Throughout the educational system, students experiencing active learning pedagogy perform better and fail less than those taught through direct instruction. Can this be ascribed to differences in learning from a neuroscientific perspective? This review examines mechanistic, neuroscientific evidence that might explain differences in cognitive engagement contributing to learning outcomes between these instructional approaches. In classrooms, direct instruction comprehensively describes academic content, while active learning provides structured opportunities for learners to explore, apply, and manipulate content. Synaptic plasticity and its modulation by arousal or novelty are central to all learning and both approaches. As a form of social learning, direct instruction relies upon working memory. The reinforcement learning circuit, associated agency, curiosity, and peer-to-peer social interactions combine to enhance motivation, improve retention, and build higher-order-thinking skills in active learning environments. When working memory becomes overwhelmed, additionally engaging the reinforcement learning circuit improves retention, providing an explanation for the benefits of active learning. This analysis provides a mechanistic examination of how emerging neuroscience principles might inform pedagogical choices at all educational levels.
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http://dx.doi.org/10.1016/j.neubiorev.2024.105737 | DOI Listing |
Sci Rep
January 2025
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally active drugs. We initially developed ML models using nine different algorithms separately on topological descriptors (referred to as simply "descriptors" in the subsequent sections of the manuscript) and MACCS fingerprints (referred to as "fingerprints" in the subsequent sections of the manuscript), thus generating 18 different ML QSAR models. Using the chemical spaces defined by the modeling descriptors and fingerprints, the similarity and error-based RASAR descriptors were computed, and the most discriminating RASAR descriptors were used to develop another set of 18 different ML c-RASAR models.
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January 2025
Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand.
Active transportation, such as cycling, improves mobility and general health. However, statistics reveal that in low- and middle-income countries, male and female cycling participation rates differ significantly. Existing literature highlights that women's willingness to use bicycles is significantly influenced by their perception of security.
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January 2025
Electronics and Communication Engineering Dept. Faculty of Engineering, Horus University, New Damietta, Egypt.
Electric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. This study presents an active cell balancing method optimized for both charging and discharging scenarios, aiming to equalize SOC across cells and improve overall pack performance.
View Article and Find Full Text PDFTalanta
December 2024
NanoBiosensors and Biodevices Lab, School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Bengal, 721302, India. Electronic address:
This work presents a robust strategy for quantifying overlapping electrochemical signatures originating from complex mixtures and real human plasma samples using nickel-based electrochemical sensors and machine learning (ML). This strategy enables the detection of a panel of analytes without being limited by the selectivity of the transducer material and leaving accommodation of interference analysis to ML models. Here, we fabricated a non-enzymatic electrochemical sensor for L-lactic acid detection in complex mixtures and human plasma samples using nickel oxide (NiO) nanoparticle-modified glassy carbon electrodes (GCE).
View Article and Find Full Text PDFDisabil Rehabil Assist Technol
January 2025
Department of Design Sciences, Lund University, Lund, Sweden.
Purpose: Assistance from artefacts and humans are traditionally viewed as separate, and it is often up to the individual to try to combine the different kinds of assistance to suit their needs and preferences. The purpose of this study was to gain new insights into the co-existence of and synergies between artefactual and human assistance in the everyday lives of persons with physical and cognitive impairments, through exploring and analysing narratives of individuals who have first-hand knowledge and experience.
Methods: Seven individuals took part in semi-structured interviews, which were then analysed with qualitative content analysis, grounded in cultural-historical activity theory.
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