The Effects of Worked-Out Example and Metacognitive Scaffolding on Problem-Solving Programming

Abstract

This study explores the effects of worked-out examples and metacognitive scaffolding on novice learners’ knowledge performance, cognitive loads, and self-regulation skills in problem-solving programming. 126 undergraduate students in a computer programming fundamentals course were randomly assigned to one of four groups: 1) task performance with a traditional WOE (TW), 2) task performance with a faded WOE (FW), 3) task performance with traditional WOE and metacognitive scaffolding (TWM), and 4) task performance with a faded WOE and metacognitive scaffolding (FWM). Over the course of 3 weeks, participants in these four groups were asked to solve programming problems using Python with WOE and metacognitive scaffolding. The results demonstrate that the provision of metacognitive scaffolding with faded WOE (FWM) is the most effective for problem-solving programming and self-regulation skills. In addition, an interaction effect exists between the two treatments for the germane load in FWM. Therefore, results in this study provide empirical insights into ways to effectively apply WOE and metacognitive scaffolding to problem-solving processes for programming-based complex problem-solving, especially for novice learners.

Keywords

problem-solving programming self-regulation cognitive load metacognitive scaffolding faded woe

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References

Abdul-Rahman

S. S.

Du Boulay

(2014). Learning programming via worked-examples: Relation of learning styles to cognitive load. Computers in Human Behavior, 30, 286–298. https://doi.org/10.1016/j.chb.2013.09.007

Agbo

F. J.

Oyelere

S. S.

Suhonen

Adewumi

(2019, November). A systematic review of computational thinking approach for programming education in higher education institutions. Proceedings of the 19th koli calling international Conference on computing education research (pp. 1–10), NY, New York, Association for Computing Machinery. https://doi.org/10.1145/3364510.3364521

Chao

P. Y.

(2016). Exploring students' computational practice, design and performance of problem-solving through a visual programming environment. Computers and Education, 95, 202–215. https://doi.org/10.1016/j.compedu.2016.01.010

Chi

M. T.

Bassok

Lewis

M. W.

Reimann

Glaser

(1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. https://doi.org/10.1207/s15516709cog1302_1

Crippen

K. J.

Earl

B. L.

(2007). The impact of web-based worked examples and self-explanation on performance, problem-solving, and self-efficacy. Computers and Education, 49(3), 809–821. https://doi.org/10.1016/j.compedu.2005.11.018

Garner

(2001). A tool to support the use of part-complete solutions in the learning of programming. In Proceeding de conférence (pp. 222–228).

Garner

(2002). Reducing the cognitive load on novice programmers. In Barker

Rebelsky

(Eds.), Proceedings of ED-MEDIA 2002--world conference on educational multimedia, hypermedia and telecommunications (pp. 578–583). Association for the advancement of computing in education (AACE). Retrieved October 6, 2022 from https://www.learntechlib.org/primary/p/10329/

Law

Tawfik

(2016). The design of scaffolding and fading: Research issues and challenges. In Minhong

M. W.

Kirschner

Bridges

(Eds.), The pre-conference workshop at the 12th international conference of the learning sciences (pp. 19–24). The University of Hong Kong.

Haseski

H. İ.

Ilic

Tugtekin

(2018). Defining a new 21st century skill-computational thinking: Concepts and Trends.International Education Studies, 11(4), 29–42. https://doi.org/10.5539/ies.v11n4p29, https://eric.ed.gov/?id=EJ1175216

10.

Jung

Hong

Shin

(2019a). The effect of faded information based on learners’ prior knowledge in problem-solving learning. Korean Association For Learner-Centered Curriculum And Instruction, 19(15), 799–820. https://doi.org/10.22251/jlcci.2019.19.15.799

11.

