Riko Kelter
Matthias Kramer
Torsten Brinda
ABSTRACT
The object-oriented programming (OOP) paradigm is quite prominent in German secondary schools. To challenge and overcome possible difficulties in the learning process it is vital for educators to have knowledge about possible (mis-)conceptions. Traditionally, these are gathered by investigating the mental models of students, e.g. towards object-orientation. While on the one side lots of misconceptions could not be reproduced in replication studies, on the other side most of ten students are asked, while teachers could provide an overview on one or several courses. To tackle both aspects at once, this paper describes the investigation of teachers views on occurring student misconceptions regarding OOP in their lessons. Therefore misconceptions were gathered from literature and were condensed into a survey. The answers of 79 teachers are analysed regarding the frequency with which teachers register misconceptions, which of those are possibly new and by fitting linear and quadratic regression models it is investigated, which external factors, such as teaching approach, work experience or educational degree, might influence the perceived frequency of registered misconceptions. All aspects show promising results for further investigations towards the research of misconceptions in OOP.
- M. S. Bartlett and M. S. 1937. Properties of Sufficiency and Statistical Tests. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 160, 901 (may 1937), 268--282.Google Scholar
- Daniel J. Benjamin and James O. Berger. 2018. Redefine statistical significance. Nature Human Behaviour 2, 1 (jan 2018), 6--10.Google ScholarCross Ref
- Joseph Bergin, Eugene Wallingford, Michael Caspersen, Michael Goldweber, and Michael Kolling. 2005. Teaching Polymorphism Early. In Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education ({ITiCSE} '05). ACM, New York, NY, USA, 342--343. Google ScholarDigital Library
- Neil C. C. Brown and Amjad Altadmri. 2017. Novice Java Programming Mistakes: Large-Scale Data vs. Educator Beliefs. Trans. Comput. Educ. 17, 2 (may 2017), 7:1--7:21. arXiv:arXiv:1502.07526v1 Google ScholarDigital Library
- Jacob Cohen. 1988. Statistical Power Analysis for the Behavioral Sciences (2 edition ed.). Routledge, Hillsdale, N.J.Google Scholar
- David Colquhoun. 2014. An investigation of the false discovery rate and the misinterpretation of p-values. Royal Society Open Science 1, 3 (nov 2014), 140216--140216. arXiv:1407.5296Google ScholarCross Ref
- David Colquhoun. 2017. The reproducibility of research and the misinterpretation of p-values. Royal Society Open Science 4, 12 (dec 2017), 171085.Google ScholarCross Ref
- James H Cross II, T Dean Hendrix, David A Umphress, and Larry A Barowski. 2008. Exploring Accessibility and Visibility Relationships in Java. In Proceedings of the 13th Annual Conference on Innovation and Technology in Computer Science Education. ACM, 103--108. Google ScholarDigital Library
- Edward E. (Edward Eugene) Cureton and Ralph B. D'Agostino. {n. d.}. Factor analysis: an applied approach.Google Scholar
- Anna Eckerdal and Michael Thuné. 2005. Novice Java Programmers' Conceptions of "Object" and "Class", and Variation Theory. In Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education ({ITiCSE} '05). ACM, New York, NY, USA, 89--93. Google ScholarDigital Library
- Pamela Flores, Nelson Medinilla, and Sonia Pamplona. 2014. What Do Software Design Students Understand About Information Hiding?: A Qualitative Case Study. In Proceedings of the 14th Koli Calling International Conference on Computing Education Research. ACM Press, New York, New York, USA, 61--70. Google ScholarDigital Library
- S B Green. 1991. How Many Subjects Does It Take To Do A Regression Analysis. Multivariate Behavioral Research 26, 3 (jul 1991), 499--510.Google ScholarCross Ref
- Simon Holland, Robert Griiths, and Mark Woodman. 1997. Avoiding Object Misconceptions. In Proceedings of the Twenty-eighth SIGCSE Technical Symposium on Computer Science Education ({SIGCSE} '97). ACM, New York, NY, USA, 131--134. Google ScholarDigital Library
- H Jonsson. 2015. Using flipped classroom, peer discussion, and just-in-time teaching to increase learning in a programming course. In 2015 {IEEE} {Frontiers} in {Education} {Conference} ({FIE}). 1--9. Google ScholarDigital Library
