Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks

Biehler, Rolf; Fleischer, Yannik

doi:10.1111/test.12279

Cited by 24 publications

(11 citation statements)

References 10 publications

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“…Second, students in this study paid close attention to the data set in engineering features and needed more support to get familiar with feature engineering practices. This phenomenon of engaging in exploring data sets aligns with several studies in the literature (eg, Biehler & Fleischer, 2021; Sakulkueakulsuk et al, 2018). However, students encountered challenges in transforming their qualitative analysis of data sets into creating meaningful features.…”

Section: Discussionsupporting

confidence: 78%

“…In designing machine learning and classification activities, researchers have focused on engaging students in reasoning about automated decisions made by AI technologies by having students build machine learning models (Ho & Scadding, 2019; Lee & Moon, 2020; Marques et al, 2020). In Biehler and Fleischer's study (Biehler & Fleischer, 2021), students created decision trees manually with a selected variable (eg, a decision tree predicting whether a person played online games frequently based on gender information). This study fills the gap of teaching the concept of a decision tree, which is an algorithm suggested by IDSSP for introducing data science in schools (IDSSP Curriculum Team, 2019).…”

Section: Related Researchmentioning

confidence: 99%

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An empirical analysis of high school students' practices of modelling with unstructured data

Jiang

Nocera

Tatar

et al. 2022

Brit J Educational Tech

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To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making. Practitioner notes What is already known about this topic Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models. While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data. There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data. What this paper adds Results show that students developed nuanced understandings of models learning patterns in data for automated decision making. Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models. Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in. Implications for practice and/or policy It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies. Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources. To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).

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Section: Discussionsupporting

confidence: 78%

Section: Related Researchmentioning

confidence: 99%

An empirical analysis of high school students' practices of modelling with unstructured data

Jiang

Nocera

Tatar

et al. 2022

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…The teaching module consists of 8 lessons of 45 minutes. It consists of 6 sections, as shown in Table 2, and is described in greater detail in Biehler & Fleischer (2021). As the final part of this instructional module, the students get a worked example of the entire modeling process using machine learning as a computational essay.…”

Section: The Machine Learning Module About Decision Treesmentioning

confidence: 99%

“…In this appendix we briefly explain the relationship of decision trees to Machine Learning (ML), and locate them in the larger area of ML. Some passages are based on explanations in Biehler and Fleischer (2021). ML consists of various fields, each containing different methods that solve similar tasks.…”

Section: A Appendixmentioning

confidence: 99%

Teaching and Learning Data-Driven Machine Learning With Educationally Designed Jupyter Notebooks

Fleischer

Biehler

Schulte

2022

SERJ

Self Cite

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This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students’ work is based on a teaching module about decision trees in machine learning and a worked example of such a modelling process. The study outlines the students’ performance in carrying out the machine learning technically and reasoning about bias in the data, different data preparation steps, the application context, and the resulting decision model. Furthermore, the context of the study and the theoretical backgrounds are presented.

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“…NetLogo was also shown to be a useful tool in helping to correct misconceptions in inorganic chemistry at the university level [27]. Another example of an interactive online tool is the common online data analysis platform (CODAP), which has been integrated into the teaching of machine learning and other data science topics [28]. Interactive activities embedded in online applets facilitate students learning at their own pace, and self-discovery has been shown to improve students' understanding and ability to retain information [29,30].…”

Section: Introductionmentioning

confidence: 99%

Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module

et al. 2022

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Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts.

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Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks

Cited by 24 publications

References 10 publications

An empirical analysis of high school students' practices of modelling with unstructured data

An empirical analysis of high school students' practices of modelling with unstructured data

Teaching and Learning Data-Driven Machine Learning With Educationally Designed Jupyter Notebooks

Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module

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