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Matsuda, Noboru; Wood, Jesse; Shrivastava, Raj; Shimmei, Machi; Bier, Norman – Journal of Educational Data Mining, 2022
A model that maps the requisite skills, or knowledge components, to the contents of an online course is necessary to implement many adaptive learning technologies. However, developing a skill model and tagging courseware contents with individual skills can be expensive and error prone. We propose a technology to automatically identify latent…
Descriptors: Skills, Models, Identification, Courseware
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Aisha Abdulmohsin Al Abdulqader; Amenah Ahmed Al Mulla; Gaida Abdalaziz Al Moheish; Michael Jovellanos Pinero; Conrado Vizcarra; Abdulelah Al Gosaibi; Abdulaziz Saad Albarrak – International Association for Development of the Information Society, 2022
The COVID-19 epidemic had caused one of the most significant disruptions to the global education system. Many educational institutions faced sudden pressure to switch from face-to-face to online delivery of courses. The conventional classes are no longer the primary means of delivery; instead, online education and resources have become the…
Descriptors: COVID-19, Pandemics, Teaching Methods, Online Courses
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Sales, Adam C.; Botelho, Anthony; Patikorn, Thanaporn; Heffernan, Neil T. – International Educational Data Mining Society, 2018
Randomized A/B tests in educational software are not run in a vacuum: often, reams of historical data are available alongside the data from a randomized trial. This paper proposes a method to use this historical data--often highdimensional and longitudinal--to improve causal estimates from A/B tests. The method proceeds in two steps: first, fit a…
Descriptors: Courseware, Data Analysis, Causal Models, Prediction
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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
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Livieris, Ioannis E.; Mikropoulos, Tassos A.; Pintelas, Panagiotis – Themes in Science and Technology Education, 2016
Educational data mining is an emerging research field concerned with developing methods for exploring the unique types of data that come from educational context. These data allow the educational stakeholders to discover new, interesting and valuable knowledge about students. In this paper, we present a new user-friendly decision support tool for…
Descriptors: Predictive Measurement, Decision Support Systems, Academic Achievement, Exit Examinations
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
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Salim, Kalbin; Tiawa, Dayang Hjh – Journal of Education and Practice, 2015
The purpose of this study was to determine the students perception on the use of animation courseware in math and to reveal how the sample courseware learning mathematical concepts to different change students' views. This research is a case study involving three mathematics students at SMAN 2 Bintan. Data were collected by means of…
Descriptors: Secondary School Students, Student Attitudes, Secondary School Mathematics, Mathematics Education
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Sim, Kwong Nui; van der Meer, Jacques – Universal Journal of Educational Research, 2015
This study investigated PhD students' computer activities in their daily research practice. Software that tracks computer usage (Manic Time) was installed on the computers of nine PhD students, who were at their early, mid and final stage in doing their doctoral research in four different discipline areas (Commerce, Humanities, Health Sciences and…
Descriptors: Computer Use, Computer Uses in Education, Doctoral Degrees, Doctoral Programs
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Gee, Kevin A. – American Journal of Evaluation, 2014
The growth in the availability of longitudinal data--data collected over time on the same individuals--as part of program evaluations has opened up exciting possibilities for evaluators to ask more nuanced questions about how individuals' outcomes change over time. However, in order to leverage longitudinal data to glean these important insights,…
Descriptors: Longitudinal Studies, Data Analysis, Statistical Studies, Program Evaluation
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Rademaker, Linnea L.; Grace, Elizabeth J.; Curda, Stephen K. – Qualitative Report, 2012
As diverse members of a college of education evaluation committee one of our charges is to support faculty as we document and improve our teaching. Our committee asked faculty to respond to three qualitative questions, documenting ways in which interdepartmental and cross-department conversations are used to promote reflective thinking about our…
Descriptors: Computer Assisted Testing, Data Analysis, Qualitative Research, Courseware
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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
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Elizondo, Juan; Parzinger, Monica J.; Welch, Orion J. – Information Systems Education Journal, 2011
This paper presents an example of a project used in an undergraduate business intelligence class which integrates concepts from statistics, marketing, and information systems disciplines. SAS Enterprise Miner software is used as the foundation for predictive analysis and data mining. The course culminates with a competition and the project is used…
Descriptors: Undergraduate Study, Integrated Curriculum, Statistics, Marketing
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Murchland, Sonya; Kernot, Jocelyn; Parkyn, Helen – Assistive Technology, 2011
This study explored the levels of satisfaction children 8-18 years experienced with assistive technology items used to assist them in their schoolwork. Modified from the Quebec User Evaluation of Satisfaction (QUEST 2.0), the QUEST 2.1: Children's Version was developed to enable scoring by children with or without parent assistance. The QUEST 2.1:…
Descriptors: Physical Disabilities, Urban Areas, Foreign Countries, Educational Technology
Pascopella, Angela – District Administration, 2012
Predicting the future is now in the hands of K12 administrators. While for years districts have collected thousands of pieces of student data, educators have been using them only for data-driven decision-making or formative assessments, which give a "rear-view" perspective only. Now, using predictive analysis--the pulling together of data over…
Descriptors: Expertise, Prediction, Decision Making, Data
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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
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