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This study examines the role of religious–moral values in supporting the inclusion of students with special educational needs (SEN) within the broader framework of inclusive education. Using a sequential explanatory mixed-methods design, the research combines a qualitative phase based on semi-structured interviews with special education teachers (N = 9 participants) and a quantitative phase involving a questionnaire administered to a larger sample (N = 324 respondents). The qualitative findings indicate that teachers associate religious–moral values with the development of socio-emotional competencies, such as empathy, respect, solidarity, and a sense of belonging, which are considered essential for inclusion. The quantitative results support these perspectives, showing high levels of agreement regarding the contribution of these values to fostering positive attitudes, social acceptance, and the classroom integration of students with SEN. The findings also suggest that teachers attribute greater importance to core values than to formal religious instruction and prefer adaptive, student-centered strategies, including narrative and experiential approaches. Overall, the study highlights the potential of religious–moral values as a resource for inclusive education when applied in a flexible, interdisciplinary, and context-sensitive manner. These findings contribute to ongoing discussions on the role of religion in education, particularly in relation to inclusion, equality, and respect for diversity. Full article

(This article belongs to the Section Religions and Humanities/Philosophies)

25 pages, 1338 KB

Open AccessArticle

Dimensional Synthesis and Optimization of Leading and Mixed-Leading Double Four-Bar Steering Mechanisms: A Comparative Metaheuristic Approach

by Yaw-Hong Kang and Da-Chen Pang

Machines 2026, 14(4), 445; https://doi.org/10.3390/machines14040445 (registering DOI) - 16 Apr 2026

Abstract

This study investigates the dimensional synthesis and optimization of multi-link steering mechanisms—namely, the leading and mixed-leading double four-bar configurations—for front-wheel-drive vehicles. To overcome the accuracy limitations of conventional steering at large angles (up to 70°), a comparative metaheuristic approach is employed, utilizing two popular metaheuristic optimizations, Improved Particle Swarm Optimization (IPSO) and Differential Evolution with golden ratio (DE-gr), to optimize the geometric parameters of these complex eight-bar steering systems. Using a track-to-wheelbase ratio of 0.5, the optimization minimizes a mean-squared structural-error objective function integrated with Grashof mobility constraints. The optimized mechanisms are validated via ADAMS kinematic simulations and further analyzed in MATLAB R2021 regarding steering accuracy, transmission angles, and mechanical advantage. The results reveal a distinct performance trade-off: mixed-leading configurations achieve superior geometric precision and mass reduction due to shorter link lengths, with IPSO yielding the highest accuracy. Conversely, leading-type mechanisms provide a more linear and stable mechanical advantage, ensuring predictable force transmission. While DE-gr exhibits faster convergence across both variants, both algorithms effectively exploit the complex parameter space of multi-link systems. Ultimately, this metaheuristic optimization-based approach offers a superior and robust framework for the dimensional synthesis of high-performance multi-link steering mechanisms, surpassing the constraints of traditional gradient-based methods. Our findings recommend the mixed-leading configuration for precision-focused applications and the leading configuration for scenarios requiring consistent mechanical performance. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled Vehicle Systems: Modeling, Control Optimization and Fault Diagnosis)

21 pages, 326 KB

Open AccessArticle

Person-First or Disease-First? Language Choices in Cancer Communication

by Anna Tsiakiri, Konstantinos Tzanas, Despoina Chrisostomidou, Spyridon Plakias, Foteini Christidi, Christos Frantzidis, Nikolaos Aggelousis, Maria Lavdaniti and Evangeli Bista

Nurs. Rep. 2026, 16(4), 143; https://doi.org/10.3390/nursrep16040143 - 16 Apr 2026

