Disequilibration for teaching the scientific method in computer science

SIGCSE Bulletin (Association for Computing Machinery, Special Interest Group on Computer Science Education)

Volumn , Issue , 2002, Pages 106-110

  Braught, Grant ^a   Reed, David ^b  

^a DICKINSON COLLEGE   (United States)

^b CREIGHTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER PROGRAMMING; COMPUTER SIMULATION; DATA REDUCTION; ENGINEERING EDUCATION; NATURAL SCIENCES COMPUTING; STUDENTS; TABLE LOOKUP; TEACHING;

SCIENTIFIC METHOD;

COMPUTER SCIENCE;

EID: 0036373671     PISSN: 00978418     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/563517.563381     Document Type: Conference Paper

References (25)

1. Making molecules into motors (gambler's paradox side-bar)
2. Teaching software testing
3. Teaching the empirical approach to designing human-computer interaction via an experimental group project
4. Teaching experimental design in an operating systems class
5. Studying conceptual change in learning physics
6. Programming in java: Student-constructed rules
7. The car and the goat
8. Misleading intuition in algorithmic problem solving
9. Avoiding object misconceptions
10. Integrating testing into the curriculum -Arsenic in small doses
11. Treating computer science as science: An experiment with sorting
12. Language-independent conceptual "bugs" Novice programming
13. Empirical investigation throughout the CS curriculum
14. Rethinking CSO with javascript
15. Developing empirical skills in an introductory computer science course
16. Empirical exploration in undergraduate operating systems
17. More testing should be taught
18. How to build a hypercomputer (crucial tasks for hypercomputers, side-bar)
19. Should computer scientists experiment more
20. Experimentation and evaluation in computer science: A quantitative study
21. A racquetball or volleyball simulation
22. The gestalt of scientific programming: Problem, model, method, implementation, assessment
23. Experimental models for validating technology