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27 results for “Concept Maps”
Unpacking the concept of "educators' data literacy in Higher Education" - Systematic Review of the literature and Keyword Map
<p>As algorithmic decision-making and data collection become pervasive within higher education, how can educators make sense of the systems that shape life and learning in the 21st century? Through a systematic review of the literature, the paper investigates the gaps in the literature, which prevent the formulation of potential pathways and principles on which educators’ data literacy can - and should - be developed and fostered. The analysis of 137 papers through the methods of classification under relevant categories, and key words mapping, showed that there is little attention on HE teachers, and most approaches to educators’ data literacy address management and technical abilities for data processing, with less concern on critical, ethical and personal approaches to datafication in education.</p> <p>The present dataset shows the full list of articles analysed.</p> <p>The dataset, and ods file, is composed by the following sheets:</p> <ol> <li>Codebook</li> <li>List of articles extracted from SCOPUS</li> <li>List of articles extracted from WOS</li> <li>List of articles extracted from ERIC</li> <li>List of articles extracted from DOAJ</li> <li>Interrater Agreement</li> <li>PRISMA workflow</li> <li>Analysis - First Level (classification of 137 articles selected)</li> <li>Analysis - Second Level (List of articles relating faculty development)</li> <li>Supplementary tables (counting articles in relation to the categories of analysis).</li> </ol> <p>As for the Keywords' Map, a second file .csv displays the text over which basis was performed the keyword maps analysis. A .txt file shows notes relating the analysis procedures using the software VOS-Viewer <a href="http://www.vosviewer.com/">http://www.vosviewer.com/</a></p> <p> </p>
Concept maps as a novel assessment tool in medical education
<p>This dataset accompanies the manuscript entitled "Concept maps as a novel assessment tool in medical education". The dataset contains data from eight participants in a pilot study investigating the use of Concept Maps (CMs) in medical education. Each participant constructed one CM for three different Problem-Based Learning (PBL) cases. Participant CMs, demographic data, results from pre-intervention questionnaire on their learning style (VARK Learning Styles Self-Assessment Questionnaire) and post-intervention questionnaire results measuring the level of clinical and critical thinking are included. In addition expert CMs are also provided for each of the three PBL cases.</p> <p> </p> <p><br> <br> <br> </p>
Figure 1. Mind mapping of Concepts and Versions of Micro learning (Hug, 2005)-Micro Learning: A Modernized Education System
<p>The methods of micro learning are in line with the way that the learner’s brain naturally takes in information, so that the body does not get stressed-out. One of the salient features of micro learning is that it allows the user to find exactly what he or she is looking for. When the mind focuses on a particular question, it is the most open to receiving that answer (www.digitalpromise.org/microcredentials dated on 10/10/2015). It allows the learner’s brain to explore its own curiosity and its own patterns.</p>
Replication Package: Product-Line Engineering for Smart Manufacturing: A Systematic Mapping Study on Security Concepts
<p><strong>Welcome to the public repository for the additional content of the paper "Product-Line Engineering for Smart Manufacturing: A Systematic Mapping Study on Security Concepts", accepted at the ICSOFT 2024.</strong></p> <p>This repository provides additional information to the conducted mapping study, including the following file:</p> <ul> <li>analysis_sheet_ICSOFT2024.csv: sheet containing information regarding the analysis results of 43 included papers based on the extraction criteria.</li> </ul>
COREQ checklist: Focus group for 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in medical Data Warehouses'
<p>Presentation of the 32 items on the consolidated criteria for reporting qualitative research (COREQ) checklist. The information is used for the report on a focus group that was conducted as part of the preparation of a publication. The title of the article is (as of submission on 18.03.2024): 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in Medical Data Warehouses'.</p>
Datasets for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings
<p><strong>Introduction</strong></p> <p>The datasets for training and evaluating our model for explainable depression detection on Twitter aided by metaphor concept mappings proposed in the following paper:</p> <p>Sooji, Han, Rui Mao, and Erik Cambria. "Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings." In Proceedings of the 29th International Conference on Computational Linguistics (COLING), 2022. in press</p> <p>Source code for our model is available at <a href="https://github.com/soojihan/HAN/blob/main/README.md">github.com/soojihan/HAN</a>.</p> <p>These datasets were generated using the dataset proposed in Shen et al., 2017. The original dataset is available at <a href="https://github.com/sunlightsgy/MDDL">github.com/sunlightsgy/MDDL</a>.</p> <p> </p> <p><strong>Description</strong></p> <p>There are three datasets. </p> <p><strong>1. mdl_HAN: </strong>This dataset contains tweets and metaphor concept mappings (MCMs) for 5,899 positive (i.e. depressed) and 4,469 negative users. Tweets in this dataset are extracted from the original MDDL dataset (Shen et al., 2017). MCMs were extracted using MetaPro (Mao et al., 2022). Please refer to our paper for more details.The name of each subfolder under the 'positive' and 'negative' subfolders is tweet userid. Each user's folder contains one or two json files:</p> <ul> <li>[userid].json: This json file contains tweet text objects, each of which is represented by [timestamp, tweet text].</li> <li>[userid]_cm.json: This json file contains MCMs, each of which is represented by [timestamp, MCM]</li> </ul> <p>Note that some users do not have MCMs. There's no [userid]_cm.json file in such users' folders.</p> <p><strong>2. imdl_HAN: </strong>This dataset has the same contents and structure as mdl_HAN except that explicit linguistic cues for depression (i.e., “I’m/I was/I am/I’ve been diagnosed depression” and words containing “depress”, “diagnos”, “anxiety”, “bipolar” and “disorder”) were removed from all tweets.