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Self-regulated learning (SRL) is an essential skill to achieve one's learning goals. This is particularly true for online learning environments (OLEs) where the support system is often limited compared to a traditional classroom setting. Likewise, existing research has found that learners often struggle to adapt their behaviour to the self-regulatory demands of OLEs. Even so, existing SRL analysis tools have limited utility for real-time or individualised support of a learner's SRL strategy during a study session. Accordingly, we explore a reinforcement learning based approach to learning optimal SRL strategies for a specific learning task. Specifically, we utilise the sequences of SRL processes acted by 44 participants, and their assessment scores for a prescribed learning task, in a purpose-built OLE to develop a long short-term memory (LSTM) network based reward function. This is used to train a reinforcement learning agent to find the optimal sequence of SRL processes for the learning task. Our findings indicate that the developed agents were able to effectively select SRL processes so as to maximise a prescribed learning goal as measured by predicted assessment score and predicted knowledge gains. The contributions of this work will facilitate the development of a tool which can detect sub-optimal SRL strategy in real-time and enable individualised SRL focused scaffolding.

Scaffolds that support self-regulated learning (SRL) have been found to improve learning outcomes. The effects of scaffolds can differ depending on how learners use them and how specific scaffolds might influence learning processes differently. Personalized scaffolds have been proposed to be more beneficial for learning due to their adaptivity to learning progress and individualized content to learning needs. The present study investigated finer-grained effects of how personalized scaffolds driven by a rule-based artificial intelligence system influenced SRL processes, especially how students learned with them. Using a pre-post experimental design, we investigated personalized scaffolds based on university students' real-time learning processes in a technologically enhanced learning environment. Students in the experimental group (n = 30) received personalized scaffolds, while the control group (n = 29) learned without scaffolds. All students completed a 45-minute learning task with trace data recorded. Findings indicated scaffold effects on students' subsequent learning behaviour. Additionally, only scaffold interaction correlated to essay performance and suggests that the increase in frequencies of SRL activities alone does not contribute directly to learning outcomes. As guidelines for real-time SRL support are lacking, this study provides valuable insights to enhance SRL support with adaptive learning technologies.

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.

The present study examined genetic, prenatal, and postnatal environmental pathways in the intergenerational transmission of anxiety and depressive symptoms from parents to early adolescents (when these symptoms start to increase), while considering timing effects of exposure to parent anxiety and depressive symptoms postnatally. The sample was from the Early Growth and Development Study, including 561 adopted children (57% male, 55% White, 13% Black/African American, 11% Hispanic/Latine, 20% multiracial, 1% other; 407 provided data in early adolescence) and their birth (BP) and adoptive parents (AP). Using a trait-state-occasion model with eight assessments from child ages 9 months to 11 years, we partitioned trait-like AP anxiety and depressive symptoms from time-specific fluctuations of AP anxiety and depressive symptoms. Offspring anxiety and depressive symptoms were assessed at 11 years (while controlling for similar symptoms at 4.5 years). Results suggested that time-specific fluctuations of AP1 (mostly mothers) anxiety/depressive symptoms in infancy (9 months) were indirectly associated with offspring anxiety/depressive symptoms at 11 years via offspring anxiety/depressive symptoms at 4.5 years; time-specific fluctuations of AP1 anxiety/depressive symptoms at child age 11 years were concurrently associated with offspring anxiety/depressive symptoms at 11 years. AP2 (mostly fathers) anxiety/depressive symptoms were not associated with offspring symptoms. Genetic and prenatal influences measured by BP internalizing problems were not associated with offspring symptoms. Results suggested infancy and early adolescence as developmental periods when children are susceptible to influences of parent anxiety and depressive symptoms. Preventive interventions should consider time-specific fluctuations in parent anxiety and depressive symptoms during these developmental periods.

Background: Many learners struggle to productively self-regulate their learning. To support the learners' self-regulated learning (SRL) and boost their achievement, it is essential to understand the cognitive and metacognitive processes that underlie SRL. To measure these processes, contemporary SRL researchers have largely utilized think aloud or trace data, however, not without challenges. Objectives: In this paper, we present the findings of a study that investigated how concurrent analysis and integration of think aloud and trace data could advance the measurement of SRL and assist in better understanding the mechanisms of SRL processes, especially those details that remain obscured by observing each data channel individually. Methods: We concurrently collected think aloud and trace data generated by 44 university students in a laboratory setting and analysed those data relative to the same timeline. Results: We found that the two data channels could be interchangeably used to measure SRL processes for only 17.18% of all the time segments identified in a learning task. Moreover, SRL processes for around 45% of all the time segments could be detected via either trace data or think aloud data. For another 27.17% of all the time segments, different SRL processes were detected in both data channels. Conclusions: Our results largely suggest that the two data collection methods can be used to complement each other in measuring SRL. In particular, we found that think aloud and trace data could provide different perspectives on SRL. The integration of the two methods further allowed us to reveal a more complex and more comprehensive temporal associations among SRL processes compared to using a single data collection method. In future research, the integrated measurement of SRL can be used to improve the detection of SRL processes and provide a fuller picture of SRL.

