ABSTRACT Recent reforms to China's college admission policies have increased access to higher education for ethnic minority students. However, these students frequently encounter distinct challenges stemming from their cultural backgrounds, language proficiency, and prior educational experiences. This study focuses on the achievement gap between ethnic minority and non‐minority students within the Chinese higher education system. By analysing data from 19,637 undergraduates across 37 universities, this study used the Chinese version of the College Student Experiences Questionnaire to examine the influence of college environment on student learning gains and to determine whether the mediating effects of academic and social activities varied across ethnic groups. The structural equation modelling results suggested that: (1) a positive association existed between college environment and learning gains for both ethnic minority and non‐minority students; (2) both academic and social activities partially mediated this relationship; and (3) the impact of social activities on learning gains was significantly less pronounced for ethnic minority students, thereby revealing a critical disparity. This study underscores the important role of college environment in fostering student learning and emphasises the necessity of tailored support mechanisms designed to address the unique challenges experienced by ethnic minority students.

<p>As researchers get more skilled at developing and testing ideas, research triangulation has gained significant traction over time. It has become a potential approach that enhances the validation of data by cross-verifying it across multiple literatures. Research triangulation specifically describes the use and synthesis of various research methodologies in the investigation of a single topic. Research provides the essential information to substantiate the theory, refining and expanding current knowledge to direct the action. In the pursuit of learning gain and understanding collection, data are gathered through interactions with human study subjects. Certain data collection techniques can be intrusive, exploitative, or ethically questionable. The study should uphold the highest standards of accepted norms. Everyone who participates in the study should abide by the ethical standards that govern it. To sustain the honesty, legitimacy, and cultural influence of any research studies, an ethical research approach must be taken (Stuart & Barnes, 2005). This paper will throw light on the importance of the adaptation of research triangulation. The discussions below will be based on the definitions of research triangulation with different types of triangulations from various literature, followed by the advantages of research triangulation. Considering these, this article will also discuss the ethical principles that can be applied in any research study.</p>

ABSTRACT This study contextualizes undergraduate research (UR) by comparing students’ perceived learning from UR and discipline-specific courses (Courses) using a within-subject, paired-sample design and construct-based measures: Academic Learning Gains (AG), Practical Learning Gains (PG), and a new Comprehensive Learning Outcomes (CO) construct. Survey responses were collected from 294 students who presented at a university-wide UR Symposium. Students rated matched items for UR and Courses side-by-side, enabling direct comparison while controlling for individual differences. Across all constructs, UR received higher mean ratings than Courses, and paired-samples tests indicated statistically significant differences with medium to large effect sizes. These results provide a context-specific benchmark for interpreting UR’s perceived contribution relative to disciplinary coursework among students who participate in research. Findings are descriptive and reflect self-reports from students who self-selected into UR at a single institution. The study is not designed to establish causality or to generalize beyond similar HIP-quality UR contexts. Even so, the study provides a replicable framework for UR-Course comparisons and introduces the CO construct to capture integrative growth. Future research is needed to validate these patterns across diverse settings, incorporate direct indicators of learning, and employ designs that yield more generalizable samples.

The effect of AI empowerment was further explored regarding academic performance, digital interaction, and gender equity in the tertiary education sector. Through an explanatory sequential mixed-method research design, 210?undergraduate students and six faculty members of a Philippine state university were engaged in the study. Quantitative data came from pre- /post-test results, a digital engagement survey, and an emerging Gender-Inclusive Pedagogy Perception Scale (GIPPS) scale; qualitative data include the focus group interviews with students, follow-up interviews with faculty, and classroom?observations. The results indicated that the learning gain of students who were instructed with AI-integrated knowledge was statistically higher (M = 5.40) than that of students in traditional instruction lessons (M = 2.10), d = 1.06, which had a large effect size. AI?users also indicated significantly more digital engagement, t (208) = 5.02, p < 001, d =.70, as well as more positive views of gender-inclusive pedagogy, t(208) = 3.41, p =?001, d = 0.47. A one-way ANOVA was significant?for gender identity on GIPPS scores, F(2, 207) = 4.15, p = 017, ?² =. 04, and the?highest perceived inclusivity came from LGBTQ+ students. Qualitative findings indicated that AI was perceived as a non-judgmental feedback partner, that consistent AI responses may diminish gendered expectations, and that AI-mediated lifelong feedback can bolster learning confidence with faculty mediation. Findings were used to design an Inclusive AI Pedagogy Framework grounded in Social Cognitive Theory, Gender Schema Theory, and Feminist Pedagogy. Results add to evidence that AI-infused?tools can hold promise for learning success and fostering gender-responsive and inclusive DLEs when used with critical teacher mediation and equity-sensitive design.

