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Although the brain can discriminate between various sweet substances, the underlying neural mechanisms of this complex behavior remain elusive. This study examines the role of the anterior paraventricular nucleus of the thalamus (aPVT) in governing sweet preference in mice. We fed the mice six different diets with equal sweetness for six weeks: control diet (CD), high sucrose diet (HSD), high stevioside diet (HSSD), high xylitol diet (HXD), high glycyrrhizin diet (HGD), and high mogroside diet (HMD). The mice exhibited a marked preference specifically for the HSD and HSSD. Following consumption of these diets, c-Fos expression levels in the aPVT were significantly higher in these two groups compared to the others. Utilizing fiber photometry calcium imaging, we observed rapid activation of aPVT neurons in response to sucrose and stevioside intake, but not to xylitol or water. Our findings suggest that aPVT activity aligns with sweet preference in mice, and notably, stevioside is the sole plant-based sweetener that elicits an aPVT response comparable to that of sucrose.

High-quality instruction is essential to facilitating student learning, prompting many professional development (PD) programmes for teachers to focus on improving classroom dialogue. However, during PD programmes, analysing discourse data is time-consuming, delaying feedback on teachers' performance and potentially impairing the programmes' effectiveness. We therefore explored the use of ChatGPT (a fine-tuned GPT-3.5 series model) and GPT-4o to automate the coding of classroom discourse data. We equipped these AI tools with a codebook designed for mathematics discourse and academically productive talk. Our dataset consisted of over 400 authentic talk turns in Chinese from synchronous online mathematics lessons. The coding outcomes of ChatGPT and GPT-4o were quantitatively compared against a human standard. Qualitative analysis was conducted to understand their coding decisions. The overall agreement between the human standard, ChatGPT output, and GPT-4o output was moderate (Fleiss's Kappa = 0.46) when classifying talk turns into major categories. Pairwise comparisons indicated that GPT-4o (Cohen's Kappa = 0.69) had better performance than ChatGPT (Cohen's Kappa = 0.33). However, at the code level, the performance of both AI tools was unsatisfactory. Based on the identified competences and weaknesses, we propose a two-stage approach to classroom discourse analysis. Specifically, GPT-4o can be employed for the initial category-level analysis, following which teacher educators can conduct a more detailed code-level analysis and refine the coding outcomes. This approach can facilitate timely provision of analytical resources for teachers to reflect on their teaching practices.

Abstract The potential long-term effects of anesthesia on cognitive development, especially in neonates and infants, have raised concerns. However, our understanding of its underlying mechanisms and effective treatments is still limited. In this study, we found that early exposure to isoflurane (ISO) impaired fear memory retrieval, which was reversed by dexmedetomidine (DEX) pre-treatment. Measurement of c-fos expression revealed that ISO exposure significantly increased neuronal activation in the zona incerta (ZI). Fiber photometry recording showed that ZI neurons from ISO mice displayed enhanced calcium activity during retrieval of fear memory compared to the control group, while DEX treatment reduced this enhanced calcium activity. Chemogenetic inhibition of ZI neurons effectively rescued the impairments caused by ISO exposure. These findings suggest that the ZI may play a pivotal role in mediating the cognitive effects of anesthetics, offering a potential therapeutic target for preventing anesthesia-related cognitive impairments.

This study investigates the potential of ChatGPT, a cutting-edge large language model in generative artificial intelligence (AI), to support the teaching of dialogic pedagogy to preservice teachers. A workshop was conducted with 29 preservice teachers, wherein ChatGPT and another prominent AI model, Bert, were sequentially integrated to facilitate real-time analysis and enhance the participants’ learning experience. Data regarding preservice teachers’ trust in and technology acceptance of ChatGPT and Bert, as well as their perceptions of ChatGPT’s efficacy in facilitating their learning, were collected and analyzed. The findings indicate that the preservice teachers exhibited greater trust and technology acceptance regarding ChatGPT compared to the baseline model, Bert, for supporting their learning. However, the participants’ satisfaction questionnaire responses indicated only moderate satisfaction with ChatGPT’s facilitation of their learning of dialogic pedagogy. Thematic analyses of interviews further revealed the perceived strengths and weaknesses of ChatGPT, as reported by preservice teachers. They expressed satisfaction with ChatGPT’s comprehensiveness, interpretability, and critical analysis, which contributed positively to their learning experiences. Nevertheless, they also identified issues related to lengthiness, readability, and answer accuracy. This exploratory study highlights the potential of ChatGPT to help teach dialogic pedagogy, paving the way for future research to integrate generative AI into teacher learning programs.

