Search

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.

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.

Education is a unique area for application of artificial intelligence (AI). In this article, the augmentation perspective and the concept of hybrid intelligence are introduced to frame our thinking about AI in education. The involvement of quadruple helix stakeholders (i.e., researchers, education professionals, entrepreneurs, and policymakers) is necessary to understand the opportunities and challenges of different educational use cases from an integrated point of view. To facilitate a meaningful dialogue, a common language about AI in education is needed. This article outlines elements of such a common language. The "detect-diagnose-act" framework is used to describe the core functions of AI in education. The "six levels of automation" model is introduced to develop our thinking about the roles of AI, learners, and teachers in educational arrangements. In this model, the transition of control between teacher and technology is articulated at different levels and related to the augmentation perspective. Finally, the future of AI in education is discussed using self-regulated learning as an example. The proposed common language will help to support a coordinated development of an interdisciplinary dialogue between quadruple helix stakeholders to strengthen meaningful application of AI for learning and teaching.

Contemporary research that looks at self-regulated learning (SRL) as processes of learning events derived from trace data has attracted increasing interest over the past decade. However, limited research has been conducted that looks into the validity of trace-based measurement protocols. In order to fill this gap in the literature, we propose a novel validation approach that combines "theory-driven" and "data-driven" perspectives to increase the validity of interpretations of SRL processes extracted from trace-data. The main contribution of this approach consists of three alignments between trace data and think aloud data to improve measurement validity. In addition, we define the match rate between SRL processes extracted from trace data and think aloud as a quantitative indicator together with other three indicators (sensitivity, specificity and trace coverage), to evaluate the "degree" of validity. We tested this validation approach in a laboratory study that involved 44 learners who learned individually about the topic of artificial intelligence in education with the use of a technology-enhanced learning environment for 45 minutes. Following this new validation approach, we achieved an improved match rate between SRL processes extracted from trace-data and think aloud data (training set: 54.24%; testing set: 55.09%) compared to the match rate before applying the validation approach (training set: 38.97%; test set: 34.54%). By considering think aloud data as "reference point", this improvement of the match rate quantified the extent to which validity can be improved by using our validation approach. In conclusion, the novel validation approach presented in this study used both empirical evidence from think aloud data and rationale from our theoretical framework of SRL, which now, allows testing and improvement of the validity of trace-based SRL measurements.

Self-regulated learning (SRL) has been linked to improved learning and corresponding learning outcomes. However, there is a need for more precise insights into how SRL during learning contributes to specific learning outcomes. We operationalised four learning outcomes that varied on two dimensions: structure/connectedness and level/deepness of knowledge. Specifically, we assessed how surface knowledge measured with a domain knowledge test (independent concepts) and a concept map (connected concepts), and deep knowledge measured with a transfer test (independent concepts) and an essay (connected concepts) were associated with frequencies of SRL activities during learning, assessed by concurrent think aloud, while taking into account students' metacognitive and prior knowledge. Forty-four university students performed a 45-minute problem-solving task integrating information about three topics to write a vision essay on the future of education. Results of the pre-/post-test analysis revealed a learning gain in domain knowledge and concept maps. Low cognitive activities were associated with all knowledge measures, except the concept maps and transfer. Furthermore, specific low cognitive activities showed either a positive or negative association; for example, processing showed a positive association with essay, but a negative association with domain knowledge. High cognitive activities were associated with the essay (connected concepts), but not with the concept map. Both metacognitive activities and knowledge were related to transfer. To conclude, taking the level and structure of knowledge into account helps specify the association between SRL activities during learning and the related learning outcomes.

In recent years, unobtrusive measures of self-regulated learning (SRL) processes based on log data recorded by digital learning environments have attracted increasing attention. However, researchers have also recognised that simple navigational log data or time spent on pages are often not fine-grained enough to study complex SRL processes. Recent advances in data-capturing technologies enabled researchers to go beyond simple navigational logs to measure SRL processes with multi-channel data. What multi-channel data can reveal about SRL processes, and to what extent can the addition of peripheral and eye-tracking data with navigational log data change and improve the measurement of SRL are key questions that require further investigation. Hence, we conducted a study and collected learning trace data generated by 25 university students in a laboratory setting, that aimed to address this problem by enhancing navigational log data with peripheral and eye-tracking data. We developed a trace-based measurement protocol of SRL, which interpreted raw trace data from multi-channel data into SRL processes. Specifically, the study compared the frequency and duration of SRL processes detected, how much duration and times of occurrences of the detected SRL processes were affected or refined. We also used a process mining technique to analyses how temporal sequencing of the detected SRL processes changed by enriching navigational log data with peripheral and eye-tracking data. The results revealed that by adding new data channels, we improved the capture of learning actions and detected SRL processes while enhancing the granularity of the measurement. In comparison to the use of navigational logs only, the completeness of temporal sequencing relationships between SRL processes with multi-channel data improved. In addition, we concluded that eye-tracking data is valuable for measuring and extracting SRL processes, and it should receive more attention in the future.

Bounded continuous data are encountered in many applications of item response theory, including the measurement of mood, personality, and response times and in the analyses of summed item scores. Although different item response theory models exist to analyze such bounded continuous data, most models assume the data to be in an open interval and cannot accommodate data in a closed interval. As a result, ad hoc transformations are needed to prevent scores on the bounds of the observed variables. To motivate the present study, we demonstrate in real and simulated data that this practice of fitting open interval models to closed interval data can majorly affect parameter estimates even in cases with only 5% of the responses on one of the bounds of the observed variables. To address this problem, we propose a zero and one inflated item response theory modeling framework for bounded continuous responses in the closed interval. We illustrate how four existing models for bounded responses from the literature can be accommodated in the framework. The resulting zero and one inflated item response theory models are studied in a simulation study and a real data application to investigate parameter recovery, model fit, and the consequences of fitting the incorrect distribution to the data. We find that neglecting the bounded nature of the data biases parameters and that misspecification of the exact distribution may affect the results depending on the data generating model.

This study examined how to improve students' regulation of task-oriented reading (TOR). TOR encompasses reading and information processing needed to perform a specific task. Previous studies suggest students can benefit from a collaboration script to enhance socially shared regulation of TOR. The collaboration script elicits discussions about task perception, strategy selection, and strategy reflection. This study aimed to examine the depth and socially sharedness of metacognitive regulation when working with a collaboration script among 44 prevocational secondary school students working in groups of four. In addition, we examined the consequent improvement of individual task representation, strategy selection, and strategy reflection after working with the script. The analysis of group discussions indicated that the collaboration script facilitated mainly low-level metacognitive regulation of TOR. However, after working with the script, students did improve their ability to determine a correct representation of a high-level task and to reflect on the most appropriate reading strategy for these tasks. Hence, we concluded that the 'Y-read?' collaboration script did elicit shared regulation during TOR.

A common approach to increase test security in higher educational high-stakes testing is the use of different test forms with identical items but different item orders. The effects of such varied item orders are relatively well studied, but findings have generally been mixed. When multiple test forms with different item orders are used, we argue that the moderating role of speededness on item order effects cannot be neglected as missing responses are commonly scored as incorrect in high-stakes testing. If test-takers run out of time while not giving answers to easy items at the end of the test, they are penalised stronger than if instead they were unable to provide answers to difficult items. Using an illustrative real-data example of a speeded test, we show that the potential consequences of ignoring item order can be substantial with respect to test fairness. Our proposed solution consists of using a fixed item order across forms from the point at which the test may become speeded for some students. In this approach, the most time-intensive items are placed at the end of the test. A simulation based on real data of two university examinations from psychology students illustrates the usefulness of this approach.

Scientific reasoning helps children understand the world around them. Teaching scientific reasoning can be challenging because not all component scientific reasoning skills develop at the same age and not all children learn these skills at the same pace. Adaptive support thus seems called for. We designed two types of adaptive instruction, based on children's standardised test scores (macro-adaptive; n = 58) or their performance in the previous lesson (micro-adaptive; n = 46), and tested their effectiveness against a non-adaptive control condition (n = 49). Analysis of pre- and post-test scores showed comparable improvements in all three instructional conditions. As many children in both adaptive conditions received medium support, additional analyses were done on children in the macro-adaptive condition who received high or low-support worksheets, and their control group counterparts. Learning gains for these groups were similar. Children's overall task performance during the lessons also improved, and this improvement interacted with condition. These results suggest that more specific information on children's performance and more frequent and precise adaptations might lead to better learning outcomes. As this was not possible in this study, future research should explore hybrid solutions that enable children to practice scientific reasoning with physical materials while receiving adaptive support via their computers.

The intelligent tutoring system of structure strategy (ITSS) is a web-based digital tutoring system proven to be effective in helping students recognize and use text structures to comprehend and recall texts. However, little is known about the dynamic learning processes within the ITSS. This study aims to investigate the effects of feedback dosage on lesson mastery throughout the progression of ITSS lessons. We applied a confirmatory factor analysis and extended vector autoregressive model to assess the dynamic relationships among three tasks embedded within the ITSS and found: (1) significant cross-regression effects among the three reading tasks; (2) distinct effects of feedback dosage on the specific reading task; and (3) different effect sizes of feedback across lessons. Results provide helpful insights on ways to design better modules in further development of the ITSS.

Mixture models have been developed to enable detection of within-subject differences in responses and response times to psychometric test items. To enable mixture modeling of both responses and response times, a distributional assumption is needed for the within-state response time distribution. Since violations of the assumed response time distribution may bias the modeling results, choosing an appropriate within-state distribution is important. However, testing this distributional assumption is challenging as the latent within-state response time distribution is by definition different from the observed distribution. Therefore, existing tests on the observed distribution cannot be used. In this article, we propose statistical tests on the within-state response time distribution in a mixture modeling framework for responses and response times. We investigate the viability of the newly proposed tests in a simulation study, and we apply the test to a real data set.

Advancing learners' agency is a key educational goal. The advent of personalized EdTech, which automatically tailor learning environments to individual learners, gives renewed relevance to the topic. EdTech researchers and practitioners are confronted with the same basic question: What is the right amount of agency to give to learners during their interactions with EdTech? This question is even more relevant for younger learners. Our aim in this paper is twofold: First, we outline and synthesize the ways in which agency is conceptualized in three key learning disciplines (philosophy, education, and psychology). We show that there are different types and levels of agency and various prerequisites for the effective exercise of agency and that these undergo developmental change. Second, we provide guiding principles for how agency can be designed for in EdTech for children. We propose an agency personalization loop in which the level of agency provided by the EdTech is assigned in an adaptive manner to strike a balance between allowing children to freely choose learning content and assigning optimal content to them. Finally, we highlight some examples from practice.

An individual's emotions system can be conceived of as a synchronized, coordinated, and/or emergent combination of physiology, experience, and behavioral components. Together, the interplay among these components produce emotional experiences through coordinated excitatory positive feedback (i.e., the mutual amplification of emotion concordance) and/or inhibitory negative feedback (i.e., the damping of emotion regulation) processes. Different system configurations produce differential psychophysiological reactivity profiles, and by implication, differential moment-to-moment emotional experience and long-term development. Applying dynamic systems models to second-by-second psychophysiological and experience time-series data collected from 130 adolescents (age 12.0 to 16.7 years) completing a social stress-inducing speech task, we describe the configuration of adolescents' emotion systems, and examine how differences in the dynamic outputs of those systems (psychophysiological reactivity profile) are related to individual differences in trait anxiety. We found substantial heterogeneity in the coordination patterns of these adolescents. Some individuals' emotion systems were characterized by negative feedback loops (emotion regulation processes), many by unidirectionally connected or independent components, and a few by positive feedback loops (emotion concordance). The reactivity dynamics of respiratory sinus arrhythmia were related to adolescents' level of trait anxiety. Results highlight how dynamic systems models may contribute to our understanding of interindividual and developmental differences.

