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作者： Vieira, Camilo Restrepo, David De La Hoz, Jose L. Universidad Del Norte Department of Education Barranquilla Colombia The University of Texas at San Antonio Department of Mechanical Engineering San AntonioTX United States

ISBN: (纸本)9798350336429

This work-in-progress paper introduces an innovative approach that uses computational notebooks to teach Finite Element Analysis (FEA) in a Mechanical Engineering undergraduate course to aid the understanding of complex phenomena in Mechanics of Materials, and enhance students' computational thinking skills. Research indicates that students, irrespective of their educational level, face difficulties in grasping fundamental concepts in mechanics of materials. These challenges arise from the inherent complexity of concepts like stress, strain, torsion, and buckling, which are difficult to observe, hindering comprehension. Therefore, this work aims to leverage the synergy between mechanics of materials and computational principles to actively engage students in advanced topics such as structural strength, failure of structures, and sensitivity analysis, through the use of computational notebooks. To evaluate the effectiveness of this approach, we first asked students to analyze truss structures using hand calculations following the discrete stiffness method within a Finite Element Analysis framework. Subsequently, we implemented the same method as a simulation tool in a MATLAB computational Notebook. Finally, we asked the students to reflect on: (1) the value of using computational methods to approach Finite Element Analysis when compared to hand calculations;(2) the difficulties they faced when implementing the activities in the MATLAB computational Notebook;(3) the support they required to successfully complete these activities;and (4) the effectiveness of the simulation tool in understanding the effect of forces and stress distributions in structures. We anticipate that computational notebooks will provide an ideal platform for sharing lessons and tutorials, enhancing student engagement, and promoting active learning. Students have access to the complete source code, allowing them to develop computational skills. Early exposure to coding, modeling, and simulation te

关键词： computational notebooks computational thinking Finite Element Analysis mechanics of materials threshold concepts

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Promoting computational Thinking Skills in Non-Computer-Science Students: Gamifying computational notebooks to Increase Student Engagement

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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2022年第3期15卷 392-405页

作者： De Santo, Alessio Farah, Juan Carlos Martinez, Marc Lafuente Moro, Arielle Bergram, Kristoffer Purohit, Aditya Kumar Felber, Pascal Gillet, Denis Holzer, Adrian Univ Neuchatel CH-2000 Neuchatel Switzerland Ecole Polytech Fed Lausanne CH-1024 Ecublens Switzerland

computational thinking (CT) skills are becoming increasingly relevant for future professionals across all domains, beyond computer science (CS). As such, an increasing number of bachelor's and master's programs outside of the CS discipline integrate CT courses within their study program. At the same time, tools such as notebooks and interactive apps designed to support the teaching of programming concepts are becoming ever more popular. However, in non-CS majors, CT might not be perceived as essential, and students might lack the motivation to engage with such tools in order to acquire solid CT skills. This article presents a field study conducted with 115 students during a full semester on a novel computational notebook environment. It evaluates computational notebooks and CT skills in an introductory course on information technology for first-year undergraduates in business and economics. A multidimensional evaluation approach makes use of pre- and post-test surveys, lectures, and self-directed laboratory sessions tracking analytics. Our findings suggest that, in the process of learning CT for non-CS students, engagement in active learning activities can be a stronger determinant of learning outcomes than initial knowledge. Furthermore, gamifying computational notebooks can serve as a strong driver of active learning engagement, even more so than initial motivational factors.

