scholarly journals Educing AI-Thinking in Science, Technology, Engineering, Arts, and Mathematics (STEAM) Education

2019 ◽  
Vol 9 (3) ◽  
pp. 184 ◽  
Author(s):  
Meng-Leong How ◽  
Wei Loong David Hung
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Thinking Skills ◽  
Supervised Machine Learning ◽  
Controlled Experiments ◽  
Machine Learn ◽  
Science Technology ◽  
Steam Education ◽  
Computer Scientists ◽  
And Mathematics

In science, technology, engineering, arts, and mathematics (STEAM) education, artificial intelligence (AI) analytics are useful as educational scaffolds to educe (draw out) the students’ AI-Thinking skills in the form of AI-assisted human-centric reasoning for the development of knowledge and competencies. This paper demonstrates how STEAM learners, rather than computer scientists, can use AI to predictively simulate how concrete mixture inputs might affect the output of compressive strength under different conditions (e.g., lack of water and/or cement, or different concrete compressive strengths required for art creations). To help STEAM learners envision how AI can assist them in human-centric reasoning, two AI-based approaches will be illustrated: first, a Naïve Bayes approach for supervised machine-learning of the dataset, which assumes no direct relations between the mixture components; and second, a semi-supervised Bayesian approach to machine-learn the same dataset for possible relations between the mixture components. These AI-based approaches enable controlled experiments to be conducted in-silico, where selected parameters could be held constant, while others could be changed to simulate hypothetical “what-if” scenarios. In applying AI to think discursively, AI-Thinking can be educed from the STEAM learners, thereby improving their AI literacy, which in turn enables them to ask better questions to solve problems.

Integrating Computational Thinking and Mathematics

2022 ◽  
pp. 175-196
Author(s):  
Marja Bertrand ◽  
Immaculate Kizito Namukasa
Keyword(s):  
Education Reform ◽  
Creative Thinking ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Non Profit ◽  
The Arts ◽  
Learning Mathematics ◽  
Steam Education ◽  
Context Of Learning ◽  
And Mathematics

Globally, computational thinking and coding in schools has become more popular as well as a growing area of interest in education reform. Coupling coding with creative thinking promises to meaningfully engage students in their learning and to improve their coding and computational thinking skills. This prompts discussions about STEAM (Science, Technology, Engineering, Arts, and Mathematics), which promotes creativity and innovation through the integration of the arts in STEM subjects. This study addresses the following question: What mathematics and computational thinking do students learn through different models of STEAM education in non-profit and in-school contexts? A small sample was taken of four different STEAM programs in Ontario, Canada. We carried out a qualitative case study with 103 participants, 19 adults and 84 students. The findings from this study have implications for designing, implementing and researching K-8 STEAM programs that promote coding and computational thinking skills in the context of learning mathematics.

Computational Expression

2021 ◽  
pp. 134-156
Author(s):  
Amanda L. Strawhacker ◽  
Amanda A. Sullivan
Keyword(s):  
Stem Education ◽  
Visual Arts ◽  
21St Century ◽  
Fine Arts ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Pedagogical Approach ◽  
The Past ◽  
Science Technology ◽  
And Mathematics

In the past two decades, STEM education has been slowly replaced by “STEAM,” which refers to learning that integrates science, technology, engineering, arts, and mathematics. The added “Arts” portion of this pedagogical approach, although an important step towards integrated 21st century learning, has long confused policymakers, with definitions ranging from visual arts to humanities to art education and more. The authors take the position that Arts can be broadly interpreted to mean any approach that brings interpretive and expressive perspectives to STEM activities. In this chapter, they present illustrative cases inspired by work in real learning settings that showcase how STEAM concepts and computational thinking skills can support children's engagement in cultural, performing, and fine arts, including painting, sculpture, architecture, poetry, music, dance, and drama.

scholarly journals Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models

2020 ◽  
Vol 49 (5) ◽  
pp. 20190441 ◽  
Author(s):  
Hakan Amasya ◽  
Derya Yildirim ◽  
Turgay Aydogan ◽  
Nazan Kemaloglu ◽  
Kaan Orhan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cervical Vertebrae ◽  
Clinical Decision ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Human Observer ◽  
Ann Model ◽  
Visual Evaluation ◽  
Cervical Vertebral Maturation

Objectives: This study aimed to develop five different supervised machine learning (ML) classifier models using artificial intelligence (AI) techniques and to compare their performance for cervical vertebral maturation (CVM) analysis. A clinical decision support system (CDSS) was developed for more objective results. Methods: A total of 647 digital lateral cephalometric radiographs with visible C2, C3, C4 and C5 vertebrae were chosen. Newly developed software was used for manually labelling the samples, with the integrated CDSS developed by evaluation of 100 radiographs. On each radiograph, 26 points were marked, and the CDSS generated a suggestion according to the points and CVM analysis performed by the human observer. For each sample, 54 features were saved in text format and classified using logistic regression (LR), support vector machine, random forest, artificial neural network (ANN) and decision tree (DT) models. The weighted κ coefficient was used to evaluate the concordance of classification and expert visual evaluation results. Results: Among the CVM stage classifier models, the best result was achieved using the ANN model (κ = 0.926). Among cervical vertebrae morphology classifier models, the best result was achieved using the LR model (κ = 0.968) for the presence of concavity, and the DT model (κ = 0.949) for vertebral body shapes. Conclusions: This study has proposed ML models for CVM assessment on lateral cephalometric radiographs, which can be used for the prediction of cervical vertebrae morphology. Further studies should be done especially of forensic applications of AI models through CVM evaluations.

