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The gap between knowledge and ability

Authors:

Andreas Mühling,

Peter HubwieserAuthors Info & Claims

Koli Calling '12: Proceedings of the 12th Koli Calling International Conference on Computing Education Research

Pages 126 - 134

https://doi.org/10.1145/2401796.2401812

Published: 15 November 2012 Publication History

Abstract

We present the results of an investigation on how well students are able to understand object-oriented programming (OOP) when learning with only very minimal guidance. We analyzed the source code that the students of a preparatory course produced during the course as well as concept maps that they were asked to draw before and after the course. Our findings show, that there are observable differences between what students know about some concepts and what they're able to do with it. Generally speaking, it seems that several OOP related concepts can be applied successfully without fully understanding the underlying concepts, while others are hard to understand and apply without a significant amount of prior knowledge. This gives rise to the suspicion that it might be possible to apply a concept without having understood it, at least with respect to some algorithmic concepts of CS.

References

[1]

D. Albert and C. M. Steiner. Empirical validation of concept maps: Preliminary methodological considerations. In IEEE, editor, Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05). 2005.

Digital Library

[2]

J. R. Anderson. Cognitive psychology and its implications: John R. Anderson. Worth Publishers, New York, 6 edition, 2005.

[3]

L. W. Anderson. A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. Longman, New York, abridged ed., {nachdr.} edition, 2009.

[4]

M. Ben-Ari. Constructivism in computer science education. In Proceedings of the twenty-ninth SIGCSE technical symposium on Computer science education, SIGCSE '98, pages 257--261, New York and NY and USA, 1998. ACM.

Digital Library

[5]

J. Bergin, K. Bruce, and M. Kölling. Objects-early tools: a demonstration. In SIGCSE'05: Proceedings of the 36th SIGCSE technical symposium on Computer science education, pages 390--391. ACM, 2005.

Digital Library

[6]

J. Börstler, Henrik B. Christensen, Jens Bennedsen, M. Nordström, Lena Kallin Westin, J. E. Moström, and Michael E. Caspersen. Evaluating oo example programs for cs1. In ITiCSE'08: Proceedings of the 13th annual conference on Innovation and technology in computer science education, pages 47--52. ACM, 2008.

Digital Library

[7]

U. Fuller, C. G. Johnson, T. Ahoniemi, D. Cukierman, I. Hernán-Losada, J. Jackova, E. Lahtinen, T. L. Lewis, D. M. Thompson, C. Riedesel, and E. Thompson. Developing a computer science-specific learning taxonomy. SIGCSE Bull, 39(4):152--170, 2007.

Digital Library

[8]

T. E. Goldsmith and D. M. Davenport. Assessing structural similarity of graphs. In Pathfinder associative networks, pages 75--87. Ablex Publishing Corp, 1990.

Digital Library

[9]

B. Hanks. Problems encountered by novice pair programmers. In Proceedings of the 3rd international workshop on Computing education research, pages 159--164. ACM, Atlanta and Georgia and USA, 2007.

Digital Library

[10]

S. A. Hansen. Analyzing programming projects. In Proceedings of the 40th ACM technical symposium on Computer science education, pages 377--381. ACM, Chattanooga and TN and USA, 2009.

Digital Library

[11]

P. Hubwieser. Analysis of learning objectives in object oriented programming. In R. T. Mittermeir and M. M. Syslo, editors, Informatics Education - Supporting Computational Thinking, Third International Conference on Informatics in Secondary Schools - Evolution and Perspectives, ISSEP 2008, Torun, Poland, July 1--4, 2008, volume 5090 of Lecture notes in computer science, pages 142--150. Springer, 2008.

Digital Library

[12]

P. Hubwieser and M. Berges. Minimally invasive programming courses: learning oop with(out) instruction. In ACM, editor, SIGCSE'11: Proceedings of the 42nd ACM technical symposium on Computer science education, pages 87--92. 2011.

Digital Library

[13]

P. Hubwieser and A. Mühling. Kowpats: Patterns of declarative knowledge (searching frequent knowledge patterns about object-orientation). In IC3K, editor, KDIR'11: Proceedings of the 3rd International Conference on Knowledge Discovery and Information Retrieval, page To appear. 2011.

[14]

P. Hubwieser and A. Mühling. What students (should) know about object oriented programming. In ACM, editor, ICER'11: Proceedings of the 7th International Computing Education Workshop, page To appear. 2011.

Digital Library

[15]

Joseph D. Novak. Meaningful learning: The essential factor for conceptual change in limited or inappropriate propositional hierarchies leading to empowerment of learners, 2002.

[16]

Joseph D. Novak and Alberto J. Cañas. The theory underlying concept maps and how to construct and use them.

[17]

Maria Araceli Ruiz-Primo, Richard J. Shavelson, and Susan Elise Schultz. On the validity of concept map-base assessment interpretations: An experiment testing the assumption of hierarchical concept maps in science, 1998.

[18]

Maria Araceli Ruiz-Primo, S. E. Schultz, M. Li, and R. J. Shavelson. Comparison of the reliability and validity of scores from two concept-mapping techniques. Journal of Research in Science Teaching, 38(2):260--278, 2001.

[19]

Maria Araceli Ruiz-Primo and R. J. Shavelson. Problems and issues in the use of concept maps in science assessment. Journal of Research in Science Teaching, 33(6):569--600, 1996.

[20]

J. R. McClure, B. Sonak, and H. K. Suen. Concept map assessment of classroom learning: Reliability, validity, and logistical practicality. Sci Teach, 36:475--492, 1999.

[21]

H. Müller and F. Weichert. Vorkurs Informatik: Der Einstieg ins Informatikstudium. Springer Fachmedien Wiesbaden, Wiesbaden, 2 edition, 2011.

[22]

D. A. Norman. Some observations on mental models. In D. Gentner and A. L. Stevens, editors, Mental models:, pages 7--14. Lawrence Erlbaum Associates, Hillsdale, NJ, 1983.

[23]

M. Pedroni and B. Meyer. Object-oriented modeling of object-oriented concepts. In J. Hromkovic, R. Královic, and J. Vahrenhold, editors, Teaching fundamental concepts of informatics, volume 5941 of Lecture notes in computer science, pages 155--169. Springer, Berlin, 2010.

Digital Library

[24]

A. Robins, P. Haden, and S. Garner. Problem distributions in a cs1 course. In D. Tolhurst and S. Mann, editors, ACE 06: Proceedings of the 8th Austalian conference on Computing education - Volume 52, page 165ff, Darlinghurst and Australia and Australia, 2006. Australian Computer Society, Inc.

Digital Library

[25]

K. Sanders, J. Boustedt, A. Eckerdal, R. McCartney, J. E. Moström, L. Thomas, and C. Zander, editors. Student understanding of object-oriented programming as expressed in concept maps, SIGCSE 2008, New York, NY, USA, 2008. ACM.

Digital Library

[26]

K. Sanders and L. Thomas. Checklists for grading object-oriented cs1 programs: concepts and misconceptions. In ITiCSE'07: Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education, pages 166--170. ACM, 2007.

Digital Library

[27]

R. J. Shavelson and M. A. Ruiz-Primo. On the psychomentrics of assessing science understanding. In J. Mintzes, J. H. Wandersee, and J. D. Novak, editors, Assessing science understanding, pages 304--341. Academic Press, San Diego, 1999.

[28]

N. Truong, P. Roe, and P. Bancroft. Static analysis of students' java programs. In Proceedings of the 6th conference on Australasian computing education - Volume 30, pages 317--325. Australian Computer Society, Inc., Dunedin and New Zealand, 2004.

Digital Library

[29]

J. Vanides, Y. Yin, M. Tomita, and Ruiz-Primo Maria Araceli. Using concept maps in the classroom. Science Scope, 28(8):27--31, 2005.

[30]

Y. Yin, J. Vanides, Maria Araceli Ruiz-Primo, C. C. Ayala, and R. J. Shavelson. Comparison of two concept-mapping techniques: Implications for scoring, interpretation, and use. Journal of Research in Science Teaching, 42(2):166--184, 2005.