Scientific reasoning is an important skill that encompasses hypothesizing, experimenting, inferencing, evaluating data and drawing conclusions. Previous research found consistent inter- and intra-individual differences in children's ability to perform these component skills, which are still largely unaccounted for. This study examined these differences and the role of three predictors: reading comprehension, numerical ability and problem-solving skills. A sample of 160 upper-primary schoolchildren completed a practical scientific reasoning task that gauged their command of the five component skills and did not require them to read. In addition, children took standardized tests of reading comprehension and numerical ability and completed the Tower of Hanoi task to measure their problem-solving skills. As expected, children differed substantially from one another. Generally, scores were highest for experimenting, lowest for evaluating data and drawing conclusions and intermediate for hypothesizing and inferencing. Reading comprehension was the only predictor that explained individual variation in scientific reasoning as a whole and in all component skills except hypothesizing. These results suggest that researchers and science teachers should take differences between children and across component skills into account. Moreover, even though reading comprehension is considered a robust predictor of scientific reasoning, it does not account for the variation in all component skills.

Teacher dashboards are a specific form of analytics in which visual displays provide teachers with information about their students; for example, concerning student progress and performance on tasks during lessons or lectures. In the present paper, we focus on the role of teacher dashboards in the context of teacher decision-making in K-12 education. There is large variation in teacher dashboard use in the classroom, which could be explained by teacher characteristics. Therefore, we investigate the role of teacher characteristics -- such as experience, age, gender, and self-efficacy -- in how teachers use dashboards. More specifically, we present two case studies to understand how diversity in teacher dashboard use is related to teacher characteristics. Surprisingly, in both case studies, teacher characteristics were not associated with dashboard use. Based on our findings, we propose an initial framework to understand what contributes to diversity of dashboard use. This framework might support future research to attribute diversity in dashboard use. This paper should be seen as a first step in examining the role of teacher characteristics in dashboard use in K-12 education.

Background: Assignments that involve writing based on several texts are challenging to many learners. Formative feedback supporting learners in these tasks should be informed by the characteristics of evolving written product and by the characteristics of learning processes learners enacted while developing the product. However, formative feedback in writing tasks based on multiple texts has almost exclusively focused on essay product and rarely included SRL processes. Objectives: We explored the viability of using product and process features to develop machine learning classifiers that identify low- and high-performing essays in a multi-text writing task. Methods: We examined learning processes and essay submissions of 163 graduate students working on an authentic multi-text writing assignment. We utilised learners' trace data to obtain process features and state-of-the-art natural language processing methods to obtain product features for our classifiers. Results and Conclusions: Of four popular classifiers examined in this study, Random Forest achieved the best performance (accuracy = 0.80 and recall = 0.77). The analysis of important features identified in the Random Forest classification model revealed one product (coverage of reading topics) and three process (elaboration/organisation, re-reading and planning) features as important predictors of writing quality. Major Takeaways: The classifier can be used as a part of a future automated writing evaluation system that will support at scale formative assessment in writing tasks based on multiple texts in different courses. Based on important predictors of essay performance, a guidance can be tailored to learners at the outset of a multi-text writing task to help them do well in the task.

BackgroundIn the general population with coronavirus disease 2019 (COVID-19), obesity is associated with an increased risk of mortality. Given the typically observed obesity paradox among patients on kidney function replacement therapy (KFRT), especially dialysis patients, we examined the association of obesity with mortality among dialysis patients or living with a kidney transplant with COVID-19.MethodsData from the European Renal Association COVID-19 Database (ERACODA) were used. KFRT patients diagnosed with COVID-19 between 1 February 2020 and 31 January 2021 were included. The association of Quetelet's body mass index (BMI) (kg/m2), divided into:

Teacher dashboards provide real-time information about students' performance and progress, which help K-12 teachers to adjust feedback to student' specific needs during learning. Prior research indicated two problems with respect to how teachers provide feedback: (i) teachers do not always select the most effective feedback to support student' learning and (ii) feedback is not allocated equally to students with different abilities. Specifically, process feedback is considered most effective yet is relatively scarce. In order to understand how dashboards influence the "type" and "allocation" of feedback, we compared characteristics of feedback given after dashboard consultation (dashboard-prompted feedback) to feedback triggered by teachers themselves or in response to students' questions (human-prompted feedback) in thirty-five K-12 classrooms. Results showed that dashboards led to equal amounts of task and process feedback, while human-prompts led to much more task than process feedback and this difference was especially large for low-ability students. Hence the different types of dashboard-prompted feedback were more equally distributed among students of different ability levels. These results indicate that dashboards can have an important equalizing effect on teacher feedback practices.

