Calibration as side effect? Computer-based learning in driver education and the adequacy of driving-task-related self-assessments

Abstract

To reduce the high risk of young, novice drivers being involved in traffic accidents, there have been several attempts to utilize computers for driver education. Previous studies have shown promising results concerning the benefits of using computers for the acquisition of driving-task-related cognitive skills. However, these studies' findings are inconclusive regarding whether using computers for driver education affects drivers' calibration skills. Underdeveloped calibration skills are considered to be an important reason explaining why young, novice drivers are at a higher risk of being involved in an accident relative to other drivers. To examine the effects of computer-based learning in driver education on drivers' calibration skills, we provided student drivers (N = 38) with two different types of learning material (computer-based vs. paper-based, approximately 90 min in duration). Two days later, we presented them with a driving simulator task. Right before the test, the participants were asked to predict the likelihood that they would be able to successfully implement their newly acquired competencies. We chose anticipatory recognition of hazardous traffic situations as the learning objective to examine both facets of calibration: accuracy of assessing driving tasks (situational or risk awareness) and accuracy of driving-task-related self-assessments (self-efficacy, state awareness). The analysis of participant's gaze data confirmed our expectation that student drivers who used computer-based learning material would not only detect situation-specific hazard cues sooner but would also demonstrate better comprehension of the information they perceived. Contrary to our expectations, the computer-based learning did not lead to more accurate predictions of test performance. However, it increased the insecurities of the participants, thereby reducing the risk that these student drivers would overestimate their own competence. Because using computers helps student drivers to develop better hazard-perception skills and more defensive self-efficacy expectations, the implementation of computers in driver education is more likely to support safe behavioral patterns in traffic than conventional methods.