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2,020 results for “schooling”
A dataset for assessing ChatGPT capabilities to create concept maps for secondary school students
<p>The dataset was compiled to examine the use of ChatGPT 3.5 in educational settings, particularly for creating and personalizing concept maps. <br>The data has been organized into three folders: Maps, Texts, and Questionnaires. The Maps folder contains the graphical representation of the concept maps and the PlanUML code for drawing them in Italian and English. The Texts folder contains the source text used as input for the map's creation The Questionnaires folder includes the students' responses to the three administered questionnaires and the submitted questionnaires.</p>
DATABASE 03 School Transformation Process
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Ten Real-World Problem Instances (Malta) of the Single-School Bus Routing Problem
<div> <div> <div> <div> <p>Each problem instance is specified in a file with the extension .bus. The format of each file is similar to that of the problem instances used in </p> <div> <div> <div> <div> <p><strong>Lewis, R. and Smith-Miles, K. (2018). A heuristic algorithm for finding cost-effective solutions to real-world school bus routing problems. <em>Journal of Discrete Algorithms</em>, 52-53:2–17</strong></p> <p>and is as follows.</p> <p> </p> </div> </div> </div> </div> <p>The first line in each file gives:</p> <ul> <li>The number of bus stops, including the school;</li> <li>The number of student addresses;</li> <li>The number of listed walks from student addresses to bus stops;</li> <li>The distance unit (K for km, M for miles);</li> <li>The minimum eligibility distance;</li> <li>The maximum walking distance;</li> <li>Further information, which can be ignored. </li> </ul> <div> <div> <div> <p>The second line contains the following details on the school:</p> <ul> <li>An “s” indicating that this line contains information about a stopping location; </li> <li>School's latitude;</li> <li>School's longitude;</li> <li>School's name. </li> </ul> <p>The next lines starting with an "s" contain the following details on the bus stops: </p> <ul> <li>Stop's latitude;</li> <li>Stop's longitude;</li> <li>Stop's name. </li> </ul> <p>The next lines starting with an "a" contain the following details on the student addresses: </p> <ul> <li>Address's latitude;</li> <li>Address's longitude;</li> <li>Number of children requiring transport to the school at this address;</li> <li>Family name at this address.</li> </ul> <p>The next lines starting with a "d" contain the following distance details between pairs of stopping locations:</p> <ul> <li>Index of start location;</li> <li>Index of end location;</li> <li>Driving distance between start and end locations;</li> <li>Driving time (seconds) between start and end locations.</li> </ul> </div> </div> </div> <p>The next lines starting with a "w" contain the following walk details between pairs of one student address and one bus stop: </p> <ul> <li>Index of address;</li> <li>Index of bus stop;</li> <li>Walking distance between address and bus stop;</li> <li>Walking time (seconds) between address and bus stop. </li> </ul> <p><em>Note that all indices start from 0. For stopping locations, 0 corresponds to the school and the rest correspond to the bus stops. </em></p> </div> </div> </div> </div>
FIGURE 2. Third grade students from Adotiva Liberato Valentin Public Elementary School, responsible for thinking a in Bringing taxonomy to school kids: Aedokritus adotivae sp. n. from Amazon (Diptera: Chironomidae)
FIGURE 2. Third grade students from Adotiva Liberato Valentin Public Elementary School, responsible for thinking a name for the new species, announcing the winner name.
FIGURE 1 in Bringing taxonomy to school kids: Aedokritus adotivae sp. n. from Amazon (Diptera: Chironomidae)
FIGURE 1. Aedokritus adotivae sp. n., male. A, wing. B, hypopygium, dorsal view. C, hypopygium with tergite IX and anal point removed, dorsal view left, ventral view right.
