Teaching Machine Learning in K-12 Education

2021 ◽  
Author(s):  
Ismaila Temitayo Sanusi
IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Matti Tedre ◽  
Tapani Toivonen ◽  
Henriikka Vartiainen ◽  
Ilkka Jormanainen ◽  
Teemu Valtonen ◽  
...  

Author(s):  
Christiane Gresse von Wangenheim ◽  
Jean C. R. Hauck ◽  
Fernando S. Pacheco ◽  
Matheus F. Bertonceli Bueno

2020 ◽  
Vol 34 (09) ◽  
pp. 13397-13403
Author(s):  
Narges Norouzi ◽  
Snigdha Chaturvedi ◽  
Matthew Rutledge

This paper describes an experience in teaching Machine Learning (ML) and Natural Language Processing (NLP) to a group of high school students over an intense one-month period. In this work, we provide an outline of an AI course curriculum we designed for high school students and then evaluate its effectiveness by analyzing student's feedback and student outcomes. After closely observing students, evaluating their responses to our surveys, and analyzing their contribution to the course project, we identified some possible impediments in teaching AI to high school students and propose some measures to avoid them. These measures include employing a combination of objectivist and constructivist pedagogies, reviewing/introducing basic programming concepts at the beginning of the course, and addressing gender discrepancies throughout the course.


2019 ◽  
Vol 19 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Elisabeth Sulmont ◽  
Elizabeth Patitsas ◽  
Jeremy R. Cooperstock

2017 ◽  
Vol 13 (8) ◽  
pp. 1584-1596 ◽  
Author(s):  
Sutanu Nandi ◽  
Abhishek Subramanian ◽  
Ram Rup Sarkar

We propose an integrated machine learning process to predict gene essentiality in Escherichia coli K-12 MG1655 metabolism that outperforms known methods.


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