A Blended-Learning Program for Implementing a Rigorous Machine-Learning Curriculum in High-Schools

Shai Perach, Giora Alexandron

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

AI, and, more specifically, deep learning, is profoundly impacting our industries and societies [1]. In recent years, machine learning (ML) 's surging impact has sparked discourse about the importance of AI education for young people, and in recent years, several initiatives and projects pursuing the mission of K-12 AI education have emerged. In 2020 Israel's Ministry of Education (MoE) approved a new comprehensive and rigorous ML curriculum targeting 11 and 12th-grade pupils majoring in computer science (CS). The curriculum is meant to be taught by the existing CS teacher workforce. However, since ML theory and practice are fundamentally different from traditional CS [2], implementing this thorough ML curriculum poses substantial challenges in developing an effective teaching workforce to deliver it. In this research, we suggest a solution for this challenge in the form of a blended-learning (BL) program. The online component of this program is based mainly on Coursera's Deep Learning Specialization MOOCs series [3]. The BL program, enhanced with pedagogical training, is also used for the professional development (PD) of the teachers who deliver the program. Out of fourteen CS teachers who participated in the PD in the summer of 2021, ten teach the BL program this year to 273 high-school pupils.

Original languageEnglish
Title of host publicationL@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale
Pages267-270
Number of pages4
ISBN (Electronic)9781450391580
DOIs
StatePublished - 1 Jun 2022
Event9th Annual ACM Conference on Learning at Scale, L@S 2022 - New York City, United States
Duration: 1 Jun 20223 Jun 2022

Conference

Conference9th Annual ACM Conference on Learning at Scale, L@S 2022
Country/TerritoryUnited States
CityNew York City
Period1/06/223/06/22

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

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