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Download full textQiwei He; Qingzhou Shi; Elizabeth L. Tighe – Grantee Submission, 2023
Increased use of computer-based assessments has facilitated data collection processes that capture both response product data (i.e., correct and incorrect) and response process data (e.g., time-stamped action sequences). Evidence suggests a strong relationship between respondents' correct/incorrect responses and their problem-solving proficiency…
Descriptors: Artificial Intelligence, Problem Solving, Classification, Data Use
Direct linkFrydenlund, Jonas Højgaard – Scandinavian Journal of Educational Research, 2023
In this ethnographic study, I present a single school's practice of registering and analysing absence from school. I show that teachers use various "dirty," interpretational contexts for understanding absence and make it classifiable in "clean" attendance categories -- a move that decontextualises the meaning of absence. When…
Descriptors: Ethnography, Attendance, Truancy, Classification
Tappel, A. P. M.; Poortman, C. L.; Schildkamp, K.; Visscher, A. J. – Journal of Educational Change, 2023
Many innovations that are implemented in schools are initially successful, but fail to become part of the schools' habits and routines. Relatively little research has followed innovations in schools for a long(er) time. In addition, few reforms last long enough to be studied longitudinally. In this exploratory study, the authors aim to find a way…
Descriptors: Intervention, Sustainability, Educational Innovation, Data Use
Yannik Fleischer; Susanne Podworny; Rolf Biehler – Statistics Education Research Journal, 2024
This study investigates how 11- to 12-year-old students construct data-based decision trees using data cards for classification purposes. We examine the students' heuristics and reasoning during this process. The research is based on an eight-week teaching unit during which students labeled data, built decision trees, and assessed them using test…
Descriptors: Decision Making, Data Use, Cognitive Processes, Artificial Intelligence
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)
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
Nachouki, Mirna; Naaj, Mahmoud Abou – International Journal of Distance Education Technologies, 2022
The COVID-19 pandemic constrained higher education institutions to switch to online teaching, which led to major changes in students' learning behavior, affecting their overall performance. Thus, students' academic performance needs to be meticulously monitored to help institutions identify students at risk of academic failure, preventing them…
Descriptors: Academic Achievement, Academic Advising, College Students, Classification
Chelsea M. Parlett-Pelleriti; Elizabeth Stevens; Dennis Dixon; Erik J. Linstead – Review Journal of Autism and Developmental Disorders, 2023
Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data--both genetic and behavioral--that are collected as part of scientific studies or a part of treatment can provide a deeper,…
Descriptors: Artificial Intelligence, Autism Spectrum Disorders, Classification, Supervision
Leher Singh; Mihaela D. Barokova; Heidi A. Baumgartner; Diana C. Lopera-Perez; Paul Okyere Omane; Mark Sheskin; Francis L. Yuen; Yang Wu; Katherine J. Alcock; Elena C. Altmann; Marina Bazhydai; Alexandra Carstensen; Kin Chung Jacky Chan; Hu Chuan-Peng; Rodrigo Dal Ben; Laura Franchin; Jessica E. Kosie; Casey Lew-Williams; Asana Okocha; Tilman Reinelt; Tobias Schuwerk; Melanie Soderstrom; Angeline S. M. Tsui; Michael C. Frank – Developmental Psychology, 2024
Culture is a key determinant of children's development both in its own right and as a measure of generalizability of developmental phenomena. Studying the role of culture in development requires information about participants' demographic backgrounds. However, both reporting and treatment of demographic data are limited and inconsistent in child…
Descriptors: Data Collection, Young Children, Demography, Cultural Traits
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
Cukurova, Mutlu; Kent, Carmel; Luckin, Rosemary – British Journal of Educational Technology, 2019
The question: "What is an appropriate role for AI?" is the subject of much discussion and interest. Arguments about whether AI should be a "human replacing" technology or a "human assisting" technology frequently take centre stage. Education is no exception when it comes to questions about the role that AI should…
Descriptors: Artificial Intelligence, Data Use, Decision Making, Debate
Zhao, Qun; Wang, Jin-Long; Pao, Tsang-Long; Wang, Li-Yu – Journal of Educational Technology Systems, 2020
This study uses the log data from Moodle learning management system for predicting student learning performance in the first third of a semester. Since the quality of the data has great influence on the accuracy of machine learning, five major data transmission methods are used to enhance data quality of log file in the data preprocessing stage.…
Descriptors: Classification, Learning, Accuracy, Prediction
Shero, Jeffrey A.; Al Otaiba, Stephanie; Schatschneider, Chris; Hart, Sara A. – Journal of Experimental Education, 2022
Many of the analytical models commonly used in educational research often aim to maximize explained variance and identify variable importance within models. These models are useful for understanding general ideas and trends, but give limited insight into the individuals within said models. Data envelopment analysis (DEA), is a method rooted in…
Descriptors: Data Analysis, Educational Research, Nonparametric Statistics, Efficiency
Andriyani, Anak Agung Ayu Dian; Ardiantari, Ida Ayu Putri Gita; Santika, I. Dewa Ayu Devi Maharani; Nurita, Wayan – Eurasian Journal of Applied Linguistics, 2022
The purpose of this study is to describe the politeness approach of the Japanese-Balinese family realm language. The study explores how the use of language politeness strategies is affected by the cultural background of both spouses in intercultural marriages. The study also examines the various factors that influence language politeness…
Descriptors: Foreign Countries, Marriage, Family Relationship, Language Usage
Huang, Anna Y. Q.; Lu, Owen H. T.; Huang, Jeff C. H.; Yin, C. J.; Yang, Stephen J. H. – Interactive Learning Environments, 2020
In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model,…
Descriptors: Academic Achievement, Data Use, Learning Analytics, Classification
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