Abstract

Background/Objectives: Cancer-related terminology is not merely descriptive and plays a critical role in shaping emotional responses, personal identity, and communication across clinical, social, and public spheres. Despite growing interest in the psychosocial dimensions of illness language, few studies have centered the lived experiences of individuals navigating cancer through the lens of terminology. This study explores how people living with and beyond cancer perceive, interpret, and emotionally respond to cancer-related language, focusing on the way terminology influences identity, stigma, and communicative interaction. Methods: A sequential mixed-methods design was employed. The quantitative phase involved 146 participants with a cancer diagnosis completing a structured questionnaire on preferred terminology and emotional impact. The qualitative phase followed, using open-ended questionnaires with 11 participants to deepen understanding of linguistic experiences. Thematic content analysis was used to identify patterns across narratives. Results: These findings reveal that labels such as “cancer patient” evoke strong negative emotional reactions, associated with stigma, fear, and identity reduction. Person-first and context-sensitive language was perceived as more respectful and empowering. Emotional responses to language varied widely, from fear to neutrality, shaped by speaker role, context, and time since diagnosis. Media representations were often seen as dramatizing or moralizing, reinforcing the need for communicative clarity, empathy, and education in both clinical and public discourse. Conclusions: Cancer-related language is a powerful psychosocial force. It shapes how individuals are seen and see themselves and can either reinforce stigma or foster dignity and resilience. This study highlights the urgent need for person-centered, context-aware communication practices across healthcare, media, and society. Full article

(This article belongs to the Special Issue Advances in Nursing Care for Cancer Patients)

25 pages, 3645 KB

Open AccessArticle

Pervaporation Mixed Matrix Membranes from Sodium Alginate/ZnO for Isopropanol Dehydration

by Roman Dubovenko, Mariia Dmitrenko, Anna Mikulan, Olga Mikhailovskaya, Anna Kuzminova, Aleksandra Koroleva, Anton Mazur, Rongxin Su and Anastasia Penkova

Molecules 2026, 31(8), 1300; https://doi.org/10.3390/molecules31081300 - 16 Apr 2026

Abstract

In this work, sodium alginate (NaAlg) membranes were enhanced with synthesized zinc oxide (ZnO) nanoplates to enable efficient pervaporation dehydration of isopropyl alcohol (IPA). A comprehensive suite of characterisation techniques—scanning electron (SEM) and atomic force (AFM) microscopy, Fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), contact angle and liquid uptake measurements—along with density functional theory (DFT) calculations, was employed to establish robust structure–property relationships and to elucidate filler–polymer interactions. Membranes with different ZnO contents were prepared, and membranes based on the optimal NaAlg-ZnO(5%) composite were cross-linked with CaCl₂ to improve stability in aqueous solutions, and supported membranes were developed for prospective applications by applying this composite onto the prepared porous cellulose acetate (CA) substrate. This developed cross-linked supported NaAlg-ZnO(5%)/CA membrane had a permeation flux increased by 2 times or more compared to a dense NaAlg membrane during dehydration of IPA (12–30 wt.% water) with a permeate water content above 99 wt.%. The integrated experimental–theoretical approach provides mechanistic insight into ZnO–NaAlg interactions and demonstrates the strong potential of these mixed matrix membranes for high-efficiency alcohol dehydration, offering a rational design paradigm for next-generation pervaporation membranes. Full article

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19 pages, 1683 KB

Open AccessArticle

Economic Viability and Carbon Sequestration of Mixed Native Forests in Southern Chile: An Integrated Faustmann Approach

by Norman Moreno-García, Roberto Moreno, Juan Ramón Molina, Beatriz López Bermúdez and Leonardo Durán-Garate