Backgroun in Computational Tools (Data Science Summer School Goettingen)
<p>Here is a dataset to understand the computational tools background of participants</p>
Changes in School-day Step Counts during a Physical Activity for Lent Intervention: A Cluster Randomized Crossover Trial of the Savior's Sandals
<p>Excel data file of 187 participants in a Physical Activity for Lent program conducted in spring 2017 at 4 Catholic middle schools in San Diego, CA, USA. Includes daily physical activity step counts and questionnaire data for religiosity, physical activity enjoyment, and situational interest motivation.</p>
Nepal School Seismology Network
<p>The Nepal School Seismology Network is a low-cost seismological network installed for both educational and observational purposes. After a pilot station installed in 2018, the network operates 22 sensors since April-May 2019 (see map).</p> <p>The sensors are RaspberryShake 1D instruments. The data is freely available to the public through IRIS (http://ds.iris.edu) and RaspberryShake (https://raspberryshake.org/).</p> <p>The online waveforms are visible at: https://raspberryshake.net/stationview/ (then zoom to Nepal).</p> <p>The station are regrouped under the "_NSSN" virtual network code. Full details on the stations can be found here: http://ds.iris.edu/mda/_NSSN/</p> <p>Further details on the Seismology at School in Nepal program can be found here: www.seismoschoolnp.org</p> <p>Both the program and the network operate on a non-profit basis.</p> <p>We are grateful to all participating schools, teachers and students for hosting the instruments.</p>
Implementing Visual Literacy Techniques among Future Educators in Pre-school and Primary School Settings
<p><span>In today's society, the pervasive influence of visual culture underscores the necessity of audiovisual literacy, particularly for primary education teachers who have to navigate a digital, image-centric world. This research aims to evaluate the impact of a visual competence intervention on early childhood and primary school teachers (N= 224) assessing its educational and social significance. Employing both quantitative and qualitative methodologies, the study involved a questionnaire on socio-demographic information and audiovisual habits, alongside an analysis of 31 open-ended questions focusing on Perception, Interpretation, Reception, and Creation/Production. Additionally, photographs taken by students post-training were examined for evidence of significant learning. The findings revealed an enhanced awareness among students regarding photographic content and messaging, confirming the hypothesis that future teachers lack essential visual competence. The study highlights the critical need for improved visual culture education among future teachers to better serve a society increasingly oriented towards audiovisual communication.</span></p>
Data and code used for 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'
<p>This repository contains all data necessary to reproduce the results for the manuscript titled 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'.</p>
STFC Astronomy and Artificial Intelligence Summer School with Public Engagement
<h3><strong>Context</strong></h3> <p><strong>Summer School. </strong>As part of the Epistemic Insight Initiative of the LASAR (Learning about Science and Religion) research and outreach centre, an <a href="https://futureofknowledge.com/summer-school/" target="_blank" rel="noopener">STFC Astronomy and Artificial Intelligence summer school with public engagement</a> was organised by Prof Berry Billingsley and Dr James Pearson, held over five days from 8th-12th July 2024 at Canterbury Christ Church University, Canterbury, UK. The summer school was aimed at astronomy PhD students as well as final year undergraduate & master's students in physics and computer science, and placed emphasis on a <a href="https://futureofknowledge.com/astronomy-and-ai-multidisciplinary-and-outreach/">multidisciplinary approach</a> of bringing together knowledge and researchers from different fields utilising AI, to devise solutions and create new opportunities. The summer school provided seminars, workshops and one-to-one conversations where participants could learn about current uses of AI, collaborate to co-create new projects and get expert perspectives on their work. Participants also explored and gained insights in three key areas:</p> <ol> <li>What are the roles of AI in astronomy and in the sciences more broadly?</li> <li>What do curiosity and knowledge creation look like in a world of AI?</li> <li>Public engagement and working with AI and immersive technologies to engage new audiences in astronomy, which involved outreach training.</li> </ol> <p><strong>Public Engagement. </strong>There was also the opportunity for attendees to participate in outreach training and contribute to a public engagement event for schools, involving working with immersive tech to communicate ideas in astronomy, and assessing generative AI as a tool for creating activities and puzzles relating to their work. The outputs of this can be found here: <a href="https://futureofknowledge.com/q-and-a-with-astronomers/">https://futureofknowledge.com/q-and-a-with-astronomers/</a></p> <p><strong>Specialist Event and Case Studies. </strong>As part of this summer school, an additional free <a href="https://futureofknowledge.com/astronomy-and-ai-online-event/" target="_blank" rel="noopener">Astronomy and AI Online Event</a> day was held the week before on Wednesday 3rd July 2024 to provide specialist talks and panel discussions about AI in astronomy, the recordings of which can be found here: <a href="../doi/10.5281/zenodo.12674685">https://zenodo.org/doi/10.5281/zenodo.12674685</a>. Alongside this event, a number of recorded case studies were released showcasing how researchers in astronomy are using AI and deep learning in their own work, which can be found here: <a href="../doi/10.5281/zenodo.12594632">https://zenodo.org/doi/10.5281/zenodo.12594632</a></p> <p><strong>Principal Investigator:</strong> Prof Berry Billingsley (Epistemic Insight Initiative)<br><strong>Project Manager:</strong> Dr James Pearson (The Open University)<strong><br>Video Editor:</strong> Mina Cullimore (Canterbury Christ Church University)</p> <h3><strong>Summer School Talks</strong></h3> <p>Here, we provide the recordings and presentation slides of the various talks. Below is an outline of the summer school's online/hybrid sessions - see the main website for the full timetable and more details about each talk.