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Background Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online (Problem Based Learning) PBL in particular. Using SNA to study students’ positions in information exchange networks, communicational activities, and interactions, we can broaden our understanding of the process of PBL, evaluate the significance of each participant role and learn how interactions can affect academic performance. The aim of this study was to study how SNA visual and mathematical analysis can be sued to investigate online PBL, furthermore, to see if students’ position and interaction parameters are associated with better performance. Methods This study involved 135 students and 15 teachers in 15 PBL groups in the course of “growth and development” at Qassim University. The course uses blended PBL as the teaching method. All interaction data were extracted from the learning management system, analyzed with SNA visual and mathematical techniques on the individual student and group level, centrality measures were calculated, and participants’ roles were mapped. Correlation among variables was performed using the non-parametric Spearman rank correlation test. Results The course had 2620 online interactions, mostly from students to students (89%), students to teacher interactions were 4.9%, and teacher to student interactions were 6.15%. Results have shown that SNA visual analysis can precisely map each PBL group and the level of activity within the group as well as outline the interactions among group participants, identify the isolated and the active students (leaders and facilitators) and evaluate the role of the tutor. Statistical analysis has shown that students’ level of activity (outdegree r s (133) = 0.27, p  = 0.01), interaction with tutors (r s (133) = 0.22, p  = 0.02) are positively correlated with academic performance. Conclusions Social network analysis is a practical method that can reliably monitor the interactions in an online PBL environment. Using SNA could reveal important information about the course, the group, and individual students. The insights generated by SNA may be useful in the context of learning analytics to help monitor students’ activity.

Social network analysis (SNA) may be of significant value in studying online collaborative learning. SNA can enhance our understanding of the collaborative process, predict the under-achievers by means of learning analytics, and uncover the role dynamics of learners and teachers alike. As such, it constitutes an obvious opportunity to improve learning, inform teachers and stakeholders. Besides, it can facilitate data-driven support services for students. This study included four courses at Qassim University. Online interaction data were collected and processed following a standard data mining technique. The SNA parameters relevant to knowledge sharing and construction were calculated on the individual and the group level. The analysis included quantitative network analysis and visualization, correlation tests as well as predictive and explanatory regression models. Our results showed a consistent moderate to strong positive correlation between performance, interaction parameters and students' centrality measures across all the studied courses, regardless of the subject matter. In each of the studied courses, students with stronger ties to prominent peers (better social capital) in small interactive and cohesive groups tended to do better. The results of correlation tests were confirmed using regression tests, which were validated using a next year dataset. Using SNA indicators, we were able to classify students according to achievement with high accuracy (93.3%). This demonstrates the possibility of using interaction data to predict underachievers with reasonable reliability, which is an obvious opportunity for intervention and support.

Background Although there is a wealth of research focusing on PBL, most studies employ self-reports, surveys, and interviews as data collection methods and have an exclusive focus on students. There is little research that has studied interactivity in online PBL settings through the lens of Social Network Analysis (SNA) to explore both student and teacher factors that could help monitor and possibly proactively support PBL groups. This study adopts SNA to investigate how groups, tutors and individual student’s interactivity variables correlate with group performance and whether the interactivity variables could be used to predict group performance. Methods We do so by analyzing 60 groups’ work in 12 courses in dental education (598 students). The interaction data were extracted from a Moodle-based online learning platform to construct the aggregate networks of each group. SNA variables were calculated at the group level, students’ level and tutor’s level. We then performed correlation tests and multiple regression analysis using SNA measures and performance data. Results The findings demonstrate that certain interaction variables are indicative of a well-performing group; particularly the quantity of interactions, active and reciprocal interactions among students, and group cohesion measures (transitivity and reciprocity). A more dominating role for teachers may be a negative sign of group performance. Finally, a stepwise multiple regression test demonstrated that SNA centrality measures could be used to predict group performance. A significant equation was found, F (4, 55) = 49.1, p  < 0.01, with an R2 of 0.76. Tutor Eigen centrality, user count, and centralization outdegree were all statistically significant and negative. However, reciprocity in the group was a positive predictor of group improvement. Conclusions The findings of this study emphasized the importance of interactions, equal participation and inclusion of all group members, and reciprocity and group cohesion as predictors of a functioning group. Furthermore, SNA could be used to monitor online PBL groups, identify important quantitative data that helps predict and potentially support groups to function and co-regulate, which would improve the outcome of interacting groups in PBL. The information offered by SNA requires relatively little effort to analyze and could help educators get valuable insights about their groups and individual collaborators.

The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting.

