NotesFAQContact Us
Collection
Advanced
Search Tips
Laws, Policies, & Programs
Assessments and Surveys
NEO Five Factor Inventory1
What Works Clearinghouse Rating
Showing all 15 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Draper, Steve; Maguire, Joseph – ACM Transactions on Computing Education, 2023
The overall aim of this article is to stimulate discussion about the activities within CER, and to develop a more thoughtful and explicit perspective on the different types of research activity within CER, and their relationships with each other. While theories may be the most valuable outputs of research to those wishing to apply them, for…
Descriptors: Computer Science Education, Educational Research, Computer Science, Classification
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Paaßen, Benjamin; Jensen, Joris; Hammer, Barbara – International Educational Data Mining Society, 2016
The first intelligent tutoring systems for computer programming have been proposed more than 30 years ago, mostly focusing on well defined programming tasks e.g. in the context of logic programming. Recent systems also teach complex programs, where explicit modelling of every possible program and mistake is no longer possible. Such systems are…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Science Education, Data
Peer reviewed Peer reviewed
PDF on ERIC Download full text
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
Peer reviewed Peer reviewed
Direct linkDirect link
Srba, Ivan; Bielikova, Maria – IEEE Transactions on Learning Technologies, 2015
In the current time of globalization, collaboration among people in virtual environments is becoming an important precondition of success. This trend is reflected also in the educational domain where students collaborate in various short-term groups created repetitively but changing in each round (e.g. in MOOCs). Students in these kind of dynamic…
Descriptors: Cooperative Learning, Online Courses, Group Dynamics, Feedback (Response)
Peer reviewed Peer reviewed
PDF on ERIC Download full text
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
Peer reviewed Peer reviewed
Direct linkDirect link
Fouh, Eric; Akbar, Monika; Shaffer, Clifford A. – Computers in the Schools, 2012
Computer science core instruction attempts to provide a detailed understanding of dynamic processes such as the working of an algorithm or the flow of information between computing entities. Such dynamic processes are not well explained by static media such as text and images, and are difficult to convey in lecture. The authors survey the history…
Descriptors: Computer Science Education, Educational Assessment, Visualization, Computer Science
Peer reviewed Peer reviewed
Direct linkDirect link
Armoni, Michal; Ben-Ari, Mordechai – Science & Education, 2009
Nondeterminism is a fundamental concept in computer science that appears in various contexts such as automata theory, algorithms and concurrent computation. We present a taxonomy of the different ways that nondeterminism can be defined and used; the categories of the taxonomy are domain, nature, implementation, consistency, execution and…
Descriptors: Computer Science Education, Fundamental Concepts, Textbooks, Semantics
Peer reviewed Peer reviewed
Direct linkDirect link
Myller, Niko; Bednarik, Roman; Sutinen, Erkki; Ben-Ari, Mordechai – ACM Transactions on Computing Education, 2009
As collaborative learning in general, and pair programming in particular, has become widely adopted in computer science education, so has the use of pedagogical visualization tools for facilitating collaboration. However, there is little theory on collaborative learning with visualization, and few studies on their effect on each other. We build on…
Descriptors: Computer Science Education, Learning Activities, Visualization, Classification
Peer reviewed Peer reviewed
PDF on ERIC Download full text
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 Peer reviewed
Direct linkDirect link
Robbins, Russell W.; Butler, Brian S. – Journal of Information Systems Education, 2009
Like any infrastructure technology, Virtual World (VW) platforms provide affordances that facilitate some activities and hinder others. Although it is theoretically possible for a VW platform to support all types of activities, designers make choices that lead technologies to be more or less suited for different learning objectives. Virtual World…
Descriptors: Computer Assisted Instruction, Barriers, Educational Objectives, Teaching Methods
Peer reviewed Peer reviewed
Gonzales, Michael G. – Computer Education, 1984
Suggests a moving pictorial tool to help teach principles in the bubble sort algorithm. Develops such a tool applied to an unsorted list of numbers and describes a method to derive the run time of the algorithm. The method can be modified to run the times of various other algorithms. (JN)
Descriptors: Algorithms, Classification, College Mathematics, Computer Programs
Peer reviewed Peer reviewed
Dryburgh, Heather – Journal of Educational Computing Research, 2000
Discusses the decline in the number of female computer science graduates and presents a categorization by educational stages of the research into this decline as well as an evaluation of the generalizability of findings to broader contexts. Show that the most extensive research is done at the post-secondary stage. (Contains 53 references.)…
Descriptors: Classification, Computer Science Education, Females, Gender Issues
Johnson, W. Lewis; And Others – 1983
Argues that a computer-based programming tutor for novice programmers needs to take into account not only the types and frequency of bugs found in the programs, but the intentions and knowledge state of the programmer. A first version of such a program was developed on the basis of the bug types found in a number of pencil-and-paper studies with…
Descriptors: Classification, Cognitive Processes, Computer Programs, Computer Science Education
Hawaii State Dept. of Education, Honolulu. Office of Instructional Services. – 1986
This guide is designed to provide teachers with guidelines and suggested activities for teaching a one-semester advanced programming course--BASIC Programming II--for the ninth through twelfth grades. Although primarily oriented toward mathematics, the guide does offer sample applications in business that also address the needs of students with a…
Descriptors: Business Education, Classification, Computer Graphics, Computer Literacy
Peer reviewed Peer reviewed
Direct linkDirect link
Abel, Marie-Helene; Benayache, Ahcene; Lenne, Dominique; Moulin, Claude; Barry, Catherine; Chaput, Brigitte – Educational Technology & Society, 2004
E-learning leads to evolutions in the way of designing a course. Diffused through the web, the course content cannot be the direct transcription of a face to face course content. A course can be seen as an organization in which different actors are involved. These actors produce documents, information and knowledge that they often share. We…
Descriptors: Course Content, Internet, College Instruction, Models