马来西亚工艺大学

High school biology learning is often hindered by abstract concepts and cognitive overload, leading to low student motivation. Although generative artificial intelligence (GenAI) and gamification show instructional potential, their combined motivational effects remain underexplored. Grounded in the ARCS model, this study examined the impact of integrating ChatGPT-based GenAI and gamification on students’ Attention, Relevance, Confidence, and Satisfaction. A longitudinal quasi-experimental repeated measures design was conducted with 52 high school biology students over eight weeks. Motivation was measured at four time points using a validated ARCS questionnaire and analyzed with Wilcoxon signed-rank tests. Results showed significant improvements across all ARCS dimensions from pre- to post-intervention (p < .001). Attention and relevance increased steadily, confidence improved in later stages, and satisfaction rose overall. Reported challenges included limited personalized AI feedback, competition-induced anxiety associated with leaderboard-based competition, and increased cognitive load resulting from complex AI-generated content. The findings indicate that GenAI-supported gamification can enhance motivation in biology learning when supported by careful pedagogical design.

Pristine biochar (PBC) and phosphoric acid-activated biochar (PAC) derived from pomegranate peel powder were produced and evaluated for the adsorption of tetracycline (TC) from aqueous solution. Physicochemical characterization using BET, FESEM and FTIR complements the development of microporous network alongside mesoporous architecture in PAC and the presence of functional groups for TC interaction. PAC exhibited high specific surface area of 1882 m2/g compared to PBC with specific surface area of 195 m2/g. Batch adsorption experiments were performed at pH 4, initial TC solution concentration of 10–150 mg/L, adsorbent dosage of 5–15 mg, contact time of 300 min and temperature range between 298 and 318 K. The adsorption kinetics was found to follow pseudo second-order model, while the Langmuir model provided the best correlation with maximum adsorption capacities of 436 mg/g and 596 mg/g at 25 °C and 45 °C, respectively. Regeneration studies shows that PAC biohar mainitained TC removal efficiency > 89% over five cycles of adsorption-desorption test. The results demonstrate the efficacy of phosphoric acid-activated pomegranate peel carbon as a sustainable adsorbent for removing pharmaceutical contaminants from water.

Distracted Driver Detection (DDD) is critical for intelligent transportation systems, yet existing methods often incur high computational costs, leading to non-real-time detection on terminals and/or limited generalization across different camera views. In this work, we propose D4RNet, a lightweight Distracted Driver Detection network that integrates knowledge Distillation with a gradient Reversal layer (GRL) for the first time in this domain. We employ a Swin-B as a teacher guide a MobileNetV2 student for training, in addition to predicting the type of distraction, the forward path extracts multi-scale, low-semantic features to predict the auxiliary tags, while during backpropagation the GRL reverses the gradient to adversarially suppress the auxiliary label, thereby enforcing the model to learn domain-invariant representations of the distraction behaviors, rather than noise such as image modality, camera angle, or vehicle scenes. Experimental results show improvements of 3.4% on D-all and 4.3% on N-all over the baseline, attains 81.32% average accuracy over eight subsets, surpassing the previous SOTA by 3.36%, while achieving 52 FPS on a real edge device, Jetson Nano via ONNX Runtime. Qualitative tests on real driving videos also demonstrate strong performance and robustness. Moreover, we introduce five new temporal-spatial combined splits for the 100-Driver dataset along with corresponding baselines, to provide a more comprehensive evaluation of model performance. We also indicate, based on numerous experiments, that future work should prioritize annotation quality and image diversity rather than architectural over-engineering. In summary, D4RNet offers a fast, accurate, and open-source solution for real-time DDD at the edge. Code, data, and models are available at https://github.com/jerodzhao/D4RNet.

Technology-Enhanced Hybrid Service-Learning (HSL) has emerged as an innovative pedagogical approach that integrates experiential community engagement with digitally mediated learning environments in higher education. Despite its growing implementation, scholarly knowledge on the intellectual structure, development trajectory, and thematic evolution of HSL remains fragmented. This study presents a comprehensive bibliometric analysis of HSL research in higher education, drawing on 154 peer-reviewed journal articles indexed in the Scopus database between 2010 and 2026. Using PRISMA-guided screening procedures and bibliometric tools including biblioMagika®, VOSviewer, and OpenRefine, the study analyses publication trends, citation patterns, influential contributors, leading institutions and countries, source titles, highly cited documents, and keyword co-occurrence networks. The findings reveal a marked acceleration in research output from 2010 onwards, coinciding with the global expansion of hybrid and online learning models. While early publications were limited in number, they demonstrated high citation impact, forming a strong conceptual foundation for subsequent scholarship. The United States leads in publication volume, whereas Malaysia and Hong Kong demonstrate particularly strong citation performance relative to output. Prominent source titles reflect the interdisciplinary nature of HSL, spanning educational technology, sustainability, and experiential learning domains. Keyword co-occurrence analysis highlights “service learning,” “higher education,” and “student engagement” as central themes, alongside growing emphasis on online learning and e-service learning modalities. Overall, the study demonstrates that Technology-Enhanced Hybrid Service-Learning represents a dynamic and increasingly mature research domain characterised by global participation, collaborative authorship, and evolving thematic integration of pedagogy and technology. By systematically mapping the field’s intellectual structure and scholarly influence, this study provides an updated foundation for future research, pedagogical innovation, and policy development in higher education.

This work demonstrates a saturable absorber (SA) based on Ti3C2Tx-zinc oxide (ZnO), for Q-switched and mode-locked generation in an erbium-doped fiber laser (EDFL) cavity. The SA was fabricated using a solution-casting method, in which both materials were mixed to form a thin-film structure. The UV-vis-NIR spectroscopy and twin-balanced detector technique reveal a 0.4 a.u. absorbance and 10.1% modulation depth, indicating its potential as SA. The film was incorporated in the EDFL cavity, initiating a Q-switched and mode-locked laser in the 1.5 & micro;m regime. The Ti3C2Tx-ZnO generates Q-switched pulses at a center wavelength of 1559.8 nm, and the center wavelength blue-shifts to 1558.65 nm when the laser operates in the mode-locking regime. The minimum pulse width of the Q-switched and mode-locked pulses is 6.56 & micro;s and 3.91 ps, respectively. The developed SA can also be used for pulsed laser generation in another near-to-mid-infrared regime, including 1-, 1.33-, and 2-& micro;m.