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Showing 1 to 15 of 20 results Save | Export
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Christine Ladwig; Taylor Webber; Dana Schwieger – Information Systems Education Journal, 2023
Data is a powerful tool for the healthcare industry to use for managing, analyzing, and reporting on critical events in the field. The analysis of broad, salient data files aids healthcare businesses in uncovering hidden patterns, market trends, and customer preferences; these details may then be used to improve the quality and delivery of care to…
Descriptors: Rural Areas, Health Services, Data Analysis, Learning Activities
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Cohausz, Lea; Tschalzev, Andrej; Bartelt, Christian; Stuckenschmidt, Heiner – International Educational Data Mining Society, 2023
Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data's sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong…
Descriptors: Information Retrieval, Data Processing, Pattern Recognition, Information Technology
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Groen, Martin; Noyes, Jan – Discourse Processes: A Multidisciplinary Journal, 2013
Communicating via text-only computer-mediated communication (CMC) channels is associated with a number of issues that would impair users in achieving dialogue coherence and goals. It has been suggested that humans have devised novel adaptive strategies to deal with those issues. However, it could be that humans rely on "classic"…
Descriptors: Foreign Countries, Computer Mediated Communication, Goal Orientation, Semantics
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Valsamidis, Stavros; Kontogiannis, Sotirios; Kazanidis, Ioannis; Theodosiou, Theodosios; Karakos, Alexandros – Educational Technology & Society, 2012
Learning Management Systems (LMS) collect large amounts of data. Data mining techniques can be applied to analyse their web data log files. The instructors may use this data for assessing and measuring their courses. In this respect, we have proposed a methodology for analysing LMS courses and students' activity. This methodology uses a Markov…
Descriptors: Foreign Countries, Electronic Learning, College Mathematics, Integrated Learning Systems
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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
Ming, Norma; Baumer, Eric – Journal of Asynchronous Learning Networks, 2011
Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help…
Descriptors: Computer Mediated Communication, Semantics, Asynchronous Communication, Discourse Analysis
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Hershkovitz, Arnon; Nachmias, Rafi – Internet and Higher Education, 2011
This research consists of an empirical study of online persistence in Web-supported courses in higher education, using Data Mining techniques. Log files of 58 Moodle websites accompanying Tel Aviv University courses were drawn, recording the activity of 1189 students in 1897 course enrollments during the academic year 2008/9, and were analyzed…
Descriptors: Higher Education, Persistence, Internet, Data Processing
Zhang, Yi – ProQuest LLC, 2011
Due to the rapid advances in computing and sensing technologies, enormous amounts of data are being generated everyday in various applications. The integration of data mining and data visualization has been widely used to analyze these massive and complex data sets to discover hidden patterns. For both data mining and visualization to be…
Descriptors: Information Technology, Data Processing, Data Analysis, Information Retrieval
Liu, Bin; Bi, Qing-sheng – Online Submission, 2010
The Verhulst model can be used to forecast the sequence, which is characterized as non-monotone and fluctuant sequence or saturated S-form sequence. According to the situation of national enrollment scale of college, this paper forecasts the quantity of students taking entrance examination to college with a Verhulst model with remedy based on data…
Descriptors: Higher Education, Foreign Countries, Mathematical Models, College Entrance Examinations
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Pistilli, Matthew D.; Arnold, Kimberly E. – About Campus, 2010
This article discusses how Purdue University is changing the academic behavior of struggling students. At Purdue, they've developed Signals as a means of helping students better understand where they stand gradewise early enough so that they can seek help and raise their grade or drop the course without the penalty of a failing grade. They knew…
Descriptors: Feedback (Response), Grades (Scholastic), Academic Achievement, Higher Education
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
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Delen, Dursun – Journal of College Student Retention: Research, Theory & Practice, 2012
Affecting university rankings, school reputation, and financial well-being, student retention has become one of the most important measures of success for higher education institutions. From the institutional perspective, improving student retention starts with a thorough understanding of the causes behind the attrition. Such an understanding is…
Descriptors: Higher Education, Student Attrition, School Holding Power, Prediction
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D'Allegro, Mary Lou; Kerns, Stefanie – Journal of College Student Retention: Research, Theory & Practice, 2011
Data mining and statistical analyses at a less selective institution reveal that the relationships between parents' educational level and some first year success indicators are not linear. Specifically, students who report that either parent or guardian(s) have an educational level beyond a baccalaureate degree or do not report parent education…
Descriptors: First Generation College Students, Educational Attainment, Data Processing, Pattern Recognition
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Alinaghi, Tannaz; Bahreininejad, Ardeshir – International Journal of Distance Education Technologies, 2011
The increasing advances of new Internet technologies in all application domains have changed life styles and interactions. E-learning and collaborative learning environment systems are originated through such changes and aim at providing facilities for people in different times and geographical locations to cooperate, collaborate, learn and work…
Descriptors: Electronic Learning, Educational Environment, Questioning Techniques, Responses
Michalski, Greg V. – Association for Institutional Research (NJ1), 2011
Excessive college course withdrawals are costly to the student and the institution in terms of time to degree completion, available classroom space, and other resources. Although generally well quantified, detailed analysis of the reasons given by students for course withdrawal is less common. To address this, a text mining analysis was performed…
Descriptors: College Instruction, Courses, Withdrawal (Education), College Students
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