The present study investigated the extent to which 18 dyads in 5th and 6th grade, who experienced low levels of social challenge, differed from 12 dyads who experience high levels of social challenge in terms of the quality of their written assignment, as well as the frequency and sequential pattern of their cognitive, metacognitive, relational, and off-task activities during a collaborative hypermedia assignment. Sequential analyses were performed by means of process mining with a fuzzy miner algorithm. Results showed that assignment quality was higher for low social challenge dyads. In addition, these more successful dyads showed more cognitive processing activities, more high-cognition, and fewer off-task activities. In terms of their process models, low and high challenge dyads showed marked differences. More specifically, high social challenge dyads showed a vicious cycle of social challenges and off-task behaviors, whereas low social challenge dyads engaged in high-cognition. In addition, for low challenge dyads, but not high challenge dyads, the various metacognitive activities were closely connected to each other. These findings indicate that social challenges not only affect assignment quality, but also fundamentally affect the overall learning process.

This paper focuses on a trend to analyse temporal characteristics of constructs important to learning and instruction. Different researchers have indicated that we should pay more attention to time in our research to enhance explanatory power and increase validity. Constructs formerly viewed as personal traits, such as self-regulated learning and motivation, are now conceptualized as a series of events that unfold over time. This raises new questions with regard to the temporal characteristics of these constructs and their dynamic interplay with learner and context characteristics. Even though the value of analyzing temporal characteristics is becoming evident, a number of challenges need to be tackled in order to make progress in the field of learning and instruction. First, we need to be aware of the paradigm shift that temporal analysis entails. Second, a common understanding of different dimensions of time and the position of temporal characteristics therein can facilitate our time-related research dialogue. Third, a better understanding how to answer time-related questions with appropriate methodological approaches needs to emerge. Fourth, researching temporal characteristics requires procedures and guidelines for segmenting time units. Fifth, temporal data are mostly collected at the micro level, whereas most theory is defined at a macro level; consequently we need to bridge these differences in the granularity used between collecting, coding and theorizing to enhance meaning making. Finally, so far, most examples of time-related research are exploratory or comparative studies; the next step is to move toward confirmative studies, which constitute the "Holy Grail" of temporal analysis.

This study investigates how teachers use dashboards in primary school classrooms. While learners practice on a tablet real-time data indicating learner progress and performance is displayed on teacher dashboards. This study examines how teachers use the dashboards, applying Verberts' learning analytics process model. Teacher dashboard consultations and resulting pedagogical actions were observed in 38 mathematics lessons. In stimulated recall interviews, the 38 teachers were asked to elaborate on how they reflect on and make sense of the information on the dashboard. The results showed that teachers consulted the dashboard on average 8.3 times per lesson. Teachers activated existing knowledge about students and the class to interpret dashboard information. Task and process feedback were the pedagogical actions most often used following dashboard consultation. Additionally, teachers who consulted the dashboard more often activated more and more diverse pedagogical knowledge to interpret the data and, consequently, gave more and more diverse feedback. These results indicated that teacher dashboards were indeed influencing teachers' pedagogical actions in their daily classroom activities. This study provided the first evidence that dashboards progressively impact teaching practice and initiate more profound behavioral changes as teachers become more proficient in using them.

Objective: This study examined the role of dispositional mindfulness in impacting the relational benefits of yoga in novice practitioners in an undergraduate academic yoga course. Participants: 21 college students enrolled in a yoga course throughout a 15-week academic semester (January-April 2016). Methods: Participants attended lab assessments at the beginning and end of the semester and were asked to complete eight consecutive Internet-based daily surveys across six separate bursts, yielding 48 data points for each participant. Results: Multivariate time-series analyses revealed within-person subgroup differences such that in general, those in the high trait mindfulness group gleaned benefits from yoga practice with respect to relational outcomes. Conclusions: This study demonstrated trait mindfulness impacts relational outcomes in novice yoga practitioners, suggesting those low in trait mindfulness may not garner immediate relational benefits from yoga and could benefit from additional strategies to bolster against any negative influences of initiating yoga practice.

In the present study, we investigated whether online learning behaviours (navigation and writing activities) mediated the relation between learner characteristics (prior knowledge, vocabulary knowledge, working memory, and motivation) and declarative knowledge. Specifically, we investigated whether the quality of participants' written assignments could further explain this relation. For this purpose, 62 fifth-grade children participated in a WebQuest hypermedia assignment on the subject of the heart. Results showed that online learning behaviours did not mediate the relations between learner characteristics and declarative knowledge when the assignment quality was not included in the model. Adding assignment quality, however, revealed two serial mediation models. Prior knowledge predicted declarative knowledge via the writing activity "copying behaviour" and assignment quality, and vocabulary knowledge predicted declarative knowledge via the navigation activity "time spent on assignment pages" and assignment quality. These results show the importance of taking into account assignment quality when investigating knowledge acquisition in hypermedia environments.

Preschool children's vocabulary mainly develops verbal through interaction. Therefore, the technology-enhanced storytelling (TES) activity Jeffy's Journey is developed to support parent-child interaction and vocabulary in preschool children. TES entails shared verbal storytelling supported by a story structure and real-time visual, auditory and textual prompts on a tablet computer. In this exploratory study, we investigated how TES influenced parent-child interaction and vocabulary. An experimental pretest-intervention-posttest design was followed with 44 3-year-old children and their parents in the experimental group and 27 peers in the control group. Results revealed that TES stimulated active child involvement and generated parent-child interaction, yet a great variety in TES characteristics both in time spent and usage of prompts was found among participants. Dyads that spent more time on story phases showed more and higher quality parent-child interaction. The usage of prompts was associated with improved parent-child interaction quality. Finally, an effect of TES was evidenced on children's productive vocabulary knowledge. To conclude, this study demonstrates that TES can be considered as a promising context for fostering parent-child interaction and children's vocabulary development.

Determining the number of factors is a critical first step in exploratory factor analysis. Although various criteria and methods for determining the number of factors have been evaluated in the usual between-subjects R-technique factor analysis, there is still question of how these methods perform in within-subjects P-technique factor analysis. A novel feature of P-technique data is that observations are usually sequentially dependent in some way. The current study evaluates 10 criteria for determining the number of factors for data simulated to resemble intensive repeated-measures data for which P-technique is usually applied. The number of factors, loading size, observed-to-latent ratio, serial-dependency, and sample size were simulation design factors with conditions simulated to resemble P-technique data. The methods are compiled from various statistical techniques and have been demonstrated in previous R-technique simulations. Finally, a real data example demonstrates applying the acceleration factor method to several time series data sets.

Some children enter elementary school with large vocabulary delays, which negatively influence their later school performance. A rich home language environment can support vocabulary development through frequent high-quality parent-toddler interaction. Elaborated picture home activities can support this rich home language environment. This study compares the effects of a multimedia versus a paper elaborated picture on the parent-toddler interaction and toddlers' vocabulary development. In a within-subjects design, 20 toddlers (age 3-4) discussed a multimedia and a paper elaborated picture with a parent. Results showed that toddlers knew significantly more words (receptively and expressively) after both activities. Moreover, the improvement in receptive vocabulary knowledge was significantly larger with the multimedia elaborated picture compared with the paper-based picture. In addition, both parent and toddler engaged in a significant higher level of decontextualized language in response to multimedia. The present study shows that multimedia elaborated pictures can support parents at home to engage in a parent-toddler interaction that is richer and supports the development of receptive vocabulary more compared with traditional paper-based activities. In the future, multimedia-based home activities for toddlers' vocabulary development, supporting parent-child interaction, can be offered relatively cheap and easily via internet.

