1. Use of Learning Analytics in Massively Open Online Courses

2. Essay Research Topic Research Topic: Major challenges currently associated with Massively Open Online Courses (MOOCs ) – High attrition – Insufficient and untimely instructor feedback – Poor self-regulated learning (SRL) skills – Ineffective use of big data and learning analytics Research Goal: Propose tools and techniques that can be used in MOOCs to promote SRL, increase feedback, effectively use big data & reduce attrition 2

3. Motivation for Research MOOCs are a fairly new and promising method for educational delivery There are several millions of students that have registered and participated in MOOCs – MOOCs already have a global presence MOOCs have yet to reach their full potential due to concerns and challenges they currently face 3

4. What is a MOOC? MOOC: Massive Open Online Course MOOCs Offer: – Education provided at low or no cost – Delivered via the web, therefore accessible to anyone that has access to the internet. NO GEOGRAPHIC BARRIERS – No pre-requisites – Enable massive student enrollment – Learning flexibility, and participation is voluntary – A learner-centered approach 4

5. Current Challenges MOOCs currently face several challenges, including: – High Attrition – Insufficient and untimely instructor feedback – Poor self-regulated learning skills – Inefficient use of collected big data and learning analytics 5

6. Challenge with High Attrition MOOCs typically have a 5 – 10% pass rate Such high attrition impacts many: – Drop outs: Feel a sense of failure Incur financial loss (if applicable) Lose the time invested into the course – Remaining students: Lose colleagues with whom they’ve formed close bonds – Educational institutions: Suffer from low graduation rates Increased financial expenses to recruit new students 6

7. Factors Contributing to High Attrition There are several factors that contribute to high attrition in MOOCs. The two factors investigated through this research are: – Insufficient instructor feedback – Poor self-regulated learning skills: 7

8. Insufficient Feedback Challenge with instructor feedback in MOOCs due to massive number of students – As the number of registered students in a MOOC increases, the more difficult it is for instructor to provide quality feedback in a timely manner Without the feedback, students: – Experience difficulty assessing progress & adjusting study habits for future improvement – May feel isolated and lost in the MOOC setting – Are likely to drop out of the course early 8

9. Poor Self-Regulated Learning Skills Most students have poor self-regulated learning (SRL) skills. SRL is a skill where students know: – How to set goals – What is required to achieve those goals – How to follow-through & achieve those goals Most students lack SRL skills & therefore experience difficulties in a MOOC setting: – Lack self-motivation & incentive to complete course – Challenged with managing time & meeting deadlines – Unable to study due to competing priorities with work/personal/family life 9

10. Importance of Addressing the Challenges It is critical that these challenges are addressed in order to: – Reduce attrition in MOOCs – Enhance the student learning experience – Continuously improve the delivery of education through MOOCs The use of big data and learning analytics may be helpful in addressing these challenges 10

11. Big Data – Massive amounts of data collected from various sources – Enables capturing information from user’s online behaviours, from which knowledge can be gained to predict future behaviour and interest Current challenge: – massive data is collected from MOOCs, but not used effectively (many lost opportunities) 11

12. Learning Analytics Interprets large amount of data on students to assess academic progress, predict future performance and identify potential issues A study on PSO-clustering revealed that students can be continuously and accurately grouped by their study habits and competencies By leveraging similar grouping techniques, it may be possible to address the feedback challenges with MOOCs 12

13. Proposed Tools & Techniques Incorporate tools & techniques to address poor self-regulated learning: – Frequent testing – Use of SCRL tool (developed by Athabasca University Learning Analytics Research group) Enables instructors & students to create goals Reminds students of existing goals Prompts students to self-reflect on progress & changes needed to improve performance Monitors student progress towards goals Facilitates interaction of students with similar goals Essentially guides students through all SRL phases 13

14. Proposed Techniques (Cont’d) Incorporate techniques to address insufficient instructor feedback: – Peer assessment Reduces instructor involvement Enables number of assessments used to be increased – Peer learning Use big data and learning analytics Cluster students into groups based on performance (high performers vs. others) Group sets of students together from each cluster to assist each other with grasping difficult learning concepts – These techniques reduce instructor involvement, but increase peer support & build a sense of community 14

15. Simulated Study A simulated study was performed to demonstrate that the clustering technique can be used for peer learning – MOOC class size of 10,000 students was used for study – Data was generated using random value generators available online – CSV file was created to store the generated data – WEKA tool was used to import the data & perform the clustering Results: – May be beneficial to use more than 2 clusters to filter students that have not logged into online course 15

16. Simulated Study Results Successful in clustering students based on performance Potential Challenges: – Students being grouped with: Students that already dropped out Students that never logged into the online course – Drastically uneven clusters Ex. Cluster 1 = 80%, Cluster 2 = 20% How to group students in such a distribution? – Selecting appropriate attributes for clustering Set of attributes used drastically changes the clustering results 16

17. Thank You! 17