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48 results for “Academic performance”
Students' Academic Performance in Case-Based Learning (CBL) based on Machine Learning Approach
<p>Paper title: Students’ Academic Performance in Case-Based Learning (CBL) based on Machine Learning Approach.</p> <p>This paper was registered in 2024 7<sup>th</sup> International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)</p>
Intrinsic motivation, perceived competence, negative feelings, and math academic performance.
<p>This dataset includes information about Primary School students' perceived competence, negative feelings, and intrinsic motivation with homework. The relationship between these variables and academic performance has been studied.</p>
Psychological Well-being, Food Insecurity, Academic Performance, and Other Risk Factors in a Sample of College Students in Jordan during Covid-19
<p>This research investigated the occurrence of psychological well-being perception and its relationship with food insecurity, academic performance, and other correlates in a sample of university students in Amman, Jordan during Covid-19. A cross-sectional study was conducted in two phases. Phase-1 translated and validated an Arabic version of the Psychological General Wellbeing Index-Short version (PGWB-S) in 122 students from the University of Jordan. In Phase-2, 414 students completed demographic questionnaire, Arabic Versions of the PGWB-S, Ryff Psychological Wellbeing Scale, and Individual Food Insecurity Experience Scale.</p> <p>Ethical considerations: This manuscript has been read and approved by all authors. The authors confirm that there are no other persons, who satisfied the criteria for authorship, but are not listed. The order of authors listed in the manuscript has been approved by all of them. They also understand that the Corresponding Author is the sole contact for the Editorial process, and holds the responsibility for communicating with the other author about progress, submissions of revisions and final approval of proofs. Moreover, the authors declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere. Furthermore, all data used in the study is confidential and the lead author has full access to the data reported in the manuscript. We confirm that there are no known conflicts of interest associated with this publication, which did not receive any financial support. Finally, the reporting of this work is compliant with The Code of Ethics of the World Medical Association (Declaration of Helsinki). In addition to that, the protocol of this research is approved by the Institutional Review Board at the University of Jordan, Amman, Jordan (ref no.: 2021-89).</p>
Student Athlete Academic Performance
<p>This is a student athlete data set.</p>
Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software.
<p><strong>If you use the data or the analysis pipeline, please refer to:</strong></p> <p>Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00006">https://doi.org/10.3389/fnins.2018.00006</a></p> <p><strong>and/or</strong></p> <p>Andersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00261">https://doi.org/10.3389/fnins.2018.00261</a></p> <p><strong>IMPORTANT</strong><br> Version 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p> <p><br> </p> <p>Dataset with tactile expectations to be analysed with pipelines for either <a href="https://mne.tools/stable/index.html">MNE-Python</a> or <a href="http://www.fieldtriptoolbox.org/">FieldTrip</a>, aiming to follow the MEG-BIDS structure</p> <p><br> <strong>Unzipping the data</strong></p> <p>Data is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called "analyses", which has a sub-directory called "omission_frontiers_BIDS-FieldTrip", which has a sub-directory called "data".<br> Thus, as an example, in my case, I should have the path: /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/data</p> <p><strong>Path:</strong><br> on a Linux system the path would be /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a macOS system the path would be /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a Windows system the path would be C:\Users\your_name\analyses\omission_frontiers_BIDS-FieldTrip\data</p> <p><strong>Steps for unzipping:</strong></p> <p>1. Set up the folder above according to your operating system, following the examples above and substitute "your_name" for your user name.<br> 2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/</p> <p>Now you are ready to run the analyses.</p> <p><br> <strong>The MEG data</strong></p> <p>Raw fif files are contained in the data folder, ordered by subject (n=20)<br> There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recording</p> <p><strong>Processed MRI data </strong></p> <p>For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects' identity</p> <p>For Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG data<br> For MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.</p> <p><br> <strong>Metadata</strong></p> <p>Each subject has a number of tsv-files:<br> *channel.tsv contain information about the channels in that recording<br> *events.tsv contain information about the events in that recording<br> removed_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)<br> ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)<br> *scans_tsv contain information about the scans conducted</p> <p><br> <strong>Scripts </strong></p> <p>Please see Github for the updated scripts at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p>
