Seeking Flow from Fine-Grained Log Data

Benjamin Ultan Cowley, Arto Hellas, Petri Ihantola, Juho Leinonen, Michiel Spape

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Flow is the experience of deep absorption in a demanding, intrinsically-motivating task conducted with skill. We consider how to measure behavioural correlates of flow from fine-grained process data extracted from programming environments. Specifically, we propose measuring affective factors related to flow non-intrusively based on log data. Presently, such affective factors are typically measured intrusively (by self-report), which naturally will break the flow. We evaluate our approach in a pilot study, where we use log data and survey data collected from an introductory programming course. The log data is fine-grained, containing timestamped actions at the keystroke level from the process of solving programming assignments, while the survey data has been collected at the end of every completed assignment. The survey data in the pilot study comprises of Likert-like items measuring perceived educational value, perceived difficulty, and students' self-reported focus when solving the assignments. We study raw and derived log data metrics, by looking for relationships between the metrics and the survey data. We discuss the results of the pilot study and provide suggestions for future work related to non-intrusive measures of programmer affect.
Original languageEnglish
Title of host publicationProceedings - 2022 ACM/IEEE 44th International Conference on Software Engineering
Subtitle of host publicationSoftware Engineering Education and Training, ICSE-SEET 2022
Number of pages7
ISBN (Electronic)9781665495929
Publication statusPublished - 13 Jun 2022
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Software Engineering: Software Engineering Education and Training - Pittsburgh, United States
Duration: 21 May 202229 May 2022
Conference number: 44


ConferenceInternational Conference on Software Engineering: Software Engineering Education and Training
Abbreviated titleICSE-SEET
Country/TerritoryUnited States
Internet address


  • 113 Computer and information sciences
  • 516 Educational sciences


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