There is a strong increase in the use of devices that measure physiological arousal through electrodermal activity (EDA). Although there is a long tradition of studying emotions during learning, researchers have only recently started to use EDA to measure emotions in the context of education and learning. This systematic review aimed to provide insight into how EDA is currently used in these settings. The review aimed to investigate the methodological aspects of EDA measures in educational research and synthesize existing empirical evidence on the relation of physiological arousal, as measured by EDA, with learning outcomes and learning processes. The methodological results pointed to considerable variation in the usage of EDA in educational research and indicated that few implicit standards exist. Results regarding learning revealed inconsistent associations between physiological arousal and learning outcomes, which seem mainly due to underlying methodological differences. Furthermore, EDA frequently fluctuated during different stages of the learning process. Compared to this unimodal approach, multimodal designs provide the potential to better understand these fluctuations at critical moments. Overall, this review signals a clear need for explicit guidelines and standards for EDA processing in educational research in order to build a more profound understanding of the role of physiological arousal during learning.

Adaptive learning technologies often provide students with immediate feedback on task performance. This feedback can elicit various emotional responses, which, in turn, influence learning. Most recent studies capture these emotions by single data streams, contradicting the multi-componential nature of emotion. Therefore, this study investigated 32 university students solving mathematical problems using an adaptive learning technology. Students received immediate feedback on every step in the solution process, after which their physiological, experiential and behavioral responses to this feedback were recorded. Physiological arousal was measured by electrodermal activity, valence was measured by self-reports (experiential), and emotion types were measured by observations of facial expressions (behavioral). Results showed more peaks in electrodermal activity after feedback than was expected based on chance. These responses were comparable in strength after feedback on failure and success. Students’ experiential responses conveyed mostly positive valence after feedback on success and mostly negative valence after feedback on failure. Behavioral observations showed more negative than positive emotion types after feedback on failure and more positive than negative emotion types after feedback on success. These results show that physiological arousal is a valuable objective indicator of emotional responses after immediate feedback but should be accompanied by other data streams in order to understand students’ emotional responses. Both valence and emotion types can be used for this purpose. These outcomes pave the way for designing adaptive learning technologies that take students’ emotions into account.

Introduction: Monitoring and controlling learning is often difficult for primary school students. This issue is partially resolved when Adaptive Learning Technologies (ALTs) take over part of these self-regulated learning (SRL) processes. Trace data in ALTs provides elaborate information on students' learning process, which can be translated into monitoring support. However, this data does not provide insight into students' goal-setting behavior, which is a crucial part of the monitoring loop. Therefore, we developed a form of co-regulation between the student and the ALT with goal-setting prompts and monitoring support.Method: This experimental study compared an experimental condition in which primary school students worked with the co-regulation intervention while practicing mathematics problems in the ALT to a control condition where they only practiced with the ALT. Firstly, we examined the effects of co-regulation with ALTs on regulation of practice behavior and learning outcomes by comparing the experimental and control conditions. Secondly, to gain further insight into students' goal-setting behavior in co-regulation, we examined how the experimental condition set their goals and attained them.Results: Results showed that students in the experimental condition were more effective in regulating their practice behavior, shown by more problems solved and higher accuracy than the control condition. Similar learning gain was found in both conditions on the easy and intermediate learning topics. For the hard learning topic, the control condition showed a higher learning gain. Higher week goals than day goals on all three learning topics and similar goals were set between the learning topics. Students' goal attainment was less for the easy compared to the hard learning topic.Discussion: Combined, these results indicate that co-regulation with an ALT positively affects students' regulation of practice behavior and goal- setting behavior but does not yet increase learning gain. A practical implication is that teachers could support students by explicitly providing students with goal-setting opportunities.

1. Around the world, there is broad awareness that AI will substantially impact our lives and lead to fundamental changes in work, health care, and education (OECD 2021, 2023). In educational pract...