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66 results for “Self-Assessment”
Self-Assessment Questions - Operating Systems and Computer Networks
<p><strong>What does the dataset contain?</strong><br> The dataset contains a list of XML files and JPG images. The XML files contain a description of a certain type of self-assessment questions to be load and processed by a plugin called <em>qtype_selfassess</em> [1] for the Moodle learning management system.</p> <p>The XML files were names as follow:</p> <pre><code><number of course unit>-<question-ID>-selfassess-<short name of the qestion>.xml</code></pre> <p>The file unit_1-262-selfassess-Caching.xml for instance, is about a self-assessment question of the first course unit ("unit_1") that has the ID 263, and is about the topic "Caching". Most of the short names are in German since the content of the files is German as well.<br> The JPG files are referenced in the XML files.</p> <p><strong>What is the purpose and origin of the data?</strong><br> In order to give students the chance of iterative improvement, the SelfAssess-plugin [1], that allows students to create and assess solutions unlimited times, was implemented in Moodle. Students can open a question to start work. The plugin shows the question and asks the student to create and upload a solution. Solutions may be uploaded as image (e.g. photo, scan) or PDF file. Thereafter, the plugin presents a list of instructor-defined assessment criteria and a link to the sample solution, and asks to assess the uploaded solution. The plugin then presents feedback to the students based on their self-assessment. For this purpose, instructors defined an error tree for each question that maps typical errors to detect one or a combination of not considered criteria on one feedback text. Feedback texts help the student to improve their solution, e.g. by providing learning goal feedback (such as misconceived concepts) and learning process feedback (such as links to relevant resources, things to improve, or activities to do), until they self-assess their solution as correct or as good enough. After receiving the feedback, students could either improve their solution by performing a new iteration of the process (create, upload, self-assess the improved solution again, receive feedback, and take or decline another iteration) or finish the exercise.</p> <p>For the mentioned plugin 43 self-assessment questions including assessment criteria and feedback have been created for fundamental computer science course about operating systems and computer networks. The course was offered at FernUniversität in Hagen, Germany, in winter term 2019/20 and 2020/21 by Professor Dr. Jörg M. Haake.</p> <p>Further information about the plugin and the proposed self-assessment question type can be found in [2] and [3].</p> <p><strong>References</strong><br> [1] Steinkohl, K., Burchart, M., Haake, J.M., Seidel, N. (2020). SelfAssess Question Type Plugin for<br> Moodle. https://github.com/D2L2/qtype_selfassess<br> [2] Haake, J. M.; Seidel, N.; Karolyi, H.; Ma, L.: Self-Assessment mit High-Information Feedback. DELFI 2020–Die 18. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V., 2020.<br> [3] Haake, J. M., Seidel, N., Karolyi, H., & Burchart, M. (2021). Accuracy of self-assessments in higher education. In DELFI 2020 – Die 19. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. Bonn.</p> <p> </p> <p><strong>Acknowledgments.</strong></p> <p>This research was supported by the Research Cluster "Digitalization, Diversity and Lifelong Learning – Consequences for Higher Education" (D2L2) of the FernUniversität in Hagen, Germany.</p>
Questionnaire for the self-assessment of digital preservation activities in institutional research repositories
<p>This dataset includes a questionnaire designed to enable institutional repository managers to conduct a self-assessment of their digital preservation strategies and activities. It consists of 46 evaluation criteria extracted and modified from the NDSA Levels of Digital Preservation and ISO 16363:2017 standards. The questionnaire is provided in queXML format, facilitating its import into various survey applications</p> <p> </p>
Dataset "What children know and want to know about climate change: a prior-knowledge self-assessment"
<p>Dataset: Responses to two open questionnaires: Questionnaire1: "What do you know about climate change?"; Questionnaire 2: "Waht do you want to know about climate change?" Applied to middle school students, from the ages of 10 (5<sup>th</sup> grade) until the ages of 13 (8<sup>th</sup> grade), at schools "Escola Básica e Secundária Dr. Pascoal José de Mello" and "Escola Nº 2 de Avelar" from the municipality of Ansião at the central region of Portugal. The survey was applied to students both individually (n=106) and in group (n= 60), encompassing a total of 417 students. Due to logistical reasons, it was no possible to gather individual questionnaires for the 5<sup>th</sup> grade.</p> <p> </p>
Investment Readiness Level - Self-assessment - BIOBEC
