In 2003, I formed the
Intelligent Educational Systems (IES) Research Group. at Athabasca University.
Thanks to the support from NSERC and AU,
the IES group completed a number of interesting projects,
such as, Broken Link Checking agent (2003-2004),
agent378 , eAdvisor for program planning (2005-2009),
Game-like QuizMASter (2009-2014), and AlgoVisAU. For details, please see my publications.
Currently, we are working on the following research projects:
- Adaptive and Personalized Learning Paths
One of the goals of adaptive learning systems is to realize adaptive learning sequencing by optimizing the
order of learning materials to be presented to different learners. This research aims to come up with
new approaches to
recommending optimal and personalized learning sequences for learners taking an online course.
- Adaptive Formative Assessment - Adaptive QuizMASter
Formative assessments help teachers identify concepts that learners are struggling to understand,
skills they are having difficulty acquiring, or learning standards they have not yet achieved so that
adjustments can be made to lesson contents, instructional approaches, and academic support.
Formative assessment should be viewed as an integral part of instruction, and it should be used in real
time for guiding the learning process. To identify the gaps quickly and accurately between what is known
and what is aimed to be known, adaptive testing has been proposed to replace static assessments.
However, existing latent factor based adaptive assessment methods need complex question pool calibration
and use the debatable premise that different questions measure one common latent trait.
Also, the knowledge tracing approaches are hard to implement and use.
To overcome these limitations, we have been developing and testing an online adaptive formative assessment system,
Adaptive Quizmaster, for learning online courses. It is an LTI-enabled system that can be integrated with other
learning platforms. The objective of this research project is to develop and test new question selection
mechanisms using Reinforcement Learning (RL).
With this framework, we aim to create formative assessments that can adaptively select questions
from a question bank according to students’ responses with as few questions as possible while ensuring
the accuracy of assessments.
- Ethical Considerations for Adaptive Cyber Learning
Adaptive learning aims to deliver learning experiences that meet the unique needs of an individual student
— whether that’s through adaptive pathways, adaptive feedback, or adaptive content.
AI technology continuously collects and analyses student data to change what a student sees
and does next automatically. There are, however, several concerns surrounding the ethics of
using AI in education. This research addresses selected ethical issues emerging
from a range of examples pertaining to adaptive learning such as (1) student modeling; (2) formative assessment.
- Interoperability issues in Open and Distribued Learning
Each adaptive learning system (ALS) has a student model.
However, what is learned about students in one system—their skills, behaviors,
and affect is not carried over to other systems. It would be beneficious to use
the information learned in other ALS and potentially improve both the effectiveness and efficiency of the system.
- X Reality based Cyber Learning
a key driver of security and prosperity for humanity. Virtual reality (VR),
Augmented Reality or Mixed Reality is a promising tool in educational technology.
Cyberlearning is based on the belief ‘learning by doing’ and where technology tools are used to
carry out and facilitate learning experiences that would otherwise be impossible without the
technology itself. VR has the promise to enable cyberlearning where a 3D environment is
beneficial to enhance learning experience and significantly boost learning outcomes.
Our goal is to develop an adaptive VR system capable of greatly enhancing learning environments
for online STEM (science, technology, engineering, and math) courses at Athabasca University.
In both formal and informal learning, models are often used to explain complex subjects that can be
unintuitive and abstract.
A cyberlearning environment with VR can employ physical movements as learning aids.
VR environments can include virtual tutors (agents) that guide learners through lessons
in more effective manners.