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

Wangenheim, Christiane Gresse von; Hauck, Jean Carlo Rossa; Pacheco, Fernando S.; Bueno, Matheus F. Bertonceli

doi:10.1007/s10639-021-10570-8

Cited by 61 publications

(27 citation statements)

References 67 publications

(84 reference statements)

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“…erefore, according to the four situations when learners answer questions, updating the probability formula that learners answer questions correctly is simplified from formulas (7) to (8).…”

Section: Bayesian Knowledge Tracking Model Based On Learningmentioning

confidence: 99%

“…Quan research applied artificial intelligence and intelligent prediction to teaching and collected different students' teaching portraits to differentiate their behaviors [7]. Christiane et al research supports the development of machine learning models and their deployment and how tools are developed and evaluated [8]. Huang studies how to use machine learning to provide more accurate teaching for teaching.…”

Section: Introductionmentioning

confidence: 99%

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Design of Experiential Teaching System for Solfeggio in Normal Universities Based on Machine Learning Algorithm

Liu

Othman

2022

Mobile Information Systems

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At present, with the rapid development of the Internet and its close connection with our life, more and more educators apply the Internet to teaching. However, there is little research on the application of the Internet in solfeggio teaching, and there are some problems in the teaching process, such as nonstandard use methods, inability to highlight teaching objectives, and mismatch with students’ professional level. On the premise of fully understanding the teaching objectives of solfeggio in China, this paper designs an experiential teaching system of solfeggio in normal universities based on machine learning algorithm, studies the application of digital technology in solfeggio, including Internet technology, multimedia digital technology, and the use of music software, and analyzes the auxiliary role of digital technology in solfeggio teaching by combining the specific teaching contents of online solfeggio teaching during the epidemic period. In this experiment, the average classification accuracy of WNB algorithm is 0.767, while that of BP algorithm is 0.683. Experimental results show that WNB algorithm outperforms BP algorithm in classification. At the same time, in terms of time efficiency, the average time consumption of WNB algorithm in this experiment is about 0.026 s, while that of BP algorithm is about 0.45 s. Compared with WNB algorithm, the time consumption of WNB algorithm is less. Through concrete practice, it is proved that the combination of solfeggio teaching and digital technology is of great significance to both teachers’ teaching and students’ learning.

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“…erefore, according to the four situations when learners answer questions, updating the probability formula that learners answer questions correctly is simplified from formulas (7) to (8).…”

Section: Bayesian Knowledge Tracking Model Based On Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of Experiential Teaching System for Solfeggio in Normal Universities Based on Machine Learning Algorithm

Liu

Othman

2022

Mobile Information Systems

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“…Even within the context of this study, we could offer students opportunities to understand model decision making in rich social contexts, such as review writers in the southern and northern United States having different comments about BBQ restaurants. Furthermore, integrating rich context reasoning into modelling unstructured data (in this study, texts) could help students to critically evaluate AI technologies (Burgsteiner et al, 2016; Gresse von Wangenheim et al, 2021; Ho & Scadding, 2019). If students are presented with the context before developing a model, they are empowered to challenge what counts as useful features.…”

Section: Discussionmentioning

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

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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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“…(Chua et al, 2019) Different levels of programming skills may result in differences with regard to the time it takes students to complete the activities with Python. (Neumann, 2019) An alternative to text-based programming languages are visual environments that are also used for teaching ML in High School (Gresse von Wangenheim et al, 2021). In this context typically workflow-based environments are used for the development of ML models, which are then deployed through block-based programming environments as intelligent games or apps.…”

Section: Advantagesmentioning

confidence: 99%

Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review

Martins¹,

Wangenheim²

2022

Informatics in Education

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Machine Learning (ML) is becoming increasingly present in our lives. Thus, it is important to introduce ML already in High School, enabling young people to become conscious users and creators of intelligent solutions. Yet, as typically ML is taught only in higher education, there is still a lack of knowledge on how to properly teach younger students. Therefore, in this systematic literature review, we analyze findings on teaching ML in High School with regard to content, pedagogical strategy, and technology. Results show that High School students were able to understand and apply basic ML concepts, algorithms and tasks. Pedagogical strategies focusing on active problem/project-based hands-on approaches were successful in engaging students and demonstrated positive learning effects. Visual as well as text-based programming environments supported students to build ML models in an effective way. Yet, the review also identified the need for more rigorous evaluations on how to teach ML.

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

Cited by 61 publications

References 67 publications

Design of Experiential Teaching System for Solfeggio in Normal Universities Based on Machine Learning Algorithm

Design of Experiential Teaching System for Solfeggio in Normal Universities Based on Machine Learning Algorithm

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

Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review

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