Reading is a foundational skill in secondary English language education, essential for academic achievement and preparing learners for global contexts. Explicit instruction in reading strategies is particularly beneficial for students with limited academic English exposure. This study examines the integration of the KWL (Know, Want to know, Learned) strategy with the digital platform Wordwall, an approach designed to improve reading comprehension for junior high school students. The KWL framework enhances comprehension, focus, and engagement by activating prior knowledge, setting clear reading purposes, and encouraging reflective learning. A mixed-methods research design was employed to evaluate this intervention. The combination of structured metacognitive guidance and interactive digital activities transformed classroom instruction. Results indicated significant improvements in reading comprehension and vocabulary acquisition. Furthermore, the approach increased student motivation and established a low-anxiety environment that encouraged participation and risk-taking. The experimental group achieved a substantially higher and more consistent mean learning gain (31.05%) compared to the control group (10.73%). Their results also exhibited greater statistical stability and a distribution closer to normality, indicating that the benefits were both reliable and equitable across participants. These findings confirm that strategically combining evidence-based reading strategies with digital tools fosters a more engaging, effective, and inclusive learning environment. This pedagogical integration ultimately leads to deepened comprehension and strengthened academic outcomes, highlighting its significant potential for contemporary English language education

The globalization and the loss of intergenerational transmission are rapidly increasing, which poses a threat to the continuation of folk cultural traditions and necessitates interactive and scalable preservation systems. By combining artificial intelligence with learning game worlds, it is possible to create adaptive storytelling, multimodal cultural simulation, and customizable learning paths that can turn passive consumption of heritage into participatory cultural experience. An experimental architecture was developed based on a multilayer design of cultural knowledge graphs, intelligent AI services, gameplay interaction, and learning analytics that were experimentally tested using controlled learner studies. Quantitative findings indicate better normalized learning gain, better retention, better persistence of engagement, and high expert-validated cultural authenticity than non-adaptive digital learning resources. Semantic coherence and responsible cultural representation was achieved through knowledge-graph grounding and community validation. The results show that AI-based cultural games can be used as both pedagogical resources and living digital preservation ecosystems that can maintain long-term engagement with intangible heritage. Scalable multilingual implementation, immersive XR implementation, and privacy-preserving community-based learning models will continue to be the primary areas of the responsible AI-driven cultural education development.

This study developed and evaluated a STEM-based digital learning module on the Anti-lock braking system (ABS) for Technical and Vocational Education and Training (TVET), implemented in a Light Vehicle Engineering (LVE) program. This pilot study uses a Research and Development (R&D) approach guided by the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. Prior to implementation, expert review was conducted to establish media and content quality. Media experts rated the module at 86.95%, highlighting strengths in interface design, software, and usefulness. Content experts rated the module at 79.68%, indicating satisfactory conceptual accuracy and pedagogical usefulness for supporting automotive competency learning, while identifying curricular alignment as the primary area requiring further improvement. The module was then piloted with 35 vocational students using a one-group pre-post test design. Learning outcomes demonstrated a statistically significant improvement in ABS knowledge, with the average score increasing from 51.49 on the pre-test to 71.81 on the post-test. The average gain was 20.32 points, representing a 39.46% improvement relative to the pre-test mean, with a very large effect size (Cohen's d = 3.67). Assuming a maximum score of 100, the normalized gain was 0.42, indicating a moderate learning gain. Overall, these results provide quantitative evidence that a STEM-oriented digital ABS module can enhance learning outcomes and support structured, interactive, and industry-relevant instruction in vocational automotive education.

