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Stephanie Wermelinger; Marco Bleiker; Moritz M. Daum – Infant and Child Development, 2025
Children's fuzziness leads to increased variance in the data, data loss, and high dropout rates in developmental studies. This study investigated the importance of 20 factors on the person (child, caregiver, experimenter) and situation (task, method, time, and date) level for the data quality as indicated via the number of valid trials in 11…
Descriptors: Infants, Young Children, Research Problems, Factor Analysis
Michael L. Chrzan; Francis A. Pearman; Benjamin W. Domingue – Annenberg Institute for School Reform at Brown University, 2025
The increasing rate of permanent school closures in U.S. public school districts presents unprecedented challenges for administrators and communities alike. This study develops an early-warning indicator model to predict mass closure events -- defined as a district closing at least 10% of its schools -- five years in advance. Leveraging…
Descriptors: Artificial Intelligence, Electronic Learning, School Districts, School Closing
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Harun Cigdem; Semiral Oncu – TechTrends: Linking Research and Practice to Improve Learning, 2025
Despite efforts to implement innovative approaches such as flipped learning leveraging computer technology, the challenge of student failure persists. Understanding the factors that contribute to student success in flipped engineering courses remains a critical issue. This study addresses this issue by investigating the impact of student…
Descriptors: Gamification, Flipped Classroom, Learner Engagement, Learning Readiness
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Rohemi Zuluaga; Alicia Camelo-Guarín; Enrique De La Hoz – Journal on Efficiency and Responsibility in Education and Science, 2023
This research aims to design a helpful methodology for estimating universities' relative impact on students as a sustainability factor in higher education. To this end, the research methodology implemented a two-stage approach. The first stage involves the relative efficiency analysis of the study units using Fuzzy Data Envelopment Analysis. The…
Descriptors: Foreign Countries, Higher Education, Educational Practices, Efficiency
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Xia, Xiaona – Interactive Learning Environments, 2023
Interactive learning environments can generate massive learning behavior data and the support of learning behavior big data can ensure the completeness of data analysis and robustness of relationship verification. In this study, learning behaviors are divided into training set and testing set, BP neural network and recurrent Elman network are…
Descriptors: Interaction, Intervention, Student Behavior, Educational Environment
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Chenguang Pan; Zhou Zhang – International Educational Data Mining Society, 2024
There is less attention on examining algorithmic fairness in secondary education dropout predictions. Also, the inclusion of protected attributes in machine learning models remains a subject of debate. This study delves into the use of machine learning models for predicting high school dropouts, focusing on the role of protected attributes like…
Descriptors: High School Students, Dropouts, Dropout Characteristics, Artificial Intelligence
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Qixuan Wu; Hyung Jae Chang; Long Ma – Journal of Advanced Academics, 2025
It is very important to identify talented students as soon as they are admitted to college so that appropriate resources are provided and allocated to them to optimize and excel in their education. Currently, this process is labor-intensive and time-consuming, as it involves manual reviews of each student's academic record. This raises the…
Descriptors: Electronic Learning, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
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Liang Zhang; Jionghao Lin; John Sabatini; Conrad Borchers; Daniel Weitekamp; Meng Cao; John Hollander; Xiangen Hu; Arthur C. Graesser – IEEE Transactions on Learning Technologies, 2025
Learning performance data, such as correct or incorrect answers and problem-solving attempts in intelligent tutoring systems (ITSs), facilitate the assessment of knowledge mastery and the delivery of effective instructions. However, these data tend to be highly sparse (80%90% missing observations) in most real-world applications. This data…
Descriptors: Artificial Intelligence, Academic Achievement, Data, Evaluation Methods
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Oscar A. Aliaga; Pradeep Kotamraju – International Journal of Adult Education and Technology, 2024
We examined the impact of secondary Career and Technical Education (CTE) credit-taking on students attaining full-time employment following graduation from high school in the United States, and whether such credit-taking impacted students entering employment in a related field. We focused on occupational programs, and we used four levels of CTE…
Descriptors: Vocational Education, Credits, Employment, Secondary Education
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Phillips, Tanner M.; Saleh, Asmalina; Ozogul, Gamze – International Journal of Artificial Intelligence in Education, 2023
Encouraging teachers to reflect on their instructional practices and course design has been shown to be an effective means of improving instruction and student learning. However, the process of encouraging reflection is difficult; reflection requires quality data, thoughtful analysis, and contextualized interpretation. Because of this, research on…
Descriptors: Reflection, Artificial Intelligence, Natural Language Processing, Data Collection
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Munise Seçkin Kapucu; I?brahim Özcan; Hülya Özcan; Ahmet Aypay – International Journal of Technology in Education and Science, 2024
Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th,…
Descriptors: Secondary School Students, Science Achievement, Artificial Intelligence, Foreign Countries
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Amy Overbay; Christopher W. Thurley – Writing Center Journal, 2024
As institutions cope with the difficult task of managing scarce resources to support student learning, college writing centers, like other student services, need to be able to articulate and, at times, quantify the benefits they offer the populations they serve. This study examined outcomes associated with visiting the writing center at one…
Descriptors: Community Colleges, Laboratories, Writing (Composition), Academic Achievement
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Aom Perkash; Qaisar Shaheen; Robina Saleem; Furqan Rustam; Monica Gracia Villar; Eduardo Silva Alvarado; Isabel de la Torre Diez; Imran Ashraf – Education and Information Technologies, 2024
Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching.…
Descriptors: Academic Achievement, Colleges, Artificial Intelligence, Predictor Variables
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Szymanska, Agnieszka; Aranowska, Elzbieta – Early Child Development and Care, 2021
The aim of the research was to determine what relations occur between the stress experienced by parents in the relationship with their children and the personality traits that they shape in them. Personal characteristics that parents shape in their children have been classified into metatraits described in the Circumplex of Personality model. The…
Descriptors: Parents, Stress Variables, Parent Child Relationship, Personality Traits
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Nirmal Ghimire; Kouider Mokhtari – AERA Online Paper Repository, 2024
This study examined the predictive power of students' demographic characteristics, reading attitudes, school characteristics, and teacher-informed reading activities on three metacognitive reading skills: understanding and remembering, summarizing, and assessing credibility and their influence on 15-year-old students' reading scores. The dataset…
Descriptors: Foreign Countries, Achievement Tests, International Assessment, Secondary School Students
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