Paper-and-Pencil Programming Strategy toward Computational Thinking for Non-Majors: Design Your Solution

Abstract

The paper-and-pencil programming strategy (PPS) is a way of representing an idea logically by any representation that can be created using paper and pencil. It was developed for non-computer majors to improve their understanding and use of computational thinking and increase interest in learning computer science. A total of 110 non-majors in their sophomore year were assigned to either a Logo or a PPS course with attendance being 2 hours per week for 15 weeks. To measure the effectiveness of PPS, the Group Assessment of Logical Thinking and a self-assessment survey pre- and post-test were used. Findings indicated that PPS not only improved students' overall logical thinking as much as did Logo programming learning, but also increased scores on one more subscale of logical thinking than did the Logo course. In addition, PPS significantly helped students understand the concept of computational thinking and increased their interest in learning computer science.

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References

Association for Computing Machinery & IEEE Computer Society. (2008). Computer science curriculum 2008: An interim revision of CS 2001. Retrieved from http://www.acm.org/education/curricula/ComputerScience2008.pdf

Basawapatna

Koh

K. H.

Repenning

Webb

D. C.

Marshall

K. S.

(2011). Recognizing computational thinking patterns. In Proceedings of the 42nd ACM Technical Symposium on Computer Science Education (pp. 245–250). doi: 10.1145/1953163.1953241

Bell

Alexander

Freeman

Grimley

(2009). Computer science unplugged: School students doing real computing without computers. The New Zealand Journal of Applied Computing and Information Technology, 13(1), 20–29.

Böszörmenyi

(2008). Guest editor's introduction: History of informatics. Annals of the History of Computing, IEEE, 30(3), 4–7. doi: 10.1109/MAHC.2008.47

Bunce

D. M.

Hutchinson

K. D.

(1993). The use of the GALT (group assessment of logical thinking) as a predictor of academic success in college chemistry. Journal of Chemical Education, 70(3), 183–187. doi: 10.1021/ed070p183

Carter

(2006). Why students with an apparent aptitude for computer science don't choose to major in computer science. ACM SIGCSE Bulletin, 38(1), 27–31. doi: 10.1145/1124706.1121352

Computer Science Teachers Association, Standards Task Force. (2011). CSTA K-12 computer science standards. Retrieved from http://www.csta.acm.org/Curriculum/sub/CurrFiles/CSTA_K-12_CSS.pdf

Deek

F. P.

Hiltz

S. R.

Kimmel

Rotter

(1999). Cognitive assessment of students' problem solving and program development skills. Journal of Engineering Education, 88(3), 317–326. Retrieved from http://www.jee.org/1999/july/666.pdf

Deek

F. P.

Kimmel

McHugh

J. A.

(1998). Pedagogical changes in the delivery of the first-course in computer science: Problem solving, then programming. Journal of Engineering Education, 87(3), 313–320. Retrieved from http://www.jee.org/1998/july/574.pdf

10.

Denning

P. J.

(2003). Great principles of computing. Communications of the ACM, 46(11), 15–20. doi: 10.1145/948383.948400

11.

Denning

P. J.

(2004). Great principles in computing curricula. ACM SIGCSE Bulletin, 36(1), 336–341. doi: 10.1145/971300.971303

12.

Denning

P. J.

McGettrick

(2005). Recentering computer science. Communications of the ACM, 48(11), 15–19. doi: 10.1145/1096000.1096018

13.

Flowers

T. R.

Gossett

K. A.

(2002). Teaching problem solving, computing, and information technology with robots. Journal of Computing Sciences in Colleges, 17(6), 45–55.

14.

Foster

A. L.

(2005). Student interest in computer science plummets. The Chronicle of Higher Education, 51(38), 31–A32. Retrieved from http://search.proquest.com/docview/214686068?accountid=10066

15.

Fowler

Cusack

(2011). Kodu game lab: Improving the motivation for learning programming concepts. In Proceedings of the 6th International Conference on Foundations of Digital Games (pp. 238–240). doi: 10.1145/2159365.2159398

16.

Gal-Ezer

Zeldes

(2000). Teaching software designing skills. Computer Science Education, 10(1), 25–38. doi: 10.1076/0899-3408(200004)10:1;1-P;FT025

17.

