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Abstract: Developing a desirable ethanol dehydrogenation process necessitates a highly efficient and selective catalyst with low cost. Herein, we show that the “complex active site” consisting of atomically dispersed Au atoms with the neighboring oxygen vacancies (Vo) and undercoordinated cation on oxide supports can be prepared and display unique catalytic properties for ethanol dehydrogenation. The “complex active site” Au−Vo−Zr3+ on Au1/ZrO2 exhibits the highest H2 production rate, with above 37,964 mol H2 per mol Au per hour (385 g H2  h−1) at 350 °C, which is 3.32, 2.94 and 15.0 times higher than Au1/CeO2, Au1/TiO2, and Au1/Al2O3, respectively. Combining experimental and theoretical studies, we demonstrate the structural sensitivity of these complex sites by assessing their selectivity and activity in ethanol dehydrogenation. Our study sheds new light on the design and development of cost‐effective and highly efficient catalysts for ethanol dehydrogenation. Fundamentally, atomic‐level catalyst design by colocalizing catalytically active metal atoms forming a structure‐sensitive “complex site”, is a crucial way to advance from heterogeneous catalysis to molecular catalysis. Our study advanced the understanding of the structure sensitivity of the active site in atomically dispersed catalysts.

Semiaquilegia danxiashanensis is currently known only from the type locality, Danxia Mountain, characterized by its spectacular red sandstone cliffscape. In this study, we assembled the complete chloroplast genome sequence of S. danxiashanensis and inferred its phylogenetic relationships. Total length of the chloroplast genome was 160,548 bp, with an overall GC content of 39%. The chloroplast genome had typical quadripartite structure and contained one LSC region (89,882 bp) and one SSC region (17,386 bp), which were separated by two IRs regions (26,640 bp, respectively). It comprised 133 genes, including 84 protein coding genes, 41 tRNA genes and eight rRNA genes. The maximum likelihood phylogenetic analysis indicated that S. danxiashanensis was sister to S. adoxoides; meanwhile, Semiaquilegia was closely related to both Urophysa and Aquilegia in Ranunculaceae. This study sheds light on the evolutionary history of Semiaquilegia and provides preliminary data for future comparative analysis of chloroplast genomes.

Background: Computer-based scaffolding has been intensively used to facilitate students' self-regulated learning (SRL). However, most previous studies investigated how computer-based scaffoldings affected the cognitive aspect of SRL, such as knowledge gains and understanding levels. In contrast, more evidence is needed to examine the effects of scaffolding on the metacognitive dimension and efficiency outcome of SRL. Objectives: This study aims to examine the role of computer-based scaffolding in students' metacognitive monitoring and problem-solving efficiency. Methods: Seventy-two medical students completed two clinical reasoning tasks in BioWorld, an intelligent tutoring system (ITS) designed for promoting medical students' diagnostic expertise. During solving the tasks, students were asked to report their confidence judgements about proposed diagnoses. Computer trace data were used to identify task completion time (CT) and students' use of three scaffolding types, that is, conceptual, strategic, and metacognitive. Then we calculated students' metacognitive monitoring accuracy (i.e., calibration) and problem-solving efficiency. Results and Conclusions: One-sample t-test demonstrated that students inaccurately monitored their learning processes and were overconfident in both tasks. Linear mixed-effects models (LMMs) indicated that the intensive use of metacognitive scaffolding positively predicted students' metacognitive monitoring accuracy. Moreover, strategic scaffolding was negatively related to problem-solving efficiency, whereas metacognitive scaffolding positively influenced problem-solving efficiency. Takeaways: This study shows the importance of metacognitive scaffolding in improving the accuracy of metacognitive monitoring and problem-solving efficiency. Findings from this study provide new insights for instructors and ITS developers to optimise the design of scaffoldings.