- Erkki Kaila, Einari Kurvinen, Erno Lokkila, and Mikko-Jussi Laakso. 2016. Redesigning an Object-Oriented Programming Course. Trans. Comput. Educ. 16, 4 (aug 2016), 18:1---18:21. Google ScholarDigital Library
- Ilana Lavy, Rami Rashkovits, and Roy Kouris. 2009. Coping with abstraction in object orientation with a special focus on interface classes. Computer Science Education 19, March 2012 (sep 2009), 155--177.Google ScholarCross Ref
- Philipp Mayring. 2010. Qualitative Inhaltsanalyse: Grundlagen und Techniken (11., aktua ed.). Beltz, Weinheim.Google ScholarCross Ref
- Jeremy Miles and Mark Shevlin. 2000. Applying Regression and Correlation: A Guide for Students and Researchers (1 edition ed.). Sage Publications Ltd, London; Thousand Oaks, Calif.Google Scholar
- V Nguyen, H H Dang, K N Do, and T D Tran. 2014. Learning and practicing object-oriented programming using a collaborative web-based {IDE}. In 2014 {IEEE} {Frontiers} in {Education} {Conference} ({FIE}) {Proceedings}. 1--9.Google Scholar
- Benjamin Pierce. 2000. Types and Programming Languages. Vol. 35. MIT Press. 20-30 pages. Google ScholarDigital Library
- Noa Ragonis and Mordechai Ben-Ari. 2005. A Long-Term Investigation of the Comprehension of OOP Concepts by Novices. Computer Science Education 15, 3 (sep 2005), 201--221.Google ScholarCross Ref
- Kate Sanders, Jonas Boustedt, Anna Eckerdal, Robert McCartney, Jan Erik Moström, Lynda Thomas, and Carol Zander. 2008. Student Understanding of Object-oriented Programming As Expressed in Concept Maps. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education ({SIGCSE} '08). ACM, New York, NY, USA, 332--336. Google ScholarDigital Library
- Kate Sanders and Lynda Thomas. 2007. Checklists for Grading Object-oriented CS1 Programs: Concepts and Misconceptions. In Proceedings of the 12th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education ({ITiCSE} 07). ACM, New York, NY, USA, 166--170. Google ScholarDigital Library
- Mariana Teif and Orit Hazzan. 2006. Partonomy and Taxonomy in Object-oriented Thinking: Junior High School Students' Perceptions of Object-oriented Basic Concepts. In Working Group Reports on ITiCSE on Innovation and Technology in Computer Science Education ({ITiCSE}-{WGR} '06). ACM, New York, NY, USA, 55--60. Google ScholarDigital Library
- Benjy Thomasson, Mark Ratcliffe, and Lynda Thomas. 2006. Identifying Novice Difficulties in Object Oriented Design. In Proceedings of the 11th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education ({ITICSE} '06). ACM, New York, NY, USA, 28--32. Google ScholarDigital Library
- Stelios Xinogalos. 2015. Object-Oriented Design and Programming: An Investigation of Novices' Conceptions on Objects and Classes. Trans. Comput. Educ. 15, 3 (jul 2015), 13:1--13:21. Google ScholarDigital Library
- Stelios Xinogalos, Maya Satratzemi, and Vassilios Dagdilelis. 2007. A Comparison of Two Object-oriented Programming Environments for Novices. In Proceedings of the 10th IASTED International Conference on Computers and Advanced Technology in Education ({CATE} '07). ACTA Press, Anaheim, CA, USA, 49--54. http://dl.acm.org/citation.cfm?id=1650165.1650176 Google ScholarDigital Library
- Stelios Xinogalos, Maya Satratzemi, and Vassilios Dagdilelis. 2007. Teaching Java with BlueJ: A Two-year Experience. In Proceedings of the 12th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education ({ITiCSE} '07). ACM, New York, NY, USA, 345. Google ScholarDigital Library
Index Terms
- Statistical Frequency-Analysis of Misconceptions In Object-Oriented-Programming: Regularized PCR Models for Frequency Analysis across OOP Concepts and related Factors
Recommendations
Programming Misconceptions for School Students
ICER '18: Proceedings of the 2018 ACM Conference on International Computing Education ResearchProgramming misconceptions have been a topic of interest in introductory programming education, with a focus on university level students. Nowadays, programming is increasingly taught to younger children in schools, sometimes as part of the curriculum. ...
What Is Object-Oriented Programming?
The meaning of the term 'object oriented' is examined in the context of the general-purpose programming language C++. This choice is made partly to introduce C++ and partly because C++ is one of the few languages that supports data abstraction, object-...
Comments