Education and Learning Technologies

This paper presents the data that is used in the article entitled “Decoding contextual factors differentiating adolescents’ high, average and low digital reading performance through machine learning methods”, which investigated the key contextual factors that synergistically differentiate high and low performers, high and average performers, and low and average performers in digital reading, through the utilization of machine learning methods, namely, support vector machine (SVM) and SVM recursive feature elimination (SVM-RFE).
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# Student Test Results Prediction based on Learning Behavior: Learning Beyond Tests
Dataset Part A: The Goal is to predict Test Results, in the form of averaged correctness, averaged timespent in the test, based only on the learning history (learning behavior records)
Dataset Part B: The objective is to predict the last test results, points and scores, based on the learning behavior records and the first test results.
# About the dataset
The raw data is provided by ALIN.ai where a large number of students participated in math learning and tests, online.
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This article was results based on the interview phase in the English language
course of even semester [the academic year 2021-2022] Institut Agama Kristen
Negeri Ambon. The majority concern is how the students of English courses respond
during even semester conducted. Moreover, only a few students are encouraged to
finish their course with moderate achievement, and half of the students are stated on
lower achievement following unconscious narration to be absent during English
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This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.
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We developed an online serious game to educate adolescents regarding human impact on freshwater ecosystems. Here, we provide the Unity codes for building the game. We also provide a data set containing survey responses of middle school students who participated in a study that assesses the effectiveness of the game, along with MatLab and R codes that analyze these data.
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The data part is the beneficial supplementary part of the article of Complex Theory and Batch Processing in Mechanical Systemic Data Extraction. It is including 2 parts. One is the about the original designed period. Another is the experimental data from 9 virtual experiments. It serves for the higher efficiency of ABRF.
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PopMedNet™ is an open-source application used to facilitate multi-site health data networks. It uses a distributed network design that enables data holders to retain full control of their data. Investigators send questions to data holders for review and response. PopMedNet eliminates the need for assembling patient records in a centralized repository, thus preserving patient privacy and confidentiality.
This Dataset contains sample data using the PCORnet Common Data Model for running the regression tests supplied with PopMedNet™.
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