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

Richard A W Tortorella, Darin Hobbs, Jeff Kurcz, Jason Bernard, Silvia Baldiris, Ting-Wen Chang, Sabine Graf

Research output: Contribution to journalJournal Articlepeer-review

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.
Original languageCanadian English
Pages (from-to)39-55
Number of pages17
JournalProceedings of Science and Technology Innovations
Publication statusPublished - 2015

Keywords

  • collaborative learning
  • context aware
  • learning analytics 40 Chapter 4
  • learning style
  • scientific competences
  • working memory capacity

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