关键词： Education Hybrid learning Codes Programming profession Encoding Python Games Active learning computational notebooks computational thinking (CT) fieldwork learning gamification motivation

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Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in computational notebooks

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ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION 2022年第5期29卷 1-33页

作者： Wang, April Yi Wang, Dakuo Drozdal, Jaimie Muller, Michael Park, Soya Weisz, Justin D. Liu, Xuye Wu, Lingfei Dugan, Casey Univ Michigan 105 South State St Ann Arbor MI 48109 USA IBM Res 75 Binney St Cambridge MA 02142 USA Rensselaer Polytech Inst 110 Eighth St Troy NY 12180 USA MIT CSAIL 32 Vassar St Cambridge MA 02139 USA

computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code, and neglect creating or updating their documentation during quick iterations. Inspired by human documentation practices learned from 80 highly-voted Kaggle notebooks, we design and implement Themisto, an automated documentation generation system to explore how human-centered AI systems can support human data scientists in the machine learning code documentation scenario. Themisto facilitates the creation of documentation via three approaches: a deep-learning-based approach to generate documentation for source code, a query-based approach to retrieve online API documentation for source code, and a user prompt approach to nudge users to write documentation. We evaluated Themisto in a within-subjects experiment with 24 data science practitioners, and found that automated documentation generation techniques reduced the time for writing documentation, reminded participants to document code they would have ignored, and improved participants' satisfaction with their computational notebook.

关键词： Code summarization computational notebooks code documentation

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Contextualized Data-Wrangling Code Generation in computational notebooks 24

Contextualized Data-Wrangling Code Generation in Computation...

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39th ACM/IEEE International Conference on Automated Software Engineering (ASE)

作者： Huang, Junjie Guo, Daya Wang, Chenglong Gu, Jiazhen Lu, Shuai Inala, Jeevana Priya Yan, Cong Gao, Jianfeng Duan, Nan Lyu, Michael R. Chinese Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Microsoft Res Redmond WA USA Microsoft Res Asia Beijing Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Data wrangling, the process of preparing raw data for further analysis in computational notebooks, is a crucial yet time-consuming step in data science. Code generation has the potential to automate the data wrangling process to reduce analysts' overhead by translating user intents into executable code. Precisely generating data wrangling code necessitates a comprehensive consideration of the rich context present in notebooks, including textual context, code context and data context. However, notebooks often interleave multiple non-linear analysis tasks into linear sequence of code blocks, where the contextual dependencies are not clearly reflected. Directly training models with source code blocks fails to fully exploit the contexts for accurate wrangling code generation. To bridge the gap, we aim to construct a high quality datasets with clear and rich contexts to help training models for data wrangling code generation tasks. In this work, we first propose an automated approach, CoCoMine to mine data-wrangling code generation examples with clear multi-modal contextual dependency. It first adopts data flow analysis to identify the code blocks containing data wrangling codes. Then, CoCoMine extracts the contextualized data-wrangling code examples through tracing and replaying notebooks. With CoCoMine, we construct CoCoNote, a dataset containing 58,221 examples for Contextualized Data-wrangling Code generation in notebooks. To demonstrate the effectiveness of our dataset, we finetune a range of pretrained code models and prompt various large language models on our task. Furthermore, we also propose DATA-CODER, which encodes data context and code&textual contexts separately to enhance code generation. Experiment results demonstrate the significance of incorporating data context in data-wrangling code generation and the effectiveness of our model. We release code and data at https://***/Jun-jie-Huang/CoCoNote.

关键词： code generation data wrangling computational notebooks large language models

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Slide4N: Creating Presentation Slides from computational notebooks with Human-AI Collaboration 23

Slide4N: Creating Presentation Slides from Computational Not...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Wang, Fengjie Liu, Xuye Liu, Oujing Neshati, Ali Ma, Tengfei Zhu, Min Zhao, Jian Sichuan Univ Chengdu Peoples R China Univ Waterloo Waterloo ON Canada IBM Res Yorktown Hts NY USA

ISBN: (纸本)9781450394215

Data scientists often have to use other presentation tools (e.g., Microsoft PowerPoint) to create slides to communicate their analysis obtained using computational notebooks. Much tedious and repetitive work is needed to transfer the routines of notebooks (e.g., code, plots) to the presentable contents on slides (e.g., bullet points, figures). We propose a human-AI collaborative approach and operationalize it within Slide4N, an interactive AI assistant for data scientists to create slides from computational notebooks. Slide4N leverages advanced natural language processing techniques to distill key information from user-selected notebook cells and then renders them in appropriate slide layouts. The tool also provides intuitive interactions that allow further refinement and customization of the generated slides. We evaluated Slide4N with a two-part user study, where participants appreciated this human-AI collaborative approach compared to fully-manual or fully-automatic methods. The results also indicate the usefulness and effectiveness of Slide4N in slide creation tasks from notebooks.

