Search Results (1,150)

Digital game-based learning environments (DGBLEs) are increasingly used in mathematics education, highlighting the need to better understand their role in supporting students’ learning, emotional experiences, and self-regulated learning (SRL). This scoping review maps and synthesizes the existing literature on DGBLEs designed for school-aged learners, with a focus on mathematics achievement, academic emotions (including math anxiety), and SRL processes. A comprehensive search of multiple databases identified 192 studies that met inclusion criteria. Of the 185 studies examining mathematics achievement, 127 (68.6%) reported positive findings, while 45 (24.3%) reported mixed or partial findings. A substantial body of research also examined academic emotions. Among the 97 studies directly examining academic emotions, 25 examined negative emotions either exclusively (n = 12) or alongside positive emotions (n = 13), with findings generally indicating reductions in negative emotional experiences, including math anxiety. In contrast, relatively few studies directly examined SRL (n = 34), with self-efficacy representing the most frequently examined SRL-related outcome. Across studies, evidence suggests that DGBLEs may support SRL processes, particularly when scaffolded through structured prompts and feedback. As a scoping review, these findings represent a synthesis of trends across heterogeneous studies rather than causal effects. Overall, this review highlights the potential of DGBLEs to support both cognitive and affective aspects of mathematics learning, while identifying key gaps in the measurement of SRL and emotional processes. Findings underscore the need for more rigorous, theory-driven, and multimodal research to better understand how achievement, emotion, and SRL interact in digital learning environments. Full article

Digital access occupies a prominent place in educational policy debates, yet its predictive contribution to mathematics achievement, relative to psychological and socioeconomic factors, remains insufficiently quantified. Existing evidence often relies on linear models, examines predictor blocks separately, or considers technological resources without placing them alongside psychological constructs within a common framework. This study compares the predictive importance of psychological, socioeconomic, demographic and ICT-access indicators for mathematics achievement in PISA 2022. The analysis used data from 141,563 students across 19 education systems and included seven predictors: mathematics self-efficacy, mathematics anxiety, sense of school belonging, economic, social and cultural status (ESCS), gender, ICT resources at home and ICT resources at school. Weighted gradient boosting models were fitted separately to each of the ten plausible mathematics values and interpreted using TreeSHAP; a weighted random forest with permutation importance was used as a robustness check. The full model explained 39.2% of the weighted test-set variance in the plausible-value outcomes (R² = 0.3922, ${SE}_{total}=0.0066$, and RMSE = 77.95 score points). Mathematics self-efficacy ranked first under both criteria (42.3% of SHAP importance; 57.8% of permutation importance), ahead of socioeconomic status (30.9%; 32.0%), while the ICT-access block contributed 6.9% and 2.1%, respectively and added 0.0122 to test-set R². The two importance rankings were identical (Spearman’s ρ = 1.000). The SHAP ranking was unchanged across all 800 plausible-value × replicate-weight runs, matched XGBoost permutation importance, and was reproduced under a school-grouped train–test split. The model also detected a non-monotonic association between school belonging and predicted achievement, together with a MATHEFF × ESCS interaction pattern, supported by a direct-outcome interaction model, in which the modelled association between self-efficacy and achievement was stronger at higher ESCS levels. Because PISA 2022 is cross-sectional and plausible values are designed for population-level inference, these findings should be interpreted as predictive and associational rather than causal. The results suggest that, in systems where ICT access is already widespread, reported access to technological resources contributes comparatively little to prediction once psychological and socioeconomic indicators are considered, although sensitivity analyses indicate that home ICT access is partly affected by missingness patterns. Full article

