Background And Purpose: Deep thinking is a desirable trait for higher education especially at a time where knowledge application, rather than knowledge acquisition, is premium. As assessment plays a critical role in shaping learning behaviors, this study attempted to evaluate the benefits of administering a 'student-designed assessment problems' (SDAP) assignment as a tool to instill deeper learning among students. The supposition was that when tasked to design assessment problems, students are challenged to higher cognitive levels of thinking on the Bloom's revised taxonomy scale.
Educational Setting And Activity: This study was conducted on a group of third year pharmacy students taking an elective module on pharmacokinetics and toxicokinetics. Students were shown an example of a finished product and were given three weeks to complete the take-home assignment. The questions that students designed were characterized according to the revised Bloom's taxonomy category by two independent reviewers. Feedback on students' experience was also evaluated.
Findings: All 18 students reading the module submitted their SDAP with questions that demonstrated all levels of thinking, with application-based questions being most significant, followed by analytical questions. Feedback from the students was positive, with clear indications of self-directed and peer learning.
Summary: This exercise offered a surprising insight into students' way of thinking, by externalizing their inquiring minds and translating their thoughts into written questions. This positive outcome informed that it has stirred deep thinking and learning among the students who participated. Evidently, SDAP is impactful as an assessment for and of learning.
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http://dx.doi.org/10.1016/j.cptl.2021.01.007 | DOI Listing |
ISA Trans
January 2025
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China.
Improving the flexible and deep peak shaving capability of supercritical (SC) unit under full operating conditions to adapt a larger-scale renewable energy integrated into the power grid is the main choice of novel power system. However, it is particularly challenging to establish an accurate SC unit model under large-scale variable loads and deep peak shaving. To this end, a data-driven modeling strategy combining Transformer-Extra Long (Transformer-XL) and quantum chaotic nutcracker optimization algorithm is proposed.
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 PDFJ Cardiovasc Electrophysiol
January 2025
Department of Cardiology, St Mary's Hospital, Rutgers's University, New Brunswick, New Jersey, USA.
This review provides a history of physiological pacing from inception to current practice and into the future. This review stems from personal experience and is not formally systematic. Physiological cardiac pacing is covered from 1960s to date.
View Article and Find Full Text PDFThe shift to pass/fail grading in undergraduate medical education was designed to reduce medical students' stress. However, this change has given rise to a "shadow economy of effort," as students move away from traditional didactic and clinical learning to engage in increasing numbers of research, volunteer, and work experiences to enhance their residency applications. These extracurricular efforts to secure a residency position are sub-phenomena of the hidden curriculum.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Laboratory Adhesion and Inflammation (LAI), Inserm UMR 1067, Cnrs Umr 7333, Aix-Marseille Université UM 61, 13009 Marseille, France.
During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are the major barriers currently met by biomedical investigators? It is suggested that during the last 2 decades there was a shift towards a growing need to elucidate complex systems, and that this was not sufficiently fulfilled by previously successful methods such as theoretical modeling or computer simulation (ii) What is the potential of AI to meet the aforementioned need? it is suggested that recent AI methods are well-suited to perform classification and prediction tasks on multivariate systems, and possibly help in data interpretation, provided their efficiency is properly validated.
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