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Jiang, Shiyan; Tang, Hengtao; Tatar, Cansu; Rosé, Carolyn P.; Chao, Jie – Learning, Media and Technology, 2023
It's critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through…
Descriptors: Artificial Intelligence, High School Students, Models, Classification
Cömert, Zeynep; Samur, Yavuz – Interactive Learning Environments, 2023
Almost in every aspect of life, classification and categorization make it easier for humans to analyze complex structures and systems. In games, the classification of the players based on their demographics, behaviors, expectations and preferences of the game is important to increase players' motivation and satisfaction. Likewise, knowing the…
Descriptors: Classification, Student Characteristics, Models, Student Motivation
Xieling Chen; Haoran Xie; Di Zou; Lingling Xu; Fu Lee Wang – Educational Technology & Society, 2025
In massive open online course (MOOC) environments, computer-based analysis of course reviews enables instructors and course designers to develop intervention strategies and improve instruction to support learners' learning. This study aimed to automatically and effectively identify learners' concerned topics within their written reviews. First, we…
Descriptors: Classification, MOOCs, Teaching Skills, Artificial Intelligence
Purificación Alcaide-Pulido; Belén Gutiérrez-Villar; Mariano Carbonero-Ruz; Helena Alves – Journal of Marketing for Higher Education, 2024
Currently, the higher education sector can be considered a marketplace, within which university education is considered a marketable service in the literature on higher education management. The analysis of the variables that generate university image has been the subject of numerous studies on higher education institutions (HEIs). The purpose of…
Descriptors: Undergraduate Students, Student Attitudes, Reputation, Institutional Characteristics
Kim, Eunsook; von der Embse, Nathaniel – Educational and Psychological Measurement, 2021
Although collecting data from multiple informants is highly recommended, methods to model the congruence and incongruence between informants are limited. Bauer and colleagues suggested the trifactor model that decomposes the variances into common factor, informant perspective factors, and item-specific factors. This study extends their work to the…
Descriptors: Probability, Models, Statistical Analysis, Congruence (Psychology)
Kim, Soyeon; Kim, Hankyul; Park, Eun Hye; Kim, Boram; Lee, Sang Min; Kim, Boyoung – Psychology in the Schools, 2021
Fourteen empirical studies on academic burnout were synthesized and reviewed with a meta-analytic approach based on the framework of job demand, control, support model. It was found that demand, control, and support were associated with academic burnout. The three dimensions of burnout were negatively related to demand and positively related to…
Descriptors: Burnout, Meta Analysis, Student Attitudes, Academic Achievement
Patel, Leigh – International Journal of Qualitative Studies in Education (QSE), 2022
In this theoretical paper, I examine the role and potential alterations to uses of social categories in qualitative research. Categories are socially constructed, imbued with power, and include race, class, gender, sexuality, and ability. These categories, although constructs and subject to change, hold durability and are leveraged in much of…
Descriptors: Social Differences, Classification, Longitudinal Studies, Ethnography
Geller, Shay A.; Gal, Kobi; Segal, Avi; Sripathi, Kamali; Kim, Hyunsoo G.; Facciotti, Marc T.; Igo, Michele; Hoernle, Nicholas; Karger, David – IEEE Transactions on Learning Technologies, 2021
This article provides computational and rule-based approaches for detecting confusion that is expressed in students' comments in couse forums. To obtain reliable, ground truth data about which posts exhibit student confusion, we designed a decision tree that facilitates the manual labeling of forum posts by experts. However, manual labeling is…
Descriptors: Identification, Misconceptions, Student Attitudes, Computer Mediated Communication
Moeller, Julia; Viljaranta, Jaana; Kracke, Bärbel; Dietrich, Julia – Frontline Learning Research, 2020
This article proposes a study design developed to disentangle the objective characteristics of a learning situation from individuals' subjective perceptions of that situation. The term objective characteristics refers to the agreement across students, whereas subjective perceptions refers to inter-individual heterogeneity. We describe a novel…
Descriptors: Student Attitudes, College Students, Lecture Method, Student Interests
Pittalis, Marios; Pitta-Pantazi, Demetra; Christou, Constantinos – Journal for Research in Mathematics Education, 2020
A theoretical model describing young students' (Grades 1-3) functional-thinking modes was formulated and validated empirically (n = 345), hypothesizing that young students' functional-thinking modes consist of recursive patterning, covariational thinking, correspondence-particular, and correspondence-general factors. Data analysis suggested that…
Descriptors: Elementary School Students, Thinking Skills, Task Analysis, Profiles
El Aissaoui, Ouafae; El Alami El Madani, Yasser; Oughdir, Lahcen; El Allioui, Youssouf – Education and Information Technologies, 2019
Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes and behaviors through surveys and questionnaires. This approach presents several weaknesses including the lack of self-awareness of…
Descriptors: Classification, Cognitive Style, Models, Electronic Learning
Ramazanoglu, Mehmet – European Journal of Educational Sciences, 2021
This paper focuses on revealing and modeling the cognitive constructs of pre-service teachers regarding the characteristics of a good IT academician. The research was carried out via the exploratory sequential design with the participation of 42 volunteer pre-service teachers enrolled in the Department of Computer and Instructional Technology. The…
Descriptors: Preservice Teachers, Student Attitudes, Information Technology, Cognitive Structures
Yayla, Ridvan; Yayla, Halime Nur; Ortaç, Gizem; Bilgin, Turgay Tugay – Open Praxis, 2021
Distance education is an education model in which the lessons can be taught simultaneously using technical material without time and space restrictions. It has gained importance after the COVID-19 pandemic processes and has been implemented as a valid educational model in all educational institutions. Due to the sudden pandemic measures, distance…
Descriptors: Classification, Distance Education, Pandemics, COVID-19
Blobstein, Ariel; Gal, Kobi; Kim, Hyunsoo Gloria; Facciotti, Marc; Karger, David; Sripathi, Kamali – International Educational Data Mining Society, 2022
Emoji are commonly used in social media to convey attitudes and emotions. While popular, their use in educational contexts has been sparsely studied. This paper reports on the students' use of emoji in an online course forum in which students annotate and discuss course material in the margins of the online textbook. For this study, instructors…
Descriptors: Computer Mediated Communication, Nonverbal Communication, Social Media, Online Courses
Sarwanto; Fajari, Laksmi Evasufi Widi; Chumdari – International Journal of Instruction, 2021
Critical thinking skills are the 21st-century life skills that are needed by students. However, in elementary schools, there are no instruments that are truly effective and efficient to measure critical thinking skills. This research aims to develop an open-ended question assessment instrument to measure students' critical-thinking skills, to test…
Descriptors: Critical Thinking, Thinking Skills, Teaching Methods, Questioning Techniques