Combining Big Data and Thick Data Analyses for Understanding Youth Learning Trajectories in a Summer Coding Camp

Fields, Deborah A.; Quirke, Lisa; Amely, Janell; Maughan, Jason

doi:10.1145/2839509.2844631

Cited by 25 publications

(18 citation statements)

References 15 publications

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“…2) can help provide deeper insights into students' learning [13]. We have explored this concept in dashboard E [6], where we visualise the sequence of an entire class across multiple activity types (see Fig.…”

Section: Lessons Learnt and Guidelinesmentioning

confidence: 99%

Creating Effective Learning Analytics Dashboards: Lessons Learnt

Charleer

Klerkx

Duval

et al. 2016

Adaptive and Adaptable Learning

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Learning Analytics (LA) dashboards help raise student and teacher awareness regarding learner activities. In blog-supported and inquiry-based learning courses, LA data is not limited to student activities, but also contains an abundance of digital learner artefacts, such as blog posts, hypotheses, and mind-maps. Exploring peer activities and artefacts can help students gain new insights and perspectives on learning efforts and outcomes, but requires effort. To help facilitate facilitate and promote this exploration, we present the lessons learnt during and guidelines derived from the design, deployment and evaluation of five dashboards.

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Section: Lessons Learnt and Guidelinesmentioning

confidence: 99%

Creating Effective Learning Analytics Dashboards: Lessons Learnt

Charleer

Klerkx

Duval

et al. 2016

Adaptive and Adaptable Learning

View full text Add to dashboard Cite

show abstract

“…By looking at student projects, field notes collected during Scratch Camps, and the JSON data from the projects, Dr. Fields and her team (Fields et al, 2016a(Fields et al, , 2016b have created a set of more complex measures related to computational thinking shown in Table 8. By looking at student projects, field notes collected during Scratch Camps, and the JSON data from the projects, Dr. Fields and her team (Fields et al, 2016a(Fields et al, , 2016b have created a set of more complex measures related to computational thinking shown in Table 8.…”

Section: Development Of Complex Measures Of Computational Thinkingmentioning

confidence: 99%

“…After Fields et al (2016aFields et al ( , 2016b created these labels and descriptions for measures of computational thinking that they found important from their ethnographic and data mining analyses, our next step was to create these complex measures within the FUN! tool.…”

Section: Development Of Complex Measures Of Computational Thinkingmentioning

confidence: 99%

Emerging Research, Practice, and Policy on Computational Thinking

Rich¹,

Hodges²

2017

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“…Fields et al examined the use of initialisation, events and parallelism in Scratch programs written by youths at a summer school [24]. They used a constructionist pedagagy and were able to make observations about different students and their development of these programming concepts.…”

Section: Analysing Coding Behaviourmentioning

confidence: 99%

“…They reported significant increases in self efficacy regarding coding in students using the system [23]. Our work differs in this phase in that we are focused on fine grained details of student coding behaviour and how this changes with changing pedagogy, as opposed to investigating the high level impact of STEAM on student self-efficacy.Fields et al examined the use of initialisation, events and parallelism in Scratch programs written by youths at a summer school [24]. They used a constructionist pedagagy and were able to make observations about different students and their development of these programming concepts.…”

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confidence: 99%

Evidencing the Value of Inquiry Based, Constructionist Learning for Student Coders

2017

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Abstract-For the last decade, there has been growing interest in the STEAM approach (essentially combining methods and practices in arts, humanities and social sciences into STEM teaching and research) with its potential to deliver better research and education, and to enable us to produce students who can work more effectively in the current and developing market-place. However, despite this interest, there seems to be little quantitative evidence of the true power of STEAM learning, especially describing how it compares and performs with respect to more established approaches. To address this, we present a comparative, quantitative study of two distinct approaches to teaching programming, one based on STEAM (with an open-ended inquiry-driven, inductive approach), the other based on a more traditional, non-STEAM approach (where constrained problems are set and solved deductively). Our key results evidence how students exhibit different styles of programming in different types of lessons and, crucially, that students who tend to exhibit more of the style of programming observed in our STEAM lessons also tend to achieve higher grades. We present our claims through a range of visualisations and statistical validations which clearly show the significance of the results, despite the small scale of the study. We believe that this work provides clear evidence for the advantages of STEAM over non-STEAM, and provides a strong theoretical and technological framework for future, larger studies.Keywords-STEAM; xAPI; coding; education; pedagogy IntroductionOver the last 10 years, we have developed and delivered a range of degree programmes at our institution that aim to bring an arts inflected pedagogy into the teaching of computer science. We make extensive use of what is considered a STEAM approach to engineering education [1], an approach we describe in detail later in the paper. Over the last decade, we have seen how a range of employers, especially from the creative industries, are keen to employ graduates from our courses. Anecdotally, the graduates demonstrate a more creative and experimental approach to engineering, and are able to adapt quickly to using new technologies in new settings.But anecdotal evidence is not enough. We would like to know the measurable impact, if any, this pedagogical approach actually has on student learning. Do students iJEP -Vol. 7, No. 3, 2017 109Paper-Evidencing the Value of Inquiry Based, Constructionist Learning for Student Coders learn to code in a different way with this approach, compared to more traditional approaches? If they do, does this impact positively upon learning outcomes? In this paper, we set out to investigate these questions, as we find there is a lack of research explicitly comparing how different pedagogical approaches impact on the way students learn programming. Our first step towards addressing this apparent gap in knowledge has been to design and deploy a novel technological platform for teaching programming. The system has been designed to support our teachin...

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Combining Big Data and Thick Data Analyses for Understanding Youth Learning Trajectories in a Summer Coding Camp

Cited by 25 publications

References 15 publications

Creating Effective Learning Analytics Dashboards: Lessons Learnt

Creating Effective Learning Analytics Dashboards: Lessons Learnt

Emerging Research, Practice, and Policy on Computational Thinking

Evidencing the Value of Inquiry Based, Constructionist Learning for Student Coders

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