Lost while searching: Difficulties in information seeking among end‐user programmers

Dorn, Brian; Stankiewicz, Adam; Roggi, Chris

doi:10.1002/meet.14505001059

Cited by 13 publications

(9 citation statements)

References 39 publications

(43 reference statements)

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“…(P1), Discussion. Prior work on end-users' example search behaviors showed that they may not know how to articulate what it is they want to see in examples [9]. Our analysis found similar results, that novices may also encounter difficulties expressing the functionality they need in an example, and instead search for items that they associate with that functionality (e.g.…”

Section: Search Barrier: How Do I Explain the Example I Want?supporting

confidence: 63%

Novices' Learning Barriers When Using Code Examples in Open-Ended Programming

Wang

Kwatra

Skripchuk

et al. 2021

Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education v. 1

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Open-ended programming increases students' motivation by allowing them to solve authentic problems and connect programming to their own interests. However, such open-ended projects are also challenging, as they often encourage students to explore new programming features and attempt tasks that they have not learned before. Code examples are effective learning materials for students and are well-suited to supporting open-ended programming. However, there is little work to understand how novices learn with examples during open-ended programming, and few real-world deployments of such tools. In this paper, we explore novices' learning barriers when interacting with code examples during open-ended programming. We deployed Example Helper, a tool that offers galleries of code examples to search and use, with 44 novice students in an introductory programming classroom, working on an openended project in Snap!. We found three high-level barriers that novices encountered when using examples: decision, search, and integration barriers. We discuss how these barriers arise and design opportunities to address them.

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Section: Search Barrier: How Do I Explain the Example I Want?supporting

confidence: 63%

Novices' Learning Barriers When Using Code Examples in Open-Ended Programming

Wang

Kwatra

Skripchuk

et al. 2021

Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education v. 1

View full text Add to dashboard Cite

show abstract

“…However, if a part of the provided code is unclear, it is difficult for them to know what to search for. Additionally, general purpose search engines may not perform well for code queries (Dorn et al 2013). For instance, if a student in a C programming course sees a line like int * p = &i;, he might wonder what * or & mean, but these special characters might be ignored by such a search engine.…”

Section: Ide Plugin and Hashtagsmentioning

confidence: 99%

“…In addition, the assumption that students know how to perform a search effectively is often not fulfilled, as they might lack prior experience with advanced search functionalities (Mavridis et al 2017;Paynter 2015). The level of expertise and the search accuracy are often positively correlated (Dorn et al 2013). As there is usually no explicit link between a task and the relevant learning material, it is especially challenging for novices to know what to search for, as already expressed in Meno's paradox of inquiry.…”

Section: Introductionmentioning

confidence: 99%

Scaffolding support through integration of learning material

Grévisse

Rothkugel

Reuter

2019

Smart Learn. Environ.

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The ever-growing amount of learning material poses a challenge to both learners and teachers. In order to perform a learning task, students often need to search for additional information. Inquiry might be hampered if they do not know what exactly to search for, especially if they lack prior knowledge in a domain. Furthermore, they might need to recall meta-information, e.g., the implicit links between learning resources or the location of an explanation within a document. In addition, their performance in a learning task might suffer from the constant need for further information. As the authoring of learning material is a time-intensive task, reusability of resources in different contexts should be enabled. In this paper, we present the ALMA-Yactul ecosystem, a holistic approach for student-centered integration of learning material. Based on semantic annotations, learners are provided with scaffolding support for retrieving material related to their current study task. This integration of resources is showcased in a variety of applications and domains, such as a plugin for an IDE or an enhanced sketchnoting app. Moreover, we show how Yactul, a gamified student response platform, can benefit from semantic annotations and integration of learning material. Additionally, teachers are provided with semi-automatic annotation support in the popular Office 365 suite to foster the reusability of their content. Apart from presenting the results of its usage in university classes, we also show how high school students without prior knowledge benefitted from this scaffolding support. A significantly better performance in resolving programming-related tasks could be observed compared to a traditional search process. Finally, the ecosystem is evaluated against a state-of-the-art technology integration model.

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“…Several studies have found that there are critical information retrieval skills necessary to successfully using online resources about computing. For example, the use of Q&A sites and code search requires people to learn search and query reformulation skills, which are non-obvious to novices (Dorn et al, 2013). DiSalvo discovered that parents looking for computer science educational resources for their children had trouble obtaining good results from what they thought were reasonable search queries (DiSalvo, 2014).…”

Section: Finding and Using Online Resourcesmentioning

confidence: 99%

Learning Outside the Classroom

Begel¹,

Ko²

2019

Preprint

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The history of computing education research is replete with studies about learning in formal contexts (i.e., students learning from teachers in school classrooms). In this chapter, we explore other contexts in which learning about computing occurs (e.g., through reading books, working through online tutorials, competing in hackathons, or asking and answering computing questions on a Q&A website). These activities are all examples of informal learning – learning that is opportunistic, rather than planned; unstructured, rather than pedagogically created; self-directed, rather than teacher-centric; and integrated authentically into life activities (Marsick & Watkins, 2001), rather than taking place in a classroom environment. We collect and synthesize research about informal learning of computing and discuss open questions around where and how it occurs and how to best support it.

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Lost while searching: Difficulties in information seeking among end‐user programmers

Cited by 13 publications

References 39 publications

Novices' Learning Barriers When Using Code Examples in Open-Ended Programming

Novices' Learning Barriers When Using Code Examples in Open-Ended Programming

Scaffolding support through integration of learning material

Learning Outside the Classroom

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