During the COVID-19 pandemic, many online lessons for emergency remote teaching (ERT) were recorded. However, little is known about how these recordings can be reused as resources for students’ active learning. This study aimed to design a post-video learning approach that uses recorded videos of lessons to remediate students’ learning losses associated with the pedagogical challenges of ERT. Two classes (54 students in total) with two teachers of general studies and mathematics in a Hong Kong primary school participated in a quasi-experimental study in which they used an approach integrating video selection, visualizations of classroom dialogues, a discussion forum, and quizzes for 10 weeks. After the intervention, the students’ mathematics knowledge had improved significantly relative to a comparison group, while the general studies class did not show significant changes. Eight students and two teachers were interviewed about their experiences with the post-video learning approach. The approach appeared to be a pedagogically meaningful way of engaging students in active learning activities and addressing the challenges raised by ERT by recycling recorded lessons. The results of this study also suggest that recorded lessons can be useful learning materials beyond tertiary education as they cater to younger learners’ needs in video-based learning by, for example, segmenting class videos, using visualization for easier viewing, and including discussion forums and quizzes for enhanced interaction. The design implications of using video segments and visualized representations of classroom interactions as learning materials and how to foster knowledge co-construction through forums and quizzes are also discussed.

Summary: The nucleus accumbens (NAc) plays an important role in motivation and reward processing. Recent studies suggest that different NAc subnuclei differentially contribute to reward-related behaviors. However, how reward is encoded in individual NAc neurons remains unclear. Using in vivo single-cell resolution calcium imaging, we find diverse patterns of reward encoding in the medial and lateral shell subdivision of the NAc (NAcMed and NAcLat, respectively). Reward consumption increases NAcLat activity but decreases NAcMed activity, albeit with high variability among neurons. The heterogeneity in reward encoding could be attributed to differences in their synaptic inputs and transcriptional profiles. Specific optogenetic activation of Nts-positive neurons in the NAcLat promotes positive reinforcement, while activation of Cartpt-positive neurons in the NAcMed induces behavior aversion. Collectively, our study shows the organizational and transcriptional differences in NAc subregions and provides a framework for future dissection of NAc subregions in physiological and pathological conditions.

IntroductionSystems thinking is one of the most important thinking skills for medical students. Most of the studies focused on designing technological-rich learning environments which usually take several weeks or months to implement. However, the occurring of COVID-19 health crisis does not allow extensive period of time to implement classroom interventions. How to support students’ systems thinking in fully online environments remains an issue. This study examines if encouraging students initiating questions on asynchronous discussion forum supports their systems thinking development.MethodsTwenty-two junior students participated in this study. We compared if and how students developed systems thinking when they were encouraged asking questions in asynchronous discussion forums in one unit with another unit in which traditional method was used. Multiple analytical methods were applied in this study, including, social network analysis, epistemic network analysis, inferential statistical analysis and qualitative analysis.ResultsQuantitative results showed that all students improved systems thinking compared with traditional teaching unit among which leader students improved most. Further analysis on students’ discussion posts suggested leader students asked high systems thinking level questions and provided high level responses. Epistemic network analysis unpacked how leader, regular and peripheral students engaged in initiating questions and providing responses differently.DiscussionThis study provides methodological and practical contributions. Methodologically, this study extends prior methods of applying network analysis beyond its original preservice teacher training contexts; practically, this study provides strategies to practitioners to support students’ asynchronous forum discussions.

Productive peer interactions are often characterized by productive peer talk moves. This study aims to synthesize an empirical list of productive peer talk moves from existing studies, quantify the efficacy of talk moves in promoting peer interaction and collaboration outcomes, and understand the preconditions of talk moves benefits in authentic settings. A total of 24 empirical studies were included in the systematic review, where 17 of the experimental studies (k = 39, n = 2636) were analyzed in meta-synthesis. The study offers three main contributions: (1) an ordered list of 24 productive peer talk moves extracted from a range of empirical studies; (2) the aggregate sizes of the positive effects that productive peer talk moves have on interaction quality (Hedges' g = 1.27), domain-specific knowledge (g = 0.96), domain-general knowledge (g = 1.02), and solution quality (g = 0.70); and (3) common explanations for the malfunction of productive peer talk moves in existing interventions. This review confirms the robust positive effects of productive peer talk moves on peer interaction, learning, and problem-solving, and may inform future research on the analysis of peer interaction or the design of peer talk scaffolds.

