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Çebi, Ayça; Güyer, Tolga – Education and Information Technologies, 2020
In this study, students' interactions with different learning activities are examined and the relation among learning performance with different interaction patterns, learning performance, self-regulated learning (SRL) strategies and motivation is presented. Learning materials including different kinds of activities are prepared and presented to…
Descriptors: Interaction, Behavior Patterns, Learning Analytics, Electronic Learning
Dani, Anita; Nasser, Ramzi – Turkish Online Journal of Educational Technology - TOJET, 2016
The purpose of this paper is to determine potential identifiers of students' academic success in foundation mathematics course from the data logs of the intelligent tutor Assessment for Learning using Knowledge Spaces (ALEKS). A cross-sectional study design was used. A sample of 152 records, which accounts to approximately 60% of the population,…
Descriptors: Postsecondary Education, Mathematics Education, Intelligent Tutoring Systems, Technology Uses in Education
Dong, Shengli; Lucas, Margaretha S. – Career Development and Transition for Exceptional Individuals, 2016
This study focused on the academic performance and use of disability support services (DSS) of students with different types of disabilities who attend a postsecondary education institution. Findings show different patterns of academic success over four semesters as well as different patterns of DSS usage. Students who requested support from DSS…
Descriptors: Academic Achievement, Postsecondary Education, Related Services (Special Education), Use Studies
Ellis, Robert A. – Active Learning in Higher Education, 2016
There is variation in the university student experience of learning. Prior research has shown that factors that shape this include student characteristics, the learning context, student perceptions of that context and approaches to learning and their learning outcomes. In blended contexts, there is a need to identify variables which can explain…
Descriptors: Student Experience, Educational Environment, Inquiry, Higher Education
Blikstein, Paulo; Worsley, Marcelo; Piech, Chris; Sahami, Mehran; Cooper, Steven; Koller, Daphne – Journal of the Learning Sciences, 2014
New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and…
Descriptors: Programming, Computer Science Education, Learning Processes, Introductory Courses
De Luca, Barbara M.; Hinshaw, Steven A. – Educational Considerations, 2013
The purpose of this research was to investigate the role of school district expenditures in predicting student achievement in Ohio for the school year 2009-2010. Building upon the concept of the "65 percent solution," the research questions that guided this study were: (1) What percentage of Ohio's school district's operating budgets…
Descriptors: Academic Achievement, Predictor Variables, Predictive Validity, Expenditures

Davis, Junius A. – 1964
The structure of characterizations of college students by faculty members, particularly observable and significant dimensions or trait patterns, were investigated. Student ratings by faculty members on 80 bi-polar traits, together with Scholastic Aptitude Test (SAT) scores and high school and college freshman average grades, were obtained.…
Descriptors: Academic Achievement, Admission Criteria, Cluster Grouping, College Faculty
Davis, Junius A. – 1964
This report, a continuation of the study described in TM 000 174 and TM 000 176, employs identical data and similar methods to determine the nature of desirability separate and apart from the rater's perception of the student's academic performance (rather than his actual performance). The results show an absorption in the faculty perception of…
Descriptors: Academic Achievement, Admission Criteria, Cluster Grouping, College Faculty