Mathematics learner profiling using behavioral, physiological and self-reporting methods

Author: 
Date created: 
2013-08-07
Identifier: 
etd7911
Keywords: 
Learner profiling, learner profile
Mathematics education, educational neuroscience
CUR framework
Motivational constructs
Embodied cognition
Behavioral and physiological methods
Abstract: 

This exploratory case study aimed at investigating learner profiles when participants are studying, self-reporting, restudying content and answering questions related to the division theorem in mathematics. It includes surveys aiming to measure participants’ epistemological beliefs, metacognitive strategies and the levels of mathematics anxiety; behavioral data including audio-visuals and screen capture embedded with eye-tracking, and physiological data including heart, respiration and eye blink rates. It uses ‘learner profiling framework’ built on previous literature and defined with a new perspective. The data are analyzed using mixed research methodology cross validating self-report, behavioral and physiological data. The results from four participants provide contributions to the literature in four aspects. First, learner profiling framework offers a new methodology to educational research with numerous benefits. Second, CUR (Calculation-Understanding-Reasoning) framework offers a new way of categorizing mathematical cognition and corresponding content. Third, qualitative approach in investigating learner motivations indicates motivational constructs are much more nuanced than previously thought. Fourth, single case approach for studying learner behavior and physiology provides successful links to underlying cognitive and affective processes. The investigations are followed by learner profiles that involve assessments from teacher’s perspective and recommendations for future work.

Document type: 
Thesis
Rights: 
Copyright remains with the author. The author granted permission for the file to be printed and for the text to be copied and pasted.
File(s): 
Senior supervisor: 
Stephen Campbell
Department: 
Education: Faculty of Education
Thesis type: 
(Dissertation) Ph.D.
Statistics: