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Faculty and Staff Research Presentations on Student Success

An Open Research Forum - Spring 2021

Faculty and Staff Research Presentations on Student Success

Teams of faculty and staff from colleges and divisions across the university will share their research finding in exploring the challenges to timely graduation. Join us in these discussions and learn how we can remove barriers to student success. Please see the information below regarding each forum and RSVP!

All presentations will be held 12pm-1pm.

Register to Attend

Retention Among College Students on Academic Probation   (4/13/21)

Forum 1: "Retention Among College Students on Academic Probation"

  • April 13th, 2021, 12:00pm - 1:00pm
  • Project Description: Academic probation is a mechanism to identify students that are at higher risk of institutional departure. In order to move the dial on postsecondary student retention, a mixed-methods study was conducted to predict first-time freshmen students’ placement on academic probation, their risk of institutional departure, and factors that support their academic recovery and persistence. In the quantitative analyses, factors predicting academic probation and subsequent institutional departure included student demographic characteristics, pre-entry and post-entry academic indicators, and academic major change. The study also explored the experiences and perceptions of students placed on academic probation who were able to recover and persist.
  • Researcher:
    • Lizzet Rojas - Academic Affairs | Advanced Studies in Education and Counseling

Finding the Right Path to Graduate   (4/14/21)

Forum 2: "Finding the Right Path to Graduate"

  • April 14th, 2021, 12:00pm - 1:00pm
  • Project Description: One of the key objectives of this project is to provide the University with strategies to improve student graduation rates and to help students find the right path to graduate. This study will utilize both econometric modeling as well as machine learning approaches. The econometric approach will help us to better understand major switching and its consequences. It will also aid our machine learning approach in building a predictive model. The results from econometric and machine learning will help to build an early recommendation system on major switching for student success.
  • Researchers:

Abating Attrition   (4/15/21)

Identify and Support Diverse Pathways to Timely Graduation in CNSM   (4/16/21)

CSULB Student Enrollment: A Dynamic Model with Projections   (4/21/21)

High-Impact Practices at CSULB: How (and for whom) Do They Promote Student Success?   (4/22/21)

Who drops out from CSULB and what factors predict their attrition?   (4/23/21)