Driver instruction for automated vehicles: Assessing the role of specific elements on learner motivation and mental model development
Authors: Sophie Feinauer, Irene Groh, Tibor Petzoldt
Abstract: Along with the increasing degree of automation of the driving task, calls for user education on automated driving have emerged. Indeed, previous studies could show positive effects of user education, e.g., on driving performance and mental model development. However, recent research has not yet examined the effectiveness of specific elements in that context. Research in educational psychology has shown that motivation to learn is crucial for learning success. Thus, in the present study we examined the role of specific instructional elements on learner motivation for automated vehicles.Following psychological needs theory, we examined the influence of autonomy and competence on the dependent variables intrinsic learning motivation and mental model development, trust in and acceptance of the automated vehicle. To that end, we developed learning material on an automated vehicle that was embedded in an online study and structured into four topics. Depending on the experimental group, different elements were added to this material to assess their effect on the dependent variables.A total of N = 193 participants took part in the online study. Participants were randomly assigned to one of four groups: (1) A group that could freely choose the order in which they read the provided topics to operationalize the aspect of autonomy. (2) A group that received feedback on answering simple yes/no questions after reading the instructions on each topic to operationalize the aspect of competence. (3) A group that received a combination of both autonomy and competence elements, and (4) a control group that read the material without any further manipulation. Participants’ mental models were assessed with a questionnaire for declarative knowledge and 17 pictures (created in a simulation program) of an automated vehicle from the driver’s perspective in different situations. Participants should anticipate the automated vehicle’s behavior in the given situation and choose the correct action. In addition, to allow for a longitudinal assessment, we also evaluated the dependent variables in a follow-up survey two weeks later.Statistical analysis indicated that combination and competence groups exhibited in tendency more motivation to learn the content than the autonomy and control groups. Incorporation of feedback elements thus seemed to have successfully facilitated learning motivation. Concerning the participants’ mental models, results indicate that the competence group showed a better declarative knowledge compared to the autonomy group, although all groups showed a decline in their knowledge after two weeks. However, concerning situation specific knowledge, no group differences were found. A significant interaction effect indicated that at follow-up, participants in the competence group reported higher trust ratings than the other groups. No significant effects for acceptance were found. Taken together, results indicate that by fostering feelings of competence, mental model formation for automated vehicles can be supported. However, contrary to our expectation, offering learners the freedom to choose their own order of learning did neither facilitate intrinsic motivation nor trust or mental model development. Thus, this study suggests that feedback elements support learning outcomes for driver instruction of automated vehicles and can be incorporated into different means of instruction.
Keywords: Automated driving, Mental model, Online study, Motivation, User education
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