Using Data to Understand How to Better Design Adaptive Learning

Min Liu, Jina Kang, Wenting Zou, Hyeyeon Lee, Zilong Pan, Stephanie Corliss

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

There is much enthusiasm in higher education about the benefits of adaptive learning and using big data to investigate learning processes to make data-informed educational decisions. The benefits of adaptive learning to achieve personalized learning are obvious. Yet, there lacks evidence-based research to understand how data such as user behavior patterns can be used to design effective adaptive learning systems. The purpose of this study, therefore, is to investigate what behavior patterns learners with different characteristics demonstrate when they interact with an adaptive learning environment. Incoming 1st-year students in a pharmacy professional degree program engaged in an adaptive learning intervention that aimed to provide remedial instruction to better prepare these professional students before they began their formal degree program. We analyzed the participants’ behavior patterns through the usage data to understand how they used the adaptive system based upon their needs and interests. Using both statistical analyses and data visualization techniques, this study found: (1) apart from learners’ cognitive ability, it is important to consider affective factors such as motivation in adaptive learning, (2) lack of alignment among various components in an adaptive system can impact how learners accessed the system and, more importantly, their performance, and (3) visualizations can reveal interesting findings that can be missed otherwise. Such research should provide much needed empirical evidences and useful insights about how the analytics can inform the effective designs of adaptive learning.

Original languageEnglish (US)
Pages (from-to)271-298
Number of pages28
JournalTechnology, Knowledge and Learning
Volume22
Issue number3
DOIs
StatePublished - Oct 1 2017

All Science Journal Classification (ASJC) codes

  • Mathematics (miscellaneous)
  • Education
  • Human-Computer Interaction
  • Computer Science Applications

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