<p><span>This document will provide you with some guidelines on how the investment readiness level assessment was conducted for each of the BBECs, which is part of Economic Requirements for BBEC task to identify the financial needs of the BBECs and to develop a tailored financial plan for each of them</span></p> <p><span>The overall goal is to provide innovative pathways and strategies with regard to access to financial resources, to encourage the economic sustainability of the implementation and maintenance phase of the BBECs, and eventually ensure their lifelong continuation of them.<br><br></span></p> <p><span>By using the self-assessment, individuals and organizations can gain a better understanding of the current state of innovative education centers, as well as identify areas for improvement or potential opportunities for growth. This can help individuals and organizations make more informed decisions about which innovative education centers to invest in, or how to improve existing education centers in order to maximize their impact on the bio-based economy.</span></p> <p><span> </span></p> <p><span><span>A)<span> </span></span></span><strong><span>Competence and structure of the BBEC: </span></strong><span>This dimension assesses the competency and structure of the leadership team and staff of the innovative education center. The questions in this dimension are designed to evaluate the level of expertise and experience of the CEO, board members, and employees, as well as the overall structure and organization of the education center.</span></p> <p><span> </span></p> <p><span><span>B)<span> </span></span></span><strong><span>Markets</span></strong><span>: This dimension evaluates the market potential of the innovative education center. The questions in this dimension are designed to assess the size and growth potential of the target market, as well as the unique value proposition that the education center offers to its target market.</span></p> <p><span> </span></p> <p><span><span>C)<span> </span></span></span><strong><span>Talent</span></strong><span>: This dimension assesses the quality and availability of talent within the innovative education center. The questions in this dimension are designed to evaluate the education center's strategy for attracting and retaining top talent, as well as its culture of learning and professional development.</span></p> <p><span> </span></p> <p><span><span>D)<span> </span></span></span><strong><span>Technology landscape:</span></strong><span> This dimension evaluates the education center's use of technology in its teaching practices and curriculum. The questions in this dimension are designed to assess the education center's access to technology tools and hardware, as well as its plan for integrating technology into its teaching practices.</span></p> <p><span> </span></p> <p><span><span>E)<span> </span></span></span><strong><span>Financials</span></strong><span>: This dimension assesses the financial sustainability of the innovative education center. The questions in this dimension are designed to evaluate the education center's business model, as well as its projected revenue, expenses, and profits.</span></p> <p><span> </span></p> <p><span><span>F)<span> </span></span></span><strong><span>Economic</span></strong><span>: This dimension evaluates the potential economic impact of the innovative education center on the local community. The questions in this dimension are designed to assess the education center's ability to create jobs and increase productivity, as well as its potential risks or challenges.</span></p> <p><span> </span></p>
Self-Assessed Radiation Safety Culture Across Sectors
<p>The aim of this work is to compare radiation safety culture in the medical and nuclear sectors, by means of a radiation protection self-assessment tool.</p> <p>This is a programme level assessment, considering the department’s culture to the extent that it exhibits the traits of a healthy safety culture described by the US Institute of Nuclear Power Operations (INPO)<sup>1</sup>. It draws on the levels of radiation safety self-assessment described in Report 162 of the National Council on Radiation Protection and Measurements (NCRP)<sup>2</sup>.</p> <p>The tool consists of fifteen good practice statements, against which users assess themselves on a five point scale. Each statement is accompanied by prompt questions, to provide evidence for the choice of response.</p> <p>The purpose of the self-assessment tool is to assist employers in taking ownership of radiation protection by recognising the common management tools and processes that apply across departments and modalities. It is also expected to aid recognition of the elements of day-to-day working that contribute to an organisation’s culture and how these express the importance of radiation safety.</p> <p>Data will be presented demonstrating how perceived safety culture varies both within and between sectors, and suggestions made as to how this approach could contribute to improving safety culture in the UK.</p> <p>The work is in keeping with the new ICRP general recommendations in that it incorporates aspects of communications and stakeholder engagement, set out in the overarching considerations in section 3 of the draft report.</p>
EvalQuiz - LLM-based Automated Generation of Self-Assessment Quizzes in Software Engineering Education
<p>Self-assessment quizzes after lectures, educational videos, or chapters are a commonly used method in software engineering (SE) education to give students the opportunity to test their gained knowledge. However, the creation of these quizzes is time-consuming, cognitively exhausting, and complex, as an expert in the field needs to create the quizzes and review the lecture material for validity. Therefore, this paper presents a concept to automatically generate self-assessment quizzes based on lecture material using a large language model (LLM) to reduce lecturers' workload and simplify the general quiz creation process. The developed prototype was handed to experts, who subsequently evaluated the approach. The results show that automatic quiz generation saves time and the quizzes cover the delivered lecture material well. However, the generated quizzes often lack originality and versatility. Therefore, further prompt engineering might be required to achieve more elaborate results.</p>