The challenges in providing quality practice place learning in pre-registration diagnostic radiography education has seen a rising interest in the use of simulation-based education (SBE) within the UK and other countries. This study aimed to determine the current use and efficacy of SBE across UK higher education institutions. An online survey was distributed to educators delivering pre-registration diagnostic radiography programmes within the UK via professional networks and social media in 2024. 18 respondents reported use of SBE within programmes to support a range of areas within the curriculum, develop skills and prepare students for placement. Reported usage ranged from between ≥50 h and 301-350 h. Staff capacity (61.1 %) and cost of equipment (55.5 %) were highlighted as the biggest challenges to delivering SBE. The majority of respondents use pre-brief (66.6 %) and debriefs (27.7 %) or feedback (50.0 %) as part of SBE. Live radiographic imaging equipment (94.4 %) and roleplay (83.3 %) were most frequently used. Methods of evaluating the learning gain include anecdotal evidence, student self-reporting and assessment results, with increased confidence most frequently cited as the impact of SBE. Despite the challenges, SBE is a widely used and accepted pedagogy in pre-registration diagnostic radiography education. Ongoing faculty training and adherence to simulation standards is important to ensure SBE provides a quality learning experience. There is an opportunity to embed more robust mechanisms for evaluating the learning gain associated with SBE.

Although visual-based approaches have been applied in Arabic writing instruction, their integration with controlled writing remains underexplored. This study examines the effectiveness of Picture-Based Controlled Writing in improving Arabic writing skills, known as mahārah kitābah, among tenth-grade students at MA Al-Falah Klender. Using a quantitative quasi-experimental design, 22 students were divided into experimental and control classes. The treatment combined sequenced visual stimuli with controlled writing tasks through guided sentence completion to help students organize ideas and use Arabic grammatical structures more accurately. To strengthen the quality and consistency of learning resources, generative AI was employed to create the visual stimuli and selected sentence prompts used in instructional materials as well as the pre-test and post-test. The experimental class showed a moderate learning gain with an N-Gain value of 0.53, suggesting the potential of the Picture-Based Controlled Writing approach in supporting Arabic writing development.

This study investigated the effects of an integrated pedagogical model, combining game-based learning (GBL) with situational language teaching (SLT), on early childhood Chinese language acquisition. The study aimed to 1) compare learning achievement in Chinese character literacy before and after learning through an integrated pedagogical model and 2) assess the learning motivation of kindergarteners. The study involved 20 children (K2) in Chonburi, Thailand, within a one-group pretest-posttest design. Data were collected using an achievement test and a motivation questionnaire and were analyzed using descriptive statistics and a dependent samples t-test. The results showed the learning achievement in Chinese character literacy was improved at a statistically significant level of 0.05, marked by a very large effect size (Cohen's d = 3.72) and a medium learning gain (N-Gain = 0.55). The learning motivation of kindergarteners was at a high level (Mean = 2.66, S.D. = 0.45), suggesting the integrated approach holds potential as a promising strategy for enhancing both literacy and engagement in young learners.

Engineering laboratory courses are essential for developing conceptual understanding and practical skills; however, the time students spend assembling prototypes and troubleshooting wiring issues often reduces opportunities for analysis, programming, and reflective learning. To address this limitation, this study designed and evaluated an integrated STM32-based educational laboratory that consolidates the main peripherals required in a microcontroller course into a single Printed Circuit Board (PCB) platform. A quasi-experimental intervention was implemented with 40 engineering students divided into a control group using traditional STM32 Blue Pill and breadboard connections and an experimental group using the integrated platform. Throughout ten laboratory sessions, data were collected through pre- and post-tests, laboratory logs, and the Motivated Strategies for Learning Questionnaire Short Form (MSLQ-SF). Results showed that the experimental group achieved a Hake normalized learning gain of 40.09% compared with 16.22% in the control group, also showing that it completed the sessions an average of 27 min faster and facilitated a substantial reduction in hardware- and connection-related errors. Significant improvements were also observed in metacognitive and improved motivational and self-regulated learning scores. Overall, the findings indicate that reducing operational barriers in laboratory work enhances both cognitive and motivational learning processes, supporting the adoption of integrated educational hardware to optimize learning outcomes in engineering laboratory courses.