Dataset on the academic performance of students in 12 programmes from a private university
<p>The dataset on the academic performance of students in 12 programmes from a private university. The overall people sampled for the observation is 2490 undergraduates excavated from 12 programmes which are as follows Computer Science (CIS), Mathematics (MAT), Electrical and Electronics Engineering (EEE), Biochemistry (BCH), Mechanical Engineering (MCE), Microbiology (MCB), Civil Engineering (CVE), Computer Engineering (CEN), Chemical Engineering (CHE), Industrial Chemistry (CHM), Information and Communication (ICE), Petroleum Engineering (PET).</p>
paper Dataset on the academic performance of students in 12 programmes from a private university
<p>Dataset on the academic performance of students in 12 programmes from a private university. The overall people sampled for the observation is 2490 undergraduates excavated from 12 programmes which are as follows Computer Science (CIS), Mathematics (MAT), Electrical and Electronics Engineering (EEE), Biochemistry (BCH), Mechanical Engineering (MCE), Microbiology (MCB), Civil Engineering (CVE), Computer Engineering (CEN), Chemical Engineering (CHE), Industrial Chemistry (CHM), Information and Communication (ICE), Petroleum Engineering (PET).</p>
Academic Excellence, Website Quality, SEO Performance: Is there a Correlation? - Dataset of measurements, test results and calculated ratings.
<p>This Dataset, in two files of xlsx format, contains the data of all measurements, test results and calculated ratings as they are described in the methodology of the research article "Academic Excellence, Website Quality, SEO Performance: Is there a Correlation".</p>
Students' Interest and Academic Performance Datasets
<p>The data are on 'Interest in Simulation and Modelling', 'Performance in Simulation and Modelling', 'Interest in Probability and Statistics', 'Performance in Probability and Statistics', 'Interest in Data Analysis', 'Performance in Data Analysis', 'Interest in Statistical Computing' and 'Performance in Statistical Computing'.</p>
Enhancing Student Academic Performance Through The Implementation of Dynamic Gamification in Educational Settings
<p><span>Dynamic gamification is a gamification strategy that uses features and game mechanics that are flexible and adaptive to engage users in a variety of tasks. Dynamic gamification, in contrast to traditional gamification, allows for real-time modification of game features in response to user behavior or other circumstances. This study aims to show whether incorporating dynamic gamification enhances students' academic achievement. Numerous factors, such as motivation, learning engagement, academic achievement, relevance, confidence, and satisfaction, will be tested in order to advance the objectives of this study. In this research, a non-probability purposive sampling strategy was used. </span><span>An online questionnaire is distributed from March 23 until May 2, 2024 which featured 477 participants from all around Indonesia. However, only 400 respondents are considered valid, as the respondents must either comprehend the concept of gamification or have prior experience using gamification. The results stated that ten out of ten hypotheses had significant effect.<span> </span><span> </span></span></p>
Data set for: Research trends of Academic Performance and Social Networking Sites
<p>Data set for: Research trends of Academic Performance and Social Networking Sites.</p>
Data base: case-based learning with or without Escape Room activities as an active learning approach for improving academic performance and satisfaction among university students of psychology of groups
<p>Data base Database that collects data on gender, age, grade at the beginning of the course, grades of the activity and satisfaction with the activity.</p> <p>An experimental study using randomisation of team work groups was developed. Some student groups developed CBL activities in combination with Escape Room activities, and other student groups developed CBL activities alone. The latter can be considered a control group.