scholarly journals Machine Learning in Context, or Learning from LANDR: Artificial Intelligence and the Platformization of Music Mastering

2019 ◽  
Vol 5 (2) ◽  
pp. 205630511984752 ◽  
Author(s):  
Jonathan Sterne ◽  
Elena Razlogova
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
New Media ◽  
Supervised Machine Learning ◽  
Media Industry ◽  
Learning In Context ◽  
Music Scene ◽  
Aesthetic Labor ◽  
Definition Of ◽  
Media Firms

This article proposes a contextualist approach to machine learning and aesthetics, using LANDR, an online platform that offers automated music mastering and that trumpets its use of supervised machine learning, branded as artificial intelligence (AI). Increasingly, machine learning will become an integral part of the processing of sounds and images, shaping the way our culture sounds, looks, and feels. Yet we cannot know exactly how much of a role or what role machine learning plays in LANDR. To parochialize the machine learning part of what LANDR does, this study spirals in from bigger contexts to smaller ones: LANDR’s place between the new media industry and the mastering industry; the music scene in their home city, Montreal, Quebec; LANDR use by DIY musicians and independent engineers; and, finally, the LANDR interface and the sound it produces in use. While LANDR claims to automate the work of mastering engineers, it appears to expand and morph the definition of mastering itself: it devalues people’s aesthetic labor as it establishes higher standards for recordings online. And unlike many other new media firms, LANDR’s connection to its local music scene has been essential to its development, growth, and authority, even as they have since moved on from that scene, and even as the relationship was never fully reciprocal.

Investigating bridge design

2015 ◽  
Vol 22 (4) ◽  
pp. 255-260
Author(s):  
Lukas J. Hefty
Keyword(s):  
Thinking Skills ◽  
Critical Thinking Skills ◽  
Student Interest ◽  
Bridge Design ◽  
Student Centered ◽  
Integrated Stem ◽  
Engineering Design Process ◽  
Science Technology ◽  
Understanding Of Science ◽  
And Mathematics

Teachers making the transition to integrated, student-centered science instruction benefit from sharing resources, and this bridge design unit offers one example. The unit uses the engineering design process to give students time to develop critical thinking skills while helping teachers assess understanding of science and mathematics content. Each month, iSTEM (Integrating Science, Technology, and Engineering in Mathematics) authors share ideas and activities that stimulate student interest in integrated STEM fields in K–grade 6 classrooms.

ALGORITHMS, MACHINE LEARNING, AND COLLUSION

2018 ◽  
Vol 14 (4) ◽  
pp. 568-607 ◽  
Author(s):  
Ulrich Schwalbe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Experimental Economics ◽  
Competition Law ◽  
Price Setting ◽  
Legal Problems ◽  
Pricing Behavior ◽  
Computer Scientists ◽  
Market Communication ◽  
Self Learning

Abstract This paper discusses whether self-learning price-setting algorithms can coordinate their pricing behavior to achieve a collusive outcome that maximizes the joint profits of the firms using them. Although legal scholars have generally assumed that algorithmic collusion is not only possible but also exceptionally easy, computer scientists examining cooperation between algorithms as well as economists investigating collusion in experimental oligopolies have countered that coordinated, tacitly collusive behavior is not as rapid, easy, or even inevitable as often suggested. Research in experimental economics has shown that the exchange of information is vital to collusion when more than two firms operate within a given market. Communication between algorithms is also a topic in research on artificial intelligence, in which some scholars have recently indicated that algorithms can learn to communicate, albeit in somewhat limited ways. Taken together, algorithmic collusion currently seems far more difficult to achieve than legal scholars have often assumed and is thus not a particularly relevant competitive concern at present. Moreover, there are several legal problems associated with algorithmic collusion, including questions of liability, of auditing and monitoring algorithms, and of enforcing competition law.

scholarly journals Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Cédric Beaulac ◽  
Fabrice Larribe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Hidden Markov Models ◽  
Real Time ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Estimation ◽  
Supervised Machine Learning ◽  
Real Time Estimation ◽  
Mobile Target

We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that estimates the unknown position of a mobile target moving in a defined environment. This narrow artificial intelligence performs two distinct tasks. First, it provides real-time estimation of the mobile agent’s position using the forward algorithm. Second, it uses the Baum–Welch algorithm as a statistical learning tool to gain knowledge of the mobile target. Finally, an experimental environment is proposed, namely, a video game that we use to test our artificial intelligence. We present statistical and graphical results to illustrate the efficiency of our method.

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