Background: In the wake of the COVID-19 pandemic, nursing students and educators are navigating an increasingly complex academic environment. The rigorous demands of nursing education, combined with diminished academic preparedness and growing mental health concerns, have heightened the need for effective wellness interventions. The SKY Happiness Retreat©, an evidence-based program incorporating breathwork, meditation, and mindfulness practices, offers a potential strategy to enhance student resilience and reduce stress. Purpose: This pilot study examined the impact of integrating the SKY Happiness Retreat© into a Bachelor of Science in Nursing (BSN) program. This study evaluated whether participation reduced perceived stress and improved students’ ability to manage stressful situations. Methods: A quasi-experimental pretest–post-test design was used with junior-level BSN students recruited through convenience sampling at a rural university. Participants completed a structured three-day retreat focused on breathwork, meditation, and mindfulness. Outcomes were measured using the Mood and Anxiety Symptom Questionnaire (Mini-MASQ), the Perceived Stress Scale (PSS), and the Brief Resilience Scale (BRS). Results: Perceived stress significantly decreased following the intervention (PSS: pre M = 18.50, SD = 4.583; post M = 17.30, SD = 5.841; t(43) = 2.072, p = 0.022). General distress also significantly improved (Mini-MASQ: pre M = 15.51, SD = 6.535; post M = 12.82, SD = 5.118; t(44) = 3.941, p < 0.001). No significant changes were observed in anxious arousal or anhedonic depression. BRS scores demonstrated a modest increase in resilience. Conclusions: The SKY Happiness Retreat© shows promise as an experiential learning strategy within undergraduate nursing education. Findings suggest participation was associated with reductions in perceived stress and general distress while promoting resilience. Integrating evidence-based wellness programs into prelicensure nursing curricula may better prepare students to manage academic and professional stress. Larger, longitudinal studies are needed to evaluate long-term effectiveness. Full article

Dihydroquercetin (DHQ), a powerful antioxidant and regulator of cellular metabolism, was proposed for therapy of neurodegenerative disorders. Alzheimer’s disease (AD) and amyotrophic lateral sclerosis (ALS) are serious neurodegenerative disorders with oxidative stress as an overlapping feature and unmet therapeutic needs. To date, few studies have explored the efficacy of DHQ in animal models of genetically driven neurodegeneration. Here, APPswe/PS1dE9 (APP/PS1) mice and their wild-type (WT) littermates were orally administered DHQ (0.6 mg/kg/day) for four months, starting at eight months of age. At the age of 12 months, behavioral evaluation was performed, followed by brain staining with Congo red for amyloid plaque scoring and immunohistochemical analysis of GFAP-positive cells for the assessment of astrogliosis. Malondialdehyde (MDA) levels in the prefrontal cortex were studied as a marker of oxidative stress. Second, two-month-old FUS[1-359]-Tg mice, which recapitulate the hallmarks of ALS, received DHQ for 1.5 months and were investigated for general physiological parameters, the onset of paralysis, motor functions, and density of motor neurons in the spinal cord. DHQ-treated APPswe/PS1dE9 mutants displayed a decrease in amyloid plaque density of small size (≤100 μm) in the cortex and thalamus, had normalized MDA levels, improved conditioned taste aversion and Y-maze learning, and ameliorated anxiety measures, whereas their hippocampus-dependent step-down and pellet displacement performance remained impaired. In the second study, DHQ-treated FUS[1-359]-Tg mice showed rescued density of spinal cord neurons, normalized liquid and diet intake, and improved coat state, while the onset of paralysis and motor scores were not significantly ameliorated. Thus, chronic administration of low doses of DHQ exerted neuroprotective effects in both AD and ALS genetic models, which partially translated to reduced manifestations of these diseases. Full article

Despite the high global prevalence of depressive and anxiety disorders, access to early clinical assessment remains limited for many. Although this field has grown rapidly, existing reviews have focused primarily on technical performance, with limited systematic attention to whether current models meet the prerequisites for clinical implementation. This scoping review mapped methodological approaches across this domain and evaluated clinical readiness using five predefined indicators: sample size adequacy, external validation, prospective data collection, real-world evaluation, and model explainability. Searches of PubMed, Web of Science, and IEEE Xplore (March 2026) identified 2463 records; 34 studies (37 dataset evaluations) were included. The majority of studies (91%) were published from 2022 onwards. Depression was the primary target in 91% of studies, while only one study addressed anxiety. Hand-crafted acoustic features were the most frequent (57%), while classical machine learning was the most common model type (32%). External validation was conducted in only 32% of evaluations and real-world testing in 8%. Clinical readiness was classified as Low in 24%, Moderate in 65%, and High in 11% of evaluations. No evaluation met all five indicators simultaneously. These findings apply primarily to voice-based depression screening; 36 of 37 evaluations targeted depression, and the evidence base for anxiety disorders is limited to a single evaluation, precluding comparable characterisation for that condition. The principal challenges to clinical implementation are insufficient external validation, reliance on laboratory conditions, narrow linguistic coverage, and inconsistent metric reporting. The framework applied in this scoping review provides a replicable structure for assessing the clinical validity of AI-driven psychiatric screening tools. Full article