</p> <p><strong>3. sampled_training_eval_data: </strong>This dataset contains 5 randomly sampled cross-validation sets. Each of the five set contains train.csv, test,csv and dev.csv. Each csv file contains user ids.</p> <p> </p> <p><strong>Remarks</strong></p> <p>If you use the datasets, please cite our paper:</p> <p>Sooji, Han, Rui Mao, and Erik Cambria. "Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings." In Proceedings of the 29th International Conference on Computational Linguistics (COLING), 2022. in press</p> <p> </p> <p><strong>Contact</strong></p> <p>If you have any questions about the datasets or source code for our model, please contact <a href="https://soojihan.github.io/">Sooji Han</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Shen, Guangyao, Jia Jia, Liqiang Nie, Fuli Feng, Cunjun Zhang, Tianrui Hu, Tat-Seng Chua, and Wenwu Zhu. "Depression detection via harvesting social media: A multimodal dictionary learning solution." In <em>IJCAI</em>, pp. 3838-3844. 2017.</p> <p>Rui Mao, Xiao Li, Mengshi Ge, and Erik Cambria. 2022. MetaPro: A computational metaphor pro- cessing model for text pre-processing. Information Fusion, 86-87:30–43.</p> <p> </p>
Replication Package: A Systematic Mapping Study on Security in Configurable Safety-critical Systems Based on Product-Line Concepts
<p><strong>Welcome to the public repository for the additional content of the paper "A Systematic Mapping Study on Security in Configurable Safety-critical Systems Based on Product-Line Concepts", accepted at the ICSOFT 2023.</strong></p> <p>This repository provides additional information to the conducted mapping study, including the following files:</p> <ul> <li>fetched_results_ICSOFT2023.csv: sheet containing all papers fetched from IEEEXplore, Scopus, and the ACM Guide to Computing Literature.</li> <li>excluded_paper.csv: sheet containing all excluded papers related to safety-critical systems but not referring to security.</li> <li>analysis_sheet_ICSOFT2023.csv: sheet containing information regarding the analysis results of 44 included papers based on the extraction criteria.</li> </ul>
Concept Mapping as a Scalable Method for Identifying Patient-Important Outcomes
ClinicalTrials.gov study NCT02792777. IPD Sharing: NO. Countries: 1. Publications: 3.
Mapping the Local Spatial Charge in Defective Diamond by Means of N-V Sensors—A Self-Diagnostic Concept
<p>Mapping the Local Spatial Charge in Defective Diamond by Means of N-V Sensors—A Self-Diagnostic Concept</p> <p>Electrically active defects have a significant impact on the performance of electronic devices based on wide-band-gap materials. This issue is ubiquitous in diamond science and technology, since the presence of charge traps in the active regions of different classes of diamond-based devices (detectors, power diodes, transistors) can significantly affect their performance, due to the formation of space charge, memory effects, and the degradation of the electronic response associated with radiation-induced damage. Among the most common defects in diamond, the nitrogen-vacancy (N-V) center possesses unique spin properties that enable high-sensitivity field sensing at the nanoscale. Here, we demonstrate that N-V ensembles can be successfully exploited to perform direct local mapping of the internal electric-field distribution of a graphite-diamond-graphite junction exhibiting electrical properties dominated by trap- and space-charge-related conduction mechanisms. By means of optically detected magnetic resonance measurements, we performed both point-by-point readout and spatial mapping of the electric field in the active region at different bias voltages. In this novel “self-diagnostic” approach, defect complexes represent not only the source of detrimental space-charge effects but also a unique tool for their direct investigation, by providing an insight on the conduction mechanisms that could not be inferred in previous studies on the basis of conventional electrical and optical characterization techniques.</p>
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>
Data for "Bridging reading and mapping: The role of reading annotations in facilitatingfeedback while concept mapping"
<p>This data includes:</p> <ul> <li>Questionnaire for Problem formulation</li> <li>UTAUT questionnaire evaluating Concept&Go</li> <li>SmartPLS model files for the UTAUT evaluation</li> </ul>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree STEM</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree gender</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree gap</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree rights</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Empirical conceptual map</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Theoretical concept map</p>
The Effect of Concept Map Use in Nursing Practice
ClinicalTrials.gov study NCT06451991. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: A citation-based map of concepts in invasion biology
Invasion biology has been quickly expanding in the last decades, so that it is now metaphorically flooded with publications, concepts and hypotheses. Among experts, there is no clear consensus about the relationships between invasion concepts, and almost no one seems to have a good overview of the literature anymore. Similar observations can be made for other research fields. Science needs new navigation tools, so that researchers within and outside of a research field as well as science journalists, students, teachers, practitioners, policy-makers and others interested in the field can more easily understand its key ideas. Such navigation tools could, for example, be maps of the major concepts and hypotheses of a research field. Applying a bibliometric method, we created such maps for invasion biology. We analysed research papers of the last two decades citing at least two of 35 common invasion hypotheses. Co-citation analysis yields four distinct clusters of hypotheses. These clusters can describe the main directions in invasion biology and explain basic driving forces behind biological invasions. The method we outline here for invasion biology can be easily applied for other research fields.
Statistical Data for The Impact of Chatbots using Concept Maps on Correction Outcomes–a Case Study of Programming Courses
<p>experiment learners' questionnaire result and test score</p>
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