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Download full textCollier, Zachary K.; Zhang, Haobai; Liu, Liu – Practical Assessment, Research & Evaluation, 2022
Although educational research and evaluation generally occur in multilevel settings, many analyses ignore cluster effects. Neglecting the nature of data from educational settings, especially in non-randomized experiments, can result in biased estimates with long-term consequences. Our manuscript improves the availability and understanding of…
Descriptors: Artificial Intelligence, Probability, Scores, Educational Research
Direct linkMusci, Rashelle J.; Kush, Joseph M.; Masyn, Katherine E.; Esmaeili, Masoumeh Amin; Susukida, Ryoko; Goulter, Natalie; McMahon, Robert; Eddy, J. Mark; Ialongo, Nicholas S.; Tolan, Patrick; Godwin, Jennifer; Bierman, Karen L.; Bierman, Karen L.; Coie, John D.; Crowley, D. Max; Dodge, Kenneth A.; Greenberg, Mark T.; Lochman, John E.; McMahon, Robert J.; Pinderhughes, Ellen E.; Wilcox, Holly C. – Prevention Science, 2023
Psychotic-like experiences (PLEs) are common throughout childhood, and the presence of these experiences is a significant risk factor for poor mental health later in development. Given the association of PLEs with a broad number of mental health diagnoses, these experiences serve as an important malleable target for early preventive interventions.…
Descriptors: Psychosis, Symptoms (Individual Disorders), Children, Adolescents
Cai, Zhiqiang; Siebert-Evenstone, Amanda; Eagan, Brendan; Shaffer, David Williamson – Grantee Submission, 2021
When text datasets are very large, manually coding line by line becomes impractical. As a result, researchers sometimes try to use machine learning algorithms to automatically code text data. One of the most popular algorithms is topic modeling. For a given text dataset, a topic model provides probability distributions of words for a set of…
Descriptors: Coding, Artificial Intelligence, Models, Probability
Antonia Petropoulou; Konstantinos Lavidas; Stamatis Papadakis – Educational Process: International Journal, 2024
Background/purpose: Awareness of the mathematical skills and knowledge children possess in their early years is widely accepted. This includes various common positive aspects, not only for educators but also for researchers and policymakers. This study presents a systematic review conducted to meticulously identify empirical studies published in…
Descriptors: Preschool Children, Mathematics Skills, Young Children, Mathematical Concepts
Lijin Zhang; Xueyang Li; Zhiyong Zhang – Grantee Submission, 2023
The thriving developer community has a significant impact on the widespread use of R software. To better understand this community, we conducted a study analyzing all R packages available on CRAN. We identified the most popular topics of R packages by text mining the package descriptions. Additionally, using network centrality measures, we…
Descriptors: Computer Software, Programming Languages, Data Analysis, Visual Aids
David Kaplan; Kjorte Harra – OECD Publishing, 2023
This report aims to showcase the value of implementing a Bayesian framework to analyse and report results from international large-scale surveys and provide guidance to users who want to analyse the data using this approach. The motivation for this report stems from the recognition that Bayesian statistical inference is fast becoming a popular…
Descriptors: Bayesian Statistics, Statistical Inference, Data Analysis, Educational Research
Soltys, Michael; Dang, Hung D.; Reyes Reilly, Ginger; Soltys, Katharine – Strategic Enrollment Management Quarterly, 2021
A Machine Learning framework for predicting enrollment is proposed. The framework consists of Amazon Web Services SageMaker together with standard Python tools for data analytics, including Pandas, NumPy, MatPlotLib, and ScikitLearn. The tools are deployed with Jupyter Notebooks running on AWS SageMaker. Based on three years of enrollment history,…
Descriptors: Enrollment Management, Strategic Planning, Prediction, Computer Software
Najib A. Mozahem – Sage Research Methods Cases, 2021
The internet has had a vast and pervasive effect on many industries. It has resulted in the creation of new industries and has overhauled the dynamics that governed existing industries. One of the most traditional industries that is now struggling to cope with the changes brought on by the internet is the industry of higher education. Students can…
Descriptors: Social Sciences, Electronic Learning, Learning Management Systems, Higher Education
Shen, Huajie; Liu, Teng; Zhang, Yueqin – International Journal of Distance Education Technologies, 2020
This study aims to create learning path navigation for target learners by discovering the correlation among micro-learning units. In this study, the learning path is defined as a sequence of learning units used to realize a learning goal, and a period used for realizing the learning goal is regarded as a learning cycle. Furthermore, the learning…
Descriptors: Correlation, Distance Education, Efficiency, Bayesian Statistics
Savi, Alexander O.; Deonovic, Benjamin E.; Bolsinova, Maria; van der Maas, Han L. J.; Maris, Gunter K. J. – Journal of Educational Data Mining, 2021
In learning, errors are ubiquitous and inevitable. As these errors may signal otherwise latent cognitive processes, tutors--and students alike--can greatly benefit from the information they provide. In this paper, we introduce and evaluate the Systematic Error Tracing (SET) model that identifies the possible causes of systematically observed…
Descriptors: Learning Processes, Cognitive Processes, Error Patterns, Models
Zieffler, Andrew; Justice, Nicola; delMas, Robert; Huberty, Michael D. – Journal of Statistics and Data Science Education, 2021
Statistical modeling continues to gain prominence in the secondary curriculum, and recent recommendations to emphasize data science and computational thinking may soon position algorithmic models into the school curriculum. Many teachers' preparation for and experiences teaching statistical modeling have focused on probabilistic models.…
Descriptors: Mathematical Models, Thinking Skills, Teaching Methods, Statistics Education
Xing, Wanli; Lee, Hee-Sun; Shibani, Antonette – Educational Technology Research and Development, 2020
Constructing scientific arguments is an important practice for students because it helps them to make sense of data using scientific knowledge and within the conceptual and experimental boundaries of an investigation. In this study, we used a text mining method called Latent Dirichlet Allocation (LDA) to identify underlying patterns in students…
Descriptors: Persuasive Discourse, Science Instruction, Scientific Concepts, Logical Thinking
Sharma, Sashi; Sharma, Shweta; Doyle, Phil; Marcelo, Louis; Kumar, Daniel – Teachers and Curriculum, 2021
Learning about probability can pose difficulties for students at all levels. Performing probability experiments using games can encourage students to develop understandings of probability grounded in real events. In this reflective paper, we explore the thinking of a group of students and teachers as they reasoned about experimental and…
Descriptors: Statistics Education, Probability, Educational Games, Learning Activities
McMaster, Kirby; Rague, Brian; Wolthuis, Stuart L.; Sambasivam, Samuel – Information Systems Education Journal, 2018
This research study provides an examination of the relatively new fields of Data Analytics and Data Science. We compare word rates in Data Analytics and Data Science documents to determine which concepts are mentioned most often. The most frequent concept in both fields is "data." The word rate for "data" is more than twice the…
Descriptors: Data Analysis, Data Collection, Comparative Analysis, Statistics
Gushchina, Oksana; Ochepovsky, Andrew – Turkish Online Journal of Distance Education, 2019
The article shows the role of data mining methods at the stages of the e-learning risk management for the various participants. The article proves the e-learning system fundamentally contains heterogeneous information, for its processing it is not enough to use the methods of mathematical analysis but it is necessary to apply the new educational…
Descriptors: Data Analysis, Information Retrieval, Electronic Learning, Risk Management
