In this study, the authors constructed structural equation models in order to determine the relationship between students' learning attitudes and their computational thinking perspectives and programming empowerment. The purpose is to understand students' perceived competence to use computational thinking effectively, along with their computer programming learning attitude regarding the C++ programming language for one semester (2 hours per week, 36 total learning hours). A total of 495 students specializing in the medical field participated in the study. Structural equation models were constructed according to three adapted scales: the computer programming learning attitude scale, the computational thinking perspectives scale, and the programming empowerment scale. The computer programming learning attitude scale is based on three factors: willingness, negativity, and necessity. The computational thinking perspectives scale also considers three factors: the ability to express, the ability to connect, and the ability to question. The programming empowerment scale is composed of four factors: meaningfulness, impact, creative self-efficacy, and programming self-efficacy. The results showed that a positive learning attitude will positively affect computational thinking perspectives and programming empowerment. However, when students have a negativity attitude, feeling that they are being forced to learn the C++ programming language, their computational thinking perspectives and programming empowerment will be negatively affected. In order to promote students' learning attitude, various teaching strategies, teaching curriculum design, and pedagogy design could be further explored.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141455 | PMC |
http://dx.doi.org/10.3390/ijerph19106005 | DOI Listing |
Alzheimers Dement
December 2024
National Ageing Research Institute, Melbourne, VIC, Australia.
Background: We have co-produced with carers of people with dementia (hereafter carers) a culturally tailored iSupport Virtual Assistant (VA), namely e-DiVA, to support English-, Bahasa- and Vietnamese-speaking carers in Australia, Indonesia, New Zealand and Vietnam. The presented research reports qualitative findings from the e-DiVA user-testing study.
Method: Family carers and healthcare professionals working in the field of dementia care were given the e-DiVA to use on their smartphone or handheld device for 1-2 weeks.
Sheng Li Xue Bao
December 2024
Virtual Simulation and Artificial Intelligence Committee, Chinese Association for Physiological Sciences.
As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, 75660, Pakistan.
Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly in uncertain cases. This sort of uncertainty can be crucial in medical image analysis; diabetic retinopathy is an example where even slight errors without an indication of confidence can have adverse impacts.
View Article and Find Full Text PDFGigascience
January 2025
School of Life, Health & Chemical Sciences, The Open University, Milton Keynes, Buckinghamshire, MK7 6AA, UK.
Background: Bioinformatics is fundamental to biomedical sciences, but its mastery presents a steep learning curve for bench biologists and clinicians. Learning to code while analyzing data is difficult. The curve may be flattened by separating these two aspects and providing intermediate steps for budding bioinformaticians.
View Article and Find Full Text PDFJ Vis
January 2025
Department of Cognitive Sciences and Neurobiology and Behavior, University of California, Irvine, California, USA.
A salience map is a topographic map that has inputs at each x,y location from many different feature maps and summarizes the combined salience of all those inputs as a real number, salience, which is represented in the map. Of the more than 1 million Google references to salience maps, nearly all use the map for computing the relative priority of visual image components for subsequent processing. We observe that salience processing is an instance of substance-invariant processing, analogous to household measuring cups, weight scales, and measuring tapes, all of which make single-number substance-invariant measurements.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!