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Figlio, David; Karbownik, Krzysztof; Salvanes, Kjell – Education Finance and Policy, 2017
Thanks to extraordinary and exponential improvements in data storage and computing capacities, it is now possible to collect, manage, and analyze data in magnitudes and in manners that would have been inconceivable just a short time ago. As the world has developed this remarkable capacity to store and analyze data, so have the world's governments…
Descriptors: Educational Research, Data, Information Utilization, Management Information Systems
Zhao, Jensen; Zhao, Sherry Y. – Journal of Education for Business, 2016
E-business, e-education, e-government, social media, and mobile services generate and capture trillions of bytes of data every second about customers, suppliers, employees, and other types of data. The growing quantity of big data is an important part of every sector in the global economy. However, there is a significant shortage of business data…
Descriptors: Business Administration Education, Business Schools, Data Analysis, Data Processing
Antoniadou, Victoria – Research-publishing.net, 2017
Reflecting the inter-connected reality of today's world, contemporary education is striving to keep up with the exponentially rapid changes that individuals around the globe are facing. Innovative educational proposals carry labels such as connective learning (Downes, 2006), e-learning 2.0 (Downes, 2005), education 2.0 (Carr et al., 2008), or…
Descriptors: Data Collection, Data Analysis, Qualitative Research, Data Processing
Rienties, Bart; Cross, Simon; Marsh, Vicky; Ullmann, Thomas – Open Learning, 2017
Most distance learning institutions collect vast amounts of learning data. Making sense of this 'Big Data' can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, the Open University UK instigated…
Descriptors: Foreign Countries, Distance Education, Data Collection, Data Interpretation
Galyardt, April; Goldin, Ilya – Journal of Educational Data Mining, 2015
In educational technology and learning sciences, there are multiple uses for a predictive model of whether a student will perform a task correctly or not. For example, an intelligent tutoring system may use such a model to estimate whether or not a student has mastered a skill. We analyze the significance of data recency in making such…
Descriptors: Achievement Rating, Performance Based Assessment, Bayesian Statistics, Data Analysis
Loh, Christian Sebastian – International Journal of Virtual and Personal Learning Environments, 2013
Today's economic situation demands that learning organizations become more diligent in their business dealings to reduce cost and increase bottom line for survival. While there are many champions and proponents claiming that game-based learning (GBL) is sure to improve learning, researchers have, thus far, been unable to (re)produce concrete,…
Descriptors: Investment, Outcomes of Education, Educational Games, Instructional Improvement
Karatepe, Akif – Educational Research and Reviews, 2012
Today's important geo-spatial technologies, GIS (Geographic Information Systems), GPS (Global Positioning Systems) and Google Earth have been widely used in geography education. Transferring spatially oriented data taken by GPS to the GIS and Google Earth has provided great benefits in terms of showing the usage of spatial technologies for field…
Descriptors: Geography Instruction, Geographic Information Systems, Field Experience Programs, Field Instruction
Jafar, Musa J. – Journal of Information Technology Education, 2010
Data mining is an emerging field of study in Information Systems programs. Although the course content has been streamlined, the underlying technology is still in a state of flux. The purpose of this paper is to describe how we utilized Microsoft Excel's data mining add-ins as a front-end to Microsoft's Cloud Computing and SQL Server 2008 Business…
Descriptors: Information Systems, Data Analysis, Teaching Methods, Integrated Curriculum
Cocea, M.; Weibelzahl, S. – IEEE Transactions on Learning Technologies, 2011
Learning environments aim to deliver efficacious instruction, but rarely take into consideration the motivational factors involved in the learning process. However, motivational aspects like engagement play an important role in effective learning-engaged learners gain more. E-Learning systems could be improved by tracking students' disengagement…
Descriptors: Prediction, Electronic Learning, Online Courses, Delivery Systems
Jones, Kenneth D., II. – ProQuest LLC, 2010
The primary purpose of this study is to determine how students are impacted by the use of Classroom Response System (CRS) technology. This research explores the nature of the outcomes experienced by students and their perceptions on the leading pedagogy and practices for using CRS technology in the classroom. The research is both quantitative and…
Descriptors: Quasiexperimental Design, Personality, Information Systems, Educational Technology
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers