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Stephen Downes – International Association for Development of the Information Society, 2023
Data literacy is the ability to collect, manage, evaluate, and apply data, in a critical manner. It is a relatively new field of study, dating only from the 2010s. It includes the skills necessary to discover and access data, manipulate data, evaluate data quality, conduct analysis using data, interpret results of analyses, and understand the…
Descriptors: Statistics Education, Data Analysis, Ethics, Data Use
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Kai Li – International Association for Development of the Information Society, 2023
Assessing students' performance in online learning could be executed not only by the traditional forms of summative assessments such as using essays, assignments, and a final exam, etc. but also by more formative assessment approaches such as interaction activities, forum posts, etc. However, it is difficult for teachers to monitor and assess…
Descriptors: Student Evaluation, Online Courses, Electronic Learning, Computer Literacy
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Jiang, Weijie; Pardos, Zachary A. – International Educational Data Mining Society, 2020
Data mining of course enrollment and course description records has soared as institutions of higher education begin tapping into the value of these data for academic and internal research purposes. This has led to a more than doubling of papers on course prediction tasks every year. The papers often center around a single prediction task and…
Descriptors: Course Descriptions, Models, Prediction, Course Selection (Students)
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Hunt-Isaak, Noah; Cherniavsky, Peter; Snyder, Mark; Rangwala, Huzefa – International Educational Data Mining Society, 2020
National failure rates seen in undergraduate introductory CS courses are quite high. In this paper, we develop a predictive model for student in-class performance in an introductory CS course. The model can serve as an early warning system, flagging struggling students who might benefit from additional support. We use a variety of features from…
Descriptors: Textbooks, Surveys, Grade Prediction, Undergraduate Students
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Calvera-Isabal, Miriam; Varas, Nuria; Santos, Patricia – International Association for Development of the Information Society, 2021
This paper describes a preliminary study of how computational methods allow us to know more about citizen science and its connection with education. Citizen science is a practice involving a general public in scientific tasks and generating knowledge and scientific results. Previous studies have shown that the education sector can take benefit of…
Descriptors: Citizen Participation, Scientific Research, Science and Society, Science Education
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Aulck, Lovenoor; Nambi, Dev; West, Jevin – International Educational Data Mining Society, 2020
Effectively estimating student enrollment and recruiting students is critical to the success of any university. However, despite having an abundance of data and researchers at the forefront of data science, traditional universities are not fully leveraging machine learning and data mining approaches to improve their enrollment management…
Descriptors: Resource Allocation, Scholarships, Artificial Intelligence, Data Analysis
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Gril, Albane; May, Madeth; Renault, Valérie; George, Sébastien – International Association for Development of the Information Society, 2021
In Technology Enhanced Learning field, learning analytics cover multiple research challenges, among which tracking data analysis and data indicator design and visualization. Part of our research effort is dedicated to changing their design process, in order to capitalize them. This would allow us to meet a need in cost savings of design workflow…
Descriptors: Comparative Analysis, Data Analysis, Cost Effectiveness, Data Use
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Zhao, Yijun; Xu, Qiangwen; Chen, Ming; Weiss, Gary M. – International Educational Data Mining Society, 2020
Predicting student success in a data science degree program is a challenging task due to the interdisciplinary nature of the field, the diverse backgrounds of the students, and an incomplete understanding of the precise skills that are most critical to success. In this study, the applicant's future academic performance in a Master of Data Science…
Descriptors: Grade Prediction, Data Analysis, Masters Programs, Admission Criteria
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Juškaite, Loreta – International Baltic Symposium on Science and Technology Education, 2019
The new research results on the online- testing method in the Latvian education system for a learning process assessment are presented. Data mining is a very important field in education because it helps to analyse the data gathered in various researches and to implement the changes in the education system according to the learning methods of…
Descriptors: Foreign Countries, Information Retrieval, Data Analysis, Data Use
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Fonseca, Gabriela; Barros, Fernando, Jr.; Santos, Daniel; Scorzafave, Luiz Guilherme – AERA Online Paper Repository, 2020
This work investigates how data availability can lead to different conclusions about the so-called fading-out effect of early childhood education (ECE). We explore a extensive dataset from 2008 and 2012 on elementary school children from a small municipality in Brazil. This data allows us to evaluate the effect of ECE attendance on language…
Descriptors: Early Childhood Education, Outcomes of Education, Sustainability, Achievement Gains
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Gulikers, Judith – AERA Online Paper Repository, 2016
This paper studies, using quantitative and qualitative data, the effectivity of a professional development approach in which a national summative assessment is used formatively to stimulate 19 teacher teams in Dutch secondary education to improve their data use for instruction and learning. The approach is based on conceptual similarities between…
Descriptors: Evidence Based Practice, Decision Making, Faculty Development, Summative Evaluation