Greitzer

F. L.

Kuchar

O. A.

Huston

(2007). Cognitive science implications for enhancing training effectiveness in a serious gaming context. Journal on Educational Resources in Computing (JERIC), 7(3), 2. doi: 10.1145/1281320.1281322

18.

Hambrusch

Hoffmann

Korb

J. T.

Haugan

Hosking

A. L.

(2009). A multidisciplinary approach towards computational thinking for science majors. ACM SIGCSE Bulletin, 41(1), 183–187. doi: 10.1145/1539024.1508931

19.

Hibbing

A. N.

Rankin-Erickson

J. L.

(2003). A picture is worth a thousand words: Using visual images to improve comprehension for middle school struggling readers. The Reading Teacher, 56(8), 758–770. Retrieved from ERIC database (EJ667704).

20.

Hur

J. W.

(2012). Learning, engagement, and technology: Middle school students' three-year experience in pervasive technology environments in South Korea. Journal of Educational Computing Research, 46(3), 295–312. doi: 10.2190/EC.46.3.e

21.

Johnson-Laird

P. N.

(1983). The computer and the mind: An introduction to cognitive science. Cambridge, MA: Harvard University Press.

22.

Kim

H. C.

(2011). A study on required elements of computation principle learning tools for computer science education. Master's thesis. Retrieved from http://www.riss.kr/link?id=A60227928

23.

Koh

K. H.

Basawapatna

Bennett

Repenning

(2010). Towards the automatic recognition of computational thinking for adaptive visual language learning. 2010 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (pp. 59–66). doi: 10.1109/VLHCC.2010.17

24.

Koschmann

T. D.

Feltovich

P. J.

Myers

A. C.

Barrows

H. S.

(1992). Implications of CSCL for problem-based learning. ACM SIGCUE Outlook, 21(3), 32–35. doi: 10.1145/130893.130902

25.

Kotovsky

Hayes

J. R.

Simon

H. A.

(1985). Why are some problems hard? Evidence from Tower of Hanoi. Cognitive Psychology, 17(2), 248–294. doi: 10.1016/0010-0285(85)90009-X

26.

Kotovsky

Simon

H. A.

(1990). What makes some problems really hard: Explorations in the problem space of difficulty. Cognitive Psychology, 22(2), 143–183. doi: 10.1016/0010-0285(90)90014-U

27.

Kramer

(2007). Is abstraction the key to computing? Communications of the ACM, 50(4), 36–42. doi: 10.1145/1232743.1232745

28.

Larkin

J. H.

Simon

H. A.

(1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11(1), 65–100. doi: 10.1111/j.1551-6708.1987.tb00863.x

29.

MacLaurin

M. B.

(2011). The design of kodu: A tiny visual programming language for children on the Xbox 360. In Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages (pp. 241–246). doi: 10.1145/1926385.1926413

30.

Mayer

R. E.

Gallini

J. K.

(1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82(4), 715. Retrieved from ERIC database (EJ440478).

31.

McDonald

A. S.

(2002). The impact of individual differences on the equivalence of computer-based and paper-and-pencil educational assessments. Computers & Education, 39(3), 299–312.doi: 10.1016/S0360-1315(02)00032-5

32.

McNerney

T. S.

(2004). From turtles to tangible programming bricks: Explorations in physical language design. Personal and Ubiquitous Computing, 8(5), 326–337. doi: 10.1007/s00779-004-0295-6

33.

Merrick

K. E.

(2010). An empirical evaluation of puzzle-based learning as an interest approach for teaching introductory computer science. IEEE Transactions on Education, 53(4), 677–680. doi: 10.1109/TE.2009.2039217

34.

Ministry of Education, Science, and Technology. (2011). The school curriculum of the Republic of Korea. Seoul, Korea: Ministry of Education, Science, and Technology.

35.

Molenda

(2003). In search of the elusive ADDIE model. Performance Improvement, 42(5), 34–37. Retrieved from ERIC database (EJ676523).

36.

Montemayor

Druin

Farber

Simms

Churaman

D'Amour

(2002). Physical programming: Designing tools for children to create physical interactive environments. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems: Changing our World, Changing Ourselves (pp. 299–306). doi: 10.1145/503376.503430

37.

Nassi

Shneiderman

(1973). Flowchart techniques for structured programming. ACM SIGPLAN Notices, 8(8), 12–26. doi: 10.1145/953349.953350

38.