Forests 2026, 17(4), 494; https://doi.org/10.3390/f17040494 - 16 Apr 2026

Abstract

This study evaluates the financial profitability and carbon sequestration in mixed native forests of the Roble-Raulí-Coigüe and evergreen types in the southern macrozone of Chile, integrating both ecosystem services into forest management decision-making. The Faustmann model and dynamic programming were applied to determine the optimal rotation periods and Land Expectation Value (LEV) under two scenarios: exclusive timber production and combined timber and carbon production. The results indicate that mixed forests consistently outperform monocultures in terms of profitability, especially in 25%–75% mix configurations and moderate densities (2000 trees/ha). The observed range of 25%–75% across different tree species is determined by the interplay of two critical factors: the average annual growth rate (AAGR) of biomass and the opportunity cost of the forest rotation. In fast-growing species, the upper limit (75%) reflects an optimisation towards early carbon sequestration, whilst in slow-growing species, the ratio shifts towards the lower limit (25%) to compensate for longer rotation periods and associated biotic risks. This range acts as an efficiency frontier that balances biological productivity with the stability of the accumulated carbon stock. The inclusion of the economic value of carbon increased the LEV and extended the optimal rotation periods, confirming the relevance of integrating ecosystem services into forest planning. These findings suggest that mixed native forests represent a competitive and sustainable alternative to monocultures, contributing to climate change mitigation and income diversification for forest owners. Full article

(This article belongs to the Special Issue Forest Ecosystem Services and Sustainable Management)

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30 pages, 12017 KB

Open AccessArticle

An Integrated Framework for Interactive and Inclusive Asynchronous Online Learning at Scale: Data Literacy in Higher Education

by Yalemisew Abgaz

Educ. Sci. 2026, 16(4), 639; https://doi.org/10.3390/educsci16040639 - 16 Apr 2026

Abstract

Online asynchronous learning offers considerable flexibility but frequently faces challenges in sustaining engagement, interactivity, and inclusivity across diverse learner populations. This study introduces the OPTIMAL framework—an Online, Pedagogy- and Technology-Integrated, Microcurricula Approach for interactive and inclusive Learning—synthesising universal design for learning, active learning, and constructive alignment with technology integration frameworks (TPACK and PICRAT), operationalised through a microcurricula-as-a-service architecture. A three-year longitudinal case study (2022/23 to 2024/25) examined the application of the framework to a data literacy and analytics module serving over 5000 students across more than 15 programs and five faculties at Dublin City University. The module design constructively aligned learning outcomes, content, and technology at three levels to support multiple learning pathways, formative assessment, and transdisciplinary engagement, deliberately fostering transformative uses of technology in a fully asynchronous environment. Mixed-methods evaluation—combining learning analytics, surveys (n = 1743), and qualitative feedback—demonstrated sustained positive outcomes across all three years, including 95–99% completion rates, consistently high satisfaction, and longitudinal gains in engagement and pass rates. These findings demonstrate how the deliberate integration of pedagogical theory, technological frameworks, and modular curriculum architecture can deliver scalable, inclusive, and high-engagement online education, offering both a transferable, evidence-based model for educators and curriculum designers and longitudinal empirical validation for researchers. Full article

(This article belongs to the Section Technology Enhanced Education)

25 pages, 18953 KB

Open AccessReview

A Systematic Taxonomy and Comparative Analysis of Mixed-Signal Simulation Methods: From Classical SPICE to AI-Enhanced Approaches

by Jian Yu, Hairui Zhu, Jiawen Yuan and Lei Jiang

Electronics 2026, 15(8), 1687; https://doi.org/10.3390/electronics15081687 - 16 Apr 2026

Abstract

Mixed-signal simulation is indispensable for verifying modern integrated circuits that tightly couple analog and digital subsystems, yet the field lacks a unified framework for systematically comparing its diverse methodologies. This paper addresses that gap by proposing a novel three-axis taxonomy that classifies simulation methods along abstraction level, solver methodology, and analysis type, together with a comparative evaluation framework based on five quantitative metrics: accuracy, throughput, capacity, convergence reliability, and scalability. Applying this framework, we systematically compare thirteen classical method categories—spanning SPICE, FastSPICE, RF/periodic steady-state, behavioral modeling, co-simulation, and model order reduction—and eight AI/ML approaches including Gaussian process surrogates, graph neural networks, physics-informed neural networks, Bayesian optimization, and reinforcement learning. Our analysis reveals a clear maturity stratification: classical methods remain the only signoff-accurate approaches, Bayesian optimization represents the most industrially validated AI contribution with integration across all three major EDA platforms, while Neural ODE solvers and LLM-based design tools remain at the research stage. We identify a persistent academic-to-industry gap driven by foundry model complexity, limited benchmark diversity, and topology-specific overfitting. The proposed taxonomy and comparative framework provide practitioners with structured guidance for simulation method selection and highlight specific research directions needed to bridge the gap between AI promise and industrial deployment. Full article

