Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 2 |
| Since 2016 (last 10 years) | 2 |
| Since 2006 (last 20 years) | 6 |
Descriptor
Source
| Journal of Educational Data… | 6 |
Author
| Boyer, Kristy Elizabeth | 1 |
| Brodersen, Kay H. | 1 |
| Buhmann, Joachim M. | 1 |
| Edwards, John | 1 |
| Ezen-Can, Aysu | 1 |
| Hart, Kaden | 1 |
| Heinimann, Hans R. | 1 |
| Jeffries, Bryn | 1 |
| Karkkainen, Tommi | 1 |
| Koprinska, Irena | 1 |
| Kupzyk, Kevin | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 6 |
| Reports - Research | 5 |
| Reports - Descriptive | 1 |
Education Level
| Higher Education | 6 |
| Postsecondary Education | 6 |
| High Schools | 1 |
| Secondary Education | 1 |
Audience
Location
| Switzerland | 1 |
| Utah | 1 |
Laws, Policies, & Programs
Assessments and Surveys
| ACT Assessment | 1 |
| Motivated Strategies for… | 1 |
What Works Clearinghouse Rating
Peer reviewed
Download full textPaassen, Benjamin; McBroom, Jessica; Jeffries, Bryn; Koprinska, Irena; Yacef, Kalina – Journal of Educational Data Mining, 2021
Educational data mining involves the application of data mining techniques to student activity. However, in the context of computer programming, many data mining techniques can not be applied because they require vector-shaped input, whereas computer programs have the form of syntax trees. In this paper, we present ast2vec, a neural network that…
Descriptors: Data Analysis, Programming Languages, Networks, Novices
Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology
Zimmermann, Judith; Brodersen, Kay H.; Heinimann, Hans R.; Buhmann, Joachim M. – Journal of Educational Data Mining, 2015
The graduate admissions process is crucial for controlling the quality of higher education, yet, rules-of-thumb and domain-specific experiences often dominate evidence-based approaches. The goal of the present study is to dissect the predictive power of undergraduate performance indicators and their aggregates. We analyze 81 variables in 171…
Descriptors: Undergraduate Students, Graduate Students, Academic Achievement, Prediction
Miller, L. Dee; Soh, Leen-Kiat; Samal, Ashok; Kupzyk, Kevin; Nugent, Gwen – Journal of Educational Data Mining, 2015
Learning objects (LOs) are important online resources for both learners and instructors and usage for LOs is growing. Automatic LO tracking collects large amounts of metadata about individual students as well as data aggregated across courses, learning objects, and other demographic characteristics (e.g. gender). The challenge becomes identifying…
Descriptors: Comparative Analysis, Data Analysis, Hierarchical Linear Modeling, Electronic Learning
Saarela, Mirka; Karkkainen, Tommi – Journal of Educational Data Mining, 2015
Curricula for Computer Science (CS) degrees are characterized by the strong occupational orientation of the discipline. In the BSc degree structure, with clearly separate CS core studies, the learning skills for these and other required courses may vary a lot, which is shown in students' overall performance. To analyze this situation, we apply…
Descriptors: Data Analysis, Academic Achievement, Undergraduate Students, Core Curriculum
Ezen-Can, Aysu; Boyer, Kristy Elizabeth – Journal of Educational Data Mining, 2015
Within the landscape of educational data, textual natural language is an increasingly vast source of learning-centered interactions. In natural language dialogue, student contributions hold important information about knowledge and goals. Automatically modeling the dialogue act of these student utterances is crucial for scaling natural language…
Descriptors: Classification, Dialogs (Language), Computational Linguistics, Information Retrieval
