Introduction: The goal of this study is to determine whether two commonly used generative learning activities for text-based lessons-writing a summary or creating a drawing-help students learn from a multimedia lesson involving animations with short text captions without prior training in the generative activities.
Methods: Students viewed a series of four annotated animations on greenhouse gases. During pauses between the animations, students were asked to generate a written summary, to create a drawing, or to do both, whereas a control group viewed the lesson without any generative learning activities. Students were tested immediately (Experiment 1) or after a one-week delay (Experiment 2).
Results: In both experiments, students who produced written summaries scored significantly higher on the posttest than those who engaged in no generative learning activities ( = 0.48 in Experiment 1, = 0.54 in Experiment 2), but there was no significant difference on the posttest for students who generated drawings compared to those who engaged in no generative learning activities. In addition, those who engaged in drawing and summarizing did not have significantly different posttest performance than those engaged in summarizing alone.
Discussion: We conclude that writing summaries during a highly visual animated lesson is effective for learning, possibly because it encourages students to engage in generative processing during learning more than drawing and we discuss potential reasons for this in the discussion. This work helps extend generative learning theory by pinpointing potential boundary conditions for learning by drawing and learning by summarizing.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11402827 | PMC |
http://dx.doi.org/10.3389/fpsyg.2024.1452385 | DOI Listing |
Biomed Phys Eng Express
January 2025
National School of Electronics and Telecommunication of Sfax, Sfax rte mahdia, sfax, sfax, 3012, TUNISIA.
Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the impact of various data augmentation strategies on enhancing the performance of a customized convolutional neural network model for corneal topographic map classification.
View Article and Find Full Text PDFThe Canadian Genomics Research and Development Initiative for Antimicrobial Resistance (GRDI-AMR) uses a genomics-based approach to understand how health care, food production and the environment contribute to the development of antimicrobial resistance. Integrating genomics contextual data streams across the One Health continuum is challenging because of the diversity in data scope, content and structure. To better enable data harmonization for analyses, a contextual data standard was developed.
View Article and Find Full Text PDFDentomaxillofac Radiol
January 2025
Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Yangsan, 50612, Korea.
Objectives: This study aimed to develop an automated method for generating clearer, well-aligned panoramic views by creating an optimized three-dimensional (3D) reconstruction zone centered on the teeth. The approach focused on achieving high contrast and clarity in key dental features, including tooth roots, morphology, and periapical lesions, by applying a 3D U-Net deep learning model to generate an arch surface and align the panoramic view.
Methods: This retrospective study analyzed anonymized cone-beam CT (CBCT) scans from 312 patients (mean age 40 years; range 10-78; 41.
J Med Internet Res
January 2025
NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, NOVA University Lisbon, Lisbon, Portugal.
Background: Heart failure (HF) is a significant global health problem, affecting approximately 64.34 million people worldwide. The worsening of HF, also known as HF decompensation, is a major factor behind hospitalizations, contributing to substantial health care costs related to this condition.
View Article and Find Full Text PDFNetwork
January 2025
Department of Computer Science and Engineering, Knowledge Institute of Technology, Salem, India.
The image retrieval is the process of retrieving the relevant images to the query image with minimal searching time in internet. The problem of the conventional Content-Based Image Retrieval (CBIR) system is that they produce retrieval results for either colour images or grey scale images alone. Moreover, the CBIR system is more complex which consumes more time period for producing the significant retrieval results.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!