NotesFAQContact Us
Collection
Advanced
Search Tips
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Showing 1 to 15 of 28 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Poitras, Eric; Butcher, Kirsten R.; Orr, Matthew; Hudson, Michelle A.; Larson, Madlyn – Interactive Learning Environments, 2022
This study mined student interactions with visual representations as a means to automate assessment of learning in a complex, inquiry-based learning environment. Log trace data of 143 middle school students' interactions with an interactive map in Research Quest (an inquiry-based, online learning environment) were analyzed. Students used the…
Descriptors: Middle School Students, Electronic Learning, Maps, Science Instruction
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Aydin, Gökhan; Duran, Volkan; Mertol, Hüseyin – International Journal of Curriculum and Instruction, 2021
This study aims to develop a computer program for the identification key to insect orders (Arthropoda: Hexapoda) and to investigate its effectiveness as teaching material. Secondly, this study is aiming at whether this program improves students' computational thinking skills or not longitudinal quasi-experimental design. Firstly, the study is…
Descriptors: Computer Software, Identification, Entomology, Computation
Lyniesha Chanell Wright – ProQuest LLC, 2020
Effectively mastering organic chemistry means having the ability to recognize structural patterns, identify properties or behaviors as a result of patterns, manipulate and transform representations, and predict future outcomes. Often students rely on rote memorization of seemingly disconnected information instead of developing a sound…
Descriptors: Organic Chemistry, Science Instruction, Visualization, Models
Leo C. Ureel II – ProQuest LLC, 2020
Students in introductory computer science courses, are learning to program. Indeed, most students perceive that learning to code is the central topic explored in the courses. Students spend an enormous amount of time struggling to learn the syntax and understand semantics of a particular language. Instructors spend a similar amount of time reading…
Descriptors: Coding, Programming, Computer Science Education, Novices
Peer reviewed Peer reviewed
Direct linkDirect link
Koury, Hannah F.; Leonard, Carly J.; Carry, Patrick M.; Lee, Lisa M. J. – Anatomical Sciences Education, 2019
Histology is a visually oriented, foundational anatomical sciences subject in professional health curricula that has seen a dramatic reduction in educational contact hours and an increase in content migration to a digital platform. While the digital migration of histology laboratories has transformed histology education, few studies have shown the…
Descriptors: Expertise, Pattern Recognition, Novices, Efficiency
Peer reviewed Peer reviewed
Direct linkDirect link
Lin, John Jr-Hung; Lin, Sunny S. J. – International Journal of Science and Mathematics Education, 2014
The present study investigated (a) whether the perceived cognitive load was different when geometry problems with various levels of configuration comprehension were solved and (b) whether eye movements in comprehending geometry problems showed sources of cognitive loads. In the first investigation, three characteristics of geometry configurations…
Descriptors: Cognitive Processes, Difficulty Level, Geometry, Comprehension
Peer reviewed Peer reviewed
Direct linkDirect link
Yang, Yang; Leung, H.; Yue, Lihua; Deng, LiQun – IEEE Transactions on Learning Technologies, 2012
In this paper, an automatic lesson generation system is presented which is suitable in a learning-by-mimicking scenario where the learning objects can be represented as multiattribute time series data. The dance is used as an example in this paper to illustrate the idea. Given a dance motion sequence as the input, the proposed lesson generation…
Descriptors: Foreign Countries, Dance Education, Lesson Plans, Pattern Recognition
Pavlik, Philip I., Jr.; Cen, Hao; Koedinger, Kenneth R. – Online Submission, 2009
Knowledge tracing (KT)[1] has been used in various forms for adaptive computerized instruction for more than 40 years. However, despite its long history of application, it is difficult to use in domain model search procedures, has not been used to capture learning where multiple skills are needed to perform a single action, and has not been used…
Descriptors: Performance Factors, Factor Analysis, Computer Software, Computer Assisted Instruction
Peer reviewed Peer reviewed
Direct linkDirect link
Moyer-Packenham, Patricia; Suh, Jennifer – Journal of Computers in Mathematics and Science Teaching, 2012
This study examined the influence of virtual manipulatives on different achievement groups during a teaching experiment in four fifth-grade classrooms. During a two-week unit focusing on two rational number concepts (fraction equivalence and fraction addition with unlike denominators) one low achieving, two average achieving, and one high…
Descriptors: Pattern Recognition, Grade 5, Mathematics Instruction, Number Concepts
Peer reviewed Peer reviewed
Direct linkDirect link
Barnes, Tiffany; Stamper, John – Educational Technology & Society, 2010
In building intelligent tutoring systems, it is critical to be able to understand and diagnose student responses in interactive problem solving. However, building this understanding into a computer-based intelligent tutor is a time-intensive process usually conducted by subject experts. Much of this time is spent in building production rules that…
Descriptors: Intelligent Tutoring Systems, Logical Thinking, Tutors, Probability
Stamper, John Carroll – ProQuest LLC, 2010
Intelligent Tutoring Systems (ITSs) that adapt to an individual student's needs have shown significant improvement in achievement over non-adaptive instruction (Murray 1999). This improvement occurs due to the individualized instruction and feedback that an ITS provides. In order to achieve the benefits that ITSs provide, we must find a way to…
Descriptors: Intelligent Tutoring Systems, Individualized Instruction, Adjustment (to Environment), Feedback (Response)
Peer reviewed Peer reviewed
Carlson, Patricia A. – Journal of Computing in Higher Education, 1991
Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…
Descriptors: Artificial Intelligence, Associative Learning, Computer Assisted Instruction, Computer Oriented Programs
Northwest Regional Educational Lab., Portland, OR. – 1982
THE FOLLOWING IS THE FULL TEXT OF THIS DOCUMENT (Except for the Evaluation Summary Table): VERSION: Atari APX-20083. PRODUCER: Atari, Inc., 60 E. Plumeria, P.O. Box 50047, San Jose, California 95050. EVALUATION COMPLETED: September 1982 by the staff and constituents of the Capital Children's Museum. Their evaluation is partly based on observation…
Descriptors: Computer Assisted Instruction, Computer Programs, Drills (Practice), Educational Games
Peer reviewed Peer reviewed
Direct linkDirect link
Villaverde, J. E.; Godoy, D.; Amandi, A. – Journal of Computer Assisted Learning, 2006
People have unique ways of learning, which may greatly affect the learning process and, therefore, its outcome. In order to be effective, e-learning systems should be capable of adapting the content of courses to the individual characteristics of students. In this regard, some educational systems have proposed the use of questionnaires for…
Descriptors: Cognitive Style, Computer Assisted Instruction, Web Based Instruction, Student Characteristics
Peer reviewed Peer reviewed
Brand, James – CALICO Journal, 1987
Describes the language learning program "Acquire," which is a sample of grammar induction. It is a learning algorithm based on a pattern-matching scheme, using both a positive and negative network to reduce overgeneration. Language learning programs may be useful as tutorials for learning the syntax of a foreign language. (Author/LMO)
Descriptors: Artificial Intelligence, Computational Linguistics, Computer Assisted Instruction, Computer Software
Previous Page | Next Page »
Pages: 1  |  2