</p> <p><strong>Monday 08 July - Big Questions stimulus and introductions</strong><br>17.00-18.00 Plenary Session - <em>Workshop lead: Mina Cullimore and Berry Billingsley</em><br><strong>Tuesday 09 July - A multidisciplinary arena</strong><br>09.30-10.45 A review of yesterday and setting the agenda for today - <em>Berry Billingsley and Mina Cullimore</em><br>11.00-12.15 Thinking about astronomy and art – and working with virtual spaces - <em>Mina Cullimore</em><br>13.15-14.30 Cultural views of astronomy and archaeoastronomy - <em>Elfneh Bariso and Kevin Walsh</em><br>14.45-16.00 Working with GenAI and your tutor - <em>William Beckwith-Chandler and Kevin Walsh</em><br>16.15-17.30 Power & Problems of the Search and Generative AI Scenarios - <em>Ted Selker</em><br><strong>Wednesday 10 July - Welcome to LASAR at CCCU</strong><br>13.15-14.30 Explainable AI (XAI): AI for tomorrow's scientific computing - <em>Konstantinos Sirlantzis</em><br>14.45-16.00 The future of AI: the loss of uncertainty - <em>Philippe de Wilde</em><br>16.15-17.30 Big data in astronomy: contemplating a potential future - <em>Matthew Graham</em></p>
EduLifeDesks Archive: Honors Desert Ecology - Empire High School (647) DwCA
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EduLifeDesks Archive: 2011 Latin School Project Week (254) DwCA
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[Dataset] NGS summer school Aarhus University
<p>Datasets for the NGS summer school at Aarhus University. The course material can be found on Github at <a href="https://github.com/hds-sandbox/NGS_summer_course_Aarhus">this repository</a> or <a href="https://hds-sandbox.github.io/NGS_summer_course_Aarhus/" target="_blank" rel="noopener">course webpage</a></p>
Social competence and behavioural problem in self and parental assessment among Polish, Spanish, and Norwegian primary school students
<p>Dataset of article: Laudańska-Krzemińska, I., Banaszak, E., Lubczyńska, A., Muller, S., Romera, E., Antypas, K., Luque González, R., Wiza, A. Social competence and behavioural problem in self and parental assessment among Polish, Spanish, and Norwegian primary school students</p> <p> </p>
A 10-day experience sampling dataset on subjective experiences of middle and secondary school students in 2022
<p>This data note presents a dataset from a 10-day experience sampling study conducted among middle and secondary school students in 2022. The dataset includes a brief start-up survey in addition to 40 momentary assessments, enabling analysis of both inter- and intraindividual processes. The start-up survey includes validated measures on students’ perceptions of school-related support from family, peers, and teachers, general self-efficacy, and academic self-efficacy, as well as items on school enjoyment and school absenteeism. The experience sampling data includes items related to positive and negative emotions, lecture characteristics, perceived teacher style, peer relationships as well as sleep quality, breakfast, school readiness, and school day satisfaction. Data were collected using the "RealLife Exp" mobile app. Multiple procedures were undertaken to validate and confirm the reliability of the dataset. The methodological approach utilized for gathering the data featured in this dataset allows for a nuanced analysis of individual and contextual influences along with lagged effects on school well-being, with the potential to discover previously overlooked patterns and trends.</p>
Introduction to the TOPS SCHOOL Project: NLU Fall 2024 Student Cohort
<p>In this video, Juan Martinez and Kytt MacManus give an overview of the TOPS SCHOOL project to the fall 2024 NLU student cohort. They provide an overview of the project, <a href="https://ciesin-geospatial.github.io/TOPSTSCHOOL/" target="_blank" rel="noopener">website</a>, <a href="https://github.com/ciesin-geospatial/TOPSTSCHOOL" target="_blank" rel="noopener">GitHub</a>, and tasks. You can watch the video below or find a link in the 'Additional details' section.</p>
Dataset for "The effects of teaching strategies on learning to think critically in primary and secondary schools: an overview of systematic reviews"
<p>A dataset for the overview of systematic reviews entitled "The effects of teaching strategies on learning to think critically in primary and secondary schools: an overview of systematic reviews". </p>
A comprehensive descriptive analysis : Assessing the guidance and counseling needs through cognitive and non-cognitive development of students in secondary level public schools
<p>This data has been collected from a secondary level public school in Karachi for a descriptive analysis . I</p> <p> </p>
Surveys reporting feedback from families attending Early Childhood Services during 5-month school closure due to COVID-19: an Italian experience
<p>This dataset reports results of two surveys distributed by <em>Cooperativa Sociale Aeris </em> during Covid19 pandemic to families attending its Early Childhood Services dedicated to 0-3 aged children. Aeris is a social enterprise based in northern Italy that deals with socio-educational and welfare services. One of Aeris activities deals with the management of the ten Early Childhood Services located in the northern Italy which are dedicated to 0-3 aged children. After the school closure occurred the 24<sup>th</sup> of February 2020, Aeris organized online activities to keep contacts with families, to support children as well as their parents. With the aim of assessing if the adopted organization and proposed activities had positive feedback on families, a first survey was distributed in May 2020 and a second survey was distributed in October 2020 to families attending the Early Childhood Services. Survey results are reported in this dataset. Even if several worldwide actions have been implemented to guarantee educational continuity during Covid19 pandemic, most of these actions targeted 3-18 years old children/adolescents, while the subgroup 0-3 was rarely included.</p>
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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.