(This article belongs to the Special Issue AI-Enhanced Mixed-Signal Simulation and EDA for Integrated Circuit Design Using CMOS Technologies)

28 pages, 2111 KB

Open AccessArticle

Simulation-Based Safety Evaluation of Mixed Traffic with Autonomous Vehicles in Seaports

by Jingwen Wang, Anastasia Feofilova, Yadong Wang, Jixiao Jiang and Mengru Shao

J. Mar. Sci. Eng. 2026, 14(8), 739; https://doi.org/10.3390/jmse14080739 - 16 Apr 2026

Abstract

The increasing deployment of autonomous vehicles in port logistics requires safety assessment methods that remain valid in mixed traffic environments. This study evaluates the safety of mixed automated guided vehicle (AGV) and human-driven vehicle (HDV) traffic in a seaport terminal connected to an external urban road network. A microscopic traffic model was developed in AIMSUN Next to represent gate areas, internal roads, storage-yard access, berth interfaces, and external container-truck traffic. HDVs were modeled using a Gipps-based car-following model, whereas AGVs were represented through an Adaptive Cruise Control framework. Vehicle trajectories were exported to the Surrogate Safety Assessment Model (SSAM), where Time-to-Collision (TTC) and Post-Encroachment Time (PET) were used to detect and classify conflicts. Six staged fleet-composition scenarios were evaluated in 36 simulation runs, ranging from fully human-driven operation to full automation. Total conflicts decreased from 89 in the fully human-driven scenario to 43 in the fully automated scenario (−51.7%), while rear-end conflicts decreased from 70 to 30 (−57.1%). Crossing conflicts remained relatively stable across scenarios. At the same time, mean TTC decreased from 0.80 to 0.24 s and mean PET from 1.57 to 0.38 s, indicating tighter but more coordinated interactions under automated control. These results show that automation improves longitudinal safety performance in port traffic, but also that conventional TTC and PET thresholds calibrated for human-driven traffic may not be directly applicable to automated port operations. Automation-sensitive surrogate safety criteria are therefore needed for seaport mixed-traffic evaluation. Full article

(This article belongs to the Special Issue Deep Learning Applications in Port Logistics Systems)

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14 pages, 6348 KB

Open AccessReview

Research on Bamboo Shoot Bud Development: A Leap from Tissue Heterogeneity to Single-Cell Spatial Atlas

by Ying Li, Xueping Li and Zhimin Gao

Plants 2026, 15(8), 1233; https://doi.org/10.3390/plants15081233 - 16 Apr 2026

Abstract

China has rich bamboo resources, with Moso bamboo (Phyllostachys edulis) being the most economically important species. Bamboo shoot bud development directly determines the eating quality of the shoots and the properties of bamboo materials; however, the intrinsic biological characteristics of this process have hindered foundational research. Traditional methods using whole shoot buds or mixed tissues obscure cellular and tissue heterogeneity, limiting our mechanistic understanding. This review synthesizes cytological features, molecular networks, and technical limitations pertaining to Moso bamboo shoot bud development, identifying four key bottlenecks: tissue homogenization masking cellular heterogeneity, loss of spatial positional information impeding analysis of position effects, challenges in single-cell technology application due to sample preparation and data interpretation issues, and unresolved coupling between chromatin accessibility and transcriptional regulation. To address these, we propose a core strategy centered on constructing a single-cell resolution, spatially resolved, multi-omics integrated, and functionally validated framework. Key approaches include developing bamboo-specific single-cell sequencing and spatial transcriptomics, integrating positional information with multi-omics data to identify spatially distinct regulatory targets, standardizing technical pipelines and functional validation platforms, and elucidating epigenetic–transcriptional coupling. Overcoming these bottlenecks will reveal the molecular basis of bamboo’s unique developmental patterns and provide key targets for the genetic improvement of the shoot quality and mechanical properties of bamboo. Full article