Understandability and completeness are essential in modern collaborative digital platforms and their learning systems. These platforms have shaken up the traditional education setting, particularly in leveraging the coauthoring approach in problem-solving and streamlining the learning behavior of cowriting or corevising. Such a learning context has attracted considerable interest from various stakeholders; however, it needs to be explored further as an independent topic. Based on social capital and social identity theories, we explore how online collaborative problem-based learning (PBL) effectiveness, relational quality, and social identity influence students’ perceived PBL performance during learning activities. Based on the core elements of online coauthoring processes (i.e., platform, cocreation, and problem-solving), this study employs a holistic view of the coauthor to discuss the effects of understandability and completeness. This study also highlights the mediating impact of trust on students’ social identity. Based on the responses of 240 students, the results support the proposed hypotheses using partial least squares analysis. The study’s implications suggest guidelines to educators on how to enhance students’ perceived PBL performance by using wiki technologies.

Objective Standardized training for resident physicians in China has been carried out for 10 years, and various new teaching methods have been widely applied in it. The quality of internal medicine teaching is directly related to whether the trainees can master the corresponding clinical skills well and become qualified clinical physicians. The purpose of this study is to systematically evaluate the effectiveness of all teaching methods in Chinese standardized training of internal medicine residents. Methods This study was registered in Inplasy. A comprehensive search of databases, including English and Chinese, was conducted from inception to 30 July 2023. Eligible studies included cohort study and randomized controlled trials (RCT) of all teaching methods in Chinese standardized training of internal medicine residents. A network meta-analysis (NMA) was performed using STATA 16.0. Statistical analysis was done using the mean and standard deviation. The literature quality and risks of bias was assessed using RevMan 5.3. Results A total of 74 articles including 5004 Chinese participants were retrieved, involving 13 interventions, of which 65 were RCT and 9 were cohort studies. This study demonstrated that, in comparison to lecture-based learning (LBL), the integration of problem-based learning (PBL) with WeChat significantly enhanced students' theoretical scores (SMD = 2.3; 95% CI 1.19–3.42; P  < 0.05; Sucra = 88%) and decreased the number of dissatisfied students (OR = 0.06; 95% CI 0.01–0.27; P  < 0.05; Sucra = 89%). Team-based learning (TBL) was beneficial in improving practical performance (SMD = 2.32; 95% CI 0.74–3.9; P  < 0.05; Sucra = 80.1%). Additionally, the PBL combined with clinical practice (PBL + CP) teaching method significantly enhanced students' performance in medical record analysis (SMD = 4.84; 95% CI 3.08–6.59; P  < 0.05; Sucra = 99.9%). Furthermore, PBL effectively improved students' self-directed learning abilities (SMD = 1.98; 95% CI 0.05–3.91; P  < 0.05; Sucra = 75.8%). Conclusion New teaching methods represented by PBL + Wechat, CBL + Wechat, PBL + CBL are more effective in improving the academic performance of Chinese resident physicians in standardized training compared to control therapy, and have gained more recognition from students.

The aim of this study was to examine the mediating role of knowledge-sharing behavior (KSB) in the relationship between employee engagement and innovative work behavior (IWB). We collected 193 completed survey responses from employees working in the service sector in the United Arab Emirates (UAE). Drawing on social exchange theory (SET), we employed hierarchical regression to analyze the research framework and the mediation effect. The primary findings indicate a significant positive association between employee engagement and IWB, as well as between employee engagement and KSB. Additionally, there is a significant positive association between KSB and IWB. Furthermore, employee engagement has an indirect effect on IWB via the mediating role of KSB. We recommend further research and practical investigation into how employee engagement contributes to enhancing knowledge-sharing behavior and IWB, ultimately improving organizational performance.This systematic review underscores the significance of learning communities as fertile grounds for skill development across diverse contexts. Furthermore, it reviews and theoretically evaluates several commonly used strategies, methods, and supports for developing skills within learning communities by synthesizing the existing literature. We followed the procedure outlined by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to ensure a transparent, comprehensive, and standardized approach to conducting and reporting our systematic review, thereby enhancing the review's credibility and reproducibility. Through an extensive analysis of the literature, we identified eleven strategies, methods, and supports (application of collaborative projects, mentorship programs, workshops and training sessions, online learning platforms, peer learning and feedback, problem-based learning, cross-collaboration initiatives, leadership development programs, inclusive learning environments, gamification and simulations, and social media and networking) that play pivotal roles in nurturing different types of skills. We describe each identified solution, its advantages and challenges, the types of skills targeted for development, and their overall contribution to skill enhancement. The findings emphasize the importance of fostering collaborative and interactive environments within learning communities to facilitate collective skill development and personal growth. Our systematic review faced some challenges (e.g., heterogeneity of studies and lack of longitudinal data) due to the overwhelming diversity of the literature on skill development across various disciplines and contexts. Overall, by synthesizing existing knowledge and identifying gaps in the literature, this review serves as a foundation for advancing theory, informing practice, and promoting continual improvement in skill development within learning communities.

