Introduction: The ongoing 4.0 industrial evolution, characterized by the rise of digital technology, has had a massive impact on human lifestyles worldwide. Faculty members in medical school are expected to respond to this industrial revolution by implementing teaching strategies, one of which is Blended learning as a suitable solution to overcome the limitations of space and time in the teaching process. For effective utilization of blended learning, it is important to conduct extensive studies on its implementation. The aim of this study was to assess the effectiveness and efficiency of implementing blended learning in the faculty of medicine in Hasanuddin University from the students' perspective.
Methods: This study used a sequential explanatory mixed method approach, combining quantitative and qualitative methods. The quantitative part involved 782 undergraduate medical students from the first, second, and third years. Data were collected through a questionnaire survey distributed among the students. The qualitative part of the research was conducted through focus group discussions involving 13 students based on the questionnaire scores, representing both high and low scores. The results of the quantitative and qualitative research were collected and integrated.
Results: Based on the results, the majority of students agreed that blended learning provided many advantages to their learning (Mean±SD: 3.79±0.78). Also, they reported e-learning platform significantly contributed to their learning process (Mean±SD: 3.88±0.67). The workload of blended learning method was still considered quite heavy by students, and good time management was highly needed (Mean±SD: 3.45±0.84). As for qualitative part, some positive results were obtained; they reported that it increased motivation for learning, enhanced the efficiency of learning and gaining adaptability, while the negative opinions were the network error in e-learning, erratic e-learning display, and video quality problem.
Conclusion: Most of the students expressed positive opinions about the advantages of blended learning; according to them, learning was more efficient and effective, it enhanced learning motivation, and it provided comprehensive accessible learning materials.
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http://dx.doi.org/10.30476/JAMP.2023.98956.1819 | DOI Listing |
Foods
December 2024
School of Physical Science and Technology, Tiangong University, Tianjin 300387, China.
The fast and accurate quantitative detection of camellia oil products is significant for multiple reasons. In this study, rice bran oil and corn oil, whose Raman spectra both hold great similarities with camellia oil, are blended with camellia oil, and the concentration of each composition is predicted by models with varying feature extraction methods and regression algorithms. Back propagation neural network (BPNN), which has been rarely investigated in previous work, is used to construct regression models, the performances of which are compared with models using random forest (RF) and partial least squares regression (PLSR).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Information Systems, College of Computing and Informatics, The University of Sharjah, Sharjah, UAE.
This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. The incorporation of nanotechnology, through the addition of nanoparticles to conventional fuels, improves fuel atomization, combustion efficiency, and emission control. Simultaneously, LSTM models are employed to analyze and predict the complex spray behavior under diverse operational and geometric conditions.
View Article and Find Full Text PDFAdv Med Educ Pract
December 2024
Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Int J Comput Assist Radiol Surg
January 2025
Faculty of Computer Science and Research Campus STIMULATE, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany.
Purpose: Structured abdominal examination is an essential part of the medical curriculum and surgical training, requiring a blend of theory and practice from trainees. Current training methods, however, often do not provide adequate engagement, fail to address individual learning needs or do not cover rare diseases.
Methods: In this work, an application for structured Abdominal Examination Training using Augmented Reality (AETAR) is presented.
J Environ Manage
January 2025
GAIKER Technology Centre, Basque Research and Technology Alliance (BRTA), Parque Tecnológico, Edificio 202, 48170, Zamudio, Spain.
Current industrial separation and sorting technologies struggle to efficiently identify and classify a large part of Waste of Electric and Electronic Equipment (WEEE) plastics due to their high content of certain additives. In this study, Raman spectroscopy in combination with machine learning methods was assessed to develop classification models that could improve the identification and separation of Polystyrene (PS), Acrylonitrile Butadiene Styrene (ABS), Polycarbonate (PC) and the blend PC/ABS contained in WEEE streams, including black plastics, to increase their recycling rate, and to enhance plastics circularity. Raman spectral analysis was carried out with two lasers of different excitation wavelengths (785 nm and 1064 nm) and varying setting parameters (laser power, integration time, focus distance) with the aim at reducing the fluorescence.
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