This systematic literature review examines the impact of teacher preparation, instructional materials, and assessment practices on the mathematical literacy of Filipino secondary students, framed by the PISA framework. Using the PRISMA methodology, a comprehensive search of the ERIC database with relevant keywords yielded 51 studies, which were screened and assessed for eligibility without duplicates. After full-text appraisal, 14 studies were included. The findings demonstrate that integrated approaches combining enhanced teacher preparation, high-quality instructional materials, and robust assessment practices lead to significant improvements in student mathematical literacy. The review highlights the importance of teacher competence, contextualized instructional materials, and multifaceted assessments aligned with PISA competencies. These results provide evidence-based recommendations for policymakers and educators to improve mathematical literacy outcomes through holistic interventions. This review underscores the value of the PISA framework as an analytic lens and advocates for evidence-based policy and practice interventions that holistically address teacher competency, teaching resources, and assessment quality to improve mathematical literacy among secondary students in contexts similar to the Philippines. Recommendations include professional development, context-driven instructional design, and diversified assessment aligned with PISA competencies to promote meaningful learning and performance gain.

This study aimed to evaluate the impact of the gamified learning tool "Anatomy Cluedo" on cognitive learning gain and engagement in cranial nerve anatomy among medical students. The quantitative research study was carried out in the Department of Anatomy, Shalamar Medical and Dental College, Lahore. In this study 40 students were divided into two groups: the experimental group engaged with "Anatomy Cluedo," while the control group participated in problem-based learning (PBL). Pre-test and post-test assessments, along with engagement metrics measured by the Flow Short Scale (FSS), were analyzed. The results showed significantly higher cognitive gains in the experimental Group-A (p < 0.001), and strong positive correlations between engagement metrics and learning outcomes (r = 0.78, p < 0.01). Key game elements, such as mechanics, feedback and storytelling, were identified as critical contributors to cognitive learning gains. This study underscores the potential of gamified tools in enhancing engagement and knowledge retention, providing a foundation for further integration of gamification into medical education curricula.

An essential goal of science education is to support students in reasoning about the underlying mechanisms of observed phenomena, which requires well-designed instructional approaches. In organic chemistry, various approaches have been designed to support students’ reasoning about mechanisms, including contrasting cases as a task format. Qualitative studies indicate that contrasting cases positively impact students’ mechanistic reasoning since this task format encourages students to identify and analyse similarities and differences in chemical phenomena. Additionally, a prior mixed-methods study showed that scaffolded contrasting cases can advance undergraduate students’ reasoning about mechanisms, but the effect varied depending on prior knowledge. Despite these valuable insights, research has not yet quantitatively analysed the effectiveness of scaffolded versus non-scaffolded contrasting cases, compared with single cases. This study quantitatively examines the effects of these instructional approaches on undergraduate organic chemistry students’ learning gains, with a particular focus on the role of prior knowledge. Our findings suggest that non-scaffolded contrasting cases increase learning gains for students with low prior knowledge. Additionally, scaffolded contrasting cases support students with low prior knowledge in their open-ended reasoning about chemical mechanisms. Given these findings, organic chemistry instructors should consider contrasting cases as an alternative task format. However, instructors should introduce the scaffolding used in this study with practice sessions as it may otherwise increase cognitive load for students unaccustomed to its demands.

Effect of Typing Ability on Learning Gain in Typing Note-taking

This study proposes an accelerated iterative learning control scheme using a fractional high-order update rule (FHUR) to improve the convergence rate for linear time-invariant systems. High- and low-order power update terms are used to handle large- and small-tracking errors, respectively, thereby accelerating convergence. Two learning mechanisms are proposed and shown to be optimal among various learning gain selections. The inherent nonlinearity in the FHUR poses significant challenges for the convergence analysis. To address this, a disturbed composite nonlinear mapping method is introduced. Using this method, the tracking errors are proven to converge either to an invariant set or to a set of limit cycles, depending on the underlying learning mechanism. Any desired tracking precision can be achieved by adjusting the parameters in the FHUR. Numerical simulations confirm that the FHUR presents a promising alternative to the commonly used proportional-type update rule for achieving accelerated convergence.

This paper proposes Social-only Particle Swarm Optimization-based Iterative Learning Control (SO-PSO-ILC) to address the limitations of conventional Iterative Learning Control (ILC) in model dependency and manual parameter tuning. The proposed method autonomously optimizes the learning gain using a social-only PSO variant. Comparative results on four distinct trajectories demonstrate superior performance: SO-PSO-ILC achieved a final RMSE of 0.0008 m in the linear path test and a precision 4.6 times higher than the baseline in the waveform path test. It also exhibits the fastest convergence rate, outperforming PSO-ILC in tracking accuracy and computational complexity while avoiding the convergence issues observed in WSA-ILC. The simulation results validate that swarm-optimized ILC provides a robust framework for repetitive tasks requiring high accuracy.

Teaching complex electrical and electronic principles to Bachelor of Science in Industrial Technology (BSIT) students presents a significant pedagogical challenge due to the abstract nature of the concepts. Traditional static methods often fail to provide the visualization required for technical mastery. This study aimed to bridge this gap by designing, developing, and evaluating an "Interactive Edu-Video App" for the course Electricity &amp; Electronics Principles at North Eastern Mindanao State University, Philippines. The study utilized a Research and Development (R&amp;D) approach grounded in the ADDIE model, combined with a quasi-experimental design (non-equivalent control group). The participants included 76 BSIT students and 16 experts (instructors and industry practitioners). Data were analyzed using weighted means for validity and t-tests for learning effectiveness. The development phase produced a mobile application integrating interactive hotspots and quizzes based on Cognitive Load Theory. Expert validation rated the app as "Very Satisfactory" (M=3.61) in terms of accessibility and engagement. Experimental results revealed that while baseline knowledge was comparable (p=0.146), the experimental group using the app achieved significantly higher posttest scores (M=25.45) compared to the control group (M=17.39), with a significant learning gain (t=18.781, p&lt;0.001). The findings confirm that interactive video is not merely a supplementary tool but a superior pedagogical strategy for technical education. The study contributes a validated, scalable mobile learning model that enhances conceptual mastery in TVET, offering a practical solution for resource-constrained industrial technology programs.

This study examines the impact of combining microlearning, virtual laboratories, and adaptive teaching algorithms on student motivation, engagement, and academic performance in an open and distance learning (ODL) setting. An experimental design involving 61 university students was used, with participants divided into experimental and control groups. The instructional module integrated short-form content, interactive virtual lab tasks, and adaptive strategies. Data were collected via structured questionnaires and academic records from the EMPRO system. Using SPSS, descriptive statistics, T-tests, N-Gain, and ANCOVA were applied. Results showed significant improvements in motivation and academic achievement in the experimental group (p &lt; 0.05), while engagement levels remained comparably high across both groups. N-Gain analysis revealed a strong learning gain (0.529) in the experimental group versus 0.112 in the control group. ANCOVA confirmed that the learning gains were primarily due to the intervention itself. Overall, the study supports the effectiveness of a microlearning-based adaptive model in enhancing student outcomes in ODL environments.

Intelligent Tutoring Systems face significant challenges in dynamically adapting to diverse student cognitive states (e.g., knowledge gaps, learning pace, and engagement levels) while maintaining high confidence in pedagogical decisions. Traditional rule-based or static Machine Learning (ML) models often fail to generalize across different learners and subjects. A confidence-aware Meta-Reinforcement Learning (Meta-RL) framework is proposed, allowing for fast adaptation to individual student needs with quantifiable uncertainty estimation. The Proposed Method (PM) leverages meta-learning to pre-train a policy on a distribution of simulated and real-world student interactions, allowing rapid fine-tuning with minimal data for new learners. The framework incorporates Bayesian Neural Networks (BNN) to assess prediction confidence, ensuring that tutoring actions (e.g., hint provision or problem difficulty adjustment) are personalized and reliable. Experiments on large-scale educational datasets (e.g., ASSISTments, MOOC logs) demonstrate that the model outperforms baseline methods (e.g., deep RL, non-adaptive ITS) in learning gain (+12%) and student engagement (+18%), with real-time deployment feasibility on edge devices. This work bridges the gap between high-speed adaptation and high-confidence AI in education, offering a scalable solution for next-generation ITS.