Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 2 |
| Since 2017 (last 10 years) | 3 |
| Since 2007 (last 20 years) | 3 |
Descriptor
| Computer Science Education | 3 |
| Prediction | 3 |
| Programming | 3 |
| Programming Languages | 3 |
| Learning Processes | 2 |
| Undergraduate Students | 2 |
| Acoustics | 1 |
| Assignments | 1 |
| Bayesian Statistics | 1 |
| Case Studies | 1 |
| Coding | 1 |
| More ▼ | |
Author
| Akar, Sacide Guzin Mazman | 1 |
| Altun, Arif | 1 |
| Barnes, Tiffany | 1 |
| Boyer, Kristy Elizabeth | 1 |
| Celepkolu, Mehmet | 1 |
| Chi, Min | 1 |
| Katuka, Gloria Ashiya | 1 |
| Ma, Yingbo | 1 |
| Price, Thomas W. | 1 |
| Shi, Yang | 1 |
Publication Type
| Reports - Research | 3 |
| Speeches/Meeting Papers | 2 |
| Journal Articles | 1 |
Education Level
| Higher Education | 2 |
| Postsecondary Education | 2 |
| Elementary Education | 1 |
| Grade 7 | 1 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
| Secondary Education | 1 |
Audience
Location
| Turkey | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
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
Ma, Yingbo; Katuka, Gloria Ashiya; Celepkolu, Mehmet; Boyer, Kristy Elizabeth – International Educational Data Mining Society, 2022
Collaborative learning is a complex process during which two or more learners exchange opinions, construct shared knowledge, and solve problems together. While engaging in this interactive process, learners' satisfaction toward their partners plays a crucial role in defining the success of the collaboration. If intelligent systems could predict…
Descriptors: Middle School Students, Cooperative Learning, Prediction, Peer Relationship
Akar, Sacide Guzin Mazman; Altun, Arif – Contemporary Educational Technology, 2017
The purpose of this study is to investigate and conceptualize the ranks of importance of social cognitive variables on university students' computer programming performances. Spatial ability, working memory, self-efficacy, gender, prior knowledge and the universities students attend were taken as variables to be analyzed. The study has been…
Descriptors: Individual Differences, Learning Processes, Programming, Self Efficacy

Peer reviewed
