ABSTRACT
Posts on on-line forums where programmers look for information often include links to Wikipedia when it can be assumed the reader will not be familiar with the linked terms. A Wikipedia article will thus often be the first exposure to a new computing concept for a novice programmer. We conducted an exploratory study with 18 novice programmers by asking them to read a Wikipedia article on a common computing concept that was new to them, while using the think-aloud protocol. We performed a qualitative analysis of the session transcripts to better understand the experience of the novice programmer learning a new computing concept using Wikipedia. We elicited five themes that capture this experience: Concept Confusion, Need for Examples, New Terminology, Trivia Clutter, and Unfamiliar Notation. We conclude that Wikipedia is not well suited as a resource for the opportunistic learning of new computing concepts, and we recommend adapting information sharing practices in on-line programmer communities to better account for the learning needs of the users.
- Maurício Aniche, Christoph Treude, Igor Steinmacher, Igor Wiese, Gustavo Pinto, Margaret-Anne Storey, and Marco Aurélio Gerosa. 2018. How Modern News Aggregators Help Development Communities Shape and Share Knowledge. In Proceedings of the 40th ACM/IEEE International Conference on Software Engineering. 499--510.Google ScholarDigital Library
- Sebastian Baltes, Lorik Dumani, Christoph Treude, and Stephan Diehl. 2018. SOTorrent: Reconstructing and Analyzing the Evolution of Stack Overflow Posts. In Proceedings of the 15th International Conference on Mining Software Repositories. 319--330. version: 2018_09_23.Google ScholarDigital Library
- Susan Bergin and Ronan Reilly. 2005. Examining the Role of Self-Regulated Learning on Introductory Programming Performance. In Proceedings of the 1st International Workshop on Computing Education Research. 81--86.Google ScholarDigital Library
- Jonas Boustedt, Anna Eckerdal, Robert McCartney, Kate Sanders, Lynda Thomas, and Carol Zander. 2011. Students' Perceptions of the Differences Between Formal and Informal Learning. In Proceedings of the 7th International Workshop on Computing Education Research. 61--68.Google ScholarDigital Library
- Joel Brandt, Philip J. Guo, Joel Lewenstein, Mira Dontcheva, and Scott R. Klemmer. 2009. Two Studies of Opportunistic Programming: Interleaving Web Foraging, Learning, and Writing Code. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 1589--1598.Google Scholar
- Virginia Braun and Victoria Clarke. 2006. Using Thematic Analysis in Psychology. Qualitative Research in Psychology 3, 2 (2006), 77--101.Google ScholarCross Ref
- John W. Creswell. 2003. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2nd ed.). Sage Publications.Google Scholar
- Peter K. Dunn, Margaret Marshman, and Robert McDougall. 2017. Evaluating Wikipedia as a Self-Learning Resource for Statistics: You Know They'll Use It. The American Statistician 73, 3 (2017), 1--8.Google Scholar
- Denis Howe et al. 2019. FOLDOC: Free On-Line Dictionary of Computing. https://foldoc.org/. Accessed 19 August 2019.Google Scholar
- Anneli Eteläpelto. 1993. Metacognition and the Expertise of Computer Program Comprehension. Scandinavian Journal of Educational Research 37, 3 (1993), 243-- 254.Google ScholarCross Ref
- Katrina Falkner, Claudia Szabo, Rebecca Vivian, and Nikolas Falkner. 2015. Evolution of Software Development Strategies. In Proceedings of the 37th IEEE/ACM International Conference on Software Engineering, Vol. 2. 243--252.Google ScholarCross Ref
- Katrina Falkner, Rebecca Vivian, and Nickolas Falkner. 2014. Identifying Computer Science Self-Regulated Learning Strategies. In Proceedings of the 19th Annual Conference on Innovation and Technology in Computer Science Education. 291--296.Google ScholarDigital Library
- Carlos Gómez, Brendan Cleary, and Leif Singer. 2013. A Study of Innovation Diffusion Through Link Sharing on Stack Overflow. In Proceedings of the 10th Working Conference on Mining Software Repositories. 81--84.Google ScholarDigital Library
- Jan Goyvaerts. 2019. Regular-Expressions.info. https://www.regular-expressions. info. Accessed 19 August 2019.Google Scholar
- Matthias Hauswirth and Andreea Adamoli. 2017. Metacognitive Calibration When Learning to Program. In Proceedings of the 17th Koli Calling International Conference on Computing Education Research. 50--59.Google ScholarDigital Library
- Ville Isomöttönen and Ville Tirronen. 2013. Teaching Programming by Emphasizing Self-Direction: How Did Students React to the Active Role Required of Them? ACM Transactions on Computing 13, 2 (2013).Google Scholar
- Carita Kiili, Leena Laurinen, and Miika Marttunen. 2008. Students Evaluating Internet Sources: From Versatile Evaluators to Uncritical Readers. Journal of Educational Computing Research 39, 1 (2008), 75--95.Google ScholarCross Ref
- Ada S. Kim and Andrew J. Ko. 2017. A Pedagogical Analysis of Online Coding Tutorials. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. 321--326.Google Scholar
- Charles Knight and Sam Pryke. 2012. Wikipedia and the University, a Case Study. Teaching in Higher Education 17, 6 (2012), 649--659.Google ScholarCross Ref
