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Ethical considerations, including transparency, play an important role when using artificial intelligence (AI) in education. Explainable AI has been coined as a solution to provide more insight into the inner workings of AI algorithms. However, carefully designed user studies on how to design explanations for AI in education are still limited. The current study aimed to identify the effect of explanations of an automated essay scoring system on students' trust and motivation. The explanations were designed using a needs-elicitation study with students in combination with guidelines and frameworks of explainable AI. Two types of explanations were tested: full-text global explanations and an accuracy statement. The results showed that both explanations did not have an effect on student trust or motivation compared to no explanations. Interestingly, the grade provided by the system, and especially the difference between the student's self-estimated grade and the system grade, showed a large influence. Hence, it is important to consider the effects of the outcome of the system (here: grade) when considering the effect of explanations of AI in education.

Background: Online and blended learning need an appropriate assessment strategy which ensures academic integrity. During the pandemic, many universities have chosen for online proctoring. Although some earlier examples suggest that online proctoring may reduce cheating, the potential side-effects of proctoring are largely unknown. Objectives: Therefore, this study aims to identify the effects of proctoring on students' self-reported temptation to cheat and potentially undesirable side-effects, including test anxiety, perceived exam difficulty, and performance. In addition, we examine which contextual and student characteristics affect test anxiety during online exams. Methods: For this, we collected four waves of survey data throughout a year of teaching during the pandemic at one faculty of a Dutch university, resulting in a total sample of 1760 students within 105 courses. Results and Conclusions: Multi-level analyses showed that while proctoring had no effect on the temptation to cheat, exam difficulty or performance, students reported higher levels of test anxiety. Some learning strategies, internet literacy, access to a reliable technology and a dedicated study space as well as gender and financial stress affect their test anxiety. To conclude, the decision to use online proctoring needs to consider the undesirable side-effect on test anxiety. We discuss practical implications for university administrators, educational designers and teachers to reduce test anxiety.

The central question of our study is which determinants drive smoke alarm ownership and intention to purchase one, and whether we can increase smoke alarm ownership by addressing these determinants in a communication‐based intervention. We first made an inventory of possible determinants for smoke alarm prevention by consulting prominent prevention behavior theories protection motivation theory and Health Belief Model and other relevant literature. We expanded this list of determinants based on interviews (n = 15) and used survey data representative for the Netherlands to decide to focus on smoke alarm ownership (rather than installation or maintenance). We then tested the determinants of smoke alarm ownership and buying intention in a survey (n = 622). Based on these results, we ran an A/B test (n = 310) of two messages to stimulate smoke alarm ownership: one emphasized the determinants we found to be strong predictors in the survey (know‐how, social norm, annoyance) and one emphasized typical determinants that are often addressed in campaigns but were poor predictors in the survey (vulnerability, severity, benefits). Results showed that the message based on the strong determinants resulted in a significant increase in smoke alarm ownership (9.1%) compared to the control group (0.9%; p = 0.027), while the message using the typical determinants did not lead to significant effects. Taken together, our results give a promising direction for interventions to increase smoke alarm ownership, and above all, show that a comprehensive problem analysis for a specific target behavior is a necessary step to induce behavioral change. [ABSTRACT FROM AUTHOR]

With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables extracted from the LMS, which makes it hard to draw general conclusions about the mechanisms underlying student performance. We first provide an overview of the theoretical arguments used in learning analytics research and the typical predictors that have been used in recent studies. We then analyze 17 blended courses with 4,989 students in a single institution using Moodle LMS, in which we predict student performance from LMS predictor variables as used in the literature and from in-between assessment grades, using both multi-level and standard regressions. Our analyses show that the results of predictive modeling, notwithstanding the fact that they are collected within a single institution, strongly vary across courses. Thus, the portability of the prediction models across courses is low. In addition, we show that for the purpose of early intervention or when in-between assessment grades are taken into account, LMS data are of little (additional) value. We outline the implications of our findings and emphasize the need to include more specific theoretical argumentation and additional data sources other than just the LMS data.