If They Build It, Will They Understand It? Exploring the Relationship between Student Code and Performance

Salac, Jean; Franklin, Diana

doi:10.1145/3341525.3387379

Cited by 26 publications

(8 citation statements)

References 10 publications

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“…We identified that because communitygenerated learning resources in the form of Q&A, tutorials, and project examples tend to be directed toward specific functional uses, those that represent common interests can become archetypes in ways that leave less room for unconventional interests. In some cases, it can also lead to a shallow understanding of underlying concepts [67]. In our sample, the almost exclusive focus on certain game elements raises concerns about whether learners who are not interested in making these elements will be well served by communitygenerated resources.…”

Section: Synthesis: a Theory Of Social Feedback Loops In Interest-dri...mentioning

confidence: 96%

“…Furthermore, kids offered code might be able to use it without understanding it [67]. We saw many examples of kids requesting working solutions and many others who seemed happy receiving code that could be copy-and-pasted into their programs.…”

Section: 22mentioning

confidence: 99%

“…Measuring learning outcomes in a community like Scratch is difficult because learners arrive with different interests and aspirations and take different paths. Although we measure the presence or absence of variables and lists in projects, we can not know whether they are being used correctly or whether project creators understand the code they write [67].…”

Section: Limitationsmentioning

confidence: 99%

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How Interest-Driven Content Creation Shapes Opportunities for Informal Learning in Scratch: A Case Study on Novices' Use of Data Structures

Cheng,

Dasgupta,

Hill

2022

Preprint

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Through a mixed-method analysis of data from Scratch, we examine how novices learn to program with simple data structures by using community-produced learning resources. First, we present a qualitative study that describes how community-produced learning resources create archetypes that shape exploration and may disadvantage some with less common interests. In a second quantitative study, we find broad support for this dynamic in several hypothesis tests. Our findings identify a social feedback loop that we argue could limit sources of inspiration, pose barriers to broadening participation, and confine learners' understanding of general concepts. We conclude by suggesting several approaches that may mitigate these dynamics. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; Empirical studies in collaborative and social computing; Collaborative and social computing theory, concepts and paradigms; Computer supported cooperative work; • Social and professional topics → Computing education.

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Section: Synthesis: a Theory Of Social Feedback Loops In Interest-dri...mentioning

confidence: 96%

Section: 22mentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

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How Interest-Driven Content Creation Shapes Opportunities for Informal Learning in Scratch: A Case Study on Novices' Use of Data Structures

Cheng,

Dasgupta,

Hill

2022

Preprint

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“…The next article on the list discusses the development of a programming project where Java applets can be dynamically updated in an undergraduate programming course (Yang, Linn, and Quadrato 1998). The paper was mentioned by several patents (30) mostly covering access to database and software design. The remaining papers with patent mentions revolve around the same themes, i.e., either enhancement to an online teaching system or teaching programming.…”

Section: Patentsmentioning

confidence: 99%

Computing Education Research in Social Media, News, Blogs, Patents And Blogs: Capturing The Impact And The Chatter with Altmetrics

Saqr

López-Pernas

Apiola

2022

Preprint

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Research impact goes beyond academia and exists in the multiplicity of digital platforms that we use to read, share, and discuss knowledge. Computing education research (CER) is no exception: it is created in academia and typical research institutions but is talked about widely on social media, blogs, and news websites. The aim of this study is to have a comprehensive analysis of how research in CER has been received, talked about in social media, discussed on blogs, and spread to the news and media. In addition to common analysis of trends of growth, we analyze trends of usage of social media and quantitative analysis of platforms, articles, and venues. The analysis also includes which articles in which subfields had a wide impact, and for whom (i.e, which platforms had more impact). The results show that Altmetrics adoption is weak, yet increasingly growing fast. Gender and diversity issues made it to popular news sites e.g., Scientific American, Los Angeles Times and Christian Science Monitor. While articles about ethics, programming education, introductory courses as well as computational thinking and inclusion have captured the attention of social media users. There was weak –or no correlation between article, author or topic impact and the traditional impact measures e.g., citation count.

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“…Salac and Franklin [13] showed that, in a primary-school context, students' use of constructs is not a reliable indicator of understanding those constructs. Similarly, in a highereducation context, Kennedy and Kraemer [14] found that there were students who could produce a program that works but were unsure about their own code and did not grasp the underlying concepts.…”

Section: Related Workmentioning

confidence: 99%

Let’s Ask Students About Their Programs, Automatically

Lehtinen¹,

Santos²,

Sorva³

2021

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)

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Students sometimes produce code that works but that its author does not comprehend. For example, a student may apply a poorly-understood code template, stumble upon a working solution through trial and error, or plagiarize. Similarly, passing an automated functional assessment does not guarantee that the student understands their code. One way to tackle these issues is to probe students' comprehension by asking them questions about their own programs. We propose an approach to automatically generate questions about student-written program code. We moreover propose a use case for such questions in the context of automatic assessment systems: after a student's program passes unit tests, the system poses questions to the student about the code. We suggest that these questions can enhance assessment systems, deepen student learning by acting as self-explanation prompts, and provide a window into students' program comprehension. This discussion paper sets an agenda for future technical development and empirical research on the topic.

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If They Build It, Will They Understand It? Exploring the Relationship between Student Code and Performance

Cited by 26 publications

References 10 publications

How Interest-Driven Content Creation Shapes Opportunities for Informal Learning in Scratch: A Case Study on Novices' Use of Data Structures

How Interest-Driven Content Creation Shapes Opportunities for Informal Learning in Scratch: A Case Study on Novices' Use of Data Structures

Computing Education Research in Social Media, News, Blogs, Patents And Blogs: Capturing The Impact And The Chatter with Altmetrics

Let’s Ask Students About Their Programs, Automatically

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