一句话：提出用编程环境细粒度日志非侵入式测量与心流相关的情感因素，并通过入门编程课程试点研究检验日志指标与问卷的关系。
心流是人们在有技能支撑、具有内在动机的挑战性任务中深度投入的体验。现有心流相关情感因素通常通过自我报告等侵入式方式测量，会打断心流。本文提出基于编程环境提取的细粒度过程数据，非侵入式地测量与心流相关的情感因素。作者在入门编程课程中开展试点研究，收集按键级、带时间戳的解题动作日志，以及每次作业后关于感知教育价值、感知难度和自我报告专注度的 Likert 问卷数据。研究分析原始与派生日志指标，并考察其与问卷数据之间的关系。论文讨论试点结果，并为未来非侵入式测量程序员情感因素提供工作建议。
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.
@inproceedings{paperbot10967,
title = {Seeking Flow from Fine-Grained Log Data},
author = {Benjamin Ultan Cowley and Arto Hellas and Petri Ihantola and Juho Leinonen and Michiel Spape},
booktitle = {ICSE},
year = {2022},
doi = {10.1109/icse-seet55299.2022.9794177}
}