Clustering student interaction data using Bloom's Taxonomy to find predictive reading patterns
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In modern educational technology we have the ability to capture click-stream interaction data from a student as they work on educational problems within an online environment. This provides us with an opportunity to identify student behaviours within the data (captured by the online environment) that are predictive of student success or failure. The constraints that exist within an educational setting provide the ability to associate these student behaviours to specific educational outcomes. This information could be then used to inform environments that support student learning while improving a student’s metacognitive skills. In this dissertation, we describe how reading behaviour clusters were extracted in an experiment in which students were embedded in a learning environment where they read documents and answered questions. We tracked their keystroke level behaviour and then applied clustering techniques to find pedagogically meaningful clusters. The key to finding these clusters were categorizing the questions as to their level in Bloom’s educational taxonomy: different behaviour patterns predicted success and failure in answering questions at various levels of Bloom. The clusters found in the first experiment were confirmed through two further experiments that explored variations in the number, type, and length of documents and the kinds of questions asked. In the final experiment, we also went beyond the actual keystrokes and explored how the pauses between keystrokes as a student answers a question can be utilized in the process of determining student success. This research suggests that it should be possible to diagnose learner behaviour even in “ill-defined” domains like reading. It also suggests that Bloom’s taxonomy can be an important (even necessary) input to such diagnosis.
DegreeDoctor of Philosophy (Ph.D.)
CommitteeGreer, Jim; Schwier, Richard; Vassileva, Julita; McQuillan, Ian
Copyright DateJanuary 2016
Reading Comprehension, Clustering, Ill-defined Domains, Bloom's Taxonomy