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) | 6 |
Descriptor
| Computer Science Education | 6 |
| Data Collection | 6 |
| Data Analysis | 3 |
| Programming | 3 |
| Classification | 2 |
| Coding | 2 |
| Foreign Countries | 2 |
| Models | 2 |
| Undergraduate Students | 2 |
| Accuracy | 1 |
| At Risk Students | 1 |
| More ▼ | |
Source
| Higher Education for the… | 1 |
| Information Systems Education… | 1 |
| International Association for… | 1 |
| Journal of Educational Data… | 1 |
| Journal of Learning Analytics | 1 |
| Journal of Statistics and… | 1 |
Author
| Aggarwal, Vaibhav | 1 |
| Allison S. Theobold | 1 |
| Amershi, Saleema | 1 |
| Borchert, Otto | 1 |
| Casey, Kevin | 1 |
| Conati, Cristina | 1 |
| Cosmano, Bob | 1 |
| Cox, Kathleen T. | 1 |
| Hokanson, Guy | 1 |
| Jin, Wei | 1 |
| Kariluoma, Matti | 1 |
| More ▼ | |
Publication Type
| Reports - Descriptive | 6 |
| Journal Articles | 5 |
| Speeches/Meeting Papers | 1 |
Education Level
| Higher Education | 4 |
| Postsecondary Education | 4 |
Audience
Location
| India | 1 |
| Ireland (Dublin) | 1 |
| New York (New York) | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Allison S. Theobold; Megan H. Wickstrom; Stacey A. Hancock – Journal of Statistics and Data Science Education, 2024
Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these…
Descriptors: Computer Science Education, Coding, Data Science, Statistics Education
Lakshminarayanan, Srinivasan; Rao, N. J. – Higher Education for the Future, 2022
There are many grey areas in the interpretation of academic integrity in the course on Introduction to Programming, commonly known as CS1. Copying, for example, is a method of learning, a method of cheating and a reuse method in professional practice. Many institutions in India publish the code in the lab course manual. The students are expected…
Descriptors: Integrity, Cheating, Duplication, Introductory Courses
Molluzzo, John C.; Lawler, James P. – Information Systems Education Journal, 2015
Big Data is becoming a critical component of the Information Systems curriculum. Educators are enhancing gradually the concentration curriculum for Big Data in schools of computer science and information systems. This paper proposes a creative curriculum design for Big Data Analytics for a program at a major metropolitan university. The design…
Descriptors: Curriculum Design, Data Analysis, Data Collection, Information Systems
Casey, Kevin – Journal of Learning Analytics, 2017
Learning analytics offers insights into student behaviour and the potential to detect poor performers before they fail exams. If the activity is primarily online (for example computer programming), a wealth of low-level data can be made available that allows unprecedented accuracy in predicting which students will pass or fail. In this paper, we…
Descriptors: Keyboarding (Data Entry), Educational Research, Data Collection, Data Analysis
Yan, Peng; Slator, Brian M.; Vender, Bradley; Jin, Wei; Kariluoma, Matti; Borchert, Otto; Hokanson, Guy; Aggarwal, Vaibhav; Cosmano, Bob; Cox, Kathleen T.; Pilch, André; Marry, Andrew – International Association for Development of the Information Society, 2013
Research into virtual role-based learning has progressed over the past decade. Modern issues include gauging the difficulty of designing a goal system capable of meeting the requirements of students with different knowledge levels, and the reasonability and possibility of taking advantage of the well-designed formula and techniques served in other…
Descriptors: Intelligent Tutoring Systems, Immersion Programs, Role Playing, Biological Sciences
Amershi, Saleema; Conati, Cristina – Journal of Educational Data Mining, 2009
In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…
Descriptors: Supervision, Classification, Models, Educational Environment

Peer reviewed
Direct link
