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O'Reilly, Una-May; Veeramachaneni, Kalyan – Research & Practice in Assessment, 2014
Because MOOCs bring big data to the forefront, they confront learning science with technology challenges. We describe an agenda for developing technology that enables MOOC analytics. Such an agenda needs to efficiently address the detailed, low level, high volume nature of MOOC data. It also needs to help exploit the data's capacity to reveal, in…
Descriptors: Data, Technology Uses in Education, Online Courses, Open Education
Rodriguez, Sheila M.; Estacion, Angela – Regional Educational Laboratory Northeast & Islands, 2014
As the name indicates, the College Readiness Data Catalog Tool focuses on identifying data that can indicate a student's college readiness. While college readiness indicators may also signal career readiness, many states, districts, and other entities, including the U.S. Virgin Islands (USVI), do not systematically collect career readiness…
Descriptors: College Readiness, Data, Educational Indicators, Data Collection
Barton, Erin E.; Reichow, Brian – Journal of Early Intervention, 2012
The interpretation of single-case data requires systematic visual analysis across and within conditions. Graphs are a vital component for analyzing and communicating single-case design data and a necessary tool for applied researchers and practitioners. Several articles have been published with task analyses for graphing data with the new versions…
Descriptors: Computer Literacy, Guidelines, Graphs, Computer Software
Zur, Ayala; Eisikovits, Rivka A. – International Journal of Qualitative Studies in Education (QSE), 2011
The study presents a phenomenologically based research procedure, whose intent is to examine people's school experience and the meaning they ascribe to "school." Participants in this investigative endeavor are instructed to sketch an "ideal school," present their plan in a visual-schematic manner, and provide an oral and written description of…
Descriptors: Research Tools, Educational Experience, Educational Environment, Phenomenology
Stamper, John; Barnes, Tiffany; Croy, Marvin – International Journal of Artificial Intelligence in Education, 2011
The Hint Factory is an implementation of our novel method to automatically generate hints using past student data for a logic tutor. One disadvantage of the Hint Factory is the time needed to gather enough data on new problems in order to provide hints. In this paper we describe the use of expert sample solutions to "seed" the hint generation…
Descriptors: Cues, Prompting, Learning Strategies, Teaching Methods
Kovacevic, Aleksandar; Ivanovic, Dragan; Milosavljevic, Branko; Konjovic, Zora; Surla, Dusan – Program: Electronic Library and Information Systems, 2011
Purpose: The aim of this paper is to develop a system for automatic extraction of metadata from scientific papers in PDF format for the information system for monitoring the scientific research activity of the University of Novi Sad (CRIS UNS). Design/methodology/approach: The system is based on machine learning and performs automatic extraction…
Descriptors: Scientific Research, Library Administration, Classification, Information Science
Interactive Computer Based Assessment Tasks: How Problem-Solving Process Data Can Inform Instruction
Zoanetti, Nathan – Australasian Journal of Educational Technology, 2010
This article presents key steps in the design and analysis of a computer based problem-solving assessment featuring interactive tasks. The purpose of the assessment is to support targeted instruction for students by diagnosing strengths and weaknesses at different stages of problem-solving. The first focus of this article is the task piloting…
Descriptors: Computer Assisted Testing, Computer Uses in Education, Problem Solving, Evaluation Methods
Felske, Herbert P. – Business Education World, 1972
Describe's an interdisciplinary approach in developing a plan to include computer programming and data processing in the course of instruction. (JS)
Descriptors: Business Education, Data Processing, Interdisciplinary Approach, Program Descriptions

Bullard, Cedric G. – Educational and Psychological Measurement, 1972
Copies of this paper, operations manuals and source listing can be obtained from the author at the University of New South Wales, Kensington 2033, Australia. (MB)
Descriptors: Computer Programs, Data Processing, Error Patterns, Program Descriptions
Rybak, John P. – Amer Ann Deaf, 1969
Descriptors: Business Education, Computer Science Education, Data Processing, Data Processing Occupations
MCKEE, R.L. – 1963
A DATA PROCESSING TRAINING PROGRAM IN WASHINGTON HAD 10 DATA PROCESSING CENTERS IN OPERATION AND EIGHT MORE IN VARIOUS STAGES OF PLANNING IN 1963. THESE CENTERS WERE FULL-TIME DAY PREPARATORY 2-YEAR POST-HIGH SCHOOL TECHNICIAN TRAINING PROGRAMS, OPERATED AND ADMINISTERED BY THE LOCAL BOARDS OF EDUCATION. EACH SCHOOL HAD A COMPLETE DATA PROCESSING…
Descriptors: Admission Criteria, Business Education, Curriculum, Data Processing
Rodriguez, Santiago; Zamorano, Juan; Rosales, Francisco; Dopico, Antonio Garcia; Pedraza, Jose Luis – Computers and Education, 2007
This paper describes a complete lab work management framework designed and developed in the authors' department to help teachers to manage the small projects that students are expected to complete as lab assignments during their graduate-level computer engineering studies. The paper focuses on an application example of the framework to a specific…
Descriptors: Data Processing, Program Design, Student Evaluation, Assignments
Weber, James F.; Greene, Richard S., Ed. – Bus Educ Forum, 1969
Descriptors: Business Education, Community Colleges, Data Processing, Educational Objectives
Hillis, Dorothy J. – J Bus Educ, 1969
Descriptors: Business Education, Data Processing, Office Machines, Program Descriptions
Ward, Obie; Poulos, Cynthia – 1973
Various aspects of the Optical Mark Reader (OMR) used by the Atlanta Public School System are discussed. First considered are the required features of the OMR scanner. Following this, methods of motivating users to record data accurately are described. Finally, a description of how forms are designed for the convenience of users is provided. (PB)
Descriptors: Data Processing, Electronic Equipment, Information Processing, Optical Scanners