Milo: A visual programming environment for Data Science Education

Rao, Arjun; Bihani, Ayush; Nair, Mydhili K.

doi:10.1109/vlhcc.2018.8506504

Cited by 29 publications

(16 citation statements)

References 5 publications

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“…23.3% (7) of the tools are intended to be used in an educational setting. Some of these tools such as [38] and [39] are utilized for teaching basic programming principles such as loops and conditionals, whereas the tool in [44] aims at teaching data science concepts to non-programmers, and the tool in [45] is intended for teaching computing and engineering concepts and for programming robots.…”

Section: Rq1-d4: Domainmentioning

confidence: 99%

Characterizing Visual Programming Approaches for End-User Developers: A Systematic Review

et al. 2021

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Recently many researches have explored the potential of visual programming in robotics, the Internet of Things (IoT), and education. However, there is a lack of studies that analyze the recent evidence-based visual programming approaches that are applied in several domains. This study presents a systematic review to understand, compare, and reflect on recent visual programming approaches using twelve dimensions: visual programming classification, interaction style, target users, domain, platform, empirical evaluation type, test participants' type, number of test participants, test participants' programming skills, evaluation methods, evaluation measures, and accessibility of visual programming tools. The results show that most of the selected articles discussed tools that target IoT and education, while other fields such as data science, robotics are emerging. Further, most tools use abstractions to hide implementation details and use similar interaction styles. The predominant platforms for the tools are web and mobile, while desktop-based tools are on the decline. Only a few tools were evaluated with a formal experiment, whilst the remaining ones were evaluated with evaluation studies or informal feedback. Most tools were evaluated with students with little to no programming skills. There is a lack of emphasis on usability principles in the design stage of the tools. Additionally, only one of the tools was evaluated for expressiveness. Other areas for exploration include supporting end users throughout the life cycle of applications created with the tools, studying the impact of tutorials on improving learnability, and exploring the potential of machine learning to improve debugging solutions developed with visual programming.INDEX TERMS Visual programming, end-user development, human-computer interaction, systematic literature review.

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Section: Rq1-d4: Domainmentioning

confidence: 99%

Characterizing Visual Programming Approaches for End-User Developers: A Systematic Review

et al. 2021

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“…Looking at recent work in AI for K-12, we see many efforts requiring familiarity with Excel [30], Rapidminer [31] or block-based programming [11,12,13,14,32]. In contrast, our paper focused on efforts that employed machine teaching 2 and did not make assumptions about children's familiarity with programming.…”

Section: B Children Training Machine Learning Modelsmentioning

confidence: 99%

Exploring Machine Teaching with Children

Dwivedi¹,

Gandhi²,

Parikh³

et al. 2021

Preprint

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Iteratively building and testing machine learning models can help children develop creativity, flexibility, and comfort with machine learning and artificial intelligence. We explore how children use machine teaching interfaces with a team of 14 children (aged 7-13 years) and adult co-designers. Children trained image classifiers and tested each other's models for robustness. Our study illuminates how children reason about ML concepts, offering these insights for designing machine teaching experiences for children: (i) ML metrics (e.g. confidence scores) should be visible for experimentation; (ii) ML activities should enable children to exchange models for promoting reflection and pattern recognition; and (iii) the interface should allow quick data inspection (e.g. images vs. gestures).

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“…Milo [17] and BlockPy [1] are tools that offer a block-based language for users, but focus on different aspects. While Milo aims to help users with no computer science background to only perform machine learning techniques, we propose a tool for data cleaning.…”

Section: Related Workmentioning

confidence: 99%

Data Curation: Towards a Tool for All

Dias

Cunha

Pereira

2020

Communications in Computer and Information Science

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Data science has started to become one of the most important skills one can have in the modern world, due to data taking an increasingly meaningful role in our lives. The accessibility of data science is however limited, requiring complicated software or programming knowledge. Both can be challenging and hard to master, even for the simple tasks. With this in mind, we have approached this issue by providing a new data science platform, termed DS4All.Curation, that attempts to reduce the necessary knowledge to perform data science tasks, in particular for data cleaning and curation. By combining HCI concepts, this platform is: simple to use through direct manipulation and showing transformation previews; allows users to save time by eliminate repetitive tasks and automatically calculating many of the common analyses data scientists must perform; and suggests data transformations based on the contents of the data, allowing for a smarter environment.

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Milo: A visual programming environment for Data Science Education

Cited by 29 publications

References 5 publications

Characterizing Visual Programming Approaches for End-User Developers: A Systematic Review

Characterizing Visual Programming Approaches for End-User Developers: A Systematic Review

Exploring Machine Teaching with Children

Data Curation: Towards a Tool for All

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