- Malcom S. Knowles, Elwood F. Holton III, and Richard A. Swanson. 2005. The Adult Learner (6th ed.). Elsevier.Google Scholar
- Essi Lahtinen, Kirsti Ala-Mutka, and Hannu-Matti Järvinen. 2005. A Study of the Difficulties of Novice Programmers. In Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education. 14--18.Google ScholarDigital Library
- Dastyni Loksa and Andrew J. Ko. 2016. The Role of Self-Regulation in Programming Problem Solving Process and Success. In Proceedings of the 12th ACM International Computing Education Research Conference. 83--91.Google Scholar
- Teun Lucassen and Jan Maarten Schraagen. 2010. Trust in Wikipedia: How Users Trust Information from an Unknown Source. In Proceedings of the 4th Workshop on Information Credibility. 19--26.Google ScholarDigital Library
- Robert McCartney, Jonas Boustedt, Anna Eckerdal, Kate Sanders, Lynda Thomas, and Carol Zander. 2016. Why Computing Students Learn on Their Own: Motivation for Self-Directed Learning of Computing. ACM Transactions on Computing Education 16, 1 (2016), 2:1--2:18.Google ScholarDigital Library
- Laurie Murphy and Josh Tenenberg. 2005. Do Computer Science Students Know What They Know?: A Calibration Study of Data Structure Knowledge. In Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education. 148--152.Google ScholarDigital Library
- David G. Novick and Karen Ward. 2006. What Users Say They Want in Documentation. In Proceedings of the 24th Annual ACM International Conference on Design of Communication. 84--91.Google Scholar
- Jennifer Parham, Leo Gugerty, and D. E. Stevenson. 2010. Empirical Evidence for the Existence and Uses of Metacognition in Computer Science Problem Solving. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education. 416--420.Google Scholar
- James Prather, Raymond Pettit, Brett A. Becker, Paul Denny, Dastyni Loksa, Alani Peters, Zachary Albrecht, and Krista Masci. 2019. First Things First: Providing Metacognitive Scaffolding for Interpreting Problem Prompts. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education. 531--537.Google ScholarDigital Library
- J. Gregory Trafton and Brian J. Reiser. 1993. Studying Examples and Solving Problems: Contributions to Skill Acquisition. In Proceedings of the 15th conference of the Cognitive Science Society. 1017--1022.Google Scholar
- Nicholas Vincent, Isaac Johnson, and Brent Hecht. 2018. Examining Wikipedia With a Broader Lens: Quantifying the Value of Wikipedia's Relationships with Other Large-Scale Online Communities. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 566:1--566:13.Google ScholarDigital Library
- Anneliese von Mayrhauser and A. Marie Vans. 1995. Program Comprehension During Software Maintenance and Evolution. Computer 28, 8 (1995), 44--55.Google ScholarDigital Library
- Ryen W. White, Susan Dumais, and Jaime Teevan. 2009. Characterizing the Influence of Domain Expertise on Web Search Behavior. In Proceedings of the 2nd ACM International Conference on Web Search and Data Mining. 132--141.Google ScholarDigital Library
- Wikipedia. 2019. What Wikipedia is not. https://en.wikipedia.org/wiki/ Wikipedia:What_Wikipedia_is_not. Accessed 10 July 2019.Google Scholar
- Carol Zander, Jonas Boustedt, Anna Eckerdal, Robert McCartney, Kate Sanders, Jan Erik Moström, and Lynda Thomas. 2012. Self-directed Learning: Stories from Industry. In Proceedings of the 12th Koli Calling International Conference on Computing Education Research. 111--117.Google ScholarDigital Library
- Rui Zhi, Thomas W. Price, Samiha Marwan, Alexandra Milliken, Tiffany Barnes, and Min Chi. 2019. Exploring the Impact of Worked Examples in a Novice Programming Environment. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education. 98--104.Google ScholarDigital Library
- Barry J. Zimmerman and Manuel Martinez Pons. 1986. Development of a Structured Interview for Assessing Student Use of Self-Regulated Learning Strategies. American Educational Research Journal 23, 4 (1986), 614--628.Google ScholarCross Ref
Index Terms
- Understanding Wikipedia as a Resource for Opportunistic Learning of Computing Concepts
Recommendations
Learning multilingual named entity recognition from Wikipedia
We automatically create enormous, free and multilingual silver-standard training annotations for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner systems rely on statistical models of annotated data to identify ...
Two-stage approach to named entity recognition using Wikipedia and DBpedia
IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and CommunicationIn natural language understanding, extraction of named entity (NE) mentions in given text and classification of the mentions into pre-defined NE types are important processes. Most NE recognition (NER) relies on resources such as a training corpus or NE ...
Evaluating Entity Linking with Wikipedia
Named Entity Linking (nel) grounds entity mentions to their corresponding node in a Knowledge Base (kb). Recently, a number of systems have been proposed for linking entity mentions in text to Wikipedia pages. Such systems typically search for candidate ...
Comments