Any Time, Any Place, Any Pace-Really? Examining Mobile Learning In A Virtual School Environment
نویسندگان
چکیده
منابع مشابه
E-learning any time any place anywhere on mobile devices
The registered screen resolution of e-learning study moments in MedicalEducation.nl was used in this research to investigate the readiness of students and medical professionals to study e-learning on a mobile device. Between January 2008 and September 2012 the use of e-learning on a mobile device by students has quintupled to 2.29 %, while medical professionals lag behind in this development. I...
متن کاملIncreasing LARC utilization: any woman, any place, any time.
Intrauterine contraceptive devices and the progestin implant are the most effective long-acting reversible contraception (LARC) methods available for preventing unintended pregnancy. LARC devices are safe, non-user-dependent methods that have the highest rates of continuation and satisfaction of all reversible contraceptives. Use of these contraceptives remains low in the United States due to s...
متن کاملAnywhere, Any Time, Any Kind: Towards a Sustainable Learning System
Forms of learning: from apprenticeship to sustainable learning Womack, et al., (1990) identified an evolutionary process from the craft production system to the mass production system to a lean production system. It is argued here that the next stage in this evolutionary process will lead to a sustainable production system. Furthermore, we may identify corresponding learning systems associated ...
متن کاملLearning Physics in a Virtual Environment: Is There Any?
With nearly one in five college students taking at least one course online, with nearly every major college and university offering courses and/or programs online and with a growing number of citizens in the work place wanting and needing education in ways which fit their work and personal schedules, e-learning is becoming more important and ubiquitous each year. The supply (courses) is there i...
متن کاملA brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention
The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully automatic, black-box learning machines for feature-based classification and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across different types of complexity. Over six rounds, participants succeeded in delivering AutoML software capable of being ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Turkish Online Journal of Distance Education
سال: 2014
ISSN: 1302-6488
DOI: 10.17718/tojde.45828