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  1. Fulltext Quality assurance in education: perception of undergraduate health professions students in a Malaysian university
    Er HM, Nadarajah VD, Ng SH, Wong AN
    Korean J Med Educ, 2020 Sep;32(3):185-195.
    PMID: 32723985 DOI: 10.3946/kjme.2020.166
    PURPOSE: Direct student involvement in quality processes in education has been suggested to encourage shared responsibilities among faculty and students. The objectives of this study were to explore undergraduate health professions students' understanding of quality assurance (QA) in education, and identify the challenges and enablers for student involvement in an Asian context.

    METHODS: Twenty semi-structured interviews were conducted among medical, dentistry, and pharmacy students in a Malaysian University. The interviews were audio-recorded, transcribed verbatim, and thematically analyzed to understand the students' perspectives of QA in education.

    RESULTS: The participants recognized the importance of QA towards ensuring the quality of their training, which will consequently impact their work readiness, employability, and quality of healthcare services. Academic governance, curriculum structure, content and delivery, faculty and student quality, teaching facilities, and learning resources were indicated as the QA areas. The challenges for students' involvement included students' attitude, maturity, and cultural barrier. To enhance their buy-in, clear objectives and impact, efficient QA mechanism, and recognition of students' contribution had been suggested.

    CONCLUSION: The findings of this study support student-faculty partnership in QA processes and decision making.

  2. A high-dimensional, multi-transceiver channel state information dataset for enhanced human activity recognition
    Wong WE, Wong AH, Peh WQ, Tan CK
    Data Brief, 2024 Aug;55:110673.
    PMID: 39049967 DOI: 10.1016/j.dib.2024.110673
    Human Activity Recognition (HAR) has emerged as a critical research area due to its extensive applications in various real-world domains. Numerous CSI-based datasets have been established to support the development and evaluation of advanced HAR algorithms. However, existing CSI-based HAR datasets are frequently limited by a dearth of complexity and diversity in the activities represented, hindering the design of robust HAR models. These limitations typically manifest as a narrow focus on a limited range of activities or the exclusion of factors influencing real-world CSI measurements. Consequently, the scarcity of diverse training data can impede the development of efficient HAR systems. To address the limitations of existing datasets, this paper introduces a novel dataset that captures spatial diversity through multiple transceiver orientations over a high dimensional space encompassing a large number of subcarriers. The dataset incorporates a wider range of real-world factors including extensive activity range, a spectrum of human movements (encompassing both micro-and macro-movements), variations in body composition, and diverse environmental conditions (noise and interference). The experiment is performed in a controlled laboratory environment with dimensions of 5 m (width) × 8 m (length) × 3 m (height) to capture CSI measurements for various human activities. Four ESP32-S3-DevKitC-1 devices, configured as transceiver pairs with unique Media Access Control (MAC) addresses, collect CSI data according to the Wi-Fi IEEE 802.11n standard. Mounted on tripods at a height of 1.5 m, the transmitter devices (powered by external power banks) positioned at north and east send multiple Wi-Fi beacons to their respective receivers (connected to laptops via USB for data collection) located at south and west. To capture multi-perspective CSI data, all six participants sequentially performed designated activities while standing in the centre of the tripod arrangement for 5 s per sample. The system collected approximately 300-450 packets per sample for approximately 1200 samples per activity, capturing CSI information across the 166 subcarriers employed in the Wi-Fi IEEE 802.11n standard. By leveraging the richness of this dataset, HAR researchers can develop more robust and generalizable CSI-based HAR models. Compared to traditional HAR approaches, these CSI-based models hold the promise of significantly enhanced accuracy and robustness when deployed in real-world scenarios. This stems from their ability to capture the nuanced dynamics of human movement through the analysis of wireless channel characteristic from different spatial variations (utilizing two-diagonal ESP32 transceivers configuration) with higher degree of dimensionality (166 subcarriers).
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