Search Results (110)

This study investigates how online discussion forums in an undergraduate Social Responsibility course support students’ SDG-oriented idea generation and collaborative knowledge construction. It also examines how participation roles, behavioral intensity, interaction-network influence, and goal-aligned discourse shape idea visibility and discussion. Using a mixed-methods learning analytics design, we analyzed forum logs and message texts across five SDG-linked themes (SDGs 6, 7, 12, 14, 15) by classifying contributor types, computing a Behavioral Participation Index (BPI), constructing a directed reply network and estimating PageRank centrality, extracting solution proposals, scoring semantic goal alignment, modelling weekly temporal dynamics, and fitting multivariate regressions predicting visibility (reads) and engagement (replies) while controlling for theme, message level, time, PageRank, and BPI. Results show role-differentiated participation (N = 514), meaningful cross-theme solution proposals that varied across academic groups, and peak-driven weekly activity. PageRank centrality emerged as the strongest and most consistent predictor of both visibility and engagement, whereas goal alignment showed weaker direct effects after controls, suggesting that SDG-aligned ideas do not necessarily diffuse without structural embeddedness. Among highly goal-aligned posts, specific communicative features differentiated which proposals attracted attention and interaction. These findings suggest that SDG forum design benefits from structured interaction pathways and scaffolded discourse strategies to support equitable diffusion and productive sustainability dialogue. The study does not evaluate the normative quality of sustainability positions but examines how interaction structures and discourse features shape the visibility and diffusion of student-generated ideas. Full article

Learner engagement is a pivotal factor affecting the effectiveness of Massive Open Online Courses (MOOCs), as it promotes collaborative learning environments. However, measuring the extent of learners’ contributions in MOOC learning forums presents challenges due to the complex nature of engagement and its variability. Given the limited research in this domain, further investigation is necessary. This study aims to address this gap by utilizing the Hidden Markov Model (HMM) to identify latent states of MOOC learners and improve their participation in learning forums. The study constructs a multidimensional observable signal sequence based on learner-generated post data from MOOC forums, with a particular focus on the widely attended course on a MOOC platform. To evaluate the predictive accuracy of HMM in forecasting learner contributions, the study employs several prominent prediction models for comparative analysis, including k-nearest neighbor, logistic regression, random forest, extreme gradient boosting tree, and the long short-term memory network. The results demonstrate that HMM provides superior accuracy in predicting learner contributions compared to other models. These findings not only validate the effectiveness of HMM but also offer significant insights and recommendations for enhancing forum management practices. This research represents a substantial advancement in addressing the challenges related to learner engagement in MOOC learning forums and underscores the potential benefits of employing the HMM approach in this context. Full article

This study aimed to examine the effects of online, video-based teacher professional development on changes in classroom instruction and student achievement in biology. The professional development program included organizing lessons based on prepared materials aligned with national curriculum outcomes, asynchronous participation in an online forum for (self-)analysis of lesson videos using the Teaching Observation Form (TOF), and synchronous participation in online communities of practice. Teachers and their eighth-grade students participated in this quasi-experimental study, which involved control and experimental student groups and pre- and post-tests of knowledge. The results indicate that students in the experimental group achieved statistically significantly higher post-test scores than those in the control group (d = 0.26), with the largest differences observed in tasks requiring higher-order cognitive skills. The findings suggest that even a relatively short professional development intervention—including continuous online support for teachers—can lead to improvements in student learning outcomes. Full article

By triangulating sentiment trends, topic models, and ideological variance, this study shows how digital publics respond to significant shifts in US foreign policy. We analyze Reddit discussions of the 20 January 2025 90-day freeze on US foreign assistance, with a focus on USAID, across partisan (r/Democrats and r/Republican) and neutral (r/fednews) subreddits. Using Structural Topic Modeling and sentiment analysis on posts and comments collected via ArcticShift, we find clear polarization in framing and tone. Overall sentiment was predominantly negative, but sources of negativity diverged: Republican forums emphasized fiscal responsibility, government waste, and national sovereignty; Democratic forums emphasized humanitarian harm and institutional erosion; r/fednews foregrounded institutional, legal, and administrative concerns. Topic-prevalence estimates reveal that themes such as executive overreach and aid justification were prominent but framed differently by the community. The findings highlight Reddit’s role as an arena for contesting and reframing policy debates. Full article

This paper explores how collective resilience is built and sustained through situated, relational, and reparative approaches to design within conditions of deep spatial inequality. Focusing on Johannesburg’s Slovo Park settlement and the long-standing 15 year collaboration between the Slovo Park Community Development Forum (SPCDF) and 1to1—Agency of Engagement, it examines how participatory tool-making—centred on two keystone tools, the Blue File (a community-held, cloud-based knowledge repository) and the Timeline Tool (a multi-workshop planning and accountability device)—supports iteration, voice change, leadership transitions, and decision-making “with the map in hand.” Grounded in Southern urbanist theory and spatial justice scholarship, the paper re-politicises resilience as ongoing negotiation, repair, and shared authorship. It details how a map-based pointing practice translated situated knowledges into spatial choices; how the Blue File preserved continuity and evidence through leadership turnover; and how the Timeline Tool embedded care and transparency. Alongside benefits, the paper surfaces key tensions—expectation management, idea overload, triage and prioritisation, and legitimacy during leadership changes—and shows the concrete decision protocols used to move from many inputs to buildable design options. It concludes with ethical reflections for practitioners working in postcolonial/post-apartheid contexts and offers transferable lessons for allied urban conditions. Full article

Compulsive fantasy, often called “maladaptive daydreaming,” involves frequent engagement with immersive fantasies that can sometimes interfere with everyday life and cause distress. This paper expands on Christoff and colleagues’ Dynamic Framework of Thought (DFT) to offer a process-based analysis of compulsive fantasy as it relates to other mental phenomena such as daydreaming and creative thought. Drawing on the existing literature and posts on online forums by self-identified maladaptive daydreamers, we also propose an account of how compulsive fantasy episodes may unfold in terms of the oscillating dynamics of various constraints on thought, and how these dynamics may be related to a perceived struggle with agency. Automatic constraints, including affective salience and mental habits, may bring about a fantasy episode. During a fantasy episode, automatic constraints may be relatively high throughout, whereas deliberate constraints may be intermittently engaged to influence the fantasy. Our analysis supports the use of “compulsive fantasy” as a more accurate designation than “maladaptive daydreaming” for this phenomenon: compulsive fantasies are not daydreams, because they are more constrained in their mental dynamics. We show that fantasy and daydreaming are not inherently harmful but can become so when they are accompanied by relatively strong and sustained automatic constraints on thought. Full article

Recognizing and understanding the nuances of mental health and how issues can present at various levels of healthcare for both patients and the interprofessional (IP) healthcare team can be crucial for the success and well-being of team members, as well as for achieving positive patient outcomes. Learners from various allied healthcare disciplines participated in a Case-Based Learning-Sequential Disclosure Activity (CBL-SDA) to address navigating appropriate approaches to fostering wellness in the clinical encounter and within healthcare teams from a multidisciplinary perspective. The CBL-SDA was delivered to a cohort of allied health students (N = 90) using a 4-step process during an interprofessional education (IPE) event of (i) Orientation, (ii) Sequential Disclosure, (iii) IPE Forum, (iv) Wrap-up. Pre- and post-activity surveys were voluntarily collected to gauge participants’ perceptions of the content and delivery method, with a response rate of 90% (N = 81). Overall, participants reported gaining confidence in their understanding of wellness, in identifying and providing support for a person struggling with wellness, in having tools to promote wellness, and also rated their own wellness higher, following the one-hour training session. It can be concluded that IPE activities highlighting wellness and mental health are beneficial and necessary in allied health care training. Full article

This essay focuses on the Greek adoptees’ search for identity and on the agrafa, or the “unwritten” territories, into which this search penetrates. The Greek adoptees represent an underresearched case study of the postwar intercountry adoption movement (1950–1975). Creating a narrative of the self is key to the adoptees’ identity formation, but their personal narrative is often undermined by stereotypes and denunciations that stunt its development. The research presented here has been guided by questions that interrogate the verdict-making or “sentencing” associated with the adoptees’ identity-shaping process: their sentencing to subjugation by stock opinions, the denouncing of their alternative viewpoints about “rescue” adoptions, and the verdict of their entrapment in feel-good master narratives. This essay also explores broader research questions pertaining to modes of interrogating “historic” adoptions from Greece. It is concerned with the why rather than with the how or the who of the oldest, post-WWII intercountry adoption flows. In what forums and genres (narrative, visual, journalistic, scholarly) are Greek adoption facts and legacies articulated, mediated, and/or materialized? How do memories, both positive and negative, underpin current projects of self-identification and transformation? What are the adoptees’ preferred outlets to speak about embodied experiences, and are those satisfactory? Based on a mixed methods approach, the essay ties these steps in identity growth to the Adoptee Consciousness Model, illustrating the five phases of consciousness that the adoptees may experience throughout their lives. Full article

