Approaches to Artificial Intelligence as a Subject in School Education

Micheuz, Peter

doi:10.1007/978-3-030-59847-1_1

Cited by 10 publications

(12 citation statements)

References 13 publications

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“…Applying the quality criteria we also excluded artifacts not providing substantial information with regard to our analysis questions (Digh, 2021;Evangelista et al, 2018;Heinemann et al, 2018), for presenting only a "lightning talk" (McBride et al, 2021), special session (Judd, 2020), poster (Posner et al, 2018) or abstract only (Haqqi et al, 2018;Young and Ringenberg, 2019). Furthermore, some potentially relevant articles not accessible via Portal Capes were also not considered (Joshua, 2021;Micheuz, 2020).…”

Section: No Of Relevant Results (Without Duplicates)mentioning

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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Section: No Of Relevant Results (Without Duplicates)mentioning

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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“…Nowadays, research on AI teaching is still scarce (Chai et al, 2020a(Chai et al, , 2020bLee et al, 2020). The acquisition of knowledge in AI represents a great pedagogical challenge for both experts and teachers, and a cognitive challenge for students (Micheuz, 2020). Some countries are making significant efforts to promote AI education in K-12 (Touretzky et al, 2019b), by developing relatively comprehensive curriculum guidelines (Yue et al, 2021).…”

Section: Integration Of Ai Into the K12 Curriculummentioning

confidence: 99%

“…Although such contributions may sometimes be short, we decided to include them as they were relevant. For instance, the works published in (Herrero et al, 2020) and (Micheuz, 2020) include the German countrywide proposal for educating about AI, through a 6-module course focusing on explaining how AI works, the social discourse on AI and reducing existing misconceptions. On the other hand, Aguar et al (2016) talk about teaching AI via an optional course which does not contribute to the final grades.…”

Section: Bibliometric Analysismentioning

confidence: 99%

“…However, the program is hindered by a lack of professionals (teachers) with adequate training (Heintz, 2021). In Germany, there are also several initiatives to pilot AI-related projects and studies (Micheuz, 2020), including the launch of a national initiative to teach a holistic view of AI. This initiative consists of a 6-module course aimed at explaining how AI works, stimulating a social discourse on AI and clarifying the abundant existing misconceptions (Micheuz, 2020).…”

Section: Proposals For Implementation Of Ai Learning At the K-12 Levelmentioning

confidence: 99%

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AI literacy in K-12: a systematic literature review

et al. 2023

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The successful irruption of AI-based technology in our daily lives has led to a growing educational, social, and political interest in training citizens in AI. Education systems now need to train students at the K-12 level to live in a society where they must interact with AI. Thus, AI literacy is a pedagogical and cognitive challenge at the K-12 level. This study aimed to understand how AI is being integrated into K-12 education worldwide. We conducted a search process following the systematic literature review method using Scopus. 179 documents were reviewed, and two broad groups of AI literacy approaches were identified, namely learning experience and theoretical perspective. The first group covered experiences in learning technical, conceptual and applied skills in a particular domain of interest. The second group revealed that significant efforts are being made to design models that frame AI literacy proposals. There were hardly any experiences that assessed whether students understood AI concepts after the learning experience. Little attention has been paid to the undesirable consequences of an indiscriminate and insufficiently thought-out application of AI. A competency framework is required to guide the didactic proposals designed by educational institutions and define a curriculum reflecting the sequence and academic continuity, which should be modular, personalized and adjusted to the conditions of the schools. Finally, AI literacy can be leveraged to enhance the learning of disciplinary core subjects by integrating AI into the teaching process of those subjects, provided the curriculum is co-designed with teachers.

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“…Machine learning (ML) is an artificial intelligence-based approach that emphasizes building a system that learns automatically from data and improves performance without being explicitly programmed. ML models are trained using a significant amount of data to find hidden patterns required to make decisions ( Winston, 1992 ; Dick, 2019 ; Micheuz, 2020 ). Artificial Neural Network (ANN), Classification and regression Trees (CART), Support vector machine (SVM), and vector quantization are some of the architectures used in ML.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive survey on computational learning methods for analysis of gene expression data

Bhandari

Walambe

Kotecha

et al. 2022

Front. Mol. Biosci.

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Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

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Approaches to Artificial Intelligence as a Subject in School Education

Cited by 10 publications

References 13 publications

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

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

AI literacy in K-12: a systematic literature review

A comprehensive survey on computational learning methods for analysis of gene expression data

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