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Study on Adaptive Learning based on Short-term Memory Capacity in Mobile Learning EnvironmentHsieh, Sheng-wen 10 March 2006 (has links)
In this new era of mobile society and information explosion, people are continuously receiving different kinds of information representation at anytime and everywhere, how to quickly learn and absorb different kinds of information to become one¡¦s own knowledge is an important challenge for modern people. Due to the rapid advancement of mobile communication & wireless transmission technology, many scholars in academia were believed that these new technologies will have a great impact on the way of learning in the future. As a matter of fact, by effectively applying short-message services as learning content delivery (LCD) methods, including SMS and MMS, provided by mobile phone system to deliver different learning content representation (LCR) types, Mobile Learning (M-learning) can be implemented accordingly. However, the most important issue is whether M-learning based on these LCD methods and LCR types can really achieve good learning outcomes and be accepted by mobile learners. In this research we will explore the restraint of short-term memory (STM) ability of psychological learning process through technology-mediated learning theory on assessing learning outcomes in M-learning environment. The finding of this study is to match different LCR types with different LCD methods to fit learners¡¦ different STM abilities would cause higher learning outcomes in M-learning environment. Therefore, we suggest that Learners with lower verbal and lower nonverbal STM capacity, the most suitable way to help their learning is just providing them the basic learning materials; learners with higher verbal and lower nonverbal STM capacity, providing them additional written annotations will help them learn better; learners with lower verbal and higher nonverbal STM capacity, providing them additional pictorial annotations will help them learn better; and Learners with higher verbal and higher nonverbal STM capacity, the best way is to cater them both written annotations and pictorial annotations in M-learning environment.
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