Nowadays, teachers must deal, as never before, with diversity in classrooms. Differentiation practices help teachers to address this diversity in an inclusive setting. However, teachers' perceptions about classroom heterogeneity are fundamental to examine whether they are competent to screen their pupils' needs. The present study used a qualitative research design in order to understand how four teachers who teach the same classroom of a secondary school recognise and address those diverse educational needs within a classroom. Results showed a distinct view of students' diversity within a classroom. Moreover, differentiation strategies to respond to this perceived diversity were only partially used. [Paper presented at the Inclusive and Supportive Education IV Conference Congresso Internacional da Pró-Inclusão (8th, Lisbon, Portugal, July 26-29, 2016).]

Transitions requiring the creation of new social networks may be challenging for individuals vulnerable to social anxiety, which may hinder successful adjustment. Using person-specific methodology, this study examined social anxiety in vulnerable university freshman away from home during their first semester of college to understand how day-to-day processes of social anxiety influenced future social anxiety and social withdrawal. Participants completed daily measures of four components of social anxiety which were examined as a process during a single day, and as a process affecting social anxiety and social withdrawal the next day. For most individuals, same-day fear of negative evaluation was associated with maladaptive cognitive processes (anticipatory processing and post-event rumination) and influenced social withdrawal behaviors. Across time relations were less robust and varied between participants suggesting the importance of situational factors and individual differences. These findings may have implications for the effectiveness of various intervention strategies.

A new and very interesting approach to the analysis of responses and response times is proposed by Goldhammer (this issue). In his approach, differences in the speed-ability compromise within respondents are considered to confound the differences in ability between respondents. These confounding effects of speed on the inferences about ability can be controlled for in experimental settings. As a result, the data for psychological or educational inferences consists of the response vectors only. Molenaar argues that the natural variability in response times can give valuable information for psychological and educational inferences about response processes and solution strategies, but that the approach by Goldhammer is very valuable if a single process or strategy needs to be measured in isolation.

In this article, Peter Molenaar responds to three commentaries (this issue) on his article, "An Interpretation of Part of Gilbert Gottlieb's Legacy: Developmental Systems Theory Contra Developmental Behavior Genetics." He addresses aspects of relational developmental systems (RDS) mentioned and questions raised in each of the commentaries. [This commentary relates to Molenaar's "An Interpretation of Part of Gilbert Gottlieb's Legacy: Developmental Systems Theory Contra Developmental Behavior Genetics" (EJ1144157) and the following commentaries: "Developmental Systems Theory and the Person-Oriented Approach" (EJ1144417), "When the Stars Align: On the Contributions of Gilbert Gottlieb and Peter C. M. Molenaar to Developmental Science Theory and Method" (EJ1144159), and "Who Are We Talking About? A Discussion of Peter Molenaar's Interpretation of Gottlieb's Legacy" (EJ1144424).]

The main theme of this paper concerns the persistent critique of Gilbert Gottlieb on developmental behavior genetics and my reactions to this critique, the latter changing from rejection to complete acceptation. Concise characterizations of developmental behavior genetics, developmental systems theory (to which Gottlieb made essential contributions), ergodic theory (the implications of which underlie my change of opinion), as well as their relevant theoretical relations are given.