Jung

M. K.

Kim

K. E.

Kwon

A. R.

Chae

H. W.

Yoon

C. S.

Kim

H. S.

Kim

D. H.

(2016). Ultrasound measurement of pediatric visceral fat thickness: Correlations with metabolic and liver profiles. Annals of Pediatric Endocrinology and Metabolism, 21(2), 75–80. https://doi.org/10.6065/apem.2016.21.2.75, https://www.jstor.org/stable/jeductechsoci.19.4.75

12.

Jung

Shin

Zumbach

(2019b). The effects of pre-training types on cognitive load, collaborative knowledge construction and deep learning in a computer-supported. collaborative learning environment. Interactive Learning Environments, 29(7), 1163–1175. https://doi.org/10.1080/10494820.2019.1619592

13.

Kalyuga

Ayres

Chandler

Sweller

(2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31. https://doi.org/10.1207/s15326985ep3801_4

14.

Leppink

Paas

Van der Vleuten

C. P.

Van Gog

Van Merriënboer

J. J.

(2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45, 1058–1072.

15.

Lister

Simon

Thompson

Whalley

J. L.

Prasad

(2006). Not seeing the forest for the trees: Novice programmers and the SOLO taxonomy. In Proceedings of the 11th annual sigcse conference on innovation and technology in computer science education (pp. 118–122). ACM. https://doi.org/10.1145/1140124.1140157

16.

Loksa

A. J.

(2016, August). The role of metacognitive in programming problem. solving process and success. Proceedings of the 2016 ACM conference on international computing education research (pp. 83–91), NY, New York, Association for Computing Machinery. https://doi.org/10.1145/1140124.1140157

17.

Loksa

A. J.

Jernigan

Oleson

Mendez

C. J.

Burnett

M. M.

(2016, May). Programming, problem-solving, and self-awareness: Effects of explicit guidance. Proceedings of the 2016 CHI Conference on human Factors in computing (pp. 1449–1461). NY, New York, Association for Computing Machinery.

18.

Loksa

Xie

Kwik

A. J.

(2020, February). Investigating novices' in situ reflections on their programming process. Proceedings of the 51st ACM Technical. Symposium on computer science education (pp. 149–155).

19.

López-Vargas

Ibáñez-Ibáñez

Racines-Prada

(2017). Students’ metacognition and cognitive style and their effect on cognitive load and learning achievement. Journal of Educational Technology and Society, 20(3), 145–157. https://www.jstor.org/stable/26196126

20.

Magana

A. J.

Fennell

H. W.

Vieira

Falk

M. L.

(2019). Characterizing the interplay. of cognitive and metacognitive knowledge in computational modeling and simulation practices. Journal of Engineering Education, 108(2), 276–303. https://doi.org/10.1002/jee.20264

21.

Molenaar

van Boxtel

C. A.

Sleegers

P. J.

(2011). Metacognitive scaffolding in an innovative learning arrangement. Instructional Science, 39(6), 785–803. https://doi.org/10.1007/s11251-010-9154-1

22.

Moreno

(2006). When worked examples don't work: Is cognitive load theory at an impasse? Learning and Instruction, 16(2), 170–181. https://doi.org/10.1016/j.learninstruc.2006.02.006

23.

National Academies of Sciences (2018). How people learn II: Learners, contexts, and cultures. National Academies Press.

24.

Paas

Tuovinen

J. E.

Tabbers

Van Gerven

P. W.

(2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71. https://doi.org/10.1207/s15326985ep3801_8

25.

Paas

F. G.

(1992). Training strategies for attaining transfer of problem-solving skill in. statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429–434. https://doi.org/10.1037/0022-0663.84.4.429

26.

Paas

F. G.

Renkl

Sweller

(2004). Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32(1/2), 1–8. https://doi.org/10.1023/b:truc.0000021806.17516.d0

27.

Paas

F. G.

Van Merriënboer

J. J.

(1994). Instructional control of cognitive load in the. training of complex cognitive tasks. Educational Psychology Review, 6(4), 351–371. https://doi.org/10.1007/BF02213420

28.