Academic emotions play an important and complex role in self-regulated learning (SRL). However, few studies have examined how academic emotions unfold in different phases of SRL and how the changes in these emotions influence learning performance. The current study examines 98 students' academic emotion trajectories and profiles across the three phases of SRL (i.e., forethought, performance, and self-reflection) as they solve a clinical problem in BioWorld. Specifically, BioWorld is a simulated learning environment where medical students are tasked with diagnosing virtual patient diseases. We identified the three phases of SRL based on students' problem-solving behaviors and we asked students to self-report their achievement and epistemic emotions at the end of each phase of SRL. The growth curve model results showed that curiosity and confusion declined across the three phases of SRL, whereas boredom increased in the self-reflection phase of SRL. The initial levels of curiosity and enjoyment positively predicted students' performance. Latent transition analysis revealed three emotion profiles: curious-positive, confused-negative, and medium-low. Curious-positive students maintained a relatively stable profile through the SRL phases, whereas students in the confused-negative and medium-low groups exhibited specific transition patterns in their emotions. This study makes theoretical contributions by highlighting the temporal and dynamic nature of emotions in problem-solving. Findings from this study have educational implications regarding the role of specific emotions in learning, the development of one's awareness of their emotions, and emotion regulation.

Emotion variability and its relationship to performance is an underexplored area of research both inside and outside the realm of medical education. We address this gap by examining the relative importance of the frequency of emotions and emotion variability that occurred in specific phases of self-regulated learning (SRL) in predicting students' performance. Specifically, 23 medical students were recruited to complete the task of diagnosing a virtual patient in a hospital-simulated environment. Students' facial expressions were video-recorded and were classified into basic emotions. We calculated the frequency of emotions and emotion variability at each SRL phase: forethought, performance, and self-reflection. Findings revealed that both the frequency of emotions and emotion variability influenced clinical reasoning performance, but they functioned differently in different SRL phases. Moreover, emotion variability negatively predicted performance regardless of which SRL phases it was tied to. This study helps shift the focus of research from the effect of emotions on performance to the joint effect of emotion and emotion variability, which has the potential to address the inconsistency in emotion-related research findings. Although we situate the study in the context of clinical reasoning, findings from this research inform the research of emotion in learning and instruction for other domains. Furthermore, this study lays the foundation for future advances in emotion-related study designs since the introduction of emotion variability leaves many questions unanswered and shows promise for new research directions.

Facilitating the collaboration of multiple groups is a critical element in problem-based learning (PBL). In face-to-face learning environments, PBL facilitators require sufficient information about a group's progress and collaboration in real time to make decisions about when and how to facilitate. The capacity of facilitator is limited as PBL scales up. Online PBL settings can mitigate this challenge by presenting data from multiple groups to support facilitators using orchestration technology. A learning analytics dashboard with visual displays is one type of orchestration technology. This study examined 10 PBL facilitators' use of such an orchestration technology to assess the collaboration patterns of multiple groups. Think-aloud protocols were collected from PBL facilitators as they used the technology to understand group collaboration patterns, which were illustrated through learning analytics visualizations. The think-aloud method enabled the PBL facilitators to verbalize their thought processes, and content analysis was conducted to analyze the transcripts. This study found that the expert facilitators made consistent formative assessments and strategically selected the most relevant visualizations to examine knowledge co-construction and group collaboration. In addition, they used multiple visualizations to calibrate and confirm their understanding of the students' learning and group processes, rather than relying on one visualization. Understanding how facilitators use the information generated from a learning analytics dashboard to assess collaboration in an online PBL environment is critical for better support of online facilitation and the design of orchestration technology.

Monitoring one's learning activities is a key component of self-regulated learning (SRL) leading to successful learning and performance outcomes across settings. Achievement emotions also play an important part in SRL and consequently student learning outcomes. However, there is little research on how specific types of monitoring (i.e. Feeling-of-Knowing, Judgement-of-Learning) and achievement emotions jointly affect performance in clinical reasoning within computer simulated environments. In this study, we explored the joint predictive value of monitoring judgements and achievement emotions in medical students' ability to make accurate and efficient clinical diagnoses using BioWorld, a computer-based learning environment designed to help medical students practice clinical reasoning skills. Multiple types of data (log files, verbal protocols, self-report questionnaires) from 27 students were analyzed using text mining and linear mixed-effects models. We found that feeling-of-knowing judgements positively predicted diagnostic efficiency whereas judgement-of-learning and the achievement emotion of anger negatively predicted diagnostic efficiency. Achievement emotion of pride positively predicted confidence in diagnosing the case correctly. This study not only provides theoretical and methodological insights but also alert medical instructors to the many dimensions of diagnostic performances and effective intervention strategies in clinical reasoning.

