Note: This research topic can be taken by Thesis and Project route students
Note: This topic is co-supervised with Dr. Oscar Lin and Dr. Maiga Chang by Dr. Hongxin Yan(https://www.athabascau.ca/science-and-technology/our-people/hongxin-yan.html) who is the leading supervisor.
Research Background and Motivation
Online STEM learners often struggle with self regulated learning (SRL), leading to high attrition rates. Existing adaptive systems frequently rely on binary correctness data and machine centric control, resulting in reduced learner autonomy, which is an essential component of effective human learning.
Research demonstrates that AI can serve as a collaborative partner when it integrates learners’ subjective metacognitive judgement and their objective performance data. This integration supports more accurate adaptation and fosters metacognitive growth in adaptive learning system.
The Confidence based Adaptive Practicing (CAP) system was proposed by Dr.Yan to embody this Human-AI partnership approach. Preliminary simulations indicate that CAP is an effective adaptive strategy; however, its implementation as a real world, operational system for online courses remains an important next step. This makes the project highly meaningful for graduate students interested in both AI in Education, machine learning and learning sciences.
Research Objectives
- Design and develop the human-AI partnership in CAP system by strengthening learner autonomy, transparency, and metacognitive support.
- Integrate CAP into real AU online courses to support self regulated learning and reduce dropout.
- Validate the effectiveness of CAP through experimentation and evaluation.
Research Methodologies
Students may use a combination of:
- Design Science Research (DSR) methodology - for systematic system development.
- System design & implementation - to develop functional components of the CAP system.
- Algorithmic experimentation - to test adaptive mechanisms and model performance.
- Pilot studies with AU students, where applicable - to evaluate usability and learning outcomes.
Expected Outcomes
Students should be able to deliver:
A fully functional CAP system suitable for deployment in real‑world online STEM courses.
A rigorous experimental design for evaluating system performance (for Thesis route students) or A system usability analysis or a pilot evaluation (for Projectroute students).
A professional final report with:
- system implementation details
- code documentation
- test cases
- validation results
Required Skills
- Strong interest in Human-AI interaction in education
- Skills in algorithm design
- Knowledge of machine learning techniques
- Proficiency in programming using Python, JavaScript, databases, JSON, XML, HTML
- Ability to work independently as a self‑motivated learner
Contact for inquiries about this research topic
Dr. Hongxin Yan
Learning Designer, Faculty of Science and Technology
Email:hongxiny@athabascau.ca
Profile: https://www.athabascau.ca/science-and-technology/our-people/hongxin-yan.html
Publications: https://scholar.google.com/citations?user=yXwH6ZUAAAAJ&hl=en
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