Chapter 20
In: Handbook of Research in Online Learning
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Abstract
Analysis of Patterns in Time (APT) is a quantitative research method for counting occurrences and durations of temporal patterns within temporal maps. APT has been used successfully in past research studies to detect patterns that linear models cannot. This is because APT measures relations directly within temporal maps, whereas linear models statistically relate variables that are measured separately.
Queries in APT yield frequency counts of matching segments within temporal maps, as well as segment durations. These query results, in turn, can be used to estimate the likelihood of temporal patterns.
Google Analytics 4 (GA4) includes features so that APT can be accomplished. GA4 tracking can be activated in websites and apps designed for online learning. This tracking results in temporal maps which are stored in Google’s cloud. Educational researchers can then use GA4 tools to do APT. This chapter illustrates how GA4 can be used to do APT to identify effective online instructional strategies.
I conclude by discussing the potential of APT temporal maps for use in machine learning to train neural networks. Online instruction and learning provide a context for efficiently collecting the big data needed for initially training a neural network. This network can, in turn, be used to adapt and individualize online instruction.