Research Vision
Learning Analytics and Big Data Regulation
E-learning offers huge possibilities for the delivery of affordable high-quality education irrespective of temporal and geographical constraints. However, many students suffer from the high demand such learning makes on the students’ ability to self-regulate, leaving them on their own and denying remedial interventions to instructors. The frustrations and low achievement of these students currently leads to high dropout rates. To counter that setback, this research proposes an AI-powered anthropomorphic pedagogical agent to assist learners and teachers in their tasks. Such APAs will meet the cognitive, metacognitive, motivational, emotional, and social needs of e-learning students, while complementing or supplementing teachers’ pedagogical responsibility.
Research News
Shedding Light on the Automated Essay Scoring Process – EDM 2019
David Boulanger presented a poster at the Educational Data Mining Conference 2019 aiming at Shedding Light on the Automated Essay Scoring Process, a subject of high interest by researchers in[…]
Read more
First Workshop on Intelligent Textbooks at AIED 2019
On June 25, 2019, David Boulanger presented ‘An Overview of Recent Developments in Intelligent e-Textbooks and Reading Analytics’ at the AIED first workshop on Intelligent Textbooks. You will find details[…]
Read more
Learning Analytics: exploring the knowledge boundary
On March 14, 2019, Professor Kumar gave a presentation on Learning Analytics (LA) and how LA caters with the strategic research orientation at AU. You will find the presentation available[…]
Read moreResearch Areas
ARTIFICIAL INTELLIGENCE IN EDUCATION
· Big data learning analytics
· Intelligent tutors and smart objects
· User modelling and model tracing
SELF-REGULATED AND CO-REGULATED LEARNING
· Computational models of self- and co-regulation
· Computer-supported collaborative learning
· Computational models of grit
HUMAN-COMPUTER INTERACTION
· Immersive technology – augmented / virtual reality
· Interactive visualizations
· Evaluation of user experiences
· Anthropomorphic agents
CAUSAL MODELLING
· Knowledge engineering, representation, and reasoning
· Game-based causality
· Longitudinal and observational causal models
· Discovery of causal models
TECHNOLOGY-ENHANCED TEACHING, LEARNING, AND RESEARCH
· Entitative learning experiences
· Inclusive and universal design for learning environments
· Open data, open science, open research
· Distributed privacy and data governance
· Reenacted companions and representations
Research Projects
Big Data Analytics – Technologies, Standards
Scaling learning analytics systems at the big data level requires the ingestion of multimodal data, standardization of data of interests, and the integration of large-scale storage and data processing technologies.
Anthropomorphic Pedagogical Agents (APA)
APAs embody human traits such as the analysis process and insightfulness of humans. They are able to interact with humans by detecting and exhibiting emotion, communicating through natural language, and enacting a pedagogical role.
Industry Training
Analytics
Industry training analytics assesses operators’ skill development during learning-by-doing processes done within immersive environments (virtual and augmented reality) in both standard and emergency settings.
Coding
Analytics
Coding analytics captures, assesses, and provides timely feedback to learners on their coding processes, testing, and debugging habits with the goal of fostering high-quality, professional programming skills demanded in the industry.
Writing
Analytics
Assisting a student during the writing process is a colossal task that requires combining the forces of state-of-the-art natural language processing and deep learning techniques. Predicting essay final scores and rubric scores is just one of many ways of providing formative feedback to students to help them reinforce their writing skills.
Observational Study
Approach
The Internet of Things improves the precision with which learners can be non-intrusively observed, increasing the potential of observational studies to approximate randomized block designs and estimate causal effects without the ethical concerns and recruitment limitations of randomized experiments.
Regulation
Analytics
Self-regulated learning analytics implies assessing the student’s ability to set goals, craft a plan to reach those goals, stick by the plan when working toward that goal, and monitor one’s progress to adapt the goal and its learning path as needed. It also provides students with feedback to guide them toward desirable self-regulatory traits.
Competence
Analytics
Competence analytics measures the knowledge and skills of learners independently of the instructional materials at their disposal during the learning processes. It also estimates the retention level of acquired knowledge and the transfer effect of learning one concept over another, which allows to fill in knowledge gaps in a new domain.
Music
Analytics
Assessing music skills, whether it is singing, playing instruments, or understanding the theoretical concepts of music, is a challenging but exciting task. Nowadays, technologies such as computer vision and audio digital processing allow to capture valuable data during the learning process, measure the level of engagement and music skills of learners, and provide timely feedback to musicians in training and teachers.
