The type of a recall task may substantially influence the effects of learning by retrieval practice. In a within-subject design, 54 university students studied two expository texts, followed by retrieval practice with either short-answer tasks (targeted retrieval) or a free-recall task (holistic retrieval). Concerning the direct effects of retrieval practice, short-answer tasks led to increased retention of directly retrieved targeted information from the learning contents, whereas free-recall tasks led to better retention of further information from the learning contents. Concerning indirect effects, short-answer tasks improved metacognitive calibration; free-recall tasks increased self-efficacy and situational interest. These findings confirm the assumption that the effects of retrieval practice depend on the type of recall task: short-answer tasks help us remember targeted information units and foster metacognitive calibration. Free-recall tasks help us remember a broader spectrum of information, and they foster motivational factors.
This study investigates the differences and effects of transparency and explainability on trust, situation awareness, and satisfaction in the context of an automated car. Three groups were compared in a between-subjects design (n = 73). Participants in every group saw six graphically manipulated videos of an automated car from the driver’s perspective with either transparency, post-hoc explanations or both combined. Transparency resulted in higher trust, higher satisfaction and higher level 2 situational awareness (SA) than explainability. Transparency also resulted in higher level 2 SA than the combined condition, but did not differ in terms of trust or satisfaction. Moreover, explainability led to significantly worse satisfaction compared to combined feedback. Although our findings should be replicated in more ecologically valid driving situations, we tentatively conclude that transparency alone should be implemented in semi self-driving cars, and possibly automated systems in general, whenever possible to make them most satisfactory, trustworthy, and resulting in higher SA.
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