboa

Mitchell

Fashola

O. S.

Hubert

Almughyirah

(2016). Using robotics and game design to enhance children’s self-efficacy, STEM attitudes, and computational thinking skills. Journal of Science Education and Technology, 25(6), 860–876. https://doi.org/10.1007/s10956-016-9628-2

44.

Leslie

S. J.

Cimpian

Meyer

Freeland

(2015). Expectations of brilliance underlie gender distributions across academic disciplines. Science, 347(6219), 262–265. https://doi.org/10.1126/science.1261375

45.

Lewis

C. M.

(2011). Is pair programming more effective than other forms of collaboration for young students? Computer Science Education, 21(2), 105–134. https://doi.org/10.1080/08993408.2011.579805

46.

Liu

Lim

Zhong

Y.Q.

(2007). Joint effects of gender composition, anonymity in communication and task type on collaborative learning. In Proceedings of the 11th Pacific-Asia Conference on Information Systems (pp. 1–7). https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1074&context=pacis2007

47.

Liu

E. Z. F.

Lin

C. H.

Chang

C. S.

(2010). Student satisfaction and self-efficacy in a cooperative robotics course. Social Behavior and Personality: An International Journal, 38(8), 1135–1146. https://doi.org/10.2224/sbp.2010.38.8.1135

48.

Lui

K. M.

Chan

K. C. C.

(2006). Pair programming productivity: Novice–novice vs. expert–expert. International Journal of Human-Computer Studies, 64(9), 915–925. https://doi.org/10.1016/j.ijhcs.2006.04.010

49.

Master

Cheryan

Moscatelli

Meltzoff

A. N.

(2017). Programming experience promotes higher STEM motivation among first-grade girls. Journal of Experimental Child Psychology, 160, 92–106. https://doi.org/10.1016/j.jecp.2017.03.013

50.

Master

Meltzoff

A. N.

(2020). Cultural stereotypes and sense of belonging contribute to gender gaps in STEM. International Journal of Gender, Science and Technology, 12(1), 152–198.

51.

McCaslin

Tuck

Wiard

Brown

Lapage

Pyle

(1994). Gender composition and small-group learning in 4th-grade mathematics. Elementary School Journal, 94(5), 467–482. https://doi.org/10.1086/461778

52.

Milto

Rogers

Portsmore

(2002). Gender differences in confidence levels, group interactions, and feelings about competition in an introductory robotics course Proceedings of the 32nd Annual Frontiers in Education (p. F4C). IEEE.

53.

Mutlu-Bayraktar

Cosgun

Altan

(2019). Cognitive load in multimedia learning environments: A systematic review. Computers and Education, 141, 103618. https://doi.org/10.1016/j.compedu.2019.103618

54.

National Science Foundation (NSF) . (2003). New formulas for America’s workforce: Girls in science and engineering. National Science Foundation.

55.

National Science Foundation (NSF) . (2014). Integrated postsecondary education data system, 2013, completions survey. National center for science and engineering statistics. June 18, 2021 https://webcaspar.nsf.gov

56.

National Science Teaching Association (NSTA) . (2019). Gender equity in science education. https://www.nsta.org/nstas-official-positions/gender-equity-science-education

57.

Nourbakhsh

I. R.

Hamner

Crowley

Wilkinson

(2004). Formal measures of learning in a secondary school mobile robotics course. In Proceedings of the 2004 International Conference on Robotics and Automation (2, pp. 1831–1836). IEEE. https://doi.org/10.1109/robot.2004.1308090

58.

Nugent

Barker

Grandgenett

Welch

(2016). Robotics camps, clubs, and competitions: Results from a US robotics project. Robotics and Autonomous Systems, 75, 686–691. https://doi.org/10.1016/j.robot.2015.07.011

59.

Payton

F. C.

Berki

(2019). Countering the negative image of women in computing. Communications of the ACM, 62(5), 56–63. https://doi.org/10.1145/3319422

60.

Puurtinen

Mappes

(2009). Between-group competition and human cooperation. Proceedings of the Royal Society of London B: Biological Sciences, 276(1655), 355–360. https://doi.org/10.1098/rspb.2008.1060

61.

Rahman

(2021). Interactions in science classrooms: A qualitative exploration of students’ perception through a gender lens [Doctoral dissertation, Brac University].

62.

Reilly

Neumann

D. L.

Andrews

(2019). Investigating gender differences in mathematics and science: Results from the 2011 trends in mathematics and science survey. Research in Science Education, 49(1), 25–50. https://doi.org/10.1007/s11165-017-9630-6

63.

Salleh

Mendes

Grundy

(2011). Empirical studies of pair programming for CS/SE teaching in higher education: A systematic literature review. IEEE Transactions on Software Engineering, 37(4), 509–525. https://doi.org/10.1109/tse.2010.59

64.

Salomon

Globerson

(1989). When teams do not function the way they ought to. Proceedings of International Journal of Educational Research, 13(1), 89–99. http://doi.org/10.1016/0883-0355(89)90018-9.

65.

Shilpi

Hasnayen

Ilahi

Parvin

Sultana

(2017). Education scenario in Bangladesh: Gender perspective.Bangladesh Bureau of Statistics, UCEP; Diakonia Bangladesh.

66.

Stefanou

C. E.

Lord

S. M.

Prince

M. J.

Chen

J. C.

(2014). Effect of classroom gender composition on students’ development of self-regulated learning competencies. International Journal of Engineering Education, 30(2), 333–342. https://www.ijee.ie/contents/c300214.html

67.

Stephenson

S. D.

(1994). The use of small groups in computer-based training: A review of recent literature. Computers in Human Behavior, 10(3), 243–259. https://doi.org/10.1016/0747-5632(94)90054-x

68.

