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Sarcona, Alessandra; Kovacs, Laura; Wright, Josephine; Williams, Christine – American Journal of Health Education, 2017
Background: Weight gain and lifestyle behaviors during college may contribute to future health problems. This population may not have sufficient self-monitoring skills to maintain healthy lifestyle behaviors. Purpose: The purpose of this study was to determine the relationship between usages of mobile health applications (apps) designed to track…
Descriptors: Handheld Devices, Computer Oriented Programs, Eating Habits, Physical Activity Level
Harris, Robert V.; King, Stephanie B. – Community College Journal of Research and Practice, 2016
The purpose of this study was to see if a relationship existed between American College Testing (ACT) scores (i.e., English, reading, mathematics, science reasoning, and composite) and student success in a computer applications course at a Mississippi community college. The study showed that while the ACT scores were excellent predictors of…
Descriptors: College Entrance Examinations, Standardized Tests, Scores, Predictor Variables
Küçük, Sevda; Kapakin, Samet; Göktas, Yüksel – Anatomical Sciences Education, 2016
Augmented reality (AR), a new generation of technology, has attracted the attention of educators in recent years. In this study, a MagicBook was developed for a neuroanatomy topic by using mobile augmented reality (mAR) technology. This technology integrates virtual learning objects into the real world and allow users to interact with the…
Descriptors: Anatomy, Medical Education, Undergraduate Students, Medical Students
Harris, Robert V. – ProQuest LLC, 2014
In this educational study, the research problem was that each semester a variable number of community college students are unable to complete an introductory computer applications course at a community college in the state of Mississippi with a successful course letter grade. Course failure, or non-success, at the collegiate level is a negative…
Descriptors: Standardized Tests, Scores, Computer Science Education, Success
Amershi, Saleema; Conati, Cristina – Journal of Educational Data Mining, 2009
In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…
Descriptors: Supervision, Classification, Models, Educational Environment