The “testing effect” refers to the finding that after an initial study opportunity, testing is more effective for long‐term retention than restudying. The testing effect seems robust and is a finding from the field of cognitive science that has important implications for education. However, it is unclear whether this effect also applies to the acquisition of problem‐solving skills, which is important to establish given the key role problem solving plays in, for instance, math and science education. Worked examples are an (...) effective and efficient way of acquiring problem‐solving skills. Forty students either only studied worked examples (SSSS) or engaged in testing after studying an example by solving an isomorphic problem (STST). Surprisingly, results showed equal performance in both conditions on an immediate retention test after 5 min, but the SSSS condition outperformed the STST condition on a delayed retention test after 1 week. These findings suggest the testing effect might not apply to acquiring problem‐solving skills from worked examples. (shrink)

Being able to categorize objects as similar or different is an essential skill. An important aspect of learning to categorize is learning to attend to relevant features and ignore irrelevant features of the to-be-categorized objects. Feature variability across objects of different categories is informative, because it allows inferring the rules underlying category membership. In this study, participants learned to categorize fictitious creatures. We measured attention to the aliens during learning using eye-tracking and calculated the attentional focus as the ratio of (...) attention to relevant versus irrelevant features. As expected, participants’ categorization accuracy improved with practice; however, in contrast to our expectations, their attentional focus did not improve with practice. When computing the attentional focus, attention to the aliens’ eyes was disregarded, because while eyes attract a lot of attention, they did not vary across aliens. Yet, an explorative analysis of attention to eyes suggested that participants’ attentional focus did become somewhat more efficient in that over time they learned to ignore the eyes. Results are discussed in the context of the need for instructional methods to improve attentional focus in learning to categorize. (shrink)

Cognitive Science, Volume 46, Issue 4, April 2022.

Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts’ (natural or didactic) problem‐solving behavior as well as their eye movements to learners. While research on expert‐novice communication mainly focused on experts’ changes in explicit, verbal communication behavior, it is as yet unclear whether and (...) how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts’ natural problem‐solving behavior by contrasting it with that of novices. Then, we explored the changes in experts’ behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts’ automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically. (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)