Deep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt and valuable feedback on their teaching practices. However, these deep learning models often have intricate structures with numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding their classroom dialogue analysis likely leads teachers to distrust and underutilize these AI-powered models. To tackle this issue, we leveraged explainable AI to unravel classroom dialogue analysis and conducted an experiment to evaluate the effects of explanations. Fifty-nine pre-service teachers were recruited and randomly assigned to either a treatment (n = 30) or control (n = 29) group. Initially, both groups learned to analyse classroom dialogue using AI-powered models without explanations. Subsequently, the treatment group received both AI analysis and explanations, while the control group continued to receive only AI predictions. The results demonstrated that teachers in the treatment group exhibited significantly higher levels of trust in and technology acceptance of AI-powered models for classroom dialogue analysis compared to those in the control group. Notably, there were no significant differences in cognitive load between the two groups. Furthermore, teachers in the treatment group expressed high satisfaction with the explanations. During interviews, they also elucidated how the explanations changed their perceptions of model features and attitudes towards the models. This study is among the pioneering works to propose and validate the use of explainable AI to address interpretability challenges within deep learning-based models in the context of classroom dialogue analysis.

A new brain circuit that contributes to aversive states, such as fear or anxiety, has been characterized in mice.

According to the complex dynamic systems (CDS) perspective, learning emerges at various system levels. This study built a coherent theoretical framework based on CDS and Bakhtinian dialogic theory and further employed the concept of attractor (i.e., certain stable states that recur over time) in CDS theory to investigate the trajectories of idea emergence and how they diversified group outcomes in dialogic collaborative problem solving (D-CPS). Two contrasting groups were compared using visual and qualitative analysis approaches. The analysis based on idea tree diagrams showed that new ideas emergent in group discussion tended to attract local utterances and performed features of attractors in CDS in both high-performing and low-performing groups. The analysis based on idea hierarchy diagrams revealed how ideas emerged at various system levels. It was also found that status problems were likely to affect the functioning of regulative feedback loops, which might give rise to different structures of idea evolution. This study proposed CDS theory as an alternative perspective, augmented by the ethical considerations of Bakhtinian dialogism, for examining the dynamics of D-CPS.

Most studies have explored how information and communication technology (ICT) factors impact adolescents' schooling, but often ignore the potential influences on their well-being; no research has further scrutinized the moderating role of self-regulated learning (SRL) as a multi-dimensional combination, that is, different types of SRL learners. This cross-cultural study simultaneously scrutinized how distinct outside-of-school ICT factors influenced adolescents' digital reading and multi-dimensional well-being. It also took a person-centered approach to identify different types of SRL learners and examined whether the influences varied across the learner types. Data were based on 10,527 students in 308 schools from one East Asian region and one Western country participating in Programme for International Student Assessment (PISA) 2018. Multivariate multiple regression analysis revealed that overall, outside-of-school ICT factors impacted adolescents' digital reading and well-being differently, which also varied across cultures. Latent profile analysis detected culturally-mixed four profiles of SRL learners: High Profile (high in all indicators), Metacognitive Profile (in-between, optimal metacognition), Cognitive Profile (in-between, optimal cognition), and Low Profile (all low). It was the relatively weaker SRL learners in the East but the stronger SRL learners in the West that were particularly susceptible to the influences, either in a beneficial or detrimental way. Moreover, the cognitive, eudemonic, emotional and social dimensions of adolescents' well-being were all affected by ICT factors, without recurring trends in patterns. The findings provide implications to strictly monitor and guide adolescents' Internet use to enhance their academic and subjective well-being.

Background: The existing research on dialogue-based learning and teaching predominantly highlights its capacity to yield productive educational outcomes, yet it often overlooks the pivotal factor of participation equity, which is fundamental to ensuring the efficacy of dialogic teaching and learning. Objectives: In this study, participation equity refers to a condition in which participation itself and opportunities to participate are fairly distributed among participants and all participants are equally listened to and respected. We designed a technology-enhanced participatory visual learning analytical approach to promote equitable participation in dialogic collaborative problem solving from four dimensions: participation (i.e., ensuring equal contributions from all participants), opportunity (i.e., promoting equal engagement with others), responsiveness (i.e., encouraging equal attentiveness and responsiveness to others' input), and respect (i.e., cultivating a respectful communication style). Methods: The intervention class of fourth-grade students (n = 59) interacted with a participatory visual learning analytical tool to reflect on their participation equity and learn productive peer talk moves to address equity issues concerning the four dimensions, while a comparison class (n = 59) only received simple feedback on participation and respect. Results: The results indicated that equal participation rates among group members were insufficient to secure participation equity. The intervention was effective in helping students realize and address equity-related issues. Intervention students were more equitable regarding responsiveness and participation opportunity than the comparison students. Conclusions: The proposed multidimensional participation equity framework has the potential to deepen the understanding of equity and promote equitable learning interactions.

