Using STM ability for Adaptive Language Learning in Mobile Learning Environment

Due to the rapid advancement in mobile communication & wireless technologies, many researchers and educators believed that these new technologies would have a great impact on the way of learning in the future. Mobile Learning (M-learning) can be implemented by effectively applying short-message services such as Short Message Service (SMS) and Multimedia Message Service (MMS) provided by mobile phone system, as Learning Content Delivery (LCD) methods, to deliver different types of Learning Content Representation (LCR). However, the most important issue is whether M-learning based on these LCD methods and LCR types can really achieve good Learning Outcomes (LOuts) and be accepted by mobile learners. Alavi and Leidner reported that most of the past studies mainly relied on the stimulus-response theory, which probed only the relation between IT (stimulus) and the LOuts (response) [2]. This over simplified the learning process of human beings. They also pointed out that the Psychological Learning Process (PLP) of learners is an important mediator which can not be neglected. Therefore, this research explores the restraint of Short-Term Memory (STM) ability, the most important part of PLP, on assessing LOuts of M-learning. Moreover, Baddeley thought that STM ability is composed of two subsystems capacities: Phonological Loop (PL) capacity and Visuo-Spatial Sketch Pad (VSSP) capacity [5]. Our study shows that if different LCR types can fit with learner’s specific STM ability would provide higher LOuts in M-learning environment. Therefore, the most suitable way to help learners with lower PL and lower VSSP STM capacity is to provide them just basic learning materials. On the other hand, providing additional written annotations to learners with higher PL and lower VSSP STM capacity will help them learn better. Providing pictorial annotations will benefit learners with lower PL and higher VSSP STM capacity. Finally, for Learners with higher PL and higher VSSP STM capacity, the best way is to cater them with both written annotations and pictorial annotations in M-learning environment. Key-Words: STM ability, Learning content delivery method, Learning content representation type, M-learning