Background: Social media represents a unique opportunity to investigate the perspectives of people with eating disorders at scale. One forum alone, r/EatingDisorders, now has 113,000 members worldwide. In less than a day, where a manual analysis might sample a few dozen items, automatic classification using large language models (LLMs) can analyze thousands of posts. Methods: Here, we compare multiple strategies for invoking an LLM, including ones that include examples (few-shot) and annotation guidelines, to classify eating disorder content across 14 predefined themes using Llama3.1:8b on 6850 social media posts. In addition to standard metrics, we calculate four novel dimensions of classification quality: a Category Divergence Index, confidence scores (overall model certainty), focus scores (a measure of decisiveness for selected subsets of themes), and dominance scores (primary theme identification strength). Results: By every measure, invoking an LLM without extensive guidance and examples (zero-shot) is insufficient. Zero-shot had worse mean category divergence (7.17 versus 3.17). Whereas, few-shot yielded higher mean confidence, 0.42 versus 0.27, and higher mean dominance, 0.81 versus 0.46. Overall, a few-shot approach improved quality measures across nearly 90% of predictions. Conclusions: These findings suggest that LLMs, if invoked with expert instructions and helpful examples, can provide instantaneous high-quality annotation, enabling automated mental health content moderation systems or future clinical research. Full article

The building sector significantly contributes to global energy consumption and carbon emissions, primarily due to the extensive use of carbon-intensive materials such as concrete and steel. Mass timber construction, particularly using cross-laminated timber (CLT), offers a promising low-carbon alternative. This study aims to calculate the embodied carbon emissions and biogenic carbon storage of a CLT-based affordable housing project, 340+ Dixwell in New Haven, Connecticut. This project was designed using a honeycomb structural system, where mass timber floors and roofs are supported by mass timber-bearing walls. The authors are not aware of a prior study that has evaluated the life cycle impacts of honeycomb mass timber construction while considering Timber Use Intensity (TUI). Unlike traditional post-and-beam systems, the honeycomb design uses nearly twice the amount of timber, resulting in higher carbon sequestration. This makes the study significant from a sustainability perspective. This study follows International Standard Organization (ISO) standards 14044, 21930, and 21931 and reports the results for both lifecycle stages A1–A3 and A1–A5. The analysis covers key building components, including the substructure, superstructure, and enclosure, with timber, concrete, metals, glass, and insulation as the materials assessed. Material quantities were extracted using Autodesk Revit^®, and the life cycle assessment (LCA) was evaluated using One Click LCA (2015)^®. The A1 to A3 stage results of this honeycomb building revealed that, compared to conventional mass timber housing structures such as Adohi Hall and Heartwood, it demonstrates the lowest embodiedf carbon emissions and the highest biogenic carbon storage per square foot. This outcome is largely influenced by its higher Timber Use Intensity (TUI). Similarly, the A1-A5 findings indicate that the embodied carbon emissions of this honeycomb construction are 40% lower than the median value for other multi-family residential buildings, as assessed using the Carbon Leadership Forum (CLF) Embodied Carbon Emissions Benchmark Study of various buildings. Moreover, the biogenic carbon storage per square foot of this building is 60% higher than the average biogenic carbon storage of reference mass timber construction types. Full article

Background/Objectives: Large-language modules facilitate accessing health information instantaneously. However, they do not provide the same level of accuracy or detail. In pediatric orthopedics, where parents have urgent concerns regarding knee deformities (bowlegs and knock knees), the accuracy and dependability of these chatbots can affect parent decisions to seek treatment. The goal of this study was to analyze how AI chatbots addressed parental concerns regarding pediatric knee deformities. Methods: A set of twenty standardized questions, consisting of ten questions each on bowlegs and knock knees, were designed through literature reviews and through analysis of parental discussion forums and expert consultations. Each of the three chatbots (ChatGPT, Gemini, and Copilot) was asked the same set of questions. Five pediatric orthopedic surgeons were then asked to rate each response for accuracy, clarity, and comprehensiveness, along with the degree of misleading information provided, on a scale of 1–5. The reliability among raters was calculated using intraclass correlation coefficients (ICCs), while differences among the chatbots were assessed using a Kruskal–Wallis test with post hoc pairwise comparisons. Results: All three chatbots displayed a moderate-to-good score for inter-rater reliability. ChatGPT and Gemini’s scores were higher for accuracy and comprehensiveness than Copilot’s (p < 0.05). However, no notable differences were found in clarity or in the likelihood of giving incorrect answers. Overall, more detailed and precise responses were given by ChatGPT and Gemini, while, with regard to clarity, Copilot performed comparably but was less thorough. Conclusions: There were notable discrepancies in performance across the AI chatbots in providing pediatric orthopedic information, which demonstrates indications of evolving potential. In comparison to Copilot, ChatGPT and Gemini were relatively more accurate and comprehensive. These results highlight the persistent requirement for real-time supervision and stringent validation when employing chatbots in the context of pediatric healthcare. Full article