In this paper, we adopted an information-processing perspective to examine the relationship between cognitive engagement and students' performance in a simulation-based training environment. In particular, we examined what forms of cognitive engagement students used while diagnosing virtual patients and whether engagement forms predicted students' diagnostic confidence and efficacy. A total of 88 medical students from a large North American university voluntarily participated in this study. We used latent profile analysis (LPA), a person-centered statistical method, to identify groups of students with similar information processing patterns. Findings from this study revealed that students displayed various forms of cognitive engagement, i.e. recipience, resource management, and task-focused. Moreover, we found that group difference in diagnostic confidence was moderated by task complexity. In terms of diagnostic efficacy, students who were task-focused or resource managers did better than the recipience students. The findings advance our understanding of theories of cognitive engagement as well as inform the design of effective interventions in developing simulation-based learning environments.

Research on self-regulated learning (SRL) in engineering design is growing. While SRL is an effective way of learning, however, not all learners can regulate themselves successfully. There is a lack of research regarding how student characteristics, such as science knowledge and design knowledge, interact with SRL. Adapting the SRL theory in the field of engineering design, this study proposes a research model to examine the mediation and causal relationships among science knowledge, design knowledge, and SRL activities (i.e. observation, formulation, reformulation, analysis, evaluation). Partial least squares modeling was utilized to examine how the science and design knowledge of 108 ninth-grade participants interacted with their SRL activities in the process of performing an engineering task. Results reveal that prior science and design knowledge positively predict SRL activities. They also show that reformulation and analysis are the two SRL activities that can lead to an improvement in post science and design knowledge, but excessive observation can hinder post design knowledge. These results have important implications for the construction of learning environments to support SRL based on students' prior knowledge levels.

Examining the sequential patterns of self-regulated learning (SRL) behaviors is gaining popularity to understand students' performance differences. However, few studies have looked at the transition probabilities among different SRL behaviors. Moreover, there is a lack of research investigating the temporal structures of students' SRL behaviors (e.g., repetitiveness and predictability) and how they related to students' performance. In this study, 75 students from a top North American university were tasked to diagnose a virtual patient in an intelligent tutoring system. We used recurrence quantification analysis and sequential analysis to analyze the temporal structures and sequential patterns of students' SRL behaviors. We compared the differences between low and high performers. We found that low performers had more single, isolated recurrent behaviors in problem-solving, whereas the recurrent behaviors of high performers were more likely to be part of a behavioral sequence. High performers also demonstrated a higher transition probability across the three phases of SRL than low performers. In addition, high performers were unique in that their behavioral state transitions were cyclically sustained. This study provided researchers with theoretical insights regarding the cyclical nature of SRL. This study has also methodological contributions to the analysis of the temporal structures of SRL behaviors.

This study examined the relationships between clinical reasoning behaviors and diagnostic efficiency in the context of diagnosing a virtual patient in BioWorld, a technology-rich environment designed for medical students to practice clinical reasoning skills. Eighty-two medical students who correctly solved a patient case with Diabetes Mellitus were included in this study. These students were grouped into efficient and less efficient groups based on the time they spent diagnosing the case using k-means clustering. Students' clinical reasoning behaviors were recorded in log files and further coded as either relevant or irrelevant to the final correct diagnosis. Independent t-tests and sequential pattern mining were then conducted to compare the differences between efficient and less efficient groups. Results revealed that students in the less efficient group collected significantly more irrelevant evidence, ordered more lab tests, and proposed more incorrect hypotheses than efficient students. Moreover, less efficient students demonstrated more disorganized behavioral patterns than efficient students. These findings underscored metacognitive skills in delivering an efficient diagnosis. This study also informs the practice of medical education in terms of the development of expertise, as well as the design of interventions and scaffolding in promoting efficient learning or clinical reasoning.