Using Learning Analytics to Improve Student Success - BC-349

Preferred Disciplines: Software, Computer Science, Big Data, PhD or Masters
Project length: 12 months
Desired start date: Nov 15, 2017
Location: Vancouver, British Columbia
No. of Positions: 1
Preferences: English Language. Data Analysis experience.
Company: Lambda Solutions

About Company:

Lambda Solutions is an e-learning company, focused on helping clients accelerate learning performance. Our technology, training, and services support over one million learners in all sectors, including education, healthcare, government, manufacturing, retail, and technology. Lambda Solutions is based in Vancouver, Canada, with offices in London, US, and Serbia.

Why work for Lambda? We are a team of smart, curious, and boundary-pushing individuals who love to solve problems. We like bad puns, Star Wars, beer on Fridays, and lots of cheesecake. Our Vancouver office is conveniently located in Gastown close to both Skytrain lines, with beautiful hardwood floors, brick walls, and a skylight where the office dogs like to bathe in the sun. We think you’ll have fun (and learn a ton!) working with us.

Project Description:

Most schools have no way to identify struggling students until they fail or withdraw. Learning analytics enables educational institutions to increase student retention by improving student success. The challenge is finding the right indicators, accessing the data to build a model that can predict students who are at risk of dropout or failure, monitor those indicators, and sharing the visualizations to help Instructors pro-actively engage struggling students.  

Background and required skills

Research Objectives/Sub-Objectives:

  • Identify the common indicators of students who are struggling and at-risk to withdraw or fail
  • Develop a flexible model that can be tuned for different learning environments who use different learning techniques (online, blended, etc.)
  • Develop an effective visualization, that is supported with detailed data, that can be used and easily understood by non-technical Instructors
  • If applicable, identify additional data sources that could complement this to improve accuracy
  • If applicable, identify potential intellectual property for future development opportunities

Methodology:

  • Interview Organizations to determine parameters of student success
  • Use real-world datasets within Learning Management Systems
  • Identify trends and differences of results that may vary across different segments of organizations, learning style, and student’s demographics
  • Use existing Learning Analytics (Zoola Analytics) technologies and complement where necessary with additional open source technologies

Expertise and Skills Needed:

  • Demonstrated experience with data analysis
  • Demonstrated experience with data structures and query methods
  • Demonstrated experience with data modeling techniques
  • Demonstrated experience with big data
  • Demonstrated experience in research techniques
  • Demonstrated competence in statistics, probability, and other relevant areas of mathematics

For more info or to apply to this applied research position, please

  1. Check your eligibility and find more information about open projects.
  2. Complete this webform. You will be asked to upload your CV. Remember to indicate the title of the project(s) you are interested in and obtain your professor’s approval to proceed!
  3. Interested students need to get the approval from their supervisor and send their CV along with a link to their supervisor’s university webpage by applying through the webform or directly to Amin Aziznia : aaziznia(a)mitacs.ca .

Program: