How programmers find online learning resources
When learning a new technology, programmers often have to sift through multiple online resources to find information that addresses their questions. Prior work has reported that information seekers use a number of different strategies, including following scents , or indicators, to locate appropriat...
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| Published in | Empirical software engineering : an international journal Vol. 28; no. 2; p. 23 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.03.2023
Springer Nature B.V |
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| Online Access | Get full text |
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| Abstract | When learning a new technology, programmers often have to sift through multiple online resources to find information that addresses their questions. Prior work has reported that information seekers use a number of different strategies, including following
scents
, or indicators, to locate appropriate resources. We present a qualitative and quantitative investigation of how programmers learning a new technology employ these strategies to navigate between online resources and evaluate the pertinence of these resources. We performed a diary and interview study with ten programmers learning a new technology, to study how users navigate from the question they have to the resource that satisfies this need. Based on our observations, we propose a resource-seeking model that represents the online resource seeking behaviour of programmers when learning a new technology. The model is comprised of six components that can be divided into two groups: Need-oriented components, i.e. Questions, Preferences, and Beliefs, and Resource-oriented components, i.e. Resources, Cues, and Impression Factors. We identified nine relations between these components and studied how the components are associated. We report on the characteristics of the components and the relationships between them, and discuss the importance of search customization and other implications of our observations for resource creators and search tools. |
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| AbstractList | When learning a new technology, programmers often have to sift through multiple online resources to find information that addresses their questions. Prior work has reported that information seekers use a number of different strategies, including following scents, or indicators, to locate appropriate resources. We present a qualitative and quantitative investigation of how programmers learning a new technology employ these strategies to navigate between online resources and evaluate the pertinence of these resources. We performed a diary and interview study with ten programmers learning a new technology, to study how users navigate from the question they have to the resource that satisfies this need. Based on our observations, we propose a resource-seeking model that represents the online resource seeking behaviour of programmers when learning a new technology. The model is comprised of six components that can be divided into two groups: Need-oriented components, i.e. Questions, Preferences, and Beliefs, and Resource-oriented components, i.e. Resources, Cues, and Impression Factors. We identified nine relations between these components and studied how the components are associated. We report on the characteristics of the components and the relationships between them, and discuss the importance of search customization and other implications of our observations for resource creators and search tools. When learning a new technology, programmers often have to sift through multiple online resources to find information that addresses their questions. Prior work has reported that information seekers use a number of different strategies, including following scents , or indicators, to locate appropriate resources. We present a qualitative and quantitative investigation of how programmers learning a new technology employ these strategies to navigate between online resources and evaluate the pertinence of these resources. We performed a diary and interview study with ten programmers learning a new technology, to study how users navigate from the question they have to the resource that satisfies this need. Based on our observations, we propose a resource-seeking model that represents the online resource seeking behaviour of programmers when learning a new technology. The model is comprised of six components that can be divided into two groups: Need-oriented components, i.e. Questions, Preferences, and Beliefs, and Resource-oriented components, i.e. Resources, Cues, and Impression Factors. We identified nine relations between these components and studied how the components are associated. We report on the characteristics of the components and the relationships between them, and discuss the importance of search customization and other implications of our observations for resource creators and search tools. |
| ArticleNumber | 23 |
| Author | Arya, Deeksha M. Robillard, Martin P. Guo, Jin L. C. |
| Author_xml | – sequence: 1 givenname: Deeksha M. orcidid: 0000-0002-3719-5011 surname: Arya fullname: Arya, Deeksha M. email: deeksha.arya@mail.mcgill.ca organization: School of Computer Science, McGill University – sequence: 2 givenname: Jin L. C. orcidid: 0000-0003-1782-1545 surname: Guo fullname: Guo, Jin L. C. organization: School of Computer Science, McGill University – sequence: 3 givenname: Martin P. orcidid: 0000-0002-0248-1384 surname: Robillard fullname: Robillard, Martin P. organization: School of Computer Science, McGill University |
