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48
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ShareScore release 0.9.0
Dataset results
48 results for “mobile learning”
HIP Mobile: A Community-based Monitoring, Rehabilitation and Learning e-System for Patients Following a Fracture
ClinicalTrials.gov study NCT03153943. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Developing a Mobile-based Interactive Simulation Scheme for Assisting the Personalized Hands-on Learning of Nasogastric Tube Insertion
ClinicalTrials.gov study NCT05742659. IPD Sharing: NO. Countries: 1. Publications: 0.
Collaborative Power Mobility Innovative Learning OpporTunity (CoPILOT) - A Pilot Study of a New Training Approach (Phase 2)
ClinicalTrials.gov study NCT02982252. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Pilot Study of the Learn, Connect, and Quit Mobile Application
ClinicalTrials.gov study NCT03978455. IPD Sharing: NO. Countries: 1. Publications: 0.
Clinical Learning Study for a Mobile Smoking Cessation Program
ClinicalTrials.gov study NCT04857515. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Mobile Chatbot with ARCS-V Motivation Theory on Learning Motivation
ClinicalTrials.gov study NCT06829628. IPD Sharing: NO. Countries: 0. Publications: 0.
Dataset used in Design Analytics for Mobile Learning: Scaling up theClassification of Learning Designs based onCognitive and Contextual Elements
<p>The following dataset has been used for the paper entitled "Design Analytics for Mobile Learning: Scaling up theClassification of Learning Designs based onCognitive and Contextual Elements".</p> <p>Abstract</p> <p>This research was triggered by the identified need in literature for large-scale studies about the kind of designs that teachers create for Mobile Learning (m-learning). These studies require analyses of large datasets of learning designs. The common approach followed by researchers when analysing designs has been to manually classify them following high-level pedagogically-guided coding strategies, which demands extensive work. Therefore, the first goal of this paper is to explore the use of Supervised Machine Learning (SML) to automatically classify the textual content of m-learning designs, through pedagogically-relevant classifications, such as the cognitive level demanded by students to carry out specific designed tasks, the phases of inquiry learning represented in the designs, or the role that the situated environment has in them. As not all the SML models are transparent, while often researchers need to understand the behaviour behind them, the second goal of this paper considers the trade-off between models’ performance and interpretability in the context of design analytics for m-learning. To achieve these goals we compiled a dataset of designs deployed through two tools, Avastusrada and Smartzoos. With it, we trained and compared different models and feature extraction techniques. We further optimized andcompared the best-performing and most interpretable algorithms (EstBERT and Logistic Regression) to consider the second goal through an illustrative case. We found that SML can reliably classify designs, with accuracy>0.86and Cohen’s kappa>0.69.</p>
Deep Learning for Real Time 3D Multi-Object Detection, Localization, and Tracking : Application to Smart Mobility
<p>In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account real-time constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detector-based on YOLOV3 [1] which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 [2,3] for monocular vision and MADNEt [4] for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in real-time condition. Object tracking is necessary in order to mitigate the risks of collisions. Unlike, traditional tracking approaches which requires target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT [5] approach for 3D object tracking. We introduce an extended Kalman filter [6] to better estimate the position of objects. Extensive experiments carried out on KITTI dataset [7] prove that our proposal outperforms state-of-the-art approches. </p>
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
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.