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Verma, Anil; Singh, Aman; Lughofer, Edwin; Cheng, Xiaochun; Abualsaud, Khalid – Journal of Computing in Higher Education, 2021
Sustainable quality education is a big challenge even for the developed countries. In response to this, education 4.0 is gradually expanding as a new era of education. This work intends to unfold some hidden parameters that are affecting the quality education ecosystem (QEE). Academic loafing, unawareness, non-participation, dissatisfaction, and…
Descriptors: Educational Quality, Ecology, Sustainability, Higher Education
Feldman-Maggor, Yael; Barhoom, Sagiv; Blonder, Ron; Tuvi-Arad, Inbal – Education and Information Technologies, 2021
Research based on educational data mining conducted at academic institutions is often limited by the institutional policy with regard to the type of learning management system and the detail level of its activity reports. Often, researchers deal with only raw data. Such data normally contain numerous fictitious user activities that can create a…
Descriptors: Data Analysis, Educational Research, Data Processing, Learning Analytics
Jakab, Imrich; Ševcík, Michal; Grežo, Henrich – Electronic Journal of e-Learning, 2017
The methods of geospatial data processing are being continually innovated, and universities that are focused on educating experts in Environmental Science should reflect this reality with an elaborate and purpose-built modernization of the education process, education content, as well as learning conditions. Geographic Information Systems (GIS)…
Descriptors: Models, Higher Education, Geographic Information Systems, Environmental Education
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
Huebner, Richard A. – Research in Higher Education Journal, 2013
Educational data mining (EDM) is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. A literature review on educational data mining topics…
Descriptors: Educational Research, Data Processing, Data Analysis, Organizational Change
Abdous, M'hammed; He, Wu – British Journal of Educational Technology, 2011
Because of their capacity to sift through large amounts of data, text mining and data mining are enabling higher education institutions to reveal valuable patterns in students' learning behaviours without having to resort to traditional survey methods. In an effort to uncover live video streaming (LVS) students' technology related-problems and to…
Descriptors: Video Technology, Student Participation, Data Analysis, Learning Experience
Mathies, Charles; Valimaa, Jussi – Tertiary Education and Management, 2013
Recent changes in European higher education have accompanied a strong desire and need by national ministries to have comparable data across institutions and a growing recognition from campus leaders that effective planning and decision-making requires reliable institutional data and analyses. This has induced changes and restructuring of duties…
Descriptors: Higher Education, Institutional Evaluation, Governance, Data Analysis
Omar, Ahmad Fairuz; MatJafri, Mohd Zubir – Physics Education, 2011
The Swift Turbidity Marker is an optical instrument developed to measure the level of water turbidity. The components and configuration selected for the system are based on common turbidity meter design concepts but use a simplified methodology to produce rapid turbidity measurements. This work is aimed at high school physics students and is the…
Descriptors: Optics, International Organizations, Data Analysis, Mathematical Applications
Niemi, David; Gitin, Elena – International Association for Development of the Information Society, 2012
An underlying theme of this paper is that it can be easier and more efficient to conduct valid and effective research studies in online environments than in traditional classrooms. Taking advantage of the "big data" available in an online university, we conducted a study in which a massive online database was used to predict student…
Descriptors: Higher Education, Online Courses, Academic Persistence, Identification
Burns, Shelley, Ed.; Wang, Xiaolei, Ed.; Henning, Alexandra, Ed. – National Center for Education Statistics, 2011
Since its inception, the National Center for Education Statistics (NCES) has been committed to the practice of documenting its statistical methods for its customers and of seeking to avoid misinterpretation of its published data. The reason for this policy is to assure customers that proper statistical standards and techniques have been observed,…
Descriptors: National Surveys, Data Processing, Statistical Data, Data Collection
Vitinš, Maris; Rasnacs, Oskars – Informatics in Education, 2012
Information and communications technologies today are used in virtually any university course when students prepare their papers. ICT is also needed after people are graduated from university and enter the job market. This author is an instructor in the field of informatics related to health care and social sciences at the Riga Stradins…
Descriptors: Educational Technology, Assignments, Information Science, Data Processing
Vialardi, Cesar; Bravo, Javier; Shafti, Leila; Ortigosa, Alvaro – International Working Group on Educational Data Mining, 2009
One of the main problems faced by university students is to take the right decision in relation to their academic itinerary based on available information (for example courses, schedules, sections, classrooms and professors). In this context, this work proposes the use of a recommendation system based on data mining techniques to help students to…
Descriptors: Data Analysis, Higher Education, Course Selection (Students), Enrollment
Ahrens, Fred – Journal of STEM Education: Innovations and Research, 2009
University student internships can be an important pre-professional experience for the student and be an immense benefit to an employer. Because of the findings of a 6-Sigma project to reduce engineering errors, a design configurator was to be rebuilt to include updated design information and expanded product coverage. Lacking available full time…
Descriptors: Graduate Students, Engineering, College Students, Higher Education
Nandeshwar, Ashutosh R. – ProQuest LLC, 2010
In the modern world, higher education is transitioning from enrollment mode to recruitment mode. This shift paved the way for institutional research and policy making from historical data perspective. More and more universities in the U.S. are implementing and using enterprise resource planning (ERP) systems, which collect vast amounts of data.…
Descriptors: Higher Education, Institutional Research, Graduation Rate, Program Effectiveness
Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing