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Kanwal Zahoor; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the…
Descriptors: Artificial Intelligence, Computer Oriented Programs, Courseware, Learning Processes
Gulzar, Zameer; Leema, A. Anny – International Journal of Web-Based Learning and Teaching Technologies, 2018
This article describes how with a non-formal education, a scholar has to choose courses among various domains to meet the research aims. In spite of this, the availability of large number of courses, makes the process of selecting the appropriate course a tedious, time-consuming, and risky decision, and the course selection will directly affect…
Descriptors: Electronic Learning, Information Retrieval, Classification, Computer Science Education
Sabitha, A. Sai; Mehrotra, Deepti; Bansal, Abhay – Education and Information Technologies, 2017
Currently the challenges in e-Learning are converging the learning content from various sources and managing them within e-learning practices. Data mining learning algorithms can be used and the contents can be converged based on the Metadata of the objects. Ensemble methods use multiple learning algorithms and it can be used to converge the…
Descriptors: Electronic Learning, Metadata, Computer System Design, Design Preferences
Dimou, Helen; Kameas, Achilles – Quality Assurance in Education: An International Perspective, 2016
Purpose: This paper aims to present a model for the quality assurance of digital educational material that is appropriate for adult education. The proposed model adopts the software quality standard ISO/IEC 9126 and takes into account adult learning theories, Bloom's taxonomy of learning objectives and two instructional design models: Kolb's model…
Descriptors: Quality Assurance, Educational Quality, Cognitive Style, Adult Education
Lang, Leah; Pirani, Judith A. – EDUCAUSE, 2014
This Spotlight focuses on data from the 2013 Core Data Service (CDS) to better understand how higher education institutions approach learning management systems (LMSs). Information provided for this Spotlight was derived from Module 8 of the Core Data Service, which contains several questions regarding information systems and applications.…
Descriptors: Management Information Systems, Technological Advancement, Information Systems, Courseware
Deliyska, Boryana; Manoilov, Peter – International Journal of Distance Education Technologies, 2010
The intelligent learning systems provide direct customized instruction to the learners without the intervention of human tutors on the basis of Semantic Web resources. Principal roles use ontologies as instruments for modeling learning processes, learners, learning disciplines and resources. This paper examines the variety, relationships, and…
Descriptors: Learning Processes, Intelligent Tutoring Systems, Curriculum Development, Lesson Plans
Wilson, Holt; Neeley, Concha; Niedzwiecki, Kelly – Journal of Instructional Pedagogies, 2009
This paper presents the findings from a survey of marketing research faculty. The study finds SPSS is the most used statistical software, that cross tabulation, single, independent, and dependent t-tests, and ANOVA are among the most important statistical tools according to respondents. Bivariate and multiple regression are also considered…
Descriptors: Marketing, Educational Research, Teaching Methods, Course Content

Bangert-Drowns, Robert L.; Pyke, Curtis – Educational Technology Research and Development, 2002
Discusses students' learning engagement and describes a study that investigated whether teachers could accurately judge elementary school students' learning engagement with educational software. Explains teacher's use of a seven-level taxonomy to rate the frequency of different forms of engagement among 42 students interacting with different types…
Descriptors: Classification, Computer Assisted Instruction, Courseware, Elementary Education
Carrier, Carol A.; Sales, Gregory C. – Educational Technology, 1987
Describes the inadequacies of the current software for computer-based instruction, and presents a framework of instructional components to guide the design of such software. A taxonomy of design considerations is then discussed, including instructional context, strategies, and technical capabilities that can be used to enhance instruction. (4…
Descriptors: Classification, Computer Assisted Instruction, Courseware, Instructional Effectiveness
Bates, Bill; Leary, Jerome – 2001
This paper describes the results of a United Kingdom research program evaluating computer-based learning software and determining its suitability in supporting the different learning styles of users. A design taxonomy is proposed that helps designers build software to target multiple specific learning styles. This enables the courseware to be more…
Descriptors: Classification, Cognitive Style, Courseware, Foreign Countries
Zechner, Klaus; Bejar, Isaac I.; Hemat, Ramin – ETS Research Report Series, 2007
The increasing availability and performance of computer-based testing has prompted more research on the automatic assessment of language and speaking proficiency. In this investigation, we evaluated the feasibility of using an off-the-shelf speech-recognition system for scoring speaking prompts from the LanguEdge field test of 2002. We first…
Descriptors: Role, Computer Assisted Testing, Language Proficiency, Oral Language

Adler, Debora L. – Computers & Education, 1994
Describes "Getting Clean with Herkimer," a software package designed to teach elementary special education students to classify personal grooming objects. A study compared this software program with another one, including results of pretests and posttests and computer use and attitude measures. (32 references) (LRW)
Descriptors: Classification, Comparative Analysis, Computer Assisted Instruction, Computer Attitudes
Spector, J. Michael; And Others – 1992
Many researchers are attempting to develop automated instructional development systems to guide subject matter experts through the lengthy and difficult process of courseware development. Because the targeted users often lack instructional design expertise, a great deal of emphasis has been placed on the use of artificial intelligence (AI) to…
Descriptors: Artificial Intelligence, Authoring Aids (Programing), Classification, Computer Assisted Instruction

Peled, Zimra; And Others – Journal of Educational Computing Research, 1992
Proposes a taxonomy to aid decision makers in selecting computer software consistent with their educational values regarding the nature of instruction and the use of information technology, based on beliefs about human development and learning. Characteristics of instruction, properties of software, and the congruence between them are discussed.…
Descriptors: Algorithms, Classification, Computer Assisted Instruction, Computer Software

Wishart, Jocelyn; Canter, David – Computers and Education, 1988
Reports the development of a classification system for microcomputer-based software according to the depth and type of involvement of the user with the software. Three studies are discussed that used the facet approach, multivariate statistical procedures, and questionnaires to investigate types of involvement produced by computer programs. (18…
Descriptors: Classification, Computer Assisted Instruction, Computer Games, Courseware
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