Within appropriate instructional frameworks, Audience Response Systems (ARS) can support active learning, retrieval practice, and feedback. However, they typically rely on selecting from predefined options, which limits active answer generation and insight into students’ reasoning. In this feasibility study, we integrated very short answers (VSA) into a hybrid ARS and examined whether free-text responses can be reliably aggregated in large classes, whether they extend student concepts beyond predefined options, and how students perceive the approach. First-semester medical students (n = 326) answered 26 questions in alternating multiple-choice (MC) and VSA formats, yielding 11,561 analyzed responses. Responses were automatically aggregated, and the ten largest aggregates per question were displayed in real time to guide discussion and feedback, capturing 74.6% of all responses and 40.3% of VSA responses. Students submitted more responses per answered question in MC than in VSA (mean difference = 0.35, 95% CI [0.31, 0.38], p < 0.001). Post hoc consolidation of conceptually identical expressions yielded 7.6 distinct concepts per question within the Top10 Aggregates, with VSA responses contributing concepts beyond the predefined MC options. Respondents perceived the approach as enhancing understanding and reducing anxiety. By eliciting student-generated concepts and reasoning, including unanticipated ones, VSA integration broadens formative feedback in ARS. Full article

Generative artificial intelligence (GenAI) has expanded opportunities for English learning beyond formal classroom settings. From an intelligence perspective, speaking in English as a foreign language (EFL) represents an applied domain in which learners must adaptively retrieve, integrate, monitor, and deploy linguistic knowledge under communicative demands. The present study examined the effects of a pedagogically guided form of AI-mediated informal digital learning of English (AI-IDLE) on Chinese university EFL learners’ speaking ability, speaking anxiety, and speaking enjoyment. To this end, 89 Chinese university EFL learners participated in a 12-week intervention and were assigned to an experimental group (EG) or a control group (CG). Pre- and post-intervention evaluations were administered to assess the results, including standardized speaking skills and valid questionnaires regarding enjoyment and speaking anxiety. The English-speaking skills test and the two questionnaires were administered at post-test and again 10 weeks after the intervention as the delayed post-test. Mixed-effects modeling (MEM) was used, and the findings showed that the EG demonstrated significantly greater improvements in speaking ability and enjoyment and a greater reduction in speaking anxiety than the CG, with these between-group advantages evident at the post-test. Overall, the findings suggest that structured AI-IDLE can provide a digitally mediated context for intelligence-relevant adaptive learning by supporting communicative performance while fostering affective conditions conducive to its development. These findings have implications for understanding how digitally mediated informal learning environments may support adaptive intelligence in applied language-learning contexts. Full article

Generative artificial intelligence (AI) is reshaping how students evaluate their abilities, occupational prospects, and the continuing value of professional education. This study examined how ability-based comparison with AI is associated with perceived professional value among journalism and communication students and why the same comparison may be linked to both threatening and adaptive responses. An explanatory sequential mixed-methods design was used. Survey data from 507 students in China were analyzed using partial least squares structural equation modeling, with PROCESS analyses as robustness checks, followed by semi-structured interviews with 20 students. Ability-based comparison with AI was positively associated with job replacement anxiety, which was negatively associated with perceived professional value, and with AI learning motivation, which was positively associated with perceived professional value. A positive direct association with perceived professional value also remained. AI hindrance appraisal strengthened the comparison–anxiety association, whereas AI challenge appraisal did not significantly moderate the comparison–motivation association. Interviews indicated that these relationships were task- and criterion-dependent and shaped by occupational interpretation and concrete learning demands. Human–AI comparison may therefore relate to professional value through simultaneous threat- and adaptation-oriented pathways rather than a uniformly positive or negative process. Full article