关键词： slides generation computational notebooks human-AI collaboration natural language processing data science

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OutlineSpark: Igniting AI-powered Presentation Slides Creation from computational notebooks through Outlines 24

OutlineSpark: Igniting AI-powered Presentation Slides Creati...

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ACM CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Wang, Fengjie Lin, Yanna Yang, Leni Li, Haotian Gu, Mingyang Zhu, Min Qu, Huamin Sichuan Univ Chengdu Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798400703300

computational notebooks are widely utilized for exploration and analysis. However, creating slides to communicate analysis results from these notebooks is quite tedious and time-consuming. Researchers have proposed automatic systems for generating slides from notebooks, which, however, often do not consider the process of users conceiving and organizing their messages from massive code cells. Those systems ask users to go directly into the slide creation process, which causes potentially ill-structured slides and burdens in further refnement. Inspired by the common and widely recommended slide creation practice: drafting outlines frst and then adding concrete content, we introduce OutlineSpark, an AI-powered slide creation tool that generates slides from a slide outline written by the user. The tool automatically retrieves relevant notebook cells based on the outlines and converts them into slide content. We evaluated OutlineSpark with 12 users. Both the quantitative and qualitative feedback from the participants verify its efectiveness and usability.

关键词： computational notebooks slides generation outlines data science

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The Design Space of computational notebooks: An Analysis of 60 Systems in Academia and Industry

The Design Space of Computational Notebooks: An Analysis of ...

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IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

作者： Lau, Sam Drosos, Ian Markel, Julia M. Guo, Philip J. Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781728169019

computational notebooks such as Jupyter are now used by millions of data scientists, machine learning engineers, and computational researchers to do exploratory and end-user programming. In recent years, dozens of different notebook systems have been developed across academia and industry. However, we still lack an understanding of how their individual designs relate to one another and what their tradeoffs are. To provide a holistic view of this rapidly-emerging landscape, we performed, to our knowledge, the first comprehensive design analysis of dozens of notebook systems. We analyzed 60 notebooks (16 academic papers, 29 industry products, and 15 experimental/R&D projects) and formulated a design space that succinctly captures variations in system features. Our design space covers 10 dimensions that include diverse ways of importing data, editing code and prose, running code, and publishing notebook outputs. We conclude by suggesting ways for researchers to push future projects beyond the current bounds of this space.

关键词： computational notebooks literate programming

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What's Wrong with computational notebooks? Pain Points, Needs, and Design Opportunities 20

What's Wrong with Computational Notebooks? Pain Points, Need...

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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Chattopadhyay, Souti Prasad, Ishita Henley, Austin Z. Sarma, Anita Barik, Titus Oregon State Univ Corvallis OR 97331 USA Microsoft Redmond WA USA Univ Tennessee Knoxville TN USA

ISBN: (纸本)9781450367080

computational notebooks-such as Azure, Databricks, and Jupyter-are a popular, interactive paradigm for data scientists to author code, analyze data, and interleave visualizations, all within a single document. Nevertheless, as data scientists incorporate more of their activities into notebooks, they encounter unexpected difficulties, or pain points, that impact their productivity and disrupt their workflow. Through a systematic, mixed-methods study using semi-structured interviews (n = 20) and survey (n = 156) with data scientists, we catalog nine pain points when working with notebooks. Our findings suggest that data scientists face numerous pain points throughout the entire workflow-from setting up notebooks to deploying to production-across many notebook environments. Our data scientists report essential notebook requirements, such as supporting data exploration and visualization. The results of our study inform and inspire the design of computational notebooks.

关键词： computational notebooks challenges data science interviews pain points survey

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