Generative preparation tasks in digital collaborative learning: actor and partner effects of constructive preparation activities on deep comprehension.

Deep learning from collaboration occurs if the learner enacts interactive activities in the sense of leveraging the knowledge externalized by co-learners as resource for own inferencing processes and if these interactive activities in turn promote the learner's deep comprehension outcomes. This experimental study investigates whether inducing dyad members to enact constructive preparation activities can promote deep learning from subsequent collaboration while examining prior knowledge as moderator. In a digital collaborative learning environment, 122 non-expert university students assigned to 61 dyads studied a text about the human circulatory system and then prepared individually for collaboration according to their experimental conditions: the preparation tasks varied across dyads with respect to their generativity, that is, the degree to which they required the learners to enact constructive activities (note-taking, compare-contrast, or explanation). After externalizing their answer to the task, learners in all conditions inspected their partner's externalization and then jointly discussed their text understanding via chat. Results showed that more rather than less generative tasks fostered constructive preparation but not interactive collaboration activities or deep comprehension outcomes. Moderated mediation analyses considering actor and partner effects indicated the indirect effects of constructive preparation activities on deep comprehension outcomes via interactive activities to depend on prior knowledge: when own prior knowledge was relatively low, self-performed but not partner-performed constructive preparation activities were beneficial. When own prior knowledge was relatively high, partner-performed constructive preparation activities were conducive while one's own were ineffective or even detrimental. Given these differential effects, suggestions are made for optimizing the instructional design around generative preparation tasks to streamline the effectiveness of constructive preparation activities for deep learning from digital collaboration.

Shaping tomorrow's dentists: a multi-institutional survey of undergraduate dental students' perceptions towards interprofessional education.

BACKGROUND: Interprofessional education (IPE) is essential to foster collaboration among healthcare professionals for holistic patient care. However, Malaysian dental education remains discipline-centric, hindering multidisciplinary learning approaches. Hence, this study aimed to explore Malaysian undergraduate dental students' perceptions of IPE. METHODS: The present cross-sectional study employed convenience sampling to survey undergraduate dental students from four Malaysian institutions using a modified questionnaire with 20 close-ended and 2 open-ended questions. The questionnaire covered three domains (effectiveness, preference, importance) to assess students' perceptions using a five-point Likert scale. Psychometric validation was performed to assure validity and reliability of the modified questionnaire. Quantitative analysis (descriptive and inferential statistics), and qualitative analysis (content analysis) were subsequently performed. RESULTS: 397 students responded, and positive perceptions were generally noted with mean scores ranging from 4.13 to 4.35 across all domains. Questions 2 and 3, assessing the improvement in understanding the roles and responsibilities, and communication among healthcare professionals, received the highest mean scores. Meanwhile, Question 15 concerning the incorporation of IPE into educational goals received the lowest mean score. Regression analysis identified gender and clinical phase as significant factors, with females and preclinical students exhibiting more favourable perceptions. Motivators for IPE included a keen interest in diverse perspectives and recognising the importance of teamwork, while barriers encompassed tightly packed schedules, lack of understanding about IPE, misconceptions regarding dental education, and students' nervousness and fear of participation. CONCLUSION: This study produced a valid and reliable instrument to measure undergraduate dental students' perceptions towards IPE. Strategic planning, such as overcoming logistical challenges, improving awareness, and creating a supportive learning environment are crucial for successful IPE integration into existing curricula, especially in resource-constrained developing countries like Malaysia.

Behavioral assessment of soft skill development in a highly structured pre-health biology course for undergraduates.

In this study, we assessed a highly structured, yearlong, case-based course designed for undergraduate pre-health students. We incorporated both content learning assessments and developed a novel method called Multiple Mini Exams for assessing course impact on the development of skills that professional schools often seek in pre-health students, focusing on students' abilities to collaborate with others, display bedside manners, synthesize patient case details, appropriately use scientific and medical language, and effectively attain patients' medical histories. This novel method utilized a rubric based on desired medical student skills to score videotaped behaviors and interactions of students role playing as doctors in a hypothetical patient case study scenario. Overall, our findings demonstrate that a highly structured course, incorporating weekly student performance and presentation of patient cases encompassing history taking, diagnosis, and treatment, can result in content learning, as well as improve desired skills specific for success in medical fields.

Collaborative learning from distributed data with differentially private synthetic data.

BACKGROUND: Consider a setting where multiple parties holding sensitive data aim to collaboratively learn population level statistics, but pooling the sensitive data sets is not possible due to privacy concerns and parties are unable to engage in centrally coordinated joint computation. We study the feasibility of combining privacy preserving synthetic data sets in place of the original data for collaborative learning on real-world health data from the UK Biobank. METHODS: We perform an empirical evaluation based on an existing prospective cohort study from the literature. Multiple parties were simulated by splitting the UK Biobank cohort along assessment centers, for which we generate synthetic data using differentially private generative modelling techniques. We then apply the original study's Poisson regression analysis on the combined synthetic data sets and evaluate the effects of 1) the size of local data set, 2) the number of participating parties, and 3) local shifts in distributions, on the obtained likelihood scores. RESULTS: We discover that parties engaging in the collaborative learning via shared synthetic data obtain more accurate estimates of the regression parameters compared to using only their local data. This finding extends to the difficult case of small heterogeneous data sets. Furthermore, the more parties participate, the larger and more consistent the improvements become up to a certain limit. Finally, we find that data sharing can especially help parties whose data contain underrepresented groups to perform better-adjusted analysis for said groups. CONCLUSIONS: Based on our results we conclude that sharing of synthetic data is a viable method for enabling learning from sensitive data without violating privacy constraints even if individual data sets are small or do not represent the overall population well. Lack of access to distributed sensitive data is often a bottleneck in biomedical research, which our study shows can be alleviated with privacy-preserving collaborative learning methods.

An integrative review of the impact of allied health student placements on current staff's knowledge and procedural skills in acute and primary care settings.

BACKGROUND: Staff shortages limit access to health services. The bidirectional benefits of allied health clinical placements are understood in the domains of student learning, health service delivery, and future workforce development. Still, the benefits to current workforce outcomes remain unknown. This review provides insights into the effects of allied health student placements in acute and primary care settings, particularly on healthcare staff's knowledge and procedural skills. METHODS: This search was based on the integrative review process established by Whittemore and Knafl in 2005. In October 2023, the first author (MH) searched five major electronic databases: Medline-EBSCO, PubMed, CINAHL, Embase, and Scopus. The CLUSTER model was used to track additional references. The first three authors (MH, SM, and SC) were involved in screening, quality appraisal, and synthesis of the studies. Data were thematically synthesised and analysed. RESULTS: MeSH headings and keywords were used in key search areas: health education, health professional training, clinical placements, and allied health professions. The systematic search yielded 12 papers on allied health student placements across various healthcare settings in rural and metropolitan areas, with no high-quality methodologies measuring student placements' impact on staff knowledge and skills. Four main themes were identified from the analysis: meaningful student integration in service delivery, targeted educational support to healthcare staff, development of staff procedural skills and confidence, and the mechanisms of why student placements work in this aspect. CONCLUSIONS: This review suggests that offering allied health student placement could be a promising approach to supporting rural healthcare staff in performing patient assessments and treatments proficiently and collaboratively. However, this requires further investigation to confirm.

Harnessing the potential of shared data in a secure, inclusive, and resilient manner via multi-key homomorphic encryption.

In this manuscript, we develop a multi-party framework tailored for multiple data contributors seeking machine learning insights from combined data sources. Grounded in statistical learning principles, we introduce the Multi-Key Homomorphic Encryption Logistic Regression (MK-HELR) algorithm, designed to execute logistic regression on encrypted multi-party data. Given that models built on aggregated datasets often demonstrate superior generalization capabilities, our approach offers data contributors the collective strength of shared data while ensuring their original data remains private due to encryption. Apart from facilitating logistic regression on combined encrypted data from diverse sources, this algorithm creates a collaborative learning environment with dynamic membership. Notably, it can seamlessly incorporate new participants during the learning process, addressing the key limitation of prior methods that demanded a predetermined number of contributors to be set before the learning process begins. This flexibility is crucial in real-world scenarios, accommodating varying data contribution timelines and unanticipated fluctuations in participant numbers, due to additions and departures. Using the AI4I public predictive maintenance dataset, we demonstrate the MK-HELR algorithm, setting the stage for further research in secure, dynamic, and collaborative multi-party learning scenarios.

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems.

In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients' medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier's error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL's effectiveness and efficiency in identifying diseases is evaluated and compared.

Collaborative learning linking nursing practice and education - Interview study with master's students and teachers.

BACKGROUND: Collaborative and innovative educational approaches are essential to building nurses' competencies in responding to healthcare challenges and to enhance high-quality nursing practice. Nurses are increasingly learning in various contexts, and thus, the understanding and organising of collaborative learning needs further exploration. AIM: To describe collaborative learning in nursing practice and education from the point of view of nurses, involved as students and teachers in master's education in nursing. DESIGN AND METHODS: Qualitative study using semi-structured focus groups involving 33 nurses as master's students and teachers from Estonia and Norway. The data were analysed using inductive content analysis. FINDINGS: The essence of collaborative learning in the context of nursing practice and education was identified as the aggregation of diverse learners' skills and reflections while working towards shared aims. This fostered the development of workplace competencies, professional attitudes, and personal growth. Organising collaborative learning requires careful planning and synchronisation between clinical and educational organisations. CONCLUSIONS: Collaborative learning is an inclusive method, aiding nurses at different phases of their career to gain knowledge and enhance their soft skills, and potentially reducing professional hierarchies. Further research is needed to develop methods for evaluating the outcomes of collaborative learning.

Wiki educativa para la Clínica Virtual de Salud de Santiago de Cuba

El programa de las tecnologías de la información y las comunicaciones se ha implementado en todas las instituciones pertenecientes al sector de la salud en Cuba, pues favorece la formación a través de los entornos de enseñanza y aprendizaje en función del desarrollo y las perspectivas académicas, mediante plataformas tecnológicas para facilitar el intercambio de información. En el presente artículo se destaca la necesidad de incrementar el uso de la wiki como herramienta educativa, que favorece el aprendizaje colaborativo como un proceso de creación de conocimiento a través de la web 2.0, donde no solo se publican contenidos sino que contribuye a desarrollar habilidades de cooperación en los procesos formativos de la salud pública.

The program of information and communications technologies has been implemented in all the institutions belonging to the health sector in Cuba, because it favors the training through the teaching and learning environments in function of development and academic perspectives, by means of technological platforms to facilitate the exchange of information. In this work the necessity to increase the wiki use as educational tool in enhanced, that favors the collaborative learning as a process of knowledge creation through the web 2.0, where contents are not only published but also contributes to develop cooperation abilities in the training processes of public health.

Is everything everywhere? A hands-on activity to engage undergraduates with key concepts in quantitative microbial biogeography.

The ubiquity and ease with which microbial cells disperse over space is a key concept in microbiology, especially in microbial ecology. The phenomenon prompted Baas Becking's famous "everything is everywhere" statement that now acts as the null hypothesis in studies that test the dispersal limitation of microbial taxa. Despite covering the content in lectures, exam performance indicated that the concepts of dispersal and biogeography challenged undergraduate students in an upper-level Microbial Ecology course. Therefore, we iteratively designed a hands-on classroom activity to supplement the lecture content and reinforce fundamental microbial dispersal and biogeography concepts while also building quantitative reasoning and teamwork skills. In a class period soon after the lecture, the students formed three-to-five-person teams to engage in the activity, which included a hands-on dispersal simulation and worksheet to guide discussion. The simulation involved stepwise neutral immigration or emigration and then environmental selection on a random community of microbial taxa represented by craft poms. The students recorded the results at each step as microbial community data. A field guide was provided to identify the taxonomy based on the pom phenotype and a reference to each taxon's preferred environmental niches. The worksheet guided a reflection of student observations during the simulation. It also sharpened quantitative thinking by prompting the students to summarize and visualize their and other teams' microbial community data and then to compare the observed community distributions to the idealized expectation given only selection without dispersal. We found that the activity improved student performance on exam questions and general student satisfaction and comfort with the biogeography concepts. Activity instructions and a list of needed materials are included for instructors to reproduce for their classrooms.

The Patient's Experience of Working with Multiple Allied Health Professional Students - A Qualitative Interview Study.

There are increasing numbers of learners in clinical settings as part of approaches to meet workforce demands. As a result, patients are now working with multiple learners at the same time, yet little is known about how people experience this. The aim of this study was to explore the patient experience of working with multiple allied health professional students. Structured interviews were carried out with 22 patients across hospital wards in one hospital in the North-West of England. Data was analysed using thematic analysis and four themes were identified: consent to work with multiple students; responses to working with multiple students; multiple students and feelings of safety; making connections with multiple students. Findings indicated that patients experienced positive relationships and feelings of safety with groups of students. However, patients were given limited advance or tailored information about working with a group of students which is an important area to address.