Learning diary emotions, competence self-assessment and difficulty experience connected with epistemic beliefs
<p>Anonym group level dataset where university students have made emotion, competence and difficulty selections on a set of study related topics and their epistemic beliefs before and after the semester long use of learning diary.</p> <p> </p> <p>Groups are formed based on the overall number of credits the participants studied in their first academic year (master's level). The number of students in each group:</p> <p>ECTS; Number of students</p> <table> <tbody> <tr> <td>0-19</td> <td>2</td> </tr> <tr> <td>20-39</td> <td>11</td> </tr> <tr> <td>40-59</td> <td>17</td> </tr> <tr> <td>60-79</td> <td>26</td> </tr> <tr> <td>>80</td> <td>8</td> </tr> </tbody> </table>
Emotional and work-related factors in the self-assessment of Work Ability
<p>Data on the study "<span>Emotional and work-related factors in the self-assessment of Work Ability"</span></p>
Disaster Resilient and Self-Assessing Multifunctional Transportation Structures
<p>Corresponding data set for Tran-SET Project No. 18STTAM02. Abstract of the final report is stated below for reference:</p> <p>"This research designs and characterizes multifunctional materials, in particular inexpensive shape memory alloys, for transportation structures that possess excellent mechanical properties and self-sensing capabilities for strengthening and health monitoring. The properties are the Fe-SMAs are sensitive to part size in that the grain size of the material, which can be grown to several inches, should exceed the smallest dimension of the part. In the current work, maximum part size of the large dimension Fe-SMA rods were determined through detailed microstructural investigations. Samples were subjected to abnormal grain growth heat treatments and found out that part size be increased up to 4.6 mm. Work to correlate computational and experimental work concerning the magnetic sensing of Fe-SMA transformation was conducted using via tensile loading of Fe-SMA wire. A model was developed to simulate a grain by grain transformation of a wire with large (4mm) grains along the wire, modeled as partitioned segments."</p>
SATO EU project | Self-assessment towards optimisation of building energy
<p>Get a glimpse of SATO project, the challenges, and technologies tested for the self-assessment and real life performance of energy use in buildings.</p>
Self-Assessment Method for Statin Side-effects Or Nocebo
ClinicalTrials.gov study NCT02668016. IPD Sharing: NO. Countries: 1. Publications: 1.
Collective self-assessment in banded mongoose intergroup contests
Open the record for dataset details and reuse information.
Self-assessment tool for Institutional Changes and Impact (RFO)
<p>As part of T8.2, which aims to investigate the institutional pathways that help embed Citizen Science (CS) in the European research landscape, Université Paris Cité (Learning Planet Institut) developed a self-assessment tool. This tool was structured around the EU-funded TIME4CS framework, which identified four key intervention areas:</p> <ol> <li><strong>Research </strong></li> <li><strong>Education & Awareness </strong></li> <li><strong>Support Resources & Infrastructure </strong></li> <li><strong>Policy & Assessment </strong></li> </ol> <p>These areas are integral to the functioning of universities and research institutes, which were the primary audience for the <a href="../records/10405577">Stock-taking" institutional support for Citizen Science in Research Performing Organisations (RPOs)</a> developed by the TIME4CS project.</p> <p>The questions in this self-assessment tool were specifically tailored for Research Funding Organisations (RFOs) to evaluate their integration of citizen science at an institutional level. The attached Excel file is the self-assessment tool, designed to generate immediate results. These results offer an overview of the organisation's integration across the four intervention areas mentioned above.</p>
Cultural diversity self-assessment model - Questionnaire dataset
Open the record for dataset details and reuse information.
Development and Validation of a Self-assessment System Based on a Mobile App to Manage Adult Growth Hormone Deficiency
ClinicalTrials.gov study NCT03525587. IPD Sharing: UNDECIDED. Countries: 1. Publications: 19.
Self-Assessment of Medical Abortion Outcome Using Serial Multi-level Pregnancy Tests
ClinicalTrials.gov study NCT02570204. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Validation of a Scale of Self-assessment of Fear Faces in Children 4 to 12 Years
ClinicalTrials.gov study NCT02675504. IPD Sharing: NO. Countries: 1. Publications: 1.
Use of Low Sensitivity Pregnancy Test for Self-assessment of Medical Abortion
ClinicalTrials.gov study NCT02231619. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Self-assessment of Flare in Osteoarthritis of Lower Limbs
ClinicalTrials.gov study NCT02892058. IPD Sharing: NO. Countries: 4. Publications: 3.
FOOTSAK (Footwear Self-Assessment Kit)
ClinicalTrials.gov study NCT06200532. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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
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DANDI Archive for NWB datasets
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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.