lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Fine-Grained Recording of Student Programming Sessions to Improve Teaching and Time Estimations
Linnaeus University, Faculty of Technology, Department of Computer Science.ORCID iD: 0000-0001-5335-5196
Linnaeus University, Faculty of Technology, Department of Computer Science.ORCID iD: 0000-0003-1154-5308
Linnaeus University, Faculty of Technology, Department of Computer Science.ORCID iD: 0000-0003-1173-5187
Linnaeus University, Faculty of Technology, Department of Computer Science.ORCID iD: 0000-0002-0835-823X
2016 (English)In: International Journal of Engineering ,Science and Innovative Technology, ISSN 0949-149X, E-ISSN 2277-3754, Vol. 32, no 3, 1069-1077 p.Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

It is not possible to directly observe how students work in an online programming course. This makes it harder for teachers to help struggling students. By using an online programming environment, we have the opportunity to record what the students actually do to solve an assignment. These recordings can be analyzed to provide teachers with valuable information. We developed such an online programming tool with fine-grained event logging and used it to observe how our students solve problems. Our tool provides descriptive statistics and accurate replays of a student's programming sessions, including mouse movements. We used the tool in a course and collected 1028 detailed recordings. In this article, we compare fine-grained logging to existing coarse-grained logging solutions to estimate assignment-solving time. We find that time aggregations are improved by including time for active reading and navigation, both enabled by the increased granularity. We also divide the time users spent into editing (on average 14.8%), active use (on average 37.8%), passive use (on average 29.0%), and estimate time used for breaks (on average 18.2%). There is a correlation between assignment solving time for students who pass assignments early and students that pass later but also a case where the times differ significantly. Our tool can help improve computer engineering education by providing insights into how students solve programming assignments and thus enable teachers to target their teaching and/or improve instructions and assignments.

Place, publisher, year, edition, pages
2016. Vol. 32, no 3, 1069-1077 p.
Keyword [en]
computer science education, learning analytics, educational data mining, computer engineering education
National Category
Computer and Information Science
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-55062ISI: 000378700600003OAI: oai:DiVA.org:lnu-55062DiVA: diva2:949940
Conference
20th Annual Conference on Innovation and Technology in Computer Science Education, JUL 06-08, 2015, Vilnius, LITHUANIA
Available from: 2016-07-26 Created: 2016-07-22 Last updated: 2017-04-19Bibliographically approved

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Toll, DanielOlsson, TobiasEricsson, MorganWingkvist, Anna
By organisation
Department of Computer Science
In the same journal
International Journal of Engineering ,Science and Innovative Technology
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

Total: 513 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf