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Abstract Eye tracking technology is a high-potential tool for different mathematic cognition research areas. Moreover, there is a dire need for more studies that provide detailed information on the quality of registered eye data. This study aimed to illustrate the applicability of eye tracking in the examination of mathematical cognition, focusing specifically on primary school students completing a computerized mental arithmetic task. Results suggested that the eye tracking device effectively captured high-quality eye movement data when primary school children engaged in this specific task. Furthermore, significant negative correlations have been found between task performance and number of eye fixations. Finally, eye movements distinctions between “Areas of Interest” have been found, indicating different visual tracking associated with different components of arithmetic calculations. This study underscores the extensive possibilities for future research employing eye tracking devices during computerized calculation tasks as assessment tools to explore the complex visual and cognitive processes.

IntroductionMonitoring 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.MethodThis 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.ResultsResults 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.DiscussionCombined, 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.

Reading comprehension is a central skill in secondary education. To be able to provide adaptive instruction, teachers need to be able to accurately estimate students’ reading comprehension. However, they tend to experience difficulties doing so. Eye tracking can uncover these reading processes by visualizing what a student looked at, in what order, and for how long, in a gaze display. The question is, however, whether teachers could interpret such displays. We, therefore, examined how teachers interpret gaze displays and perceived their potential use in education to foster tailored support for reading comprehension. Sixty teachers in secondary education were presented with three static gaze displays of students performing a reading comprehension task. Teachers were asked to report how they interpreted these gaze displays and what they considered to be the promises and pitfalls of gaze displays for education. Teachers interpreted in particular reading strategies in the gaze displays quite well, and also interpreted the displays as reflecting other concepts, such as motivation and concentration. Results showed that teachers’ interpretations of the gaze displays were generally consistent across teachers and that teachers discriminated well between displays of different strategies. Teachers were generally positive about potential applications in educational practice. This study provides first insights into how teachers experience the utility of gaze displays as an innovative tool to support reading instruction, which is timely as rapid technological developments already enable eye tracking through webcams on regular laptops. Thus, using gaze displays in an educational setting seems to be an increasingly feasible scenario.

Background: Even though monitoring and control enactment are key aspects of self-regulated learning (SRL), Adaptive learning technologies (ALTs) may reduce the need for learners to monitor and control their learning. Personalized dashboards are effective in supporting learners' monitoring and can potentially support control behaviour. Allowing learners to enact control over their learning process, seems to hold potential for increasing their motivation. Objectives: Therefore, this study's aim was to investigate the relation between control enactment and motivation. We examined how learners enacted control while learning with an ALT with personalized dashboards and examined the relation between learners' enactment of control and their motivation. Methods: Seventy-eight primary school learners (Grade 5) participated. During the lesson, learners worked on mathematics in the ALT and concurrently were shown personalized visualizations that supported monitoring and enacting control over their learning process. Learners could enact control to change problems' difficulty to easy, medium, or hard. Motivation was measured before and after learning. Results: The SEM analyses showed that how learners enacted control was related to their motivation. Choosing difficult problems was related to more enjoyment and competence while choosing easy problems related to more pressure and tension. Learners who complied with the suggested difficulty level experienced less choice, but also less pressure/tension and more enjoyment and competence. Conclusions: These results provide avenues to account for broader learner characteristics like SRL and motivation to optimize learning. This way, hybrid systems in which control enactment is a shared responsibility of the system and learner, can be improved to support SRL development.