Dyadic practice for the acquisition of laparoscopic skills (DALS)-A randomized controlled trial.

BACKGROUND: Laparoscopic simulation is integral to surgical education but requires significant resources. We aimed to compare the effectiveness of dyadic practice (DP), involving two individuals working together, to individual practice (IP) for novices acquiring laparoscopic skills and assess their learning experience. METHODS: We conducted a Randomized Controlled Trial comparing DP and IP for novice medical students who completed a laparoscopic simulation workshop. Participants were assessed individually pre-course (test 1), post-course (test 2), and 8-week retention (test 3) using a validated quantitative method. A post-course questionnaire and interview, analyzed with thematic analysis, assessed the learning experience. RESULTS: In total, 31 DP and 35 IP participants completed the study. There was no difference in mean scores between DP and IP groups in all three tests: test 1 (p = 0.55), test 2 (p = 0.26), test 3 (p = 0.35). In trend analysis, the DP group improved post-course (test 1 vs. 2: p = 0.02) and maintained this level at the retention test (2 vs. 3: p = 0.80, 1 vs. 3: p = 0.02). Whilst the IP group also improved post-course (test 1 vs. 2: p < 0.001), this improvement was not retained (2 vs. 3: p = 0.003, 1 vs. 3: p = 0.32). Thematic analysis revealed that DP participants valued peer support, peer feedback and observation time, but also acknowledged the limitations of reduced practical time and issues with teamwork. CONCLUSION: DP is non-inferior to IP for novices learning laparoscopic skills, is well received and may lead to superior long-term skill retention.

Comparison of the Effects of E-learning Blended with Collaborative Learning and Lecture-Based Teaching Approaches on Academic Self-Efficacy among Undergraduate Nursing Students: A Quasi-Experimental Study.

Introduction: Applying new technologies in teaching has led to the phenomenon of blended learning (BL), which is currently flourishing as a specific requirement for higher self-efficacy and success in increasingly complex healthcare environments. Although various forms of novel education are on the rise worldwide, the effects of electronic learning (EL), combined with collaborative learning (CL) and lecture-based teaching (LBT) approaches, have not yet been validated on academic self-efficacy among undergraduate nursing students. Methods: Utilizing a pre-/post-test comparison-group design, this quasi-experimental study was conducted on 70 undergraduate nursing students of Urmia University of Medical Sciences, Urmia, Iran, selected by the census sampling technique in 2020. The eligible participants were allocated to intervention groups, viz., the EL+LBT group (n=34) and the EL+CL group (n=36). A learning management system (LMS) was used for both intervention groups along with the LBT approach, and then 10 steps were integrated into the CL approach during 14 sessions, lasting 150 minutes. Afterward, a demographic information form and the College Academic Self-Efficacy Scale (CASES) were administered to collect the data. The data were analyzed using descriptive statistics, Chi-square test, independent-samples t-test, and analysis of covariance (ANCOVA). Results: No significant difference was observed in the CASE scores between the students were taught using the EL+LBT (113.76±16.98) and the EL+CL approaches (107.66±16.70) before the interventions (P=0.136). However, the CL+EL approaches resulted in the highest changes in the CASE scores at the pre- (107.66±16.70) and post-test (119.08±25.49) stages (P=0.019). Moreover, the female students attending the CL+EL classrooms experienced significantly positive differences in their CASE scores (127.12±30.34), compared to the males (112.65±19.30) (P=0.011). Conclusion: Blending the EL and CL approaches significantly promoted CASE among the undergraduate nursing students in this study by providing sufficient collaboration, essential educational equipment, and better technical support.

The Daily Fact Pile: Exploring Mutual Microlearning in Neurology Resident Education.

