Connecting a Conduit of Learning Analytics to Proactive Student Support
Concurrent Session 7
To connect to this real time data stream, our Learning Center implemented a year-long pilot which leveraged student learning analytics to prompt student outreach, coaching and review sessions. We discuss the impact on student performance and give you the opportunity to apply key takeaways to your own student support network.
The flow of learning analytics is increasing in our gateway courses, especially those in STEM. With grant support from the Association of Public and Land-grant Universities and the Gates Foundation, the Active and Adaptive initiative at our institution implements adaptive technology in those courses with challenges in an effort to improve student achievement. Much of this work has been in STEM courses such as Biology, Chemistry, Computer Science, Math/Statistics, and Physics. Faculty in these courses are beginning to use the learning analytics to connect the student experience in the adaptive system to an active classroom.
The learning analytics from the adaptive courseware provides student performance and usage data which can indicate at what points students begin to struggle or disengage. Faculty focus on students’ academic success within their course, but many times they don’t have the bandwidth and tools to act on these data points as they come up. With this in mind, the Office of Academic Innovation initiated a conversation with the Learning Center and faculty from Physics to discuss how to collaborate on a student support model informed by these learning analytics. It was agreed that the Learning Center would hire an academic coach who, after conferring with the General Physics instructor, would proactively reach out to students based on patterns of disengagement or low performance. In addition, the Learning Center would also provide a peer assistant to attend the class one day a week to help with activities and set up bi-weekly review sessions. A pilot was then formed for one section of a General Physics course in Fall term 2019 with the following objectives:
Evaluate how the learning analytics indicate a pattern which prompts student outreach, then assess student achievement following these interventions
Measure how adaptive learning analytics informed interventions in the first two weeks of class (prior to the drop date) and impacted the withdrawal rate
Identify an effective system to notate each student intervention and interaction
Gain insights from the academic coach’s communication to students on the types of support needed and how to effectively triage those
Describe a preferred method of communication between the academic coach and instructor in different stages of the course
Define what training is required for peer assistants to contribute to student outreach (in the later stages of the course)
Define the time required by an academic coach to support one section of a course
Traditionally, the Learning Center has provided on-demand tutoring and academic coaching while each STEM department has deployed its own level of student support for its courses such as: offering one credit supplemental workshops; or placing student learning assistants or graduate teaching assistants in the classroom. The departments may choose to continue some form of this support, however, our goal is to transform the role of the Learning Center into a hub for student outreach & support with spokes into multiple gateway STEM courses. Through learning analytics, the LC would deploy a pull strategy to engage students, especially for those who typically wouldn’t seek assistance, providing timely outreach and a direct onramp for their services.
Our intent in this session is to introduce the Learning Center pilot for General Physics and share the different objectives as described above. We will then discuss our key findings and measures of impact we found in the Fall and Winter terms. We’ll then wrap up our presentation with how we’ll move forward to scaling this approach to multiple STEM courses in the next academic year.
Level of Participation:
To kick off this session, we’ll immediately engage participants by asking them to share their roles and gauge their use of learning analytics at their respective institutions through a poll. This will be followed by our 30 minute presentation where we’ll invite questions as they come up. Participants will then be invited to reflect in small groups on what sources of analytics their courses provide and how they could be better utilized to inform student support. We end the session with a general discussion and Q&A. All material will be available in the slides.
Individuals attending this session will learn how learning analytics can help to inform proactive communication & coaching to students. They will be able to identify who in their student support network can collaborate directly with instructors to deploy this level of support. They will be able to select key takeaways from the pilot study and apply them to their own student support network.