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127 results for “learning outcomes”
Attributes: A Curriculum Analytics System for measuring learning outcomes - Overview
<p><span><strong>Link to video </strong><a href="https://vimeo.com/1015456231?share=copy#t=0"><strong>https://vimeo.com/1015456231?share=copy - t=0</strong></a><br><br>The Curriculum Analytics System at the Instituto Tecnológico de Costa Rica, integrated into TEC Digital, supports faculty, coordinators, and students in assessing engineering learning outcomes during accreditation processes. The system offers two key user modules: one for coordinators to map and manage learning outcomes, and another for instructors to conduct assessments through the course portal. Coordinators oversee course and attribute mapping using visual representations of study plans, control points, and outcome visualizations. Instructors configure assignments and evaluate student submissions with standardized rating scales. The system tracks progress in real-time and generates</span> <span>comprehensive reports with performance metrics, facilitating continuous improvement in academic programs.</span></p> <p><strong><span>Key words: </span></strong><span>attributes, learning outcomes, TEC Digital, curriculum analytics, continuous improvement. </span></p>
Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer
<p>Data supporting the findings of "<a href="https://doi.org/10.1158/2326-6066.CIR-23-0873" target="_blank" rel="noopener">Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer</a>" publication. Files include patient and tissue region metadata (in metadata folder) and output of multiplex immunohistochemistry computational image processing workflow for each tissue region (in mIHC_files folder). The code used to produce the results of this study is available at: <a href="https://github.com/kblise/PDAC_mIHC_paper">https://github.com/kblise/PDAC_mIHC_paper</a>.</p>
Direct Measurement of Learning Outcomes in Engineering Programs: A Proposal for Nine Standardized Scales
<p>This database presents the results of nine different scales aimed at directly evaluating learning outcomes as generic attributes in engineering programs, as defined by the Washington Accord and the International Alliance of Engineering. Data was collected at a higher education institution focused on engineering and technology as part of quality assurance processes. Each scale features a distinct number of indicators. The data correspond to the following scales: AC (Lifelong Learning), AF (Project Management and Finance), AP (Problem Analysis), DI (Design/Development of Solutions), EE (Ethics), HC (Communication), HI (Tool Usage), IN (Investigation), and TE (Individual and Collaborative Teamwork).</p>
Rating results obtained during a review of original articles on radiomics and machine learning for outcome prediction based on PET
<p>This upload provides Open Data associated with the publication "Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)" by Rogasch JMM <em>et al.</em> (2023).</p> <p>The upload contains the item-by-item results of rating for all criteria and all 100 original articles. PubMed IDs are also included.</p> <p>Furthermore, a description of all variable names and how the rating categories were encoded in the data tables can be found in the PDF file "ML_prediction_Dictionary_2023_08_27.pdf".</p>
The Impact of Generative AI on Student Learning Outcomes: A Statistical Analytical Approach - Dataset
Open the record for dataset details and reuse information.
Patient and Provider Outcomes of E-Learning Training in Collaborative Assessment and Management of Suicidality
ClinicalTrials.gov study NCT00905827. IPD Sharing: Not stated. Countries: 1. Publications: 4.
The Learning Outcome of Resuscitation Teamwork Training in Postgraduate Year Doctors and Nurses
ClinicalTrials.gov study NCT05302414. IPD Sharing: NO. Countries: 1. Publications: 14.
Improving STEM Outcomes for Young Children With Language Learning Disabilities
ClinicalTrials.gov study NCT03438760. IPD Sharing: NO. Countries: 1. Publications: 2.
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
ClinicalTrials.gov study NCT06092450. IPD Sharing: NO. Countries: 1. Publications: 1.
The impact of learning modality on team-based learning (TBL) outcomes in anatomical sciences education
<p>Team-based learning (TBL) is an instructional methodology that has been increasingly used in anatomy and physiology education in recent years. The appropriateness of TBL methods for students with diverse preferred sensory learning modalities has not been adequately examined. This study aimed to show the influence of students preferred sensory modality for learning on TBL and traditional academic outcome measures (tests, assignments, etc.). 157 American undergraduate Communication Sciences and Disorders students taking anatomy and physiology courses took the VARK, a questionnaire quantifying their preferred learning modality. These students traditional and TBL measures of academic success were compared by preferred learning modality. A one-way MANOVA failed to find any difference in TBL or other academic outcomes by preferred learning modality (<em>p</em> = .252). The results of this study support the effectiveness of TBL methods for undergraduate students regardless of what sensory mode they prefer to receive information</p>
Early life learning ability predicts adult social structure, with potential implications for fitness outcomes in the wild
1. Social environments influence important ecological processes and can determine how selection acts on traits. Cognitive abilities can shape these social environments and in turn, affect individuals' fitness. 2. To understand how cognitive abilities evolve, we need to understand the complex interplay between an individual's cognitive abilities, the social environment that they inhabit and the fitness consequences of these relationships. 3. We measured the associative learning ability of pheasant chicks, Phasianus colchicus, then released them into the wild where we quantified their social position by observing their associations at feeding stations and monitored the number of days survived. 4. We observed disassortative mixing by learning performance at the population level, and poor learners had more associates than good learners. Learning was beneficial for survival when focal individuals had fewer than four associates, but survival probability across learning abilities equalized for individuals with more than four associates. 5. While the mechanisms underlying these relationships remain to be determined, the patterns of association exhibited by pheasants at feeders can be predicted by individual variation in cognitive performances and we suspect these patterns are related to differences in information use. Critically, these resulting patterns of association have fitness consequences for individuals that cannot be explained directly by their cognitive ability, but which could mediate selection on cognition.
Contrast MR Based Radiomics and Machine Learning Analysis to assess clinical outcomes following liver resec-tion in Colorectal Liver Metastases: a preliminary study
<p>We uploaded the images of the manuscript "Contrast MR Based Radiomics and Machine Learning Analysis to assess clinical outcomes following liver resection in Colorectal Liver Metastases: a preliminary study" accepted on Cancers.</p> <p>Vincenza Granata1*, Roberta Fusco2, Federica De Muzio3, Carmen Cutolo4, Sergio Venanzio Setola1, Federica dell’ Aversana5, Alessandro Ottaiano6, Antonio Avallone6, Guglielmo Nasti6, Francesca Grassi5, Vincenzo Pilone4, Vittorio Miele7-8, Luca Brunese3, Francesco Izzo9, Antonella Petrillo1</p> <p>1Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy</p> <p>2Medical Oncology Division, Igea SpA, Napoli, Italy</p> <p>3Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy</p> <p>4Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy</p> <p>5Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy</p> <p>.6Division of Abdominal Oncology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy</p> <p>7Division of Radiology, “Azienda Ospedaliera Universitaria Careggi”, Florence, Italy</p> <p>8Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122 Milan, Italy</p> <p>9Division of Epatobiliary Surgical Oncology,“Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy</p> <p> </p>
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis
<p>data files, including ML models of dynamic classifiers, trajectories of electronic structure and geometric features, optimized geometries, and final csv files.</p>
Dataset from Research: Effect of active lecture and webquest on learning outcomes in higher education
<p>Dataset in a xlsx file used by Remedios Aguilar-Moya & collaborators in the research “Effect of direct instruction and webquest on learning outcomes in higher education”.</p>
Technology Acquisition Plans to Foster Supply Chain Risk Management Learning Outcomes in Project-Based Software Development Courses: Raw Data and TAP Template
<p>This is a data set and TAP template to accompany the paper</p> <p>"Technology Acquisition Plans to Foster<span> </span>Supply Chain Risk Management Learning Outcomes in Project-Based Software Development Courses" by Tenbergen and Mead published at the 36th International Conference on Software Engineering Education and Training.</p>
Development of a Semester Learning Plans System Based on OBE (Outcome-Based Education)
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Figure data for "Machine Learning For Early Dynamic Prediction Of Functional Outcome After Stroke"
Open the record for dataset details and reuse information.
Coding and results Literature. Review: Professional Learning Communities and Professional Capital in the Improvement of Learning Outcomes
<p>Project PID2020-117020GB-I00 , funded by : Ministerio de Ciencia e Innovación de España/<br>AEI/10.13039/501100011033 and by the predoctoral contracts grant for the training of PhD implemented by<br>the [grant number PRE2021-098075]</p>
Study to Learn More About Outcomes Reported by Patients Suffering From Type 2 Diabetes Using a Digital Data Collection Tool
ClinicalTrials.gov study NCT04383041. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
COVID-19 Lessons Learned: the Outcome of TOMEKA® Project is to Teach
ClinicalTrials.gov study NCT04424940. IPD Sharing: YES. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.