This article explores the effect of computerized scaffolding with different scaffolds (structuring vs. problematizing) on intra-group metacognitive interaction. In this study, we investigate 4 types of intra-group social metacognitive activities; namely ignored, accepted, shared and co-constructed metacognitive activities in 18 triads (6 control groups; no scaffolds and 12 experimental groups; 6 structuring scaffolds and 6 problematizing scaffolds). We found that groups receiving scaffolding showed significantly more intra-group interactions in which the group members co-construct social metacognitive activities. Groups receiving problematizing scaffolds showed significantly less ignored and more co-constructed social metacognitive interaction compared to groups receiving structuring scaffolds. These findings indicate that scaffolding positively influenced the group members' intra-group social metacognitive interaction. We also found a significant relation between students' participation in intra-group social metacognitive interaction and students' metacognitive knowledge. Twelve percent of the variance in students' metacognitive knowledge was explained by their participation in intra-group shared social metacognitive interaction. Therefore, future research should consider how to design scaffolds that elicit intra-group social metacognitive interaction among group members to enhance the development of students' metacognitive knowledge.

Extending past research showing that regulative activities (metacognitive and relational) can aid learning, this study tests whether sequences of cognitive, metacognitive and relational activities affect subsequent cognition. Scaffolded by a computer avatar, 54 primary school students (working in 18 groups of 3) discussed writing a report about a foreign country for 51,338 turns. Statistical discourse analysis (SDA) of these sequences of talk showed that after low cognition, high cognition, planning or evaluation, both low and high cognition were more likely (some effects lasted 6 conversation turns). After monitoring or positive relational activities (confirm, engage), low cognition was more likely. After a denial however, high cognition was less likely. These results suggest that metacognitive planning organizes subsequent cognitive activities and facilitates the transition between acquisition of knowledge and meaning making, while relational activities help enact them. These insights can inform micro-temporal theories of social regulation and shared knowledge construction.

The fields of psychology and human development are experiencing a resurgence of scientific inquiries about phenomena that unfold at the level of the individual. This article addresses the issues of analyzing intraindividual psychological/developmental phenomena using standard analytical techniques for interindividual variation. When phenomena are characterized by intraindividual variation, analyses must be done at the level of the individual. Research on these processes generally uses methods analyzing interindividual variation, rather than intraindividual, however, a noteworthy fundamental difference exists between the two. The current article provides theoretical and empirical illustrations of the fundamental contrast between variation within subjects and variation between subjects. First, we explain this distinction through the classical ergodic theorems. Next, we discuss the utility of analysis of intraindividual variation for developmental processes. We then provide an empirical example, using recently-developed methods for analyzing person-specific developmental processes. This article presents for the first time a methodological approach in which first the factor model is determined by exploratory and confirmatory P-technique, and then in a separate second phase the dynamic model for the latent factor series is determined.

Measurement invariance is an important prerequisite for the adequate comparison of group differences in test scores. In psychology, measurement invariance is typically investigated by means of linear factor analyses of subtest scores. These subtest scores typically result from summing the item scores. In this paper, we discuss 4 possible problems related to this common practice. Specifically, we discuss (a) nonlinearity of the latent variable to subtest relation; (b) suboptimality of the total score as a proxy for the latent variable measured through the item scores; (c) non-normality of the subtest score; and (d) differences in the nature of the latent variable at the item level as compared to the latent variable at the subtest level. Additionally, we give guidelines to overcome these problems and illustrate the issues by analysing data that pertain to a performal IQ data set. (Contains 7 tables, 6 figures, and 1 note.)

The aim of this study is to test the effects of dynamically scaffolding social regulation of middle school students working in a computer-based learning environment. Dyads in the scaffolding condition (N=56) are supported with computer-generated scaffolds and students in the control condition (N=54) do not receive scaffolds. The scaffolds are dynamically adjusted to dyads' progress with an attention management system. The scaffolds support two aspects of socially regulated learning namely the metacognitive and cognitive activities. We analyzed the effects of dynamic scaffolding on dyads' performance, their perception of the learning environment and students' knowledge acquisition. We found that scaffolding had a positive effect on the dyads' learning performance, but did not affect students' domain knowledge. The repeated measurements of perception of the learning environment showed that dyads in the experimental condition were more positive about their teachers and their collaborators than students in the control condition. With respect to their perception of the software and the 3D embodied agent delivering the scaffolds, we found a stronger decrease of appreciation over time in the scaffolding condition compared to the control condition. (Contains 1 table and 3 figures.)