Cited By

Li RYin YDai LShen SLin XSu YChen EAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)PSTProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531903(2601-2606)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531903
Liang YPeng TPu YWu W(2022)HELP-DKT: an interpretable cognitive model of how students learn programming based on deep knowledge tracingScientific Reports10.1038/s41598-022-07956-012:1Online publication date: 7-Mar-2022
https://doi.org/10.1038/s41598-022-07956-0
Gutiérrez LGuerrero CLópez-Ospina H(2022)Ranking of problems and solutions in the teaching and learning of object-oriented programmingEducation and Information Technologies10.1007/s10639-022-10929-527:5(7205-7239)Online publication date: 8-Feb-2022
https://doi.org/10.1007/s10639-022-10929-5
Show More Cited By

Index Terms

The gap between knowledge and ability
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education
        Information systems education
      2. Model curricula

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Koli Calling '12: Proceedings of the 12th Koli Calling International Conference on Computing Education Research

November 2012

187 pages

ISBN:9781450317955

DOI:10.1145/2401796

Conference Chairs:
Mikko-Jussi Laakso
University of Turku
,
Robert McCartney
University of Connecticut

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Univ. Eastern Finland: University of Eastern Finland
Tampere University of Technology
Univ. Turku: University of Turku
Aalto University

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SIGCSE: ACM Special Interest Group on Computer Science Education

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2012

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Koli Calling '12

Sponsor:

Univ. Eastern Finland
Univ. Turku

Koli Calling '12: 12th Koli Calling International Conference on Computing Education Research

November 15 - 18, 2012

Koli, Finland

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Overall Acceptance Rate 80 of 182 submissions, 44%

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Cited By

Li RYin YDai LShen SLin XSu YChen EAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)PSTProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531903(2601-2606)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531903
Liang YPeng TPu YWu W(2022)HELP-DKT: an interpretable cognitive model of how students learn programming based on deep knowledge tracingScientific Reports10.1038/s41598-022-07956-012:1Online publication date: 7-Mar-2022
https://doi.org/10.1038/s41598-022-07956-0
Gutiérrez LGuerrero CLópez-Ospina H(2022)Ranking of problems and solutions in the teaching and learning of object-oriented programmingEducation and Information Technologies10.1007/s10639-022-10929-527:5(7205-7239)Online publication date: 8-Feb-2022
https://doi.org/10.1007/s10639-022-10929-5
Lukkarinen ALehtinen THaaranen LMalmi L(2021)An Event Listener or an Event Handler?Proceedings of the 21st Koli Calling International Conference on Computing Education Research10.1145/3488042.3488051(1-10)Online publication date: 17-Nov-2021
https://dl.acm.org/doi/10.1145/3488042.3488051
Luxton-Reilly ABecker BCao YMcDermott RMirolo CMühling APetersen ASanders KSimon Whalley JSheard JKorhonen A(2018)Developing Assessments to Determine Mastery of Programming FundamentalsProceedings of the 2017 ITiCSE Conference on Working Group Reports10.1145/3174781.3174784(47-69)Online publication date: 30-Jan-2018
https://dl.acm.org/doi/10.1145/3174781.3174784
Daun MBrings JObe PPohl KMoser SSchumacher HRieB M(2017)Teaching Conceptual Modeling in Online Courses: Coping with the Need for Individual Feedback to Modeling Exercises2017 IEEE 30th Conference on Software Engineering Education and Training (CSEE&T)10.1109/CSEET.2017.30(134-143)Online publication date: Nov-2017
https://doi.org/10.1109/CSEET.2017.30
Berges MHubwieser PDagienė VSchulte CJevsikova T(2015)Evaluation of Source Code with Item Response TheoryProceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education10.1145/2729094.2742619(51-56)Online publication date: 22-Jun-2015
https://dl.acm.org/doi/10.1145/2729094.2742619
Staubitz TKlement HRenz JTeusner RMeinel C(2015)Towards practical programming exercises and automated assessment in Massive Open Online Courses2015 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE)10.1109/TALE.2015.7386010(23-30)Online publication date: Dec-2015
https://doi.org/10.1109/TALE.2015.7386010
Capovilla DBerges MMühling AHubwieser P(2015)Handling Heterogeneity in Programming Courses for FreshmenProceedings of the 2015 International Conference on Learning and Teaching in Computing and Engineering10.1109/LaTiCE.2015.18(197-203)Online publication date: 9-Apr-2015
https://dl.acm.org/doi/10.1109/LaTiCE.2015.18
Berges MHubwieser PCarter JUtting IClear A(2013)Concept specification mapsProceedings of the 18th ACM conference on Innovation and technology in computer science education10.1145/2462476.2462503(291-296)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1145/2462476.2462503

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