(This article belongs to the Special Issue Genetic and Omics Insights into Plant Adaptation and Growth)

19 pages, 3441 KB

Open AccessArticle

Lactopontin in a Simulated Infant Formula Protein Matrix Promotes Bone Development via the Gut–Bone Axis in Growing Rats

by Yipin Lyu, Jie Zhang, Chi Cheng, Xue Tang, Pantian Huang, Feitong Liu, Ruibiao Hu, Thom Huppertz, Xinyan Wang and Peng Zhou

Nutrients 2026, 18(8), 1265; https://doi.org/10.3390/nu18081265 - 16 Apr 2026

Abstract

Background: Lactopontin (L-OPN) is a pivotal bioactive protein present in breast milk that supports bone development, but its efficacy in a formula matrix is unknown. This study aimed to evaluate the effects of L-OPN-fortified formula on bone growth in a growing rat model and to explore the underlying mechanisms. Methods: Weanling rats (n = 8/group) received daily gavage for four weeks: (1) CON—deionized water; (2) PRO—750 mg/kg·BW mixed protein; or (3) L-OPN—750 mg/kg·BW of the PRO formula fortified with L-OPN. Results: The results showed that the formula fortified with L-OPN could significantly increase bone volume and trabecular bone number (p < 0.05). Furthermore, both femur length and thickness, as well as overall body length, were significantly increased (p < 0.001). In addition, the L-OPN-fortified formula specifically increased the relative abundance of Bacteroides and Parabacteroides in rat feces (p < 0.05). Metabolomic analysis revealed that L-OPN supplementation significantly altered bile acid metabolism, notably increasing serum levels of 12-ketolithocholic acid (12-KLCA), which correlated strongly with bone metrics. Conclusion: These preclinical findings provide a basis for future research in infant formula. Full article

(This article belongs to the Special Issue Food Functional Factors and Nutritional Health)

16 pages, 465 KB

Open AccessSystematic Review

Interactions Between Blood Nutritional Biomarkers and Apolipoprotein E ε4 in the Progression of Mild Cognitive Impairment in Alzheimer’s Disease

by Rasheedat Lawal, Sanjay Kumar, Rosemary Chigevenga and Shelly Coe

Nutrients 2026, 18(8), 1263; https://doi.org/10.3390/nu18081263 - 16 Apr 2026

Abstract

Background/Objectives: Mild cognitive impairment (MCI), the prodromal stage of Alzheimer’s disease, may be influenced by nutritional status and genetic susceptibility. This systematic review synthesised evidence on how nutritional biomarkers interact with genetic variants, particularly APOE ε4, to influence cognitive outcomes in individuals with MCI. Methods: Following PRISMA 2020 guidelines, seven studies were included (three longitudinal, two randomised controlled trials, and two cross-sectional) involving adults aged ≥55 years with MCI. Nutritional exposures comprised plasma or serum concentrations of vitamins A, D, E, the vitamin B group, lipids, selenium, and ketogenic medium-chain triglycerides. Genetic risk was assessed primarily through APOE ε4 status. Risk of bias was assessed using RoB 2 and ROBINS-I, and certainty of evidence using GRADE. Due to heterogeneity in biomarkers, cognitive tools, and study designs, findings were synthesised narratively. Results: Across nutrient categories, higher concentrations of vitamin D, selenium, and antioxidants were associated with better cognitive outcomes. kMCT supplementation improved episodic memory and brain energy metabolism. Evidence for nutrient–gene interactions was mixed: APOE ε4 modified responses to vitamin B group and selenium but showed limited influence on vitamin D, lipids, or kMCT effects. Heterogeneity in biomarker assays, cognitive tools, and genetic stratification limited comparability across studies. Conclusions: Nutritional biomarkers appear to influence cognitive trajectories in MCI, and some associations may differ by APOE ε4 status. However, small samples and limited genetic stratification constrain interpretation. Future research should prioritise standardised biomarker measurement, genetically stratified cohorts, and individual participant data meta-analyses to clarify nutrient–gene interactions in MCI. Full article