</p> <p>This innovative teaching project was performed by social work students at the University of Zaragoza (Spain). This degree comprises 240 ECTS credits spread out over four years. Specifically, this experimental study was created for "Social Work with Groups" , a compulsory subject taught during the second semester of the second academic year of the Social Work degree programme. It is divided into two parts: the first one is presented from a social psychology perspective, and it is made up of five course curriculum topics. The second one is taught from a social work/social services perspective, which focuses more on the specifics of the profession (four course curriculum topics). This experiment was conducted in February and March 2023, during the delivery of the social psychology part of the course. There are taught five course curriculum topics that fall within the domain of social psychology (psychology of groups). These topics are: 1) group meaning and types; 2) group growth processes, cohesion, conflict, obedience and group violence, group decision-making; 3) group structure: definition, status, roles, norms, group culture; 4) leadership and 5) group characteristics such as communication and empathy.</p> <p>The participants were students enrolled in the “Social Work with Groups” course at the University of Zaragoza (Spain) during the 2022-2023 academic year. The sample size was 111 students: 56 performed CBL activities with Escape Room activities, and 55 performed CBL activities without Escape Room activities.</p> <p>The variable outcome of this experimental study was academic performance, assessed by the grade obtained in the mark for CBL activities with a rating from 0 to 10, where the higher score indicated a better performance. This mark showed the number of correct concepts that were identified and extracted from the case. This score was translated to a categorical assessment going from <em>fail</em> (between 0 and 4.9), to <em>pass</em> (between 5.0 and 6.9), to <em>merit</em> (between 7.0 and 8.9), to <em>outstanding</em> (between 9.0 and 10).</p> <p><em>Secondary outcomes</em></p> <p>The secondary variables were: 1) quantitative and qualitative exam score (on the psychology of groups´ contents) 2) students´ satisfaction with the activity, and 3) time needed for performing the activities.</p> <p>The academic performance data were collected using the exam score for the subject (psychology of groups´ contents). This exam consisted of 40 multiple-choice questions with three response options, taking the chance factor into account (so marks were deducted for wrong answers). The quantitative rating of each academic score can range between 0 and 10, with a higher score denoting a higher percentage of correct answers. The categorical holistic assessment of achievement goes from <em>fail</em> (between 0 and 4.9), to <em>pass</em> (between 5.0 and 6.9), to <em>merit</em> (between 7.0 and 8.9), to <em>outstanding</em> (between 9.0 and 10).</p> <p>The data on students´ satisfaction with the activity performed were collected using a self-reporting questionnaire made up of seven statements on the course and teaching methodology used (Gómez-Poyato et al. 2020; Oliván-Blázquez et al. 2022; Olivan-Blázquez et al. 2019), which were answered on a Likert scale from 0 to 4, with 0 meaning <em>not at all</em> and 4 meaning <em>to a great extent</em>. The statements to be evaluated were as follows: the teaching methodology used has encouraged new knowledge acquisition; it has favoured deep learning; it has helped me to think more critically; it has helped me to apply theoretical content to practice; it has helped me to apply theoretical content to assessments; it has helped me to understand concepts better; I believe it is an appropriate teaching methodology. A free response section was also included so that students could express themselves openly.</p> <p>The data for the time used to carry out the activities were also collected, measured in minutes used for finishing the activities.</p> <p>Age, gender and university admittance mark data were also obtained in order to to determine if the student groups were in the same conditions regarding these values at the start of the analysis.</p>
Cognitive Fatigue, Self-Regulation, and Academic Performance: A Physiological Study
ClinicalTrials.gov study NCT05012293. IPD Sharing: NO. Countries: 1. Publications: 2.
Effects of Litebook EDGE™ Phototherapy on Academic Performance and Brain Activity
ClinicalTrials.gov study NCT05383690. IPD Sharing: NO. Countries: 1. Publications: 37.
Effect of Self-regulated Learning on Academic Performance Among Physical Education Students
ClinicalTrials.gov study NCT06608524. IPD Sharing: NO. Countries: 1. Publications: 0.
Mental Health Literacy and Academic Performance
ClinicalTrials.gov study NCT06217744. IPD Sharing: NO. Countries: 1. Publications: 1.
Optimizing Cardiometabolic Health, Cognition and Academic Performance in Children (HAPHC)
ClinicalTrials.gov study NCT04956003. IPD Sharing: NO. Countries: 1. Publications: 2.
Effect of Extra-curricular Sports Activities After School on Primary School Children's Academic Performance
ClinicalTrials.gov study NCT04587765. IPD Sharing: NO. Countries: 1. Publications: 10.
ACTIVE SCHOOL - Effects on Academic Performance of Novel Approaches to Increase Physical Activity in School-children
ClinicalTrials.gov study NCT05602948. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effectiveness of a Physical Activity Intervention to Prevent Obesity and Improve Academic Performance
ClinicalTrials.gov study NCT01971827. 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.