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Thontirawong, Pipat; Chinchanachokchai, Sydney – Marketing Education Review, 2021
In the age of big data and analytics, it is important that students learn about artificial intelligence (AI) and machine learning (ML). Machine learning is a discipline that focuses on building a computer system that can improve itself using experience. ML models can be used to detect patterns from data and recommend strategic marketing actions.…
Descriptors: Marketing, Artificial Languages, Career Development, Time Management
Bälter, Olle; Zimmaro, Dawn – Interactive Learning Environments, 2018
It is challenging for students to plan their work sessions in online environments, as it is very difficult to make estimates on how much material there is to cover. In order to simplify this estimation, we have extended the Keystroke-level analysis model with individual reading speed of text, figures, and questions. This was used to estimate how…
Descriptors: Keyboarding (Data Entry), Data Analysis, Time Management, Online Courses
Nguyen, Quan; Huptych, Michal; Rienties, Bart – Journal of Learning Analytics, 2018
Extensive research in learning science has established the importance of time management in online learning. Recently, learning analytics (LA) has shed further lights on the temporal characteristics of learning by allowing researchers to capture authentic digital footprints of student learning behaviours. Nonetheless, students' timing of…
Descriptors: Time Management, Online Courses, Educational Technology, Technology Uses in Education
Dvorak, Tomas; Jia, Miaoqing – Journal of Learning Analytics, 2016
This study analyzes the relationship between students' online work habits and academic performance. We utilize data from logs recorded by a course management system (CMS) in two courses at a small liberal arts college in the U.S. Both courses required the completion of a large number of online assignments. We measure three aspects of students'…
Descriptors: Online Courses, Educational Technology, Study Habits, Academic Achievement
Miyamoto, Yohsuke R.; Coleman, Cody A.; Williams, Joseph Jay; Whitehill, Jacob; Nesterko, Sergiy; Reich, Justin – Journal of Learning Analytics, 2015
A long history of laboratory and field experiments have demonstrated that dividing study time into many sessions is often superior to massing study time into few sessions, a phenomenon known as the "spacing effect." We use this well-established finding from the psychology literature as inspiration for investigating how students…
Descriptors: Online Courses, Large Group Instruction, Educational Technology, Technology Uses in Education
Pardos, Zachary A. – Journal of Learning Analytics, 2015
In Miyamoto et al. (2015, this issue) the authors looked to substantiate the presence of the spacing effect, referenced from the psychology literature, in several MOOCs. Their secondary analyses constituted a robust, empirical finding on the correspondence between session distribution and certification but with only a coarse, analogous…
Descriptors: Online Courses, Large Group Instruction, Educational Technology, Technology Uses in Education
Kilgore, Debra V. – ProQuest LLC, 2011
The purpose of this research study was to explore how college alternative teacher certification (ATC) participants experience mentoring support. The goal was to obtain a rich and deep understanding of the nature of the mentoring experience in a college ATC program through the perspectives of mentees and their mentors. The ATC program was the…
Descriptors: Program Design, Mentors, Data Analysis, Field Experience Programs
Yau, Jane Y.-K.; Joy, Mike; Dickert, Stephan – Educational Technology & Society, 2010
We report the results of a diary study to determine whether a diary approach could be used as a successful way of retrieving a) the user's learning contexts, b) which learning contexts are significant for consideration within an m-learning application, and c) which learning materials are appropriate for which learning situation. Analyses of data…
Descriptors: Foreign Countries, Educational Technology, Data Analysis, Student Characteristics
Community College Survey of Student Engagement, 2006
Each year, the Community College Survey of Student Engagement ("CCSSE") presents the results of its annual survey--and helps colleges use that information to improve student learning and persistence. "CCSSE" results give community colleges objective and relevant data about students' experiences at their colleges so they can better understand how…
Descriptors: Learner Engagement, Community Colleges, Public Agencies, Student Participation
Community College Survey of Student Engagement, 2006
Each year, the Community College Survey of Student Engagement ("CCSSE") presents the results of its annual survey--and helps colleges use that information to improve student learning and persistence. "CCSSE" results give community colleges objective and relevant data about students' experiences at their colleges so they can better understand how…
Descriptors: Learner Engagement, Community Colleges, Public Agencies, Student Participation
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