The Graded Response Model (GRM; Samejima, "Estimation of ability using a response pattern of graded scores," Psychometric Monograph No. 17, Richmond, VA: The Psychometric Society, 1969) can be derived by assuming a linear regression of a continuous variable, Z, on the trait, [theta], to underlie the ordinal item scores (Takane & de Leeuw in "Psychometrika," 52:393-408, 1987). Traditionally, a normal distribution is specified for Z implying homoscedastic error variances and a normally distributed [theta]. In this paper, we present the Heteroscedastic GRM with Skewed Latent Trait, which extends the traditional GRM by incorporation of heteroscedastic error variances and a skew-normal latent trait. An appealing property of the extended GRM is that it includes the traditional GRM as a special case. This enables specific tests on the normality assumption of Z. We show how violations of normality in Z can lead to asymmetrical category response functions. The ability to test this normality assumption is beneficial from both a statistical and substantive perspective. In a simulation study, we show the viability of the model and investigate the specificity of the effects. We apply the model to a dataset on affect and a dataset on alexithymia.

In this short contribution I introduce myself to the readership of the "International Journal of Developmental Science" by describing some high points in my scientific career, leading up to a major change in my perspective on the proper way to conduct empirical research in developmental science.

This study investigated the performance of fit indexes in selecting a covariance structure for longitudinal data. Data were simulated to follow a compound symmetry, first-order autoregressive, first-order moving average, or random-coefficients covariance structure. We examined the ability of the likelihood ratio test (LRT), root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis Index (TLI) to reject misspecified models with varying degrees of misspecification. With a sample size of 20, RMSEA, CFI, and TLI are high in both Type I and Type II error rates, whereas LRT has a high Type II error rate. With a sample size of 100, these indexes generally have satisfactory performance, but CFI and TLI are affected by a confounding effect of their baseline model. Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) have high success rates in identifying the true model when sample size is 100. A comparison with the mixed model approach indicates that separately modeling the means and covariance structures in structural equation modeling dramatically improves the success rate of AIC and BIC. (Contains 4 tables and 5 figures.)

Molenaar (2003, 2011) showed that a common factor model could be transformed into an equivalent model without factors, involving only observed variables and residual errors. He called this invertible transformation the Houdini transformation. His derivation involved concepts from time series and state space theory. This article verifies the Houdini transformation on a general latent variable model using algebraic methods. The results show that the Houdini transformation is illusory in the sense that the Houdini-transformed model remains a latent variable model. Contrary to common knowledge, a model that is a path model with only observed variables and residual errors may, in fact, be a latent variable model.

Metacognitive scaffolding in a computer-supported learning environment can influence students' metacognitive activities, metacognitive knowledge and domain knowledge. In this study we analyze how metacognitive activities mediate the relationships between different avatar scaffolds on students' learning. Multivariate, multilevel analysis of the 51,339 conversation turns by 54 elementary school students working in triads showed that scaffolding has an effect on students' learning. Students receiving structuring or problematizing metacognitive scaffolds displayed more metacognitive knowledge than students in the control group. Metacognitive activities mediated the effects of scaffolding, and increased metacognitive activities supported students' metacognitive knowledge. Moreover, students who were engaged in proportionately more cognitive activities or fewer off-task activities also outperformed other students on the metacognitive knowledge test. Only problematizing scaffolds led to more domain knowledge and metacognitive activities mediated the effects of the problematizing scaffolds. Moreover, students in the problematizing condition who engaged in more cognitive activities or whose group mates used more relational activities had greater domain knowledge acquisition than other students. (Contains 9 tables, 5 examples, and 2 figures.)