“I do and I understand”: mastery model learning for a large non-major course

Authors:
Mark Urban-Lurain;Donald J. Weinshank
Affiliations:
Michigan State University;Michigan State University
Venue:
SIGCSE '99 The proceedings of the thirtieth SIGCSE technical symposium on Computer science education
Year:
1999

Citing 3
Cited 7

Teaching computer science concepts and problem solving with a spreadsheet

SIGCSE '95 Proceedings of the twenty-sixth SIGCSE technical symposium on Computer science education
Turning liabilities into assets in a general education course

SIGCSE '98 Proceedings of the twenty-ninth SIGCSE technical symposium on Computer science education
The computer as a problem solving tool: a unifying view for a non-majors course

SIGCSE '98 Proceedings of the twenty-ninth SIGCSE technical symposium on Computer science education

Using Ada-based robotics to teach computer science

Proceedings of the 5th annual SIGCSE/SIGCUE ITiCSEconference on Innovation and technology in computer science education
Teaching computer science with robotics using Ada/Mindstorms 2.0

Proceedings of the 2001 annual ACM SIGAda international conference on Ada
New roles for students, instructors, and computers in a lab-based introductory programming course

SIGCSE '03 Proceedings of the 34th SIGCSE technical symposium on Computer science education
Introduction to computing for future elementary and middle-school teachers

Journal of Computing Sciences in Colleges
Content and laboratories of a computing science course for non-majors in the 21st Century

Journal of Computing Sciences in Colleges
Making computing attractive for non-majors: a course design

Journal of Computing Sciences in Colleges
Expert code review and mastery learning in a software development course

Journal of Computing Sciences in Colleges

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe the infrastructure we have created for a large enrollment (1700 / semester) non-major course. The course combines collaborative, lab-based problem-solving instruction with modified mastery-learning assessment. The infrastructure supports continuous improvement to respond to client department needs, incoming student experience, course design flaws and computing platform upgrades.