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79 results for “e-learning,”
Japanese FAQ dataset for e-learning system
<p>This dataset includes FAQ data and their categories to train a chatbot specialized for e-learning system used in Tokyo Metropolitan University. We report accuracies of the chatbot in the following paper.</p> <p><em>Yasunobu Sumikawa, Masaaki Fujiyoshi, Hisashi Hatakeyama, and Masahiro Nagai "Supporting Creation of FAQ Dataset for E-learning Chatbot", Intelligent Decision Technologies, Smart Innovation, IDT'19, Springer, 2019, to appear.</em></p> <p><em>Yasunobu Sumikawa, Masaaki Fujiyoshi, Hisashi Hatakeyama, and Masahiro Nagai "An FAQ Dataset for E-learning System Used on a Japanese University", Data in Brief, Elsevier, in press.</em></p> <p>This dataset is based on real Q&A data about how to use the e-learning system asked by students and teachers who use it in practical classes. The duration we collected the Q&A data is from April 2015 to July 2018.</p> <p>We attach an English version dataset translated from the Japanese dataset to ease understanding what contents our dataset has. Note here that we did not perform any evaluations on the English version dataset; there are no results how accurate chatbots responds to questions.</p> <p><strong>File contents</strong>:</p> <ul> <li>FAQ data (*.csv) <ol> <li>Answer2Category.csv: Categories of answers.</li> <li>Answer2Tag.csv: Titles of answers.</li> <li>Answers.csv: IDs for answers and texts of answers.</li> <li>Categories.csv: Names of categories for answers.</li> <li>Questions.csv: Texts of questions and their corresponding answer IDs.</li> <li>Answers_english.csv: IDs for answers and texts of answers written in English.</li> <li>Categories_english.csv: Names of categories for answers and their corresponding English names.</li> <li>Questions_english.csv: Texts of questions and their corresponding answer IDs written in English.</li> </ol> </li> <br> <li>Statistics (*.tsv) <p> Results of statistical analyses for the dataset. We used Calinski and Harabaz method, mutual information, Jaccard Index, TF-IDF+KL divergence, and TF-IDF+JS divergence in order to measure qualities of the dataset. In the analyses, we regard each answer as a cluster for questions. We also perform the same analyses for categories by regarding them as clusters for answers.</p> </li> </ul> <p><strong>Grants</strong>: JSPS KAKENHI Grant Number 18H01057</p>
Conformity of E-Learning for Teaching and Learning in Ethiopian Higher Education: Analysis on User-Friendliness of E-Resources
<p>The study investigated the practicability of e-learning for teaching and learning in Ethiopian higher education in terms of availability, clarity, accessibility in terms of accommodation and economy. The research looked into the roles of e-provisions based on the country’s Information and Communication Technology (ICT) in education policy guidelines as benchmark. Descriptive survey research design was used in the research since the study focused on indicating status than in-depth institution-based analysis of technology-use in education. Two higher institutions were selected for their relative proximity and viability for data collection. Data for the research were collected from 150 students, 4 technical support renderers and 30 teachers. Instruments of data collection were binary mode questionnaires and semi-structured interviews. Findings indicated shortage in a purpose-orientation, weak cross-institutional interchange and low mainstreaming of e-resources for course-provision. Though initiatives were high to use e-resources across lessons, shortage in internet access and prevailing digital divides were common barriers. Selective use of e-learning was witnessed on the part of technical support providers. multiplier effects in sharing experiences were not practiced among the teachers. Purpose-conformity was met on highly individualized bases. inter-institutional experiential exchange was insufficient though there was high emphasis on supporting selected instructional strings.</p>
Como o apoio Institucional influencia a intençao de reuso do e-learning no interior e nas ciudades metropolitanas?
<p>Video resumen de un artículo presentado en el VI Congreso Latinoamericano de Marketing Social en Brasil</p>
MODEL PEMBELAJARAN DI MASA PANDEMI COVID-19: SYNCHRONOUS AND ASYNCHRONOUS (BLENDED E-LEARNING)
<p>Pandemic Covid-19 telah membawa dampak besar pada beberapa sektor kehidupan, salah satunya adalah Pendidikan. Dalam menghadapi situasi ini, proses pembelajaran dituntut untuk menyesuaikan diri. Namun sayangnya, tidak semua pegiat Pendidikan paham mengenai inovasi terbaru yang bisa dipakai sebagai sistem belajar untuk melakukan pembelajaran selama pandemic. Tulisan ini menggambarkan model pembelajaran yang cukup inovatif yang bisa digunakan pendidik dalam pengajaran model baru di era kebiasaan baru. Tulisan ini bertujuan untuk mendeskripsikan bagaimana sejatinya model pembelajaran di masa pandemic Covid-19 dan bagaimana penggunaan blended e-learning pada beberapa aplikasi pembelajaran tertentu. Model pembelajaran yang menjadi fokus kajian dalam tulisan ini adalah Project Based Learning dan Integrated Learning sebagai bagian dari Blended Learning dan Synchronous and Asynchronous Learning. Sedangkan media pengajaran yang menjadi focus kajian adalah penggunaan Canvas Network dan Google Doc.</p>
Effect of an E-learning Intervention on Knowledge, Attitudes and Beliefs of Healthcare Professionals Managing Low Back Pain.
ClinicalTrials.gov study NCT05284669. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
E-learning in Patient Education to Patients With Rheumatoid Arthritis
ClinicalTrials.gov study NCT04669340. IPD Sharing: YES. Countries: 1. Publications: 39.
Orsi Surgical Skills E-learning Trial
ClinicalTrials.gov study NCT04541615. IPD Sharing: NO. Countries: 1. Publications: 2.
e-Learning & Development of an Evidence-based Psychoeducational Programme for First Episode Psychosis
ClinicalTrials.gov study NCT01783457. IPD Sharing: Not stated. Countries: 1. Publications: 1.
E-learning to Improve Oral Anticoagulant Use in Hospitalized Older People With AF
ClinicalTrials.gov study NCT03188211. IPD Sharing: NO. Countries: 1. Publications: 3.
Cognitive Style and Mobile Technology in E-learning in Undergraduate Medical Education
ClinicalTrials.gov study NCT02971735. IPD Sharing: NO. Countries: 0. Publications: 7.
Training Health Care Professional in Detecting BCC on OCT Using E-learning and CUSUM-analysis
ClinicalTrials.gov study NCT05634421. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
The Effect of Blended E-Learning Method on Students' Knowledge and Psychomotor Skills on Basic Life Support
ClinicalTrials.gov study NCT05346003. IPD Sharing: NO. Countries: 1. Publications: 4.
Self-management and HeAlth Promotion in Early-stage Dementia With E-learning for Carers
ClinicalTrials.gov study NCT04286139. IPD Sharing: NO. Countries: 3. Publications: 1.
e-Learning About Cognitive Impairment and Dementia Prevention
ClinicalTrials.gov study NCT05383118. IPD Sharing: NO. Countries: 1. Publications: 1.
Randomized, Single Center Study About the Impact of an E-learning Dedicated to Myocardial Infarction Patient
ClinicalTrials.gov study NCT03949608. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
TRACE E-learning Implementation in Belgium
ClinicalTrials.gov study NCT03265028. IPD Sharing: NO. Countries: 1. Publications: 5.
E-learning Proficiency Based Progression Training Programme for Clinical Communication on ISBAR Performance (eISBAR)
ClinicalTrials.gov study NCT02937597. IPD Sharing: NO. Countries: 1. Publications: 4.
Reuptake of an E-Learning Programme in General Practice
ClinicalTrials.gov study NCT00392483. IPD Sharing: Not stated. Countries: 1. Publications: 1.
E-learning Module to Improve Laboratory Prescriptions
ClinicalTrials.gov study NCT03551106. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Impact of an E-learning Tool on Patient Satisfaction, Knowledge and Medication Adherence in Solid Organ Transplantation: A Randomized Controlled Trial
ClinicalTrials.gov study NCT01826721. IPD Sharing: Not stated. Countries: 1. Publications: 2.
ScienceDex guides
Understand access before you commit
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.