NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 7 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Gontzis, Andreas F.; Kotsiantis, Sotiris; Panagiotakopoulos, Christos T.; Verykios, Vassilios S. – Interactive Learning Environments, 2022
Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students' failure,…
Descriptors: Student Attrition, Distance Education, At Risk Students, Achievement
Peer reviewed Peer reviewed
Direct linkDirect link
Gkontzis, Andreas F.; Kotsiantis, Sotiris; Panagiotakopoulos, Christos T.; Verykios, Vassilios S. – Interactive Learning Environments, 2022
Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze, and predict students' failure,…
Descriptors: Student Attrition, Distance Education, At Risk Students, Achievement
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Huang, Shiu-Li; Shiu, Jung-Hung – Educational Technology & Society, 2012
The success of Web 2.0 inspires e-learning to evolve into e-learning 2.0, which exploits collective intelligence to achieve user-centric learning. However, searching for suitable learning paths and content for achieving a learning goal is time consuming and troublesome on e-learning 2.0 platforms. Therefore, introducing formal learning in these…
Descriptors: Foreign Countries, Intelligent Tutoring Systems, Computer System Design, Computer Science Education
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Olsen, Robert J. – Journal of Chemical Education, 2008
I describe how data pooling and data visualization can be employed in the first-semester general chemistry laboratory to introduce core statistical concepts such as central tendency and dispersion of a data set. The pooled data are plotted as a 1-D scatterplot, a purpose-designed number line through which statistical features of the data are…
Descriptors: Familiarity, Visualization, Chemistry, Laboratories
Peer reviewed Peer reviewed
Direct linkDirect link
Patton, Rob; Johnson, Diane; Bimber, Bruce; Almeroth, Kevin; Michaels, George – AACE Journal, 2004
College students exploit information technology to cheat on papers and assignments, but for the most part university faculty employ few technological techniques to detect cheating. This paper reports on a trial of software for the detection of cheating in a large undergraduate survey class. The paper discusses the decision to adopt electronic…
Descriptors: College Students, Student Attitudes, Plagiarism, Cheating