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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
2015 (English)In: IFIP TC3 Working Conference “A New Culture of Learning: Computing and next Generations” / [ed] Andrej Brodnik, Cathy Lewin, 2015, 264-274 p.Conference paper, Published paper (Refereed)
Abstract [en]

To have direct observation of students during an online programming course is impossible. 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. We can analyse the recordings and provide teachers with valuable information. We developed and used an online programming toolwith fine-grained event logging 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. We compare fine-grained logging with existing coarsegrained logging solutions to estimate assignment-solving time. We find that time aggregations are improved by including time for active reading and navigation 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 also estimate time used for breaks (on average 18.2%).Finally wesee a correlation between early student submission results and students that hand in later, but also see an example where the results differ significantly.

Place, publisher, year, edition, pages
2015. 264-274 p.
Keyword [en]
Computer Science Education, Learning Analytics, Educational Data Mining
National Category
Computer Science
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-49403ISBN: 978-609-95760-0-8 (print)OAI: oai:DiVA.org:lnu-49403DiVA: diva2:898719
Conference
IFIP TC3 Working Conference "A New Culture of Learning: Computing and Next Generations" July 1-3, 2015, Vilnius, Lithuania
Available from: 2016-01-29 Created: 2016-01-29 Last updated: 2017-03-09Bibliographically approved

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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