National Research Council. (2010). Report of a workshop on the scope and nature of computational thinking. Washington, DC: National Academies Press.

39.

National Research Council. (2011). Report of a workshop on pedagogical aspects of computational thinking. Washington, DC: National Academies Press.

40.

Newby

M. R.

(1972). Piaget's theory of the development of concrete logical thinking: Implications for the elementary curriculum. Doctoral dissertation. Retrieved from ERIC database (ED080937).

41.

Norman

(1986). Cognitive engineering. In Norman

D. A.

Draper

S. W.

(Eds.), User centered system design: New perspectives on human-computer interaction (pp. 31–62). Hillsdale, NJ: Lawrence Erlbaum Associates.

42.

Norman

D. A.

(1981). What is cognitive science? Norwood, NJ: Ablex Publishing Corp.

43.

Nwana

H. S.

(1997). Is computer science education in crisis? ACM Computing Surveys (CSUR), 29(4), 322–324. doi: 10.1145/267580.267582

44.

Oslapas

(1993). Beyond brainstorming: Creative problem-solving techniques. In Proceedings of Frontier in Education Conference, IEEE (p. 837). doi: 10.1109/FIE. 1993.405392

45.

Patterson

D. A.

(2005). Restoring the popularity of computer science. Communications of the ACM, 48(9), 25–28. doi: 10.1145/1081992.1082011

46.

Piaget

Inhelder

(1969). The psychology of the child. New York, NY: Basic Books.

47.

Prensky

(2003). Digital game-based learning. Computers in Entertainment (CIE), 1(1), 21. doi: 10.1145/950566.950596

48.

Rasmussen

(1983). Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE Transactions on System, Man and Cybernetics, 13(3), 257–266.

49.

Roadrangka

Yeany

R. H.

Padilla

M. J.

(1983, April). The construction and validation of Group Assessment of Logical Thinking (GALT). Paper presented at the annual meeting of the National Association for Research in Science Teaching, Dallas, TX.

50.

Schwill

(1994). Fundamental ideas of computer science. Bulletin-European Association for Theoretical Computer Science, 53, 274–295.

51.

Schwill

(1997). Fundamental ideas: Rethinking computer science education. Learning & Leading with Technology, 25(1), 28–31. Retrieved from ERIC database (EJ550784).

52.

Shannon

C. E.

(1956). A universal Turing machine with two internal states. Automata Studies, 34, 157–165.

53.

Smith

Caputi

(2007). Cognitive interference model of computer anxiety: Implications for computer-based assessment. Computers in Human Behavior, 23(3), 1481–1498. doi: 10.1016/j.chb.2005.07.001

54.

Softronics, Inc. (2002). Welcome to MSWLogo. Retrieved October 8, 2012 from http://www.softronix.com/logo.html

55.

Taub

Armoni

Ben-Ari

(2012). CS unplugged and middle-school students' views, attitudes, and intentions regarding CS. ACM Transactions on Computing Education (TOCE), 12(2), 8. doi: 10.1145/2160547.2160551

56.

Trimmel

Strässler

Knerer

(2001). Brain DC potential changes of computerized tasks and paper/pencil tasks. International Journal of Psychophysiology, 40(3), 187–194. doi: 10.1016/S0167-8760(00)00186-0

57.

Turing

A. M.

(1950). Computing machinery and intelligence. Mind, 59(236), 433–460. doi: 10.1093/mind/LIX.236.433

58.

Warschauer

(2006). Going one-to-one. Educational Leadership, 63(4), 34–38. Retrieved from ERIC database (EJ745473).

59.

Wing

J. M.

(2006). Computational thinking. Communications of the ACM, 49(3), 33–35. doi: 10.1145/1118178.1118215

60.

Wing

J. M.

(2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725. doi: 10.1098/rsta.2008.0118

61.

Zendler

Klaudt

(2012). Central computer science concepts to research-based teacher training in computer science: An experimental study. Journal of Educational Computing Research, 46(2), 153–172. doi: 10.2190/EC.46.2.c

62.

Zhang

Norman

D. A.

(1994). Representations in distributed cognitive tasks. Cognitive Science, 18(1), 87–122. doi: 10.1207/s15516709cog1801_3