(This article belongs to the Special Issue Advances in Nutrition, Genetics, and Metabolism to Improve Outcomes in Inherited Diseases)

19 pages, 3775 KB

Open AccessArticle

A Time-Partitioned Dual-Layer LSTM Based on Route Spatiotemporal for Electric Bus Energy Prediction

by Yue Wang, Yu Wang, Shiqi Liu, Yanpeng Zhu, Bo Wang, Yixin Li, Guoqun Yao and Wei Zhong

World Electr. Veh. J. 2026, 17(4), 210; https://doi.org/10.3390/wevj17040210 - 16 Apr 2026

Abstract

Existing energy consumption models suffer from accuracy degradation and limited robustness in complex urban environments due to insufficient consideration of the route spatiotemporal characteristics of electric buses. To address this limitation, a Time-Partitioned Dual-Layer LSTM (TP-D-LSTM) framework driven by cloud data and spatiotemporal characteristics is proposed. First, a spatiotemporal characteristics analysis is conducted on urban bus routes to reveal the underlying traffic flow dynamics. Based on these insights, a time-partitioning strategy is developed to classify the continuous operating data into independent periods while preserving the kinematic continuity of individual trips. Subsequently, a Dual-Layer LSTM (D-LSTM) is constructed to precisely capture the distinct energy consumption mechanisms within each partitioned scenario. Experiments based on real-world cloud-logged data demonstrate that the proposed TP-D-LSTM framework is superior to existing baseline models. By alleviating the limitations of global mixed modeling, the TP-D-LSTM significantly reduces the Root Mean Square Error (RMSE) to 6.15, achieving an improvement of over 50% compared to the D-LSTM, and exhibits remarkable stability under highly volatile traffic conditions. Full article

(This article belongs to the Section Energy Supply and Sustainability)

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20 pages, 1034 KB

Open AccessArticle

LLM-Based Adaptive Control Code Generation Framework with Digital Twin-Integrated Verification for Heterogeneous Robot Systems

by Young-Hoon Lee, Taemin Nam, Deun-Sol Cho and Won-Tae Kim

Appl. Sci. 2026, 16(8), 3883; https://doi.org/10.3390/app16083883 - 16 Apr 2026

Abstract

High-Mix Low-Volume (HMLV) manufacturing increasingly relies on heterogeneous robot fleets, but automatic generation of vendor-specific robot control code remains difficult due to platform fragmentation and safety-critical feasibility constraints. Although recent Large Language Model (LLM)-based approaches have shown promise for translating natural language into robot programs, they remain largely limited to single-platform or simulation-oriented settings and are vulnerable to physical hallucination, including spatially inconsistent commands and dynamically infeasible motions. This paper proposes a Digital Twin-integrated verification framework for adaptive control code generation in heterogeneous robot systems. The framework uses a structured intermediate task representation to support runtime spatial grounding, robot selection, pre-execution dynamics validation, and adaptive motion scaling before vendor-specific code generation and execution. Evaluation on 170 task-description scenarios and eight robot selection tasks showed improved ranking discriminability in lightweight stress cases where conventional baselines exhibited limited separation. In addition, adaptive dynamics scaling enabled safe execution in all analytically verified test cases, compared with 50% without scaling. These results suggest that Digital Twin-grounded verification and adaptive feasibility control can improve the reliability of LLM-based multi-vendor robot programming and help mitigate physical hallucination in heterogeneous robot systems. Full article