Traditional assessment practices in multilingual higher education continue to privilege text-based and monolingual demonstrations of learning, often limiting multilingual students’ opportunities to draw on their full linguistic repertoires and increasing assessment-related anxiety. Responding to calls for more equitable and inclusive assessment, this study investigates PLAYMOBIL pro-mediated Serious Play integrated with translanguaging as an innovative multimodal assessment framework that supports reflection, conceptual understanding, communication, and socio-emotional well-being. Employing a qualitative, comparative action-research design, the study was conducted across three higher education institutions in the United Kingdom, Albania, and Kosovo through three iterative action-research cycles involving implementation, observation, reflection, and pedagogical refinement. Data were triangulated through students’ three-dimensional PLAYMOBIL pro models accompanied by multilingual oral narratives, end-of-module reflective reports, qualitative post-intervention surveys, and lecturers’ observational field notes. Reflexive thematic analysis revealed that PLAYMOBIL pro-mediated translanguaging created an inclusive, dialogic, and low-anxiety assessment environment in which students exercised greater agency by drawing on their full linguistic and semiotic repertoires. The tactile and spatial affordances of PLAYMOBIL pro models enabled students to connect abstract disciplinary concepts with personal, cultural, and socio-economic experiences. Comparative findings highlighted contextual variations across sites. Students in the United Kingdom frequently described reduced English communication anxiety and increased confidence in expressing complex ideas, whereas students in Albania and Kosovo more often drew on translanguaging and spatial representations when engaging with regional and international business concepts. Across all settings, successive action-research cycles were associated with richer reflection and more elaborate conceptual explanations, suggesting that iterative pedagogical refinement may have contributed to these developments alongside the affordances of the PLAYMOBIL pro-mediated approach. This study advances scholarship on multilingual assessment by proposing an evidence-informed framework with potential applicability across multilingual higher education contexts and by illustrating how PLAYMOBIL pro-mediated Serious Play may support equity, learner agency, and meaningful participation. Full article

Background/Objectives: Glucocorticoids have been shown to improve fear extinction learning in preclinical and human studies. We hypothesized an impact of the circadian rhythm of cortisol on exposure therapy in patients with anxiety spectrum disorders. Methods: Sixteen patients suffering from obsessive–compulsive disorder and eight with agoraphobia participated in a pilot randomized prospective cohort study. During two therapist-guided sessions of exposure therapy with response prevention, both starting either at 08:00 (morning group) or at 16:00 (late afternoon group), subjective units of distress (SUD) and salivary cortisol levels were assessed repeatedly. Results: While expected significant diurnal differences in salivary cortisol emerged between daytime groups, after controlling for chronotype, no significant differences were detected in our exposure process indicators of within-session and between-session habituation and distress-related expectancy violation towards maximal SUD and SUD at end. Nearly one-hundred subjects in each group would be needed to gain statistically significant daytime differences for these indicators as per case number estimation. Conclusions: Our preliminary results from this transdiagnostic sample do not provide support for scheduling exposure sessions in the morning. Further research should consider cortisol administration before exposure sessions to test whether different doses of exogenous glucocorticoids can augment exposure therapy outcomes. Full article

Background: Aging is a risk factor for Alzheimer’s disease and related dementias, which are associated with synaptic dysfunction and cognitive decline. Elderberry (Sambucus spp.) is rich in anthocyanins with antioxidant and anti-inflammatory properties. Docosahexaenoic acid (DHA), an essential fatty acid, plays a key role in neuronal membrane integrity during brain aging. However, it remains unclear whether elderberry and DHA exert overlapping or distinct effects on brain aging and how these relate to molecular signaling. This study aimed to characterize molecular signatures induced by dietary supplementation and to determine their relationships with behavioral outcomes. Methods: 44-week-old male C57BL/6J mice were randomly assigned to control, elderberry, DHA, or combined diets for 12 weeks. Behavioral testing assessed anxiety-like behavior, spatial learning and memory. Brain tissues underwent proteomic and phosphoproteomic profiling and fatty-acid analysis. Data were analyzed using Ingenuity Pathway Analysis to identify enriched pathways, upstream regulators, and functional associations. Results: Elderberry as well as DHA supplementation induced targeted remodeling of the proteome and phosphoproteome, with pathway enrichment involving synaptogenesis, glutamatergic signaling, and long-term potentiation. Upstream-regulator analysis predicted elderberry-associated CDK5 signaling, accompanied by reduced MAPT/Tau phosphorylation at selected sites, whereas DHA supplementation was associated with CAMK-related signaling. DHA supplementation altered fatty-acid composition, increasing the n-3/n-6 ratio. Elderberry reduced anxiety-like behavior and improved target-directed search during the Barnes maze probe test. Molecular signatures were examined in relation to the measured behavioral outcomes. Conclusions: Elderberry and DHA are associated with distinct molecular networks related to synaptic function and behavioral outcomes in the aging male mouse brain. These findings support further investigation of elderberry and DHA as dietary interventions targeting molecular and behavioral features of brain aging. Full article

