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22 results for “Dropout Prediction”
Predict students' dropout and academic success
<p>A dataset created from a higher education institution (acquired from several disjoint databases) related to students enrolled in different undergraduate degrees, such as agronomy, design, education, nursing, journalism, management, social service, and technologies.</p> <p>The dataset includes information known at the time of student enrollment (academic path, demographics, and social-economic factors) and the students' academic performance at the end of the first and second semesters.</p> <p>The data is used to build classification models to predict students' dropout and academic success. The problem is formulated as a three category classification task (dropout, enrolled, and graduate) at the end of the normal duration of the course.<br> </p> <p><strong>Funding</strong><br> We acknowledge support of this work by the program "SATDAP - Capacitação da Administração Pública under grant POCI-05-5762-FSE-000191, Portugal"</p> <p> </p>
Data Set: In-session dropout prediction model
<p>In-session dropout prediction model</p> <p>This project describes an in-session prediction model that predicts student early dropout from online learning exercises.<br> Dropout prediction models for Massive Open Online Courses (MOOCs) have shown high accuracy rates in<br> the past and make personalized interventions possible. While MOOCs have traditionally high dropout rates,<br> school homework and assignments are supposed to be completed by all learners. In the pandemic, online<br> learning platforms were used to support school teaching. In this setting, dropout predictions have to be designed differently as a simple dropout from the (mandatory) class is not possible. The aim of our work is to<br> transfer traditional temporal dropout prediction models to in-session dropout prediction for school-supporting<br> learning platforms. For this purpose, we used data from more than 164,000 sessions by 52,000 users of the<br> online language learning platform orthografietrainer.net. We calculated time-progressive machine learning<br> models that predict dropout after each step (completed sentence) in the assignment using learning process<br> data. The multilayer perceptron is outperforming the baseline algorithms with up to 87% accuracy. By extending the binary prediction with dropout probabilities, we were able to design a personalized intervention<br> strategy that distinguishes between motivational and subject-specific interventions. <br> A random state is not set, thus, results might differ marginally.</p> <p>Whole project described in: <br> N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart<br> Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios<br> Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,<br> SciTePress, 2022, ISBN 978-989-758-562-3 </p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
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.