Purpose To investigate the effectiveness of problem-based learning (PBL) and case-based learning (CBL) teaching methods in clinical practical teaching in transarterial chemoembolization (TACE) treatment in China. Materials and methods A comprehensive search of PubMed, the Chinese National Knowledge Infrastructure (CNKI) database, the Weipu database and the Wanfang database up to June 2023 was performed to collect studies that evaluate the effectiveness of problem-based learning and case-based learning teaching methods in clinical practical teaching in TACE treatment in China. Statistical analysis was performed by R software (4.2.1) calling JAGS software (4.3.1) in a Bayesian framework using the Markov chain-Monte Carlo method for direct and indirect comparisons. The R packages “gemtc”, “rjags”, “openxlsx”, and “ggplot2” were used for statistical analysis and data output. Results Finally, 7 studies (five RCTs and two observational studies) were included in the meta-analysis. The combination of PBL and CBL showed more effectiveness in clinical thinking capacity, clinical practice capacity, knowledge understanding degree, literature reading ability, method satisfaction degree, learning efficiency, learning interest, practical skills examination scores and theoretical knowledge examination scores. Conclusions Network meta-analysis revealed that the application of PBL combined with the CBL teaching mode in the teaching of liver cancer intervention therapy significantly improves the teaching effect and significantly improves the theoretical and surgical operations, meeting the requirements of clinical education.

Traditional uncertainty sampling methods in active learning often neglect category information, leading to imbalanced sample selection in multi-class computer vision tasks. Our approach integrates category information with uncertainty sampling through a novel active learning framework to address this limitation. Our method employs a pre-trained VGG16 architecture and cosine similarity metrics to efficiently extract category features without requiring additional model training. The framework combines these features with traditional uncertainty measures to ensure balanced sampling across classes while maintaining computational efficiency. Extensive experiments across both object detection and image classification tasks validate our method’s effectiveness. For object detection, our approach achieves competitive mAP scores while ensuring balanced category representation. For image classification, our method achieves accuracy comparable to state-of-the-art approaches while reducing computational overhead by up to 80%. The results validate our approach’s ability to balance sampling efficiency with dataset representativeness across different computer vision tasks. This work offers a practical, efficient solution for large-scale data annotation in domains with limited labeled data and diverse class distributions.

Background Team-Based Learning (TBL) has garnered considerable attention in education research. To consolidate the existing evidence, we conducted an umbrella review with four objectives: (a) to identify TBL review characteristics, (b) to synthesize findings from previous reviews regarding TBL effectiveness and outcomes, (c) to determine which student groups benefit most, and (d) to identify the most and least researched elements. Methods The Joanna Briggs Institute (JBI) methodology was followed: [1] Search strategy and literature search [2] Screening and Study Selection [3] Assessment of methodological quality [4] Data collection, and [5] Data summary. We utilized Endnote, Excel, and MAXQDA for efficient project management and analyzing data. Results Analyzing twenty-three reviews spanning from 2013 to 2024, we found a peak in TBL research in 2022 including more than 312 unique primary studies involving more than 63,987 participants. Notably, the United States and China accounted for over 61% of the total primary articles focused on students from medicine, nursing, pharmacy, and dentistry. Evidence supports the superiority of TBL in enhancing cognitive outcomes. However, findings related to retention are mixed. Insufficient evidence exists to draw robust conclusions when comparing TBL with other active learning methods. TBL demonstrates favorable outcomes in terms of clinical performance and engagement. Non-technical skills show mixed results. Notably, TBL positively impacts self-study, learning ability, decision-making, and emotional intelligence. Faculty experiences reveal an initial increase in workload, but generally hold positive attitude. Faculty development remain limited in duration and scope. Freshmen, academically weaker students, undergraduates, Chinese female students, and nursing students appear to benefit most from TBL. Team formation and size are the most frequently studied elements. Conclusion TBL holds promise for improving learning outcomes, but ongoing investigation is essential to maximize its impact in diverse educational contexts. This umbrella review underscores the need for further research in specific areas i.e. effective pre-class learning methods and faculty workload.