Publication Date
In 2025 | 1 |
Since 2024 | 2 |
Since 2021 (last 5 years) | 6 |
Since 2016 (last 10 years) | 8 |
Since 2006 (last 20 years) | 8 |
Descriptor
Bayesian Statistics | 8 |
Learning Processes | 8 |
Undergraduate Students | 8 |
Teaching Methods | 4 |
Comparative Analysis | 3 |
Computer Science Education | 3 |
Foreign Countries | 3 |
Data Analysis | 2 |
Instructional Effectiveness | 2 |
Problem Solving | 2 |
Programming | 2 |
More ▼ |
Source
Author
Aitor Garcés-Manzanera | 1 |
Ayesha Sohail | 1 |
Barnes, Tiffany | 1 |
Berenson, Mark | 1 |
Chang, Maiga | 1 |
Chen, Nian-Shing | 1 |
Chi, Min | 1 |
Denden, Mouna | 1 |
Dorambari, Diedon | 1 |
Essalmi, Fathi | 1 |
Huma Akram | 1 |
More ▼ |
Publication Type
Journal Articles | 7 |
Reports - Research | 7 |
Guides - Classroom - Teacher | 1 |
Reports - Descriptive | 1 |
Speeches/Meeting Papers | 1 |
Education Level
Higher Education | 8 |
Postsecondary Education | 7 |
Early Childhood Education | 1 |
Elementary Education | 1 |
Location
California (Los Angeles) | 1 |
Ireland | 1 |
Spain | 1 |
Tunisia | 1 |
Laws, Policies, & Programs
Assessments and Surveys
Big Five Inventory | 1 |
What Works Clearinghouse Rating
Ayesha Sohail; Huma Akram – Pedagogical Research, 2025
The ability to properly evaluate one's own academic progress has long been considered a predictor of academic success. However, its distinctive role in the context of computational mathematics remains underexplored. Grounded in social cognitive theory, this study investigates the critical role of self-regulated learning (SRL) strategies in…
Descriptors: Undergraduate Students, Mathematics Education, Mathematics Achievement, Self Evaluation (Individuals)
Johnson, Marina E.; Misra, Ram; Berenson, Mark – Decision Sciences Journal of Innovative Education, 2022
In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods--Bayesian analysis and Markov chains--can be combined to enhance student learning using the Analytics…
Descriptors: Bayesian Statistics, Programming Languages, Artificial Intelligence, Data Analysis
Shi, Yang; Chi, Min; Barnes, Tiffany; Price, Thomas W. – International Educational Data Mining Society, 2022
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In…
Descriptors: Programming, Knowledge Level, Prediction, Instructional Innovation
Aitor Garcés-Manzanera – Language Teaching Research Quarterly, 2024
Learning a second language (L2) is dependent upon numerous external and internal factors, among which motivation plays a relevant role. In fact, motivation has been recognized as crucial in the L2 learning process (Ushioda, 2012). Such has been its importance that interest in L2 motivation has led to the development of theories such as the L2…
Descriptors: Learning Motivation, Second Language Learning, Second Language Instruction, Learning Processes
Dorambari, Diedon – International Journal of Education and Practice, 2022
This study examined whether instructional humor (IH) was not just another type of seductive detail when covariates such as humor pre-disposition, prior-knowledge, and working memory capacity were controlled. Participants were students (N = 228) from universities who were randomly assigned two stimuli conditions in the classic experimental design.…
Descriptors: Humor, Multimedia Instruction, Prior Learning, Short Term Memory
Tlili, Ahmed; Denden, Mouna; Essalmi, Fathi; Jemni, Mohamed; Chang, Maiga; Kinshuk; Chen, Nian-Shing – Interactive Learning Environments, 2023
The ability of automatically modeling learners' personalities is an important step in building adaptive learning environments. Several studies showed that knowing the personality of each learner can make the learning interaction with the provided learning contents and activities within learning systems more effective. However, the traditional…
Descriptors: Learning Analytics, Learning Management Systems, Intelligent Tutoring Systems, Bayesian Statistics
Wang, Felix Hao; Mintz, Toben H. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2018
The structure of natural languages give rise to many dependencies in the linear sequences of words, and within words themselves. Detecting these dependencies is arguably critical for young children in learning the underlying structure of their language. There is considerable evidence that human adults and infants are sensitive to the statistical…
Descriptors: Artificial Languages, Sentences, Second Language Learning, Undergraduate Students
Loftus, Mary; Madden, Michael G. – Teaching in Higher Education, 2020
How do we teach and learn with our students about data literacy, at the same time as Biesta (2015) calls for an emphasis on 'subjectification' i.e. 'the coming into presence of unique individual beings'? (Good Education in an Age of Measurement: Ethics, Politics, Democracy. Routledge) Our response to these challenges and the datafication of higher…
Descriptors: Teaching Methods, Data Analysis, Literacy, Learning Processes