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Showing 1 to 15 of 41 results Save | Export
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Kathleen Lynne Lane; Katie Scarlett Lane Pelton; Nathan Allen Lane; Mark Matthew Buckman; Wendy Peia Oakes; Kandace Fleming; Rebecca E. Swinburne Romine; Emily D. Cantwell – Behavioral Disorders, 2025
We report findings of this replication study, examining the internalizing subscale (SRSS-I4) of the revised version of the Student Risk Screening Scale for Internalizing and Externalizing behavior (SRSS-IE 9) and the internalizing subscale of the Teacher Report Form (TRF). Using the sample from 13 elementary schools across three U.S. states with…
Descriptors: Data Analysis, Decision Making, Data Use, Measures (Individuals)
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Luna, J. M.; Fardoun, H. M.; Padillo, F.; Romero, C.; Ventura, S. – Interactive Learning Environments, 2022
The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery (SD) approach based on MapReduce. The proposed SD approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope…
Descriptors: Online Courses, Student Characteristics, Classification, Student Behavior
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Kathleen Lynne Lane; Katie Scarlett Lane Pelton; Nathan Allen Lane; Wendy Peia Oakes; Mark Matthew Buckman; Kandace Fleming; Rebecca E. Swinburne Romine; Emily D. Cantwell – Behavioral Disorders, 2025
We report results from this psychometric study examining convergent validity between internalizing subscale (SRSS-I4) scores from the revised version of the teacher-completed Student Risk Screening Scale for Internalizing and Externalizing behavior (SRSS-IE 9) and the internalizing subscale from the Teacher Report Form (TRF). Using the sample of…
Descriptors: Screening Tests, Cutting Scores, Data Use, Decision Making
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Mubarak, Ahmed Ali; Ahmed, Salah A. M.; Cao, Han – Interactive Learning Environments, 2023
In this study, we propose a MOOC Analytic Statistical Visual model (MOOC-ASV) to explore students' engagement in MOOC courses and predict their performance on the basis of their behaviors logged as big data in MOOC platforms. The model has multifunctions, which performs on visually analyzing learners' data by state-of-the-art techniques. The model…
Descriptors: MOOCs, Learner Engagement, Performance, Student Behavior
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Tenison, Caitlin; Sparks, Jesse R. – Large-scale Assessments in Education, 2023
Background: Digital Information Literacy (DIL) refers to the ability to obtain, understand, evaluate, and use information in digital contexts. To accurately capture various dimensions of DIL, assessment designers have increasingly looked toward complex, interactive simulation-based environments that afford more authentic learner performances.…
Descriptors: Students, Search Strategies, Student Behavior, Simulation
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Boels, Lonneke – Teaching Statistics: An International Journal for Teachers, 2023
Gaze data are still uncommon in statistics education despite their promise. Gaze data provide teachers and researchers with a new window into complex cognitive processes. This article discusses how gaze data can inform and be used by teachers both for their own teaching practice and with students. With our own eye-tracking research as an example,…
Descriptors: Statistics Education, Eye Movements, Data, Cognitive Processes
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Riden, Benjamin S.; Markelz, Andrew M.; Taylor, Jonté C. – Intervention in School and Clinic, 2021
Teachers who work with students who display challenging behaviors need to implement interventions to support them in the classroom. A daily behavior report card (DBRC) is one intervention that research suggests can reduce challenging behaviors and replace them with more socially appropriate behaviors. With step-by-step instructions, this column…
Descriptors: Intervention, Student Behavior, Behavior Problems, Report Cards
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Duncan Culbreth; Rebekah Davis; Cigdem Meral; Florence Martin; Weichao Wang; Sejal Foxx – TechTrends: Linking Research and Practice to Improve Learning, 2025
Monitoring applications (MAs) use digital and online tools to collect and track data on student behavior, and they have become increasingly popular among schools. Empirical research on these complex surveillance platforms is scant, and little is known about the efficacy or impact that they have on students. This study used a multi-method…
Descriptors: High School Students, COVID-19, Pandemics, Progress Monitoring
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Ean Teng Khor; Dave Darshan – International Journal of Information and Learning Technology, 2024
Purpose: This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course. Design/methodology/approach: The exploration and visualisation of the…
Descriptors: Prediction, Academic Achievement, Electronic Learning, Artificial Intelligence
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Jelena Andelkovic Labrovic; Nikola Petrovic; Jelena Andelkovic; Marija Meršnik – Journal of Computing in Higher Education, 2025
The focus of this study was on identifying patterns of student behavior to support data-informed decision-making which would then improve the learning experience and learning outcomes of online English language courses. Learning analytics approach (or more specifically cluster analysis) was used to identify engagement patterns in online learning.…
Descriptors: Electronic Learning, Online Courses, Behavior Patterns, Student Behavior
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Susnjak, Teo; Ramaswami, Gomathy Suganya; Mathrani, Anuradha – International Journal of Educational Technology in Higher Education, 2022
This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our…
Descriptors: Learning Analytics, Computer Interfaces, Artificial Intelligence, Prediction
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HaeJin Lee; Nigel Bosch – International Journal of STEM Education, 2024
Self-regulated learning (SRL) strategies can be domain specific. However, it remains unclear whether this specificity extends to different subtopics within a single subject domain. In this study, we collected data from 210 college students engaged in a computer-based learning environment to examine the heterogeneous manifestations of learning…
Descriptors: Computer Assisted Instruction, Self Management, Intellectual Disciplines, College Students
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Kathleen Lynne Lane; Nathan Allen Lane; Mark Matthew Buckman; Katie Scarlett Lane Pelton; Kandace Fleming; Rebecca E. Swinburne Romine – Behavioral Disorders, 2025
We report the results of a convergent validity study examining the externalizing subscale (SRSS-E5, five items) of the adapted Student Risk Screening Scale for Internalizing and Externalizing (SRSS-IE 9) with the externalizing subscale of the Teacher Report Form (TRF) with two samples of K-12 students. Results of logistic regression and receiver…
Descriptors: Data Analysis, Decision Making, Data Use, Test Validity
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Quin-Anne Hinrichs; Chelsea R. Johnston; Laura Feuerborn; Ashli Tyre – Beyond Behavior, 2025
Implementation of a culturally responsive positive behavioral interventions and supports (PBIS) framework is associated with positive outcomes for secondary students when implemented schoolwide. Yet, educators often report more implementation challenges in secondary school as compared to elementary school settings. Difficulties obtaining student…
Descriptors: Behavior Modification, Positive Behavior Supports, Student Behavior, Behavior Problems
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Baig, Maria Ijaz; Shuib, Liyana; Yadegaridehkordi, Elaheh – International Journal of Educational Technology in Higher Education, 2020
Big data is an essential aspect of innovation which has recently gained major attention from both academics and practitioners. Considering the importance of the education sector, the current tendency is moving towards examining the role of big data in this sector. So far, many studies have been conducted to comprehend the application of big data…
Descriptors: Educational Research, Educational Trends, Learning Analytics, Student Behavior
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