The inherent uncertainty and information asymmetry in financial markets create significant challenges for accurate price forecasting. Although investor sentiment analysis has gained traction in recent research, the temporal dimension of sentiment dynamics remains underexplored. This study develops a novel framework that enhances stock price prediction by integrating time-partitioned investor sentiment, while improving model interpretability via Shapley additive explanations (SHAP) analysis. Employing the ERNIE (enhanced representation through knowledge integration) 3.0 model for sentiment extraction from China’s Eastmoney Guba stock forum, we quantitatively distinguish intraday and post-market investor sentiment then integrate these temporal components with technical indicators through neural network architecture. Our results indicate that temporal sentiment partitioning effectively reduces uncertainty. Empirical evidence demonstrates that our long short-term memory (LSTM) model integrating intraday and post-market sentiment indicators achieves better prediction accuracy, and SHAP analysis reveals the importance of intraday and post-market investor sentiment to stock price prediction models. Implementing quantitative trading strategies based on these insights generates significantly more annualized returns for representative stocks with controlled risk, outperforming sentiment-agnostic and non-temporal sentiment models. This research provides methodological innovations for processing temporal unstructured data in finance, while the SHAP framework offers regulators and investors actionable insights into sentiment-driven market dynamics. Full article

This paper investigates, applies, and evaluates state-of-the-art Large Language Models (LLMs) for the classification of posts from a dark web hackers’ forum into four cyber-security categories. The LLMs applied included Mistral-7B-Instruct-v0.2, Gemma-1.1-7B, Llama-3-8B-Instruct, and Llama-2-7B, with zero-shot learning, few-shot learning, and fine-tuning. The four cyber-security categories consisted of “Access Control and Management”, “Availability Protection and Security by Design Mechanisms”, “Software and Firmware Flaws”, and “not relevant”. The hackers’ posts were also classified and labelled by a human cyber-security expert, allowing a detailed evaluation of the classification accuracy per each LLM and customization/learning method. We verified LLM fine-tuning as the most effective mechanism to enhance the accuracy and reliability of the classifications. The results include the methodology applied and the labelled hackers’ posts dataset. Full article

Background: Cancer diagnosis causes a range of different emotions. It is also a factor that causes feelings of severe stress. Coping with stress is individual and depends on the person’s nature, environment, and support they receive. Aim: This study aimed to assess how women diagnosed with breast cancer cope with stress caused by the disease. Methods: A total of 111 women diagnosed with breast cancer participated in the study. The questionnaires were distributed electronically using Google Forms in online forums and groups on social media. The survey consisted of two parts: the original questions and the Mini-COPE questionnaire. The relationships between stress-coping strategies and age, having children, marital status, and life satisfaction were tested. The Mann–Whitney U test, Kruskal–Wallis test, and Dunn’s post-hoc test with Bonferroni correction were used for the analyses (p < 0.05). Results: Of the surveyed women, 54.9% reported that the moment of diagnosis was the most stressful. Feelings of fear and anxiety accompanied 30.5% of the women, and 24.7% at the time of diagnosis could not provide information about the disease. The dominant strategies were seeking emotional support (mean 2.12 ± 0.56) and seeking instrumental support (mean 2.06 ± 0.48). Women in the older age group, married women, and women with children were most likely to adopt the strategy of turning to religion. Conclusions: The dominant strategies were seeking emotional and instrumental support. The strategy of turning to religion was used more often by older patients and patients with children. Full article

With the wide adoption of Learning Management Systems (LMSs) in educational institutions, ample data have become available demonstrating students’ online behavior. Digital traces are widely applicable in Learning Analytics (LA). This study aims to explore and extract behavioral features from Moodle logs and examine their effect on undergraduate students’ performance. Additionally, traditional factors such as demographics, academic history, family background, and attendance data were examined, highlighting the prominent features that affect student performance. From January to April 2019, a total of 64,231 students’ Moodle logs were collected from a private university in Malaysia for analyzing students’ behavior. Exploratory Data Analysis, correlation, statistical tests, and post hoc analysis were conducted. This study reveals that age is found to be inversely correlated with student performance. Tutorial attendance and parents’ occupations play a crucial role in students’ performance. Additionally, it was found that online engagement during the weekend and nighttime positively correlates with academic performance, representing a 10% relative increase in the student’s exam score. Ultimately, it was found that course views, forum creation, overall assignment interaction, and time spent on the platform were among the top LMS variables that showed a statistically significant difference between successful and failed students. In the future, clustering analysis can be performed in order to reveal heterogeneous groups of students along with specific course-content-based logs. Full article