What Is Hard about Teaching Machine Learning to Non-Majors? Insights from Classifying Instructors’ Learning Goals

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


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

2020 ◽  
pp. 283-321
Author(s):  
Lívia S. MARQUES ◽  
Christiane GRESSE VON WANGENHEIM ◽  
Jean C. R. HAUCK

Author(s):  
Bram van der Vlist ◽  
Rick van de Westelaken ◽  
Christoph Bartneck ◽  
Jun Hu ◽  
Rene Ahn ◽  
...  

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