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Automated gaze-based mind wandering detection during computerized learning in classrooms

机译：在教室中的计算机学习过程中基于注视的自动思维游荡检测

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We investigate the use of commercial off-the-shelf (COTS) eye-trackers to automatically detect mind wandering-a phenomenon involving a shift in attention from task-related to task-unrelated thoughts-during computerized learning. Study 1 (N = 135 high-school students) tested the feasibility of COTS eye tracking while students learn biology with an intelligent tutoring system called GuruTutor in their classroom. We could successfully track eye gaze in 75% (both eyes tracked) and 95% (one eye tracked) of the cases for 85% of the sessions where gaze was successfully recorded. In Study 2, we used this data to build automated student-independent detectors of mind wandering, obtaining accuracies (mind wandering F-1 = 0.59) substantially better than chance (F-1 = 0.24). Study 3 investigated context-generalizability of mind wandering detectors, finding that models trained on data collected in a controlled laboratory more successfully generalized to the classroom than the reverse. Study 4 investigated gaze- and video- based mind wandering detection, finding that gaze-based detection was superior and multimodal detection yielded an improvement in limited circumstances. We tested live mind wandering detection on a new sample of 39 students in Study 5 and found that detection accuracy (mind wandering F-1 = 0.40) was considerably above chance (F1 = 0.24), albeit lower than offline detection accuracy from Study 1 (F-1 = 0.59), a finding attributable to handling of missing data. We discuss our next steps towards developing gaze-based attention-aware learning technologies to increase engagement and learning by combating mind wandering in classroom contexts.
机译：我们调查了使用现成的商业（COTS）眼动仪来自动检测思维游荡的现象，这种现象涉及在计算机学习过程中注意力从与任务相关的思想转向与任务无关的思想的转变。研究1（N = 135名高中学生）测试了COTS眼动追踪的可行性，同时学生在教室中使用名为GuruTutor的智能辅导系统学习生物学。在成功记录凝视的85％的会话中，我们可以成功跟踪75％（两只眼睛都跟踪）和95％（一只眼睛已经跟踪）的案例中的眼睛凝视。在研究2中，我们使用此数据构建了独立于学生的自动心理游荡检测器，其准确度（思维游荡F-1 = 0.59）远胜于机会（F-1 = 0.24）。研究3调查了思维游荡检测器的上下文可概括性，发现在受控实验室中收集的数据所训练的模型比反向的方法更成功地推广到了教室。研究4调查了基于凝视和视频的思维游荡检测，发现基于凝视的检测优越，在有限的情况下多模式检测带来了改善。我们在研究5中对39名学生的新样本进行了实时思维游荡检测的测试，发现检测准确性（思想徘徊F-1 = 0.40）大大高于机会（F1 = 0.24），尽管低于研究1的离线检测准确性（ F-1 = 0.59），这归因于丢失数据的处理。我们讨论了开发基于注视的注意力感知学习技术的下一步，以通过消除课堂环境中的思维游荡来提高参与度和学习能力。

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