Problem: A significant proportion of learning during residency takes place through informal channels. Spontaneous collaboration among medical learners significantly contributes to this informal learning and is increasingly recognized as a component of the hidden curriculum in medical education. Yet historically, a disproportionate emphasis in medical education has been placed on didactic, structured, and faculty-initiated methods, leaving an important force in medical education understudied and underutilized. We hypothesize that there is significant educational potential in studying and deploying targeted tools to facilitate collaboration among medical learners. Intervention: At our institution, neurology residents implemented the "Daily Fact Pile" (DFP), a resident-led, email-based collaboration that served as a platform to share clinical pearls in an informal, digital way. Participation was voluntary and participants were encouraged to share facts that were new to them and thought to be clinically relevant. Motivated by the positive collective experience, we conducted a retrospective examination of this phenomenon. In this context, we developed the concept of "mutual microlearning" to characterize this efficient, multidirectional exchange of information. Context: Thirty-six residents in a single neurology residency program utilized the DFP at a large university hospital in the USA between 2018 and 2019. After 21 months of spontaneous and voluntary participation, we assessed the feasibility of the DFP, its impact on the education and morale of neurology residents, and compared its mutual microlearning approach to traditional lectures. This was done through a survey of the DFP participants with a response rate of 80.7%, and analysis of the statistics of participation and interaction with the DFP. Impact: Most participants felt that the DFP was beneficial to their education and thought they often or always learned something new from reading the DFP. The impact of the DFP extended beyond education by improving interest in neurology, morale, and sense of teamwork. The DFP was feasible during neurology residency and participation was high, though participants were more likely to read facts than share them. Lessons learned: Mutual microlearning represents an opportunity to augment residents' education, and well-designed mutual microlearning tools hold promise for complementing traditional teaching methods. We learned that efficiency, ease of use, and a supportive, non-judgmental environment are all essential to the success of such tools. Future research should delve deeper into the underlying mechanisms of mutual microlearning to establish its position within the theoretical frameworks of medical education.

Dataset of mathematics learning and assessment of higher education students using the MathE platform.

Higher education institutions are promoting the adoption of innovative methodologies and instructional approaches to engage and promote personalized learning paths to their students. Several strategies based on gamification, artificial intelligence, and data mining are adopted to create an interactive educational setting centred around students. Within this personalized learning environment, there is a notable boost in student engagement and enhanced educational outcomes. The MathE platform, an online educational system introduced in 2019, is specifically crafted to support students tackling difficulties in comprehending higher-education-level mathematics or those aspiring to deepen their understanding of diverse mathematical topics - all at their own pace. The MathE platform provides multiple-choice questions, categorized under topics and subtopics, aligning with the content taught in higher education courses. Accessible to students worldwide, the platform enables them to train their mathematical skills through these resources. When the students log in to the training area of the platform, they choose a topic to study and specify whether they prefer basic or advanced questions. The platform then selects a set of seven multiple-choice questions from the available ones under the chosen topic and generates a test for the student. After completing and submitting the test, the answers are recorded and stored on the platform. This paper describes the data stored in the MathE platform, focusing on the 9546 answers to 833 questions, provided by 372 students from 8 countries who use the platform to practice their skills using the questions (and other resources) available on the platform. The information in this paper will help research about active learning tools to support the improvement of future education, especially at higher educational level. Furthermore, these data are valuable for understanding student learning patterns, assessing platform efficacy, gaining a global perspective on mathematics education, and contributing to the advancement of active learning tools for higher education.

Enhancing human-human musical interaction through kinesthetic haptic feedback using wearable exoskeletons: theoretical foundations, validation scenarios, and limitations.

In this perspective paper, we explore the use of haptic feedback to enhance human-human interaction during musical tasks. We start by providing an overview of the theoretical foundation that underpins our approach, which is rooted in the embodied music cognition framework, and by briefly presenting the concepts of action-perception loop, sensorimotor coupling and entrainment. Thereafter, we focus on the role of haptic information in music playing and we discuss the use of wearable technologies, namely lightweight exoskeletons, for the exchange of haptic information between humans. We present two experimental scenarios in which the effectiveness of this technology for enhancing musical interaction and learning might be validated. Finally, we briefly discuss some of the theoretical and pedagogical implications of the use of technologies for haptic communication in musical contexts, while also addressing the potential barriers to the widespread adoption of exoskeletons in such contexts.

Collaborative Modality Fusion for Mitigating Language Bias in Visual Question Answering.

