Stoplights for student success SIGNALS Stoplights for student success
Agenda Student performance tracking Stoplights - Signals Context – John Campbell’s model Process – what it could involve Overview – how it is implemented now Pilot results & feedback Sneak Preview How to get started
Student Performance Tracking Tracking progress Running grade totals Calculating midterm grades Class grade statistics Available methods Blackboard grade book Blackboard tracking data
What is Signals Signals detects early warning signs and provides interventions for students who may not be performing to the best of their abilities before they reach a critical point in their class.
Project Context Model based on J.P.Campbell’s (2007) work Goals Identify at-risk students early Intervene early Improve chances of success Goals Creation of independent learners Retention to in-demand degrees Improved student experience Preservation of marketing/recruitment dollars
Signals – The Process Predicted Student Success Algorithm Effort Tracking Data Grades Help Seeking Behavior Intervene to determine “risk-groups” Communicate students’ performance
Signals - Overview Combines predictive modeling with data- mining from Blackboard Interventions are based on Grades and Effort Each student is assigned an individual “risk- group” high likelihood of failing the course potential problem with succeeding in course high likelihood of success in the course
Signals – Overview contd. Communicating students’ performance Sending Intervention emails Personal emails to individuals Mass class emails grouped by risk-groups Releasing Stoplights in Blackboard Providing resources for improvement - 1-1 meetings - online help/resources - mentors - etc.
Pilot Study Results – Spring 09 900 students in Biology and Math courses Higher levels of help-seeking behavior Higher level of Bs and Cs and lower levels of Ds and Fs More drops and withdrawals Greatest success with moderate risk students
Risk Groups Most remained “at risk”. Still unlikely to take advantage of resources. Majority were able to leave the “at risk” status – as long as feedback continued. More likely to take advantage of resources. Least affected. Instructors can still send positive feedback.
Feedback from Students “Your message was a ‘kick in the butt’ that woke me up.” “Really appreciate knowing how I’m doing in the class before I get too far into the course.” “This biology lab is the hardest I’ve ever taken, but your message let me know that I need to get more help. Also, I can see that this lab is helping me in my biology lecture course and in my chemistry lab.”
Feedback from Instructors “"I have not heard first hand, nor anecdotally that students don’t like these messages; I have not heard one complaint. Actually, I gave a talk last spring and there were students in the audience because they seemingly had a class assignment to go to my talk, and in their evaluation of my talk they wrote, I wish I had this [messages] in all my classes.“ Dr. Laurie Iten, Associate Professor of Biology
Why do it? Signals communicates directly to students facilitating faculty/student communication Provides resources to help students meet demands of their classes Early academic success for students Students like the help
How it works Works in three different ways Provides real-time feedback Intervention starts early Frequent and ongoing feedback
Fall 2009: 9-10,000 students Class No. of Students CHEM 111 2600 1078 STAT 301 900 SPAN 101 300 SPAN 102 500 MA 154 MA 153 600 AGRY 105 60 BIOL 195 150 CDFS 210 455 ENGR 126 1400 IT 345 80 GS 101
Sneak Preview
Who can use this application? Signals is open to all instructors and can be used for any course Specifically targeted to large enrollment gateway courses Must use Blackboard Vista in WL academic institution Future – looking for ways to include regional campuses
How do I get involved? Project Contact Kim Arnold - kimarnold@purdue.edu Signals Support Contact IDC – tlt-consulting@purdue.edu; 496-3257 Join the listserv signals@lists.purdue.edu Look for more information in Purdue Today