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| Cites_doi | 10.1145/1518701.1518944 10.1016/j.ipm.2011.08.009 10.1145/1985429.1985440 10.1016/B978-0-12-805390-4.00006-6 10.1109/ICSME46990.2020.00052 10.1075/hts.1.thi1 10.1109/ICSE-SEET.2019.00026 10.2307/2529310 10.1109/SANER48275.2020.9054828 10.1145/3369255.3369258 10.1007/s10664-020-09857-0 10.1109/WPC.1998.693322 10.1145/357436.357440 10.1109/ICSME.2017.17 10.1109/ASE.1998.732658 10.1109/BigData50022.2020.9378083 10.1145/2786805.2786855 10.1109/MS.2021.3090978 10.1109/ICSM.2015.7332447 10.1016/B978-0-12-805390-4.00011-X 10.1007/s10791-017-9305-y 10.1145/985692.985745 10.1145/1181775.1181779 10.1145/3387904.3389274 10.1109/HICSS.1990.205259 10.1145/2858036.2858469 10.1109/ICSE.2007.45 10.1007/3-540-44963-9_8 10.1145/1357054.1357261 10.1145/3358931.3358937 10.1145/3449240 10.1037/0033-295X.106.4.643 10.1109/ICSE.2012.6227187 10.1007/s10664-017-9514-4 10.1145/3377811.3380330 10.1145/2775441.2775457 10.1145/3194793.3194796 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Online learning resources Information seeking Software documentation User study Diary study Qualitative analysis Quantitative analysis |
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A framework and system for evaluating summarized knowledge. In: Proceedings of the ACM on human-computer interaction (CSCW1) Arya DM, Nassif M, Robillard MP (2021) A data-centric study of software tutorial design. IEEE Softw Erdos K, Sneed HM (1998) Partial comprehension of complex programs (enough to perform maintenance). In: Proceedings. 6th International workshop on program comprehension. IWPC’98, pp 98–105 Nadi S, Treude C (2020) Essential sentences for navigating stack overflow answers. In: International conference on software analysis, evolution and reengineering (SANER). IEEE, pp 229–239 Duala-Ekoko E, Robillard MP (2012) Asking and answering questions about unfamiliar apis: an exploratory study. In: 2012 34th International conference on software engineering (ICSE). IEEE, pp 266–276 Lazar J, Feng JH, Hochheiser H (2017b) Chapter 6—diaries. In: Lazar J, Feng J H, Hochheiser H (eds) Research methods in human computer interaction, 2nd edn. Morgan Kaufmann, Boston, pp 135–152 Dondio P, Shaheen S (2019) Is Stack Overflow an effective complement to gaining practical knowledge compared to traditional computer science learning?. In: Proceedings of the international conference on education technology and computers (ICETC), pp 132–138 PirolliPCardSInformation foragingPsychol Rev199910664367510.1037/0033-295X.106.4.643 Earle RH, Rosso MA, Alexander KE (2015) User preferences of software documentation genres. In: Proceedings of the 33rd annual international conference on the design of communication, SIGDOC ’15. Association for Computing Machinery SharpeDChi-square test is statistically significant: now what?Pract Assess Res Eval20152018 Abdi H et al (2007) Bonferroni and šidák corrections for multiple comparisons. In: Encyclopedia of measurement and statistics, vol 3, pp 103–107 Sadowski C, Stolee KT, Elbaum S (2015) How developers search for code: a case study. 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| References_xml | – reference: Erdos K, Sneed HM (1998) Partial comprehension of complex programs (enough to perform maintenance). In: Proceedings. 6th International workshop on program comprehension. IWPC’98, pp 98–105 – reference: MehtaCRPatelNRIBM SPSS exact tests2011ArmonkIBM Corporation – reference: Lazar J, Feng JH, Hochheiser H Lazar J, Feng J H, Hochheiser H (eds) (2017a) Chapter 11—analyzing qualitative data. Morgan Kaufmann, Boston – reference: Arya DM, Nassif M, Robillard MP (2021) A data-centric study of software tutorial design. IEEE Softw – reference: Chattopadhyay S, Nelson N, Au A, Morales N, Sanchez C, Pandita R, Sarma A (2020) A tale from the trenches: cognitive biases and software development. In: Proceedings of the 42nd ACM/IEEE international conference on software engineering, ICSE ’20. Association for Computing Machinery, New York, pp 654–665 – reference: Duala-Ekoko E, Robillard MP (2012) Asking and answering questions about unfamiliar apis: an exploratory study. In: 2012 34th International conference on software engineering (ICSE). IEEE, pp 266–276 – reference: Sillito J, Murphy GC, De Volder K (2006) Questions programmers ask during software evolution tasks. In: Proceedings of the SIGSOFT international symposium on foundations of software engineering (FSE). Association for Computing Machinery, pp 23–34 – reference: LandisJRKochGThe measurement of observer agreement for categorical dataBiometrics19773315917410.2307/25293100351.62039 – reference: Sadowski C, Stolee KT, Elbaum S (2015) How developers search for code: a case study. In: Proceedings of the 10th joint meeting on foundations of software engineering, ESEC/FSE. Association for Computing Machinery, pp 191–201 – reference: SharpeDChi-square test is statistically significant: now what?Pract Assess Res Eval20152018 – reference: SprentPFisher exact test2011BerlinSpringer524525 – reference: Lazar J, Feng JH, Hochheiser H (2017b) Chapter 6—diaries. In: Lazar J, Feng J H, Hochheiser H (eds) Research methods in human computer interaction, 2nd edn. Morgan Kaufmann, Boston, pp 135–152 – reference: LuYHsiaoIHPersonalized information seeking assistant (pisa): from programming information seeking to learningInf Retr20172043345510.1007/s10791-017-9305-y – reference: MackRLLewisCHCarrollJMLearning to use word processors: problems and prospectsACM Trans Inf Syst19831325427110.1145/357436.357440 – reference: Earle RH, Rosso MA, Alexander KE (2015) User preferences of software documentation genres. In: Proceedings of the 33rd annual international conference on the design of communication, SIGDOC ’15. Association for Computing Machinery – reference: Brandt J, Guo PJ, Lewenstein J, Dontcheva M, Klemmer SR (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, CHI ’09. Association for Computing Machinery, pp 1589–1598 – reference: Robillard MP, Marcus A, Treude C, Bavota G, Chaparro O, Ernst N, Gerosa MA, Godfrey M, Lanza M, Linares-vásquez M, Murphy GC, Moreno L, Shepherd D, Wong E (2017) On-demand developer documentation. In: International conference on software maintenance and evolution (ICSME). IEEE, pp 479–483 – reference: Marques A, Bradley NC, Murphy GC (2020) Characterizing task-relevant information in natural language software artifacts. 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