Background: Deep discussions play an important role in students' online learning. However, researchers have largely focused on engaging students in deep discussions in online asynchronous forums. Few studies have investigated how to promote deep discussion via mobile instant messaging (MIM). Objectives: In this study, we applied learning analytical tools (i.e., KBdeX and word clouds) to enhance students' shared epistemic agency and thereby support their deep discussions in MIM. Methods: Forty Chinese college students participated in this study and reflected on their MIM engagement by participating in the learning analytics (LA)-augmented meta-discourse sessions. The study used multiple data analysis methods, including content analysis, statistical analysis, epistemic network analysis and lag sequential analysis. Results: We found that LA engaged students in deep discussions and shared epistemic agency-related discourse, such as creating shared understanding, creating knowledge objects, and projective and regulative processes. In particular, word clouds engaged students in more complete shared epistemic agency discourse trajectory which started from creating awareness of unknowns, then progressed to setting projective plans and sharing information, and ultimately, creating shared understanding. Moreover, our analysis indicated that epistemic agency discourse moves of creating shared understanding led students to a high level of deep discussion. Implications: This study contributes to research by extending the 'comparison paradigm', which focuses on comparing (a)synchronous forums with MIM, to a 'design paradigm', which mobilises design features from (a)synchronous forums to MIM and using learning analytical tools to engage students in deep online discussions by promoting their epistemic agency.

In this study, we examine the design process for a visual learning analytics (VLA) approach in addition to its effect on developing students' epistemic understanding of discourse and relationships to promote knowledge-building inquiry. Over a 3-month period, an experimental class of secondary school students took a visual arts course using a knowledge-building design augmented by VLA, while a comparison class took the same course within a regular knowledge-building environment. The VLA approach implemented in the experimental class emphasized collective reflections using assessment tools to visualize the students' online and classroom discussions. The analysis of the students' questionnaire responses and Knowledge Forum discourse showed that the VLA approach helped the students develop a deeper epistemic understanding of discourse for productive inquiry and greater domain knowledge. In addition, the students' epistemic understanding was associated with their performance in productive Knowledge Forum inquiry. Qualitative analysis of the classroom and group discussions, interview responses and learning artefacts showed that the VLA design helped them understand the nature of discourse deeply by encouraging them to notice community orientations, increase their awareness of productive discourse moves and advance idea development. The implications of the VLA design in improving epistemic understanding of discourse are discussed. We contribute to the literature by examining the epistemic dimensions of discourse and implementing VLA as an epistemic tool for knowledge building.

It is well established that COVID-19 pandemic is associated with arousal of feelings of hopelessness and negativity in adolescents regarding future career development outcomes. However, the current literature has yet to clarify the roles teacher social support play in facilitating students' positive career development during the pandemic. This study investigated the effects of career-related teacher support (CRTS) on ameliorating students' feelings of hopelessness and increasing their career self-efficacy among 402 senior secondary students (Grades 10-12) in Hong Kong, China. Results from mediated moderation analyses showed that academic ability was only weakly related to career self-efficacy. Hopelessness was not a significant moderator concerning the link between academic ability and career self-efficacy when CRTS functioned as the mediator.

This study explored the latent profiles of self-regulated learning (SRL) strategies (cognitive, metacognitive, and motivational regulation) endorsed by Chinese English-as-a-foreign-language (EFL) readers in a high-stakes testing environment, and also their associations with individual factors (gender, grade, reading proficiency, and motivational beliefs). With a sequential explanatory mixed-methods design, students in grades 11 and 12 (n = 1,113) completed a reading comprehension test and a questionnaire regarding their strategy use and individual factors, and some (n = 16) were randomly selected for follow-up semi-structured interviews. Findings revealed three SRL profiles, characterized by high, medium, and low levels of SRL-strategy use. Self-efficacy and extrinsic motivation most powerfully predicted an individual's profile membership; all the intrinsic and extrinsic motivation variables were significantly higher for learners from the higher strategy-use profile. Moreover, reading proficiency did not significantly predict profile membership, but more self-regulated students still achieved higher reading scores as a group tendency.