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My name is Stephen Pollard, professor of economics and statistics at Los Angeles. I am interested in working on a proposal for the Learner Analytics. Not sure how this project works is it campus based or CSU based?


Hi Stephen. The projects will be campus based for the most part (in my opinion) where the campus is either teaching or adopting an exemplary practice . I'm not sure who might be "leading" CSULA's efforts but you might want to check with your Dean. The grant program was just announced so there might not be anyone "leading" CSULA's effort yet.

Hi Gerry,

Thanks for the reply. Yes, before your reply, I have been in touch with those at CSULA who also want to work on learner analytics. We are going to put forth a proposal.


Hello All,

As part of the CSU System-Wide Learning Management Systems and Services project, we're planning to build a Moodle analytics module that builds on the current queries being used at SF State to assess their Moodle impact.

A description of the project follows below - if you or your campus is interested in collaborating, please contact me and there's plenty of room for participation!

Title: Analytics to Increase Student Persistence and Engagement in Gatekeeper Courses through a “Checkup” Moodle Reporting Module

The proposed project will create a real-time analytics Moodle module for faculty and students. Rapid development will build out existing San Francisco State (SF State) queries used to analyze Learning Management System (LMS) activity and existing Moodle reporting functionality. The module will include both predefined and ad-hoc report functionality to provide user-friendly service that allows customization to the diverse ways that faculty use the LMS. These reports will trigger alerts that enable faculty to identify students at risk of failing a course (hereforth “at-risk students”) and suggest interventions to improve increase student completion, persistence, and other learning challenge areas of NGLC.

Best, John (
The EOP Early Warning System (TEWS) at CSUN. Realizing the need for a more aggressive approach to increase retention and student persistence, EOP at CSUN created The Early Warning System (TEWS). TEWS is a technology-enabled support system that is student centered. It was created with the sole purpose of facilitating the early interaction of faculty, students, advisors and other student service areas to create deeper learning and engagement. TEWS is a tool that can ensure the early identification of students who may be in need of some level of intervention to ensure their persistence and academic success at the university. TEWS recognizes the importance of: (1) easing the new student's transition into the college environment, (2) early systematic identification of those students who may be academically at risk, (3) early identification of students who may be having difficulties in and out of the classroom and, (4) establishing a common communication link and real-time learner analytics between students and their instructors, advisors and relevant student services. The system is currently used for first-year students who are enrolled in developmental reading, writing and math classes. This fall semester the campus is piloting the “Stretch” Writing program and TEWS has been activated for these courses as well. We will expand use of TEWS by other areas on campus. This spring semester we will pilot TEWS in a select cross section of regular lower division courses. The system has been adopted by one other CSU campus and can be scaled to others as well.
Another very interesting idea - Humboldt is potentially interested in partnering with Northridge on this! I'll put you in contact with the right folks here for further dialog. :-)
Sacramento State will be submitting a proposal entitled something like Using Targeted Learner Analytic Data To Optimize Persistence and Progress for At-Risk Cohorts On the Roadmap To Graduation. The project is based on research by the Institutue for Higher Education Leadership & Policy at Sac State (, including research on at-risk student flow done last year for the CSU system office. The research is based on the use of so-called Success Indicators related to defined Milestones and Gateway Courses that correlate with success in retention and graduation. Milestones to be addressed include preparation of student curriculum roadmaps, completion of remediation, completion of key gateway GE and major pre-requisite courses, declaration of major or major intent, and completion of 30 GE credits. Success indicators are student behaviors and outcomes related to success at achieving those milestones; for example, a success indicator for a student required to complete remediation might be enrolling only in concurrent courses that have a correlation with high success rate for students in that remediation cohort. The technology in use for this project is timely application of learner analytic data to allow for providing roadmap information to students, faculty, and advisors that will most correlate with academic success and also avoid behaviors that correlate with non-retention. Aggregate learner analytic data will be used to allow curriculum planners and class scheduling to ensure needed courses are available to at-risk students. For example, analysis of success indicators may show that retention of remefial students is highly correlated with taking math by the third semester. Aggregate data would predict how many sections of math would be needed to meet student demand.


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