Language bias stands as a noteworthy concern in visual question answering (VQA), wherein models tend to rely on spurious correlations between questions and answers for prediction. This prevents the models from effectively generalizing, leading to a decrease in performance. In order to address this bias, we propose a novel modality fusion collaborative de-biasing algorithm (CoD). In our approach, bias is considered as the model's neglect of information from a particular modality during prediction. We employ a collaborative training approach to facilitate mutual modeling between different modalities, achieving efficient feature fusion and enabling the model to fully leverage multimodal knowledge for prediction. Our experiments on various datasets, including VQA-CP v2, VQA v2, and VQA-VS, using different validation strategies, demonstrate the effectiveness of our approach. Notably, employing a basic baseline model resulted in an accuracy of 60.14% on VQA-CP v2.

Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition.

BACKGROUND: Glomerular lesions reflect the onset and progression of renal disease. Pathological diagnoses are widely regarded as the definitive method for recognizing these lesions, as the deviations in histopathological structures closely correlate with impairments in renal function. METHODS: Deep learning plays a crucial role in streamlining the laborious, challenging, and subjective task of recognizing glomerular lesions by pathologists. However, the current methods treat pathology images as data in regular Euclidean space, limiting their ability to efficiently represent the complex local features and global connections. In response to this challenge, this paper proposes a graph neural network (GNN) that utilizes global attention pooling (GAP) to more effectively extract high-level semantic features from glomerular images. The model incorporates Bayesian collaborative learning (BCL), enhancing node feature fine-tuning and fusion during training. In addition, this paper adds a soft classification head to mitigate the semantic ambiguity associated with a purely hard classification. RESULTS: This paper conducted extensive experiments on four glomerular datasets, comprising a total of 491 whole slide images (WSIs) and 9030 images. The results demonstrate that the proposed model achieves impressive F1 scores of 81.37%, 90.12%, 87.72%, and 98.68% on four private datasets for glomerular lesion recognition. These scores surpass the performance of the other models used for comparison. Furthermore, this paper employed a publicly available BReAst Carcinoma Subtyping (BRACS) dataset with an 85.61% F1 score to further prove the superiority of the proposed model. CONCLUSION: The proposed model not only facilitates precise recognition of glomerular lesions but also serves as a potent tool for diagnosing kidney diseases effectively. Furthermore, the framework and training methodology of the GNN can be adeptly applied to address various pathology image classification challenges.

Defining Learning Communities in the Veterinary Medicine Curriculum.

Learning communities (LCs) are a type of high-impact, collaborative educational practice where students cooperate with other students in two or more classes to improve learning. Additional criteria used for defining LCs vary across the literature and have focused on high school and undergraduate programs. Similar benefits may also be experienced in professional programs; however, before further studies can investigate and describe these benefits, the structure for LCs in the veterinary curriculum must first be defined. To accomplish this, a survey instrument was developed and distributed to veterinary students attending Iowa State University College of Veterinary Medicine during the 2019 to 2020 academic year. The survey focused on a number of criteria and included both qualitative and quantitative questions. Criteria included demographics, prior experiences, and structure of an ideal LC. Recurring themes in the qualitative responses were identified, reviewed, and categorized based on an established coding theme. Significant correlations included an association between respondents' opinions on the ideal number of people in a LC and prior participation in LCs; age and preference for LC focus; ideal number of LC members and LC focus; and gender identity and thoughts on LC diversity. Based on the survey results, the typical veterinary student would consider an ideal LC to (1) be selected by the students of the LC, (2) have faculty mentors, (3) have a focus on coursework with some social aspects, (4) have fewer than seven members, (5) be composed of friends, (6) meet both during and after class, and (7) be diverse.

Investigating Linguistic Alignment in Collaborative Dialogue: A Study of Syntactic and Lexical Patterns in Middle School Students.

Linguistic alignment, the tendency of speakers to share common linguistic features during conversations, has emerged as a key area of research in computer-supported collaborative learning. While previous studies have shown that linguistic alignment can have a significant impact on collaborative outcomes, there is limited research exploring its role in K-12 learning contexts. This study investigates syntactic and lexical linguistic alignments in a collaborative computer science-learning corpus from 24 pairs (48 individuals) of middle school students (aged 11-13). The results show stronger effects of self-alignment than partner alignment on both syntactic and lexical levels, with students often diverging from their partners on task-relevant words. Furthermore, student self-alignment on the syntactic level is negatively correlated with partner satisfaction ratings, while self-alignment on lexical level is positively correlated with their partner's satisfaction.