Abstract
Performance and consistency play a large role in learning. Decreasing the effort that one invests into course work may have short-term benefits such as reduced stress. However, as courses progress, neglected work accumulates and may cause challenges with learning the course content at hand. In this work, we analyze students' performance and consistency with programming assignments in an introductory programming course. We study how performance, when measured through progress in course assignments, evolves throughout the course, study weekly uctuations in students' work consistency, and contrast this with students' performance in the course final exam. Our results indicate that whilst uctuations in students' weekly performance do not distinguish poor performing students from well performing students with a high accuracy, more accurate results can be achieved when focusing on the performance of students on individual assignments which could be used for identifying struggling students who are at risk of dropping out of their studies.
Original language | English |
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Title of host publication | ACE 2017 |
Subtitle of host publication | Proceedings of the 19th Australasian Computing Education Conference |
Place of Publication | New York, NY |
Publisher | Association for Computing Machinery |
Pages | 11-16 |
Number of pages | 6 |
ISBN (Electronic) | 9781450348232 |
DOIs | |
Publication status | Published - 2017 |
Externally published | Yes |
Event | Australasian Computing Education Conference (19th : 2017) - Geelong, Australia Duration: 31 Jan 2017 → 3 Feb 2017 |
Conference
Conference | Australasian Computing Education Conference (19th : 2017) |
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Abbreviated title | ACE 2017 |
Country/Territory | Australia |
City | Geelong |
Period | 31/01/17 → 3/02/17 |
Keywords
- source code snapshot analysis
- educational data mining
- CS1