Research
Analytics
Research analytics aims at promoting and facilitating the sharing and integration of scientific study results with the purpose to discover new and finer-grained insights in education to optimize the learning and teaching processes and the environments in which they occur. The ultimate goal is to avoid actionable insights to sleep over decades, and rather optimize the research benefits to the education community.
Sport
Analytics
Sport analytics consists of designing sportswear that tracks players’ movements as well as developing sensors in sports equipment that assess players’ dexterity and velocity. The data analyzed allow to provide real-time and concise feedback that help teams optimize their performance and assist referees/umpires in the decision-making process, with careful attention to not interfere with the sport itself.
Math
Analytics
The Knowledge Space Theory allows to capture the dependencies between a set of related math concepts to form a knowledge space within which the student’s learning state is constantly recorded and his/her overall learning path tracked. Through continuous adaptive formative assessments, gaps in the student’s comprehension are easily detected and remedied. This is crucial to help students build their confidence.
Instructional Designs Analytics
Evaluates the quality and effectiveness of learning resources and course designs on student performance and provides recommendations on how to improve the learning experience.
Healthcare
Analytics
Healthcare analytics automates through education data the detection of mental health disorders such as ADHD and remedies to these disorders through educational rehabilitation.
Sentiment
Analytics
Sentiment analytics is the detection of the learner’s emotion during the learning process and the identification of the cause identified through computer vision, audio, neurological, physical, and text data.
Traffic
Analytics
Traffic analytics leverages techniques developed in educational data mining and learning analytics to innovatively apply them to better determine and improve traffic conditions thanks to smart technologies.
Lean & Agile in Collaborations
Adapting the Lean & Agile methods of project management to software development projects in the context of academia-academia or industry-academia collaborations.
User
Experience
Identifying and minimizing barriers to learning analytics adoption to analyze the effectiveness of analytics-generated feedback and optimize the educational benefits.
Reading
Analytics
Analyzes reading behaviors and recommends concepts ready to be learned based on learner’s profile and when to transition from practice to reading (vice versa) to optimize learning.
Communication Skills Analytics
Assesses and nurtures skills for effective verbal and non-verbal communication including listening/understanding complex language, and articulating and pronouncing ideas thoroughly.
Research Experiments
Our learning analytics research group, under the umbrella of Athabasca University, is conducting different experiments of our learning analytics tools and solutions in collaboration with various institutions and industries around the world.
OUR EXPERIMENTS
Research Team
FULL TEAM
Prof Vive Kumar
PhD in Computer Science
Lead Researcher –
Learning Analytics in AI
Prof Shawn Fraser
PhD in PE and Recreation
Researcher –
Data Analysis, Experimental Design, Technology UX
David Boulanger
UGRD in Computer Science
Research Assistant –
Data Scientist
Jeremie Seanosky
UGRD in Computer Science
Research Assistant –
Information Systems Engineer
Chris Maton
B.S. in Computer Sc.
Information Architect
Isabelle Guillot
UGRD in Management
Research Assistant –
Agile Project Manager & Communication Coordinator
Rebecca Guillot
UGRD in Computer Science
Research Assistant –
Systems Architect & Software Developer
Claudia Guillot
UGRD in Design
Research Assistant –
3D Modeling Graphic Designer
Geof Glass
PhD in Communication
Researcher –
Sport Analytics
Diane Mitchnick
Grad in Computer Science
Researcher –
Health Analytics
Colin B. Pinnell
UGRD in Computer Science
Research Assistant –
Software Developer
Research Publications
ALL OUR PUBLICATIONSInternational Conference on Advanced Learning Technologies (ICALT 2019), July 15-18, Maceió-AL, Brazil
Assessing Learning Analytics Impact on Coding Competence Growth
Boulanger, D., Seanosky, J., Guillot, R., Guillot, I., Guillot, C., Fraser, S., Kumar, V., & Kinshuk (2019, accepted)
In D. Sampson, D. Ifenthaler, J.M. Spector, P. I Isaías, S. Sergis (Eds.) Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment (pp. 123-151). Springer, Cham
Performance analysis of a serial NLP pipeline for scaling analytics of academic writing process
Boulanger, D., Clemens, C., Seanosky, J., Fraser, S., Kumar, V.S. (2019)
Educational Data Mining (EDM 2019), Poster, July 2-5, Montreal, Canada
Shedding Light on the Automated Essay Scoring Process
Boulanger, D., & Kumar V. (2019)
Others’ Research Publications
Contact Us
If you have any questions regarding the Learning Analytics please feel free to email us.
Useful Links