Imbalance in classes is one of the critical issues in machine learning practice, especially in the context of healthcare and organizational applications, where the class of interest, representing subjects at high risk of some adverse event, holds significant clinical and organizational value. In this paper, a comparative machine learning framework is presented to assess the influence of Synthetic Minority Over-sampling Technique (SMOTE) and stratified validation strategies on burnout risk prediction under extreme class imbalance scenarios. There was an apparent class imbalance ratio of 7.16:1 between the two classes of Not at Risk and At Risk (Moderate or High). There were sixteen possible experimental setups that were used, where each algorithm was evaluated using two approaches for addressing the class imbalance problem, with and without using SMOTE, along with hold-out and 5-Fold Stratified Cross-Validation. Based on the results, SMOTE provided a significant improvement in the performance of minority class detection in all the models evaluated without having any negative impact on the global model accuracy. Additionally, the application of SMOTE along with Stratified Cross-Validation was found to be the most effective in terms of achieving balance in classification performance. Overall, the use of XGBoost along with SMOTE and 5-Fold Stratified Cross-Validation provided the best results in this respect, with the highest minority class F1-Score and ROC-AUC performance. In particular, the SHAP-based analysis showed that SMOTE contributed to redistributing the attributions among a wide set of clinically relevant features such as stress level, hours of work overtime, and anxiety. Overall, the use of SMOTE and Stratified Cross-Validation was proven to significantly improve the performance of burnout risk classification in imbalanced data samples. Full article

Introduction: Cognitive dysfunction (“brain fog”) is a common manifestation of post-acute COVID-19 syndrome (PACS) and may persist long after the acute infection. While cross-sectional studies have described cognitive deficits, longitudinal evidence on recovery trajectories remains limited. Methods: We conducted a longitudinal observational study of neurocognitive performance and neuropsychiatric symptoms in patients with PACS. Participants underwent assessment with 20 standardized tests covering five cognitive domains (memory, attention, language, executive functions, psychomotor processing speed); anxiety, depression, and sleep quality were assessed at three time points. Changes were analysed using the Friedman test. Results: Forty-two patients were included (median age 57 years; 35.7% female) from a predominantly hospitalized cohort (81% hospitalised; 66.7% requiring respiratory support). Patients who completed all three assessments (completers, n = 42) were compared with those who attended the first evaluation but did not complete follow-up (non-completers, n = 544); completers were more severely ill during the acute phase rather than healthier or more motivated. At the group level, statistically significant improvements over time were observed across the whole sample in verbal short-term learning, visuospatial memory, working memory, constructional praxis, phonological verbal fluency, and psychomotor processing speed (all p ≤ 0.05); after Benjamini–Hochberg adjustment across the twenty cognitive outcomes, visuospatial span forward and backward and psychomotor processing speed remained significant (all FDR-adjusted p ≤ 0.013), with the change confined to the first six months. Sleep quality also improved (p < 0.0001). Conclusion: In this cohort, group-level performance improved in six of the twenty tests administered, of which three remained significant after correction for multiple comparisons, while 28 of 42 patients (66.7%) still scored in the impaired range on at least one test at 12 months, and 17 (40.5%) on two or more. These findings highlight the importance of long-term neuropsychological monitoring and integrated cognitive-psychiatric evaluation in post-COVID care. Given the small, predominantly hospitalized sample, improvements should be interpreted cautiously and confirmed in larger controlled studies, although the use of alternate forms for part of the battery makes task-specific learning an incomplete explanation. Full article