Self-regulation is an essential skill for lifelong learning. Research has shown that self-regulated learning (SRL) leads to greater academic achievement and sustainable education, but students often struggle with SRL. Scaffolds are widely reported as an effective and efficient support method for SRL. To further improve digital scaffolds' effectiveness, real-time detection of learning behavior can be used to personalize scaffolds. Therefore, the present study aimed to inform the field of scaffolding SRL by reporting on the design and evaluation of digital scaffolds. We present decisions made during the design process of personalized scaffolds to inform future scaffold designs. We evaluated how scaffolds were personalized based on real-time detection SRL, how university students respond to the scaffolds (i.e., compliance), and how this response is related to learning outcomes (i.e., quality of an essay). The research design was a pre-posttest with three conditions (no, generalized (same for all), or personalized scaffolds). A 45-minute reading and writing task was used, during which SRL processes were recorded in real-time. Findings revealed that different real-time SRL processes could be used to personalize scaffolds, meaning that we were able to personalize the content of scaffold based on students' actual learning behavior. In addition, students in the personalized condition complied more with the scaffolds than students in the other conditions. This compliance with the scaffolds was generally associated with better learning outcomes. To conclude, our approach showed how design decisions could be evaluated and provided insight into the personalization of scaffolds.

This study investigated macro-level navigation patterns in children's hypermedia learning, and how they related to task structure and learning outcomes. For this purpose, 5th and 6th grade learners performed a hypermedia assignment in which a high (n=57) versus a low (n=54) level of structure was provided. By means of qualitative analyses of their navigation activities, 6 macro-level navigation patterns were distinguished: linear reading, selective reading, video viewing, massed writing, late onset writing, and unpredictable reading. Results showed that the linear reading pattern was more frequent in the high structure environment, and that both the high structure environment and the linear reading pattern were associated with the highest quality of the children's written assignments. Navigation patterns and task structure did not clearly predict children's declarative knowledge gains or knowledge transfer. These findings show that there are multiple ways to navigate through a hypermedia environment, but that these are not all equally successful for learning. Moreover, the provided task structure in the environment may affect the occurrence of successful navigation patterns.

In technology empowered classrooms teachers receive real-time data about students' performance and progress on teacher dashboards. Dashboards have the potential to enhance teachers' feedback practices and complement human-prompted feedback that is initiated by teachers themselves or students asking questions. However, such enhancement requires teachers to integrate dashboards into their professional routines. How teachers shift between dashboard-and human-prompted feedback could be indicative of this integration. We therefore examined in 65 K-12 lessons: i) differences between human- and dashboard-prompted feedback; ii) how teachers alternated between human- and dashboard-prompted feedback (distribution patterns); and iii) how these distribution patterns were associated with the given feedback type: task, process, personal, metacognitive, and social feedback. The three sources of feedback resulted in different types of feedback: Teacher-prompted feedback was predominantly personal and student-prompted feedback mostly resulted in task feedback, whereas dashboard-prompted feedback was equally likely to be task, process, or personal feedback. We found two distribution patterns of dashboard-prompted feedback within a lesson: either given in one sequence together (blocked pattern) or alternated with student- and teacher-prompted feedback (mixed pattern). The distribution pattern affected the type of dashboard-prompted feedback given. In blocked patterns, dashboard-prompted feedback was mostly personal, whereas in mixed patterns task feedback was most prevalent. Hence, both sources of feedback instigation as well as the distribution of dashboard-prompted feedback affected the type of feedback given by teachers. Moreover, when teachers advanced the integration of dashboard-prompted feedback in their professional routines as indicated by mixed patterns, more effective types of feedback were given.

To develop guidelines for more effective news literacy interventions, we conducted focus groups with early adolescents (12-15 years old) on what these interventions should be like. Participants (N = 55) discussed that motivating early adolescents is a challenge, but did provide more insight into their preferences and needs. Future interventions should be easy to find and use, make news literate behaviors more accessible, should be interactive, and should tailor the content toward the target group. Developing interventions based on these guidelines could positively influence various predictors of news literate behaviors in early adolescents, and, in turn, lead to more news literate behaviors.