Pedrosa

Cravino

Morgado

Barreira

(2016, June). Self-regulated learning in. Computer programming: Strategies students adopted during an assignment. In International conference on immersive learning (pp. 87–101). Springer.

29.

Pintrich

P. R.

Smith

D. A. F.

Garcia

McKeachie

W. J.

(1991). A manual for the use. Of the motivated Strategies for learning questionnaire (MSLQ) (tech. Report No. 91-B-004). Board of Regents, University of Michigan.

30.

Prather

Becker

B. A.

Craig

Denny

Loksa

Margulieux

(2020, August). What do we think we think we are doing? Metacognition and metacognitive in programming. Proceedings of the 2020 ACM conference on international computing education research (pp. 2–13).

31.

Puntambekar

Hubscher

(2005). Tools for scaffolding students in a complex learning environment: What have we gained and what have we missed? Educational Psychologist, 40(1), 1–12. https://doi.org/10.1207/s15326985ep4001_1

32.

Renkl

(2002). Worked-out examples: Instructional explanations support learning by self-explanations. Learning and Instruction, 12(5), 529–556. https://doi.org/10.1016/S0959-4752(01)00030-5

33.

Renkl

Atkinson

R. K.

Große

C. S.

(2004). How fading worked solution steps works–a cognitive load perspective. Instructional Science, 32(1/2), 59–82. https://doi.org/10.1023/B:TRUC.0000021815.74806.f6

34.

Renkl

Atkinson

R. K.

Maier

U. H.

(2000, August). From studying examples to. solving problems: Fading worked-out solution steps helps learning. In Gleitman

Joshi

A. K.

(Eds), In proceeding of the 22nd annual Conference of the cognitive science society (pp. 393–398). Lawrence Erlbaum Associates, Inc.

35.

Salleh

S. M.

Shukur

Judi

H. M.

(2018). Scaffolding model for efficient programming. learning based on cognitive load theory. International Journal of Pure and Applied Mathematics, 118(7), 77–83.

36.

Schraw

Crippen

K. J.

Hartley

(2006). Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning. Research in Science Education, 36(1-2), 111–139. https://doi.org/10.1007/s11165-005-3917-8

37.

Shin

Song

(2022). The effects of self-regulated learning support on learners’. Task. Performance and cognitive load in computer programing. Journal of Educational Computing Research, 60(6), 1490–1513. https://doi.org/10.1177/07356331211052632

38.

Stachel

Marghitu

Brahim

T. B.

Sims

Reynolds

Czelusniak

(2013). Managing cognitive load in introductory programming courses: A cognitive aware scaffolding tool. Journal of Integrated Design and Process Science, 17(1), 37–54. https://doi.org/10.3233/jid-2013-0004

39.

Sweller

Van Merrienboer

J. J. G.

Paas

(1998). Cognitive architecture and. instructional design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/A:1022193728205

40.

van Merriënboer

J. J.

Kirschner

(2012). Ten steps to complex learning: A systematic. Approach to four-component instructional design (2nd ed.). Routledge/Taylor and Francis Group.

41.

van Rossum & Python development team (2018). Python Tutorial Release 3.7.0. Python Software Foundation.

42.

Vieira

Yan

Magana

(2015). Exploring design characteristics of worked examples to support programming and algorithm design. The Journal of Computational Science Education, 6(1), 2–15. https://doi.org/10.22369/issn.2153-4136/6/1/1

43.

Xie

Loksa

Nelson

G. L.

Davidson

M. J.

Dong

Kwik

Tan

A. H.

Hwa

A. J.

(2019). A theory of instruction for introductory programming skills. Computer Science Education, 29(2-3), 205–253. https://doi.ort/10.1080/08993408.2019.1565235

44.

Yousoof

Sapiyan

Kamaluddin

(2007). Reducing cognitive load in learning. computer programming. International Journal of Computer and Information Engineering, 1(12), 4100–4103.

45.

Zimmerman

Schunk

D. H.

(2011). Handbook of self-regulation of learning and performance. Taylor and Francis.