Computational models for Mobile Learning Objects
Mobile learning is considered the newest step of eLearning, supported by mobile computing (Caudill, 2007). Learning objects are proposed in order to enhance reusability of digital educative contents in different learning contexts (Polsani, 2003). Mobile Learning Objects (MLOs) are learning objects aimed at being used in mobile learning environments (Castillo, 2007). In this paper we present our proposal of computational models for the design, development and use of MLOs. These models support the learning approaches and corresponding awareness which sustain mobile learning (Sharpies, 2005)(Wang, 2001). We present three models: personalization model, aimed at supporting personalized learning, and knowledge awareness, collaboration model, aimed at supporting collaborative learning, with social and knowledge awareness, and interaction model, aimed at supporting situated learning, and context awareness. These computational models were implemented as belief systems based on DLV (Datalog Disjunctive), a programming system of the Answer Set Programming paradigm (Leone, 2002). We also present results of testing these models in a simulated mobile learning environment.
Mobile Learning Learning Objects Computational models Answer Set Programming
Sergio Luis Castillo-Valerio Gerardo Ayala San Martin
Universidad Anahuac Xalapa Universidad de las Americas Puebla
国际会议
10th World Conference on Mobile and Contextual Learning(第十届移动学习国际会议)
北京
英文
104-110
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）