Existing research shows that people can improve their decision skills by learning what experts paid attention to when faced with the same problem. However, in domains like financial education, effective instruction requires frequent, personalized feedback given at the point of decision, which makes it time‐consuming for experts to provide and thus, prohibitively costly. We address this by demonstrating an automated feedback mechanism that allows amateur decision‐makers to learn what information to attend to from one another, rather than from an expert. (...) In the first experiment, eye movements of N = 100 subjects were recorded while they repeatedly performed a standard behavioral finance investment task. Consistent with previous studies, we found that a significant proportion of subjects were affected by decision bias. In the second experiment, a different group of N = 100 subjects faced the same task but, after each choice, they received individual, machine learning‐generated feedback on whether their pre‐decision eye movements resembled those made by Experiment 1 subjects prior to good decisions. As a result, Experiment 2 subjects learned to analyze information similarly to their successful peers, which in turn reduced their decision bias. Furthermore, subjects with low Cognitive Reflection Test scores gained more from the proposed form of process feedback than from standard behavioral feedback based on decision outcomes. (shrink)

In online lectures, unlike in face-to-face lectures, teachers lack access to (nonverbal) cues to check if their students are still “with them” and comprehend the lecture. The increasing availability of low-cost eye-trackers provides a promising solution. These devices measure unobtrusively where students look and can visualize these data to teachers. These visualizations might inform teachers about students’ level of “with-me-ness” (i.e., do students look at the information that the teacher is currently talking about) and comprehension of the lecture, provided that (...) (1) gaze measures of “with-me-ness” are related to comprehension, (2) people not trained in eye-tracking can predict students’ comprehension from gaze visualizations, (3) we understand how different visualization techniques impact this prediction. We addressed these issues in two studies. In Study 1, 36 students watched a video lecture while being eye-tracked. The extent to which students looked at relevant information and the extent to which they looked at the same location as the teacher both correlated with students’ comprehension (score on an open question) of the lecture. In Study 2, 50 participants watched visualizations of students’ gaze (from Study 1), using six visualization techniques (dynamic and static versions of scanpaths, heatmaps, and focus maps) and were asked to predict students’ posttest performance and to rate their ease of prediction. We found that people can use gaze visualizations to predict learners’ comprehension above chance level, with minor differences between visualization techniques. Further research should investigate if teachers can act on the information provided by gaze visualizations and thereby improve students’ learning. (shrink)