Academic Advising Discussion Notes - Metro Atlanta Regional Meeting

Academic advising was one of three shared areas of interest for participants at the Metro Atlanta CCG Regional Meeting held at Atlanta Metropolitan State College on April 4, 2016. Brief notes from the breakout conversation on this issue at the meeting follow:

Key Concerns:

• Acquisition, interpretation and application of data
• Unclear who on campus is using data, especially in faculty advisor models. Questions around what departments should be involved to use data most effectively
• Online course implementation and advising

Key Observations:

• Dual enrollment advising presents unique challenges across several campuses
  
  • Scale and momentum of initiative has greatly increased volume, difficult for campuses to keep up
  • Questions regarding the benefit to individual campuses

Solutions:

• Developing resources to use data already available, without analytics tools
  
  • Convening institutions with similar tools and abilities
• Best practices in advising are needed for campuses to reference
• Definition of academic advising
• USG help
  
  • Getting data earlier—retention and graduation numbers
  • Guidance for Move on When Ready Students—how to count them in enrollment numbers

Discussion Notes:

• CCG is a lot of work with many moving pieces for campuses to keep up with
• Several campuses asked if their were additional resources available to campuses to aid in completing CCG work
• Members appreciated the flexibility and freedom to develop customized models
• Importance of using data to determine what populations to focus advising resources
  
  • Some campuses were initially focused on extreme high and low performers, now focusing on middle performers that can be moved
• Critical to the success of advising initiatives on campus is developing a culture that appreciates advising and understands its value
  
  • “Faculty are taught to support the rule of evidence,” will appreciate advising once they see research supporting it’s value
  • One institution found success in winning over support after their data was cleaned up and one uniform source for information was established
  • Presidential level support was important in one institutions transition to centralized advising
• Differences across campuses in how faculty are involved in advising
  
  • “Professional advisors best positioned to monitor academic progression. Faculty still maintain role of mentor, career/professional advising, research…”
  • Some institutions considering similar separation of responsibilities—professional advisors managing academic progression of courses and keeping them on track while faculty mentor and advise on working in the particular field
• Advising loads
  
  • Difficulty of maintaining lower case loads during mergers.
• Discussion of academic maps—different definitions for each campus. One campus notes that academic maps are central to their advising process
  
  • Using data to forecast number of seats needed through the use of academic maps
  • Striking right balance of when courses are offered and knowing enrollment make-up (e.g. part-time students in need of courses in the evening)
• Distribution of resources and time dedicated to student population
  
  • Mandatory advising for first-year students, as needed for most students, targeting those most in need based on analytics.
    • Specialized advising programs for first-year students, special student groups. Proactively helping students register for appropriate courses
  • Requiring students to meet with advisors before changing majors as a strategy to help students register for correct courses
• Faculty Advisor Model
  
  • Faculty complete annual training and handle most advising cases. Advising Center manages specialized cases—Learning Support, transfer, off-track
• There is a need for faculty to receive additional information about students that comes from data
  
  • Request for System Office to interpret data for institutions with lower capacity
  • Tiers of data interpretation
    • Institutions with predictive analytics that are proficient in its usage
    • Institutions that have analytics tools that need training on how to use it most efficiently
    • Institutions using native data to a high degree to inform advising
    • Institutions that need training on how to use native data most efficiently
    • **Several campuses indicate difficulty in translating data for faculty to use**
  • Campuses indicate a need for a road maps on how to effectively use data
    • Predictive analytics
    • Degree Works