Improving Learning Based on the Identification of Working Memory Capacity, Adaptive Context Systems, Collaborative Learning and Learning Analytics

Abstract

Working memory capacity and learning styles play key roles within adaptive learning environments. In addition, the concepts of collaborative efforts, context awareness, ensuring student engagement and the identification of students at risk of dropping out, play vital roles and are key to any successful learning environment. In this chapter, key concepts and mechanisms for each of them are discussed along with various approaches and frameworks. A means of utilizing artificial intelligence to improve working memory capacity identification and learning styles identification is discussed in the second section. Adaptation is discussed in both the third and fourth section, as it pertains to collaborative learning environments and adaptive context-aware expert systems. The final two sections address the problem of student drop-out rates as it pertains to improving the promotion of scientific competencies and the identification of students at risk of dropping out. All these concepts assist in providing learners with adaptive and improved learning environments that aid in supporting learners in the learning process.