(This article belongs to the Special Issue Digital Twin and IoT, 2nd Edition)

24 pages, 3028 KB

Open AccessArticle

AD-PDAF-Net: Noise-Adaptive and Dual-Attention Cooperative Network for PQD Identification

by Tianwei He and Yan Zhang

Energies 2026, 19(8), 1930; https://doi.org/10.3390/en19081930 - 16 Apr 2026

Abstract

Classifying power quality disturbances (PQDs) under strong noise conditions remains challenging for existing deep learning models. These models typically separate denoising from feature extraction, often rely on attention mechanisms that operate along only a single dimension, and tend to achieve high accuracy at the cost of high complexity, which limits their performance under low signal-to-noise ratio conditions and hinders practical deployment. To address these limitations, this paper proposes AD-PDAF-Net, which organically integrates three key mechanisms through a co-design strategy. Unlike conventional methods that depend on preprocessing, an adaptive soft thresholding denoising layer is embedded into a lightweight residual network to progressively suppress noise during feature extraction, thereby unifying denoising with feature learning. A parallel dual attention module independently refines features along the channel and temporal dimensions, then adaptively fuses them using learnable weights to capture both frequency domain and temporal characteristics of disturbances. The lightweight network entry replaces aggressive downsampling with small convolutions to preserve transient details, and a bidirectional long short-term memory network (BiLSTM) efficiently captures temporal dependencies. Evaluated on a dataset of 25 disturbance categories defined in IEEE Std 1159-2019, the model achieves a classification accuracy of 97.26% and a Kappa coefficient of 97.02% under 20 dB white Gaussian noise, along with an accuracy of 98.78% under mixed noise conditions. The model has only 0.36 million parameters and a computational cost of just 1.50 GFLOPS. Through this co-design, AD-PDAF-Net achieves both high noise robustness and high classification accuracy with minimal computational overhead, offering an effective solution for time series signal recognition in resource constrained environments. Full article

23 pages, 679 KB

Open AccessArticle

Enhancing Statistical Thinking in Higher Education Through Pedagogically Designed Use of Interactive Whiteboards

by Roman Yavich

Educ. Sci. 2026, 16(4), 636; https://doi.org/10.3390/educsci16040636 - 16 Apr 2026

Abstract

Although interactive technologies such as interactive whiteboards are increasingly used in higher education, empirical evidence regarding their pedagogical role in statistics education remains limited. Existing studies often focus on technology adoption rather than instructional design. This study examines the effectiveness of interactive whiteboards when embedded within a pedagogically designed instructional framework aimed at supporting statistical thinking. A mixed-methods, quasi-experimental design with pre- and post-test measures (N = 126) was employed to compare learning outcomes and student perceptions in an introductory university statistics course taught either through traditional lectures or through an interactive approach emphasizing dynamic visualization, collective interpretation, and formative feedback. Mediation was tested using bootstrapped indirect effects and complemented by qualitative thematic analysis. Students in the interactive condition demonstrated significantly greater gains in statistical reasoning (Cohen’s d = 0.94, 95% CI [0.57, 1.31]), particularly in tasks involving data interpretation and reasoning about variability. Mediation analysis indicated that two student self-report measures—perceived clarity of instruction and formative feedback quality—together accounted for 63% of the total effect. The interactive format was especially beneficial for students with lower prior knowledge, reducing achievement gaps by 34%. These findings are consistent with the view that interactive technologies support conceptual learning most effectively when embedded in deliberate pedagogical designs promoting visualization, collective reasoning, and real-time feedback, highlighting the central role of instructional design over technological presence. Full article

(This article belongs to the Special Issue AI in Education: Transforming Curriculum, Pedagogy, and Assessment)

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