Takeda

Homberg

(2014). The effects of gender on group work process and achievement: An analysis through self- and peer-assessment. British Educational Research Journal, 40(2), 373–396. https://doi.org/10.1002/berj.3088

69.

Thomas

Ratcliffe

Robertson

(2003). Code warriors and code-a-phobes: A study in attitude and pair programming. ACM SIGCSE Bulletin, 35(1), 363–367. https://doi.org/10.1145/792548.612007

70.

Underwood

Wood

(2000). When does gender matter? Interactions during computer-based problem solving. Learning and Instruction, 10(5), 447–462. https://doi.org/10.1016/s0959-4752(00)00008-6

71.

U.S. Department of Education, National Center for Education Statistics (NCES) . (2014). Digest of education statistics. https://nces.ed.gov/programs/digest/2014menu_tables.asp

72.

Wainer

Ferrari

Dautenhahn

Robins

(2010). The effectiveness of using a robotics class to foster collaboration among groups of children with autism in an exploratory study. Personal and Ubiquitous Computing, 14(5), 445–455. https://doi.org/10.1007/s00779-009-0266-z

73.

Waite

W. M.

Jackson

M. H.

Diwan

Leonardi

P. M.

(2004, March). Student culture vs group work in computer science. ACM SIGCSE Bulletin, 36(1), 12–16. http://doi.org/10.1145/1028174.971308.

74.

Wang

M. T.

Degol

J. L.

(2017). Gender gap in science, technology, engineering, and mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educational Psychology Review, 29(1), 119–140. https://doi.org/10.1007/s10648-015-9355-x

75.

Wei

Lin

Meng

Tan

Kong

S.-C.

(2021). The effectiveness of partial pair programming on elementary school students’ computational thinking skills and self-efficacy. Computers and Education, 160, 104023. https://doi.org/10.1016/j.compedu.2020.104023

76.

Werner

L. L.

Denner

Bean

(2004). Pair programming strategies for middle school girls. Proceedings of the CATE, (pp. 1–6). http://doi.org/10.13140/2.1.2533.2165

77.

Werner

L. L.

Hanks

McDowell

(2004). Pair-programming helps female computer science students. Journal on Educational Resources in Computing (JERIC), 4(1), 4. https://doi.org/10.1145/1060071.1060075

78.

Williams

McDowell

Nagappan

Fernald

Werner

(2003). Building pair programming knowledge through a family of experiments. In Proceedings of the 2003 International Symposium on Empirical Software Engineering. Proceedings (pp. 143–152). IEEE.

79.

Xia

Zhong

(2018). A systematic review on teaching and learning robotics content knowledge in K-12. Computers and Education, 127, 267–282. https://doi.org/10.1016/j.compedu.2018.09.007

80.

Xie

(2011). Older adults, e-health literacy, and collaborative learning: An experimental study. Journal of the American Society for Information Science and Technology, 62(5), 933–946. https://doi.org/10.1002/asi.21507

81.

Xie

Antle

A. N.

Motamedi

(2008, February). Are tangibles more fun? Comparing children’s enjoyment and engagement using physical, graphical and tangible user interfaces. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction, 18–20 February 2008, Bonn, Germany (pp. 191–198). http://doi.org/10.1145/1347390.1347433

82.

Yuen

T. T.

Boecking

Tiger

E. P.

Gomez

Guillen

Arreguin

Stone

(2014). Group tasks, activities, dynamics, and interactions in collaborative robotics projects with elementary and middle school children. Journal of STEM Education, 15(1), 39–45. https://www.jstem.org/jstem/index.php/JSTEM/article/view/1853/1585

83.

Zhan

Fong

P. S. W.

Mei

Liang

(2015). Effects of gender grouping on students’ group performance, individual achievements and attitudes in computer-supported collaborative learning. Computers in Human Behavior, 48, 587–596. https://doi.org/10.1016/j.chb.2015.02.038

84.

Zhong

(2020). Can pair learning improve students’ troubleshooting performance in robotics education? Journal of Educational Computing Research, 58(1), 220–248. https://doi.org/10.1177/0735633119829191

85.

Zhong

Wang

(2021a). Exploring the non-significant difference on students’ cognitive load imposed by robotics tasks in pair learning. International Journal of Social Robotics, 14(1), 3–13. https://doi.org/10.1007/s12369-021-00764-y

86.

Zhong

Wang

(2021b). Effects of roles assignment and learning styles on pair learning in robotics education. International Journal of Technology and Design Education, 31(1), 41–59. https://doi.org/10.1007/s10798-019-09536-2

87.

Zhong

Wang

Chen

(2016). The impact of social factors on pair programming in a primary school. Computers in Human Behavior, 64, 423–431. https://doi.org/10.1016/j.chb.2016.07.017

88.

Zhong

Wang

Chen

(2017). Investigating the period of switching roles in pair programming in a primary school. Journal of Educational Technology and Society, 20(3), 220–233. https://www.jstor.org/stable/26196132

89.

Zhong

Xia

(2020). A systematic review on exploring the potential of educational robotics in mathematics education. International Journal of Science and Mathematics Education, 18(1), 79–101. https://doi.org/10.1007/s10763-018-09939-y

90.

Zhong

Xia

(2021). Effects of new coopetition designs on learning performance in robotics education. Journal of Computer Assisted Learning, 38(1), 223–236. https://doi.org/10.1111/jcal.12606

91.

Zhong

Zheng

Zhan

(2020). An exploration of combining virtual and physical robots in robotics education. Interactive Learning Environments, 1–13. https://doi.org/10.1080/10494820.2020.1786409