Background: Cervical cancer remains a major public health concern worldwide, particularly in low- and middle-income countries. Limited knowledge regarding cervical cancer prevention and elevated anxiety levels may hinder women’s participation in preventive measures. Mindfulness-Based Social–Emotional Learning integrates mindfulness practices with social–emotional learning strategies and may enhance health knowledge while reducing anxiety. Materials and Methods: A quasi-experimental pretest–posttest non-equivalent control group design was conducted among 80 women attending gynecological outpatient clinics in Egypt. Participants were assigned to a study group (n = 40) and a control group (n = 40). Data were collected using a structured interview questionnaire, a cervical cancer knowledge questionnaire, and the Beck Anxiety Inventory (BAI). The study group received an eight-week MBSEL intervention incorporating mindfulness exercises, social–emotional learning activities, and cervical cancer education, while the control group received routine health education. Pre- and post-intervention assessments were performed. Results: Baseline characteristics were comparable between groups. Following the intervention, the study group had significantly higher mean knowledge scores regarding cervical cancer prevention, with mean knowledge scores increasing from 38.71 ± 10.41 to 66.25 ± 8.95 compared with 42.09 ± 15.95 in the control group (t = 8.355, p < 0.001). Good knowledge levels increased from 2.5% to 35.0%, while poor knowledge declined from 62.5% to 0.0%. Anxiety levels significantly improved in the study group, with mean BAI scores decreasing from 38.68 ± 6.32 to 22.20 ± 3.91 compared with 34.28 ± 9.53 in the control group (t = 7.414, p < 0.001). Low anxiety increased from 2.5% to 62.5% in the study group compared with 7.5% to 12.5% in the control group, and severe anxiety was eliminated from 27.5% to 0.0% in the study group compared with 32.5% to 22.5% in the control group after the intervention. Conclusions: The MBSEL approach effectively enhanced women’s knowledge regarding cervical cancer prevention and significantly reduced anxiety levels. Integrating mindfulness practices with social–emotional learning and health education appears to strengthen cognitive engagement and emotional regulation. Clinical Trial Registration: Registered in accordance with WHO and ICMJE standards (Trial number: PACTR202605512267205). Registration date: 29 May 2026. Full article

Artificial intelligence (AI) can be appraised simultaneously as useful, capable, threatening, and demanding. Yet research typically treats attitudes toward AI as a single favorable-unfavorable continuum, obscuring how these evaluations coexist within individuals. Using a three-wave, time-lagged survey of 379 first-year university students in China, we examined configurations of six AI-related appraisal indicators: perceived humanlikeness, adaptability, and quality of AI; AI use anxiety; awareness of smart technology, artificial intelligence, robotics, and algorithms (STARA) as a career threat; and AI creative self-efficacy. Latent profile analysis supported four profiles: Positive Empowerment (19.2%), Anxious Acceptance (37.5%), Low-Perception Detached (37.2%), and High-Perception High-Vigilance (6.1%). R3STEP models showed that learning agility, digital literacy, future work self salience, and perceived university digital support were prospectively associated with profile membership, particularly in distinguishing the Low-Perception Detached profile from the more engaged profiles. BCH comparisons further showed that the Positive Empowerment and High-Perception High-Vigilance profiles reported relatively high levels of both career crafting and self-perceived employability, whereas the Low-Perception Detached profile reported the lowest levels of both outcomes. Notably, the High-Perception High-Vigilance profile combined elevated AI use anxiety and STARA awareness with strong career-development engagement, while the Low-Perception Detached profile combined comparatively low threat perceptions with weak career crafting and employability. These findings demonstrate that higher AI-related threat was not necessarily associated with weaker career preparation and, conversely, that low perceived threat was not necessarily associated with greater adaptive readiness. Overall, the results position students’ responses to AI as configurations of opportunity appraisal, threat, and AI creative self-efficacy rather than as uniformly positive or negative attitudes, thereby highlighting the need for differentiated university career education and AI-readiness interventions. Full article