Can You Teach Me To Machine Learn?

Sulmont, Elisabeth; Patitsas, Elizabeth; Cooperstock, Jeremy R.

doi:10.1145/3287324.3287392

Cited by 54 publications

(36 citation statements)

References 8 publications

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“…Although few of these resources are understandable to the general public, ML will certainly be used by people from a range of educational backgrounds and occupations. Currently, many educators who teach ML to non-technical audiences actively avoid teaching with math altogether [36]. However, our findings motivate the importance of developing resources that teach the necessary math and logic skills in concurrence with ML skills specifically for these non-technical audiences.…”

Section: Discussionmentioning

confidence: 77%

Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making

Suresh

Lao

Liccardi

2020

12th ACM Conference on Web Science

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ML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gaps in their ability, but recognize signals that the system might be incorrect. We measured how people's trust in ML recommendations differs by expertise and with more system information through a task-based study of 175 adults. We used two tasks that are difficult for humans: comparing large crowd sizes and identifying similar-looking animals. Our results provide three key insights: (1) People trust incorrect ML recommendations for tasks that they perform correctly the majority of the time, even if they have high prior knowledge about ML or are given information indicating the system is not confident in its prediction; (2) Four different types of system information all increased people's trust in recommendations; and (3) Math and logic skills may be as important as ML for decision-makers working with ML recommendations.

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Section: Discussionmentioning

confidence: 77%

Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making

Suresh

Lao

Liccardi

2020

12th ACM Conference on Web Science

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“…To achieve the learning of ML competencies on an application level, this requires students to learn how to develop ML applications for them to become creators of intelligent solutions Kandlhofer et al, 2016;Long & Magerko, 2020;Sulmont et al, 2019;Touretzky et al, 2019b). Building such a custom ML application in a human-centric manner is an iterative process that requires students to execute a sequence of steps as presented in Table 1 (Amazon, 2019;Amershi et al, 2019;Mathewson, 2019;Watanabe et al, 2019).…”

Section: Machine Learning Education In K-12mentioning

confidence: 99%

“…Building such a custom ML application in a human-centric manner is an iterative process that requires students to execute a sequence of steps as presented in Table 1 (Amazon, 2019;Amershi et al, 2019;Mathewson, 2019;Watanabe et al, 2019). Yet, as machine learning is a complex knowledge area, students may have difficulties with the first steps when learning ML (Sulmont et al, 2019). And, as K-12 students often do not have any prior computing experiences, it is important to carefully define the sequence of learning goals to be achieved.…”

Section: Machine Learning Education In K-12mentioning

confidence: 99%

“…On the other hand, as it may be impracticable for students to collect data during the activities or in order to assure certain characteristics of the datasets, for example, low-dimensional datasets when initially introducing concepts or datasets that Fig. 6 Frequency of tool types and steps of the ML process supported are on purpose "messy" when demonstrating issues of bias, it would be helpful to have some example datasets available (D'Ignazio, 2017;Sulmont et al, 2019). However, only a few tools provide easy access to datasets.…”

Section: What Are Their Characteristics Concerning the ML Platform?mentioning

confidence: 99%

“…Developing an ML model involves several tasks from acquiring a (labeled) set of examples, selecting an appropriate learning algorithm and its parameters, training the model, and evaluating the model's performance (Lwakatare et al, 2019;Ramos et al, 2020). It requires an understanding of complex algorithms and working processes, as well as a constantly increasing zoo of architectures, frameworks, etc., which makes choosing a suitable one a difficult task for novices (Gillies, 2016;Gutosk, 2017;Sulmont et al, 2019) as well as requiring the user to have a certain level of programming skills (Xie et al, 2019). As a consequence, students typically face several difficulties when starting to learn ML, making the process of building ML models inaccessible to many people (Ramos et al, 2020;Sankaran et al, 2018;Tamilselvam et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Visual tools for teaching machine learning in K-12: A ten-year systematic mapping

et al. 2021

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Teaching Machine Learning in school helps students to be better prepared for a society rapidly changing due to the impact of Artificial Intelligence. This requires age-appropriate tools that allow students to develop a comprehensive understanding of Machine Learning in order to become creators of smart solutions. Following the trend of visual languages for introducing algorithms and programming in K-12, we present a ten-year systematic mapping of emerging visual tools that support the teaching of Machine Learning at this educational stage and analyze the tools concerning their educational characteristics, support for the development of ML models as well as their deployment and how the tools have been developed and evaluated. As a result, we encountered 16 tools targeting students mostly as part of short duration extracurricular activities. Tools mainly support the interactive development of ML models for image recognition tasks using supervised learning covering basic steps of the ML process. Being integrated into popular block-based programming languages (primarily Scratch and App Inventor), they also support the deployment of the created ML models as part of games or mobile applications. Findings indicate that the tools can effectively leverage students’ understanding of Machine Learning, however, further studies regarding the design of the tools concerning educational aspects are required to better guide their effective adoption in schools and their enhancement to support the learning process more comprehensively.

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Data science knowledge integration: Affordances of a computational cognitive apprenticeship on student conceptual understanding

Sánchez‐Peña

Vieira

Magana

2022

Comp Applic In Engineering

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This study implements a computational cognitive apprenticeship framework for knowledge integration of Data Science (DS) concepts delivered via computational notebooks. This study also explores students' conceptual understanding of the unsupervised Machine Learning algorithm of K‐means after being exposed to this method. The learning of DS methods and techniques has become paramount for the new generations of undergraduate engineering students. However, little is known about effective strategies to support student learning of DS and machine learning (ML) algorithms. The research questions are: How do students conceptualize their understanding of an unsupervised ML method after engaging with interactive visualizations designed using the computational cognitive apprenticeship approach? How do the affordances of the interactive visualizations support or hinder student knowledge integration of an unsupervised machine learning method? Design‐based research allowed for the iterative design, implementation, and validation of the pedagogy in the context of a working classroom. For this, data collection methods often take the form of student artifacts. We performed a qualitative content analysis of students' written responses and reflections elicited during the learning process. Results suggest that the computational cognitive apprenticeship promoted knowledge integration. After interacting with the computational notebooks, most students had accurate conceptions of the goal and the nature of the method and identified factors affecting the output of the algorithm. Students found it useful to have a concrete representation of the method, which supported its conceptual understanding and showcased the acquisition of strategic knowledge for its appropriate execution. However, we also identified important misconceptions students held about the algorithm.

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Can You Teach Me To Machine Learn?

Cited by 54 publications

References 8 publications

Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making

Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making

Visual tools for teaching machine learning in K-12: A ten-year systematic mapping

Data science knowledge integration: Affordances of a computational cognitive apprenticeship on student conceptual understanding

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