Student
Retention
    In
 Online
Programs
            Phil Ice, Ed.D.

            SLN SOL Summit

            Syracuse, 2...
The State of Online Learning
 approximately 4 million students taking
 online courses with 12.9% growth rate
 outpacing ...
Retention
 a concern in higher education since the late
 1800’s
 GPA, SAT, ACT, ect. traditional predictors of
 retentio...
social presence         cognitive presence




           teaching presence
Social Presence
 the ability of participants in a community of
 inquiry to project themselves socially and
 emotionally -...
Social Presence - Elements
 affective expression (expressing emotion,
 self-projection)
 open communication (learning cl...
APUS Study
 American Public University System
 Approximately 60,000 students
 100% online
 monthly course starts
 CoI...
Findings
 21 of the 34 items were found to be
 significant predictors
 21.1% of variance accounted for
 two items accou...
Subsequent Research
 Inclusion of Transfer Credit, Age, Gender,
 Ethnicity, GPA, Last Course Grade, Military /
 Civilian ...
Conclusions
 students perceptions of adequacy of the
 online medium for social interaction may be
 significant for retent...
Moving Forward
 Transactional data needed to understand
 what is occurring in the LMS / other learning
 environments
 Ev...
The Technology Fix
 Occasional and poor connectivity are
 problems
 PLE’s to overcome low media richness
 Implementing ...
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Phil Ice's: Student Retention in Online Programs

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SLN SOLsummit 2010
http://slnsolsummit2010.edublogs.org
February 26, 2010

Phil Ice, Director of Course Design, Research & Development, American Public University System

Student Retention in Online Programs
As the growth of online programs continues to rapidly accelerate, concern over retention is increasing. Models for understanding student persistence in the face-to-face environment are well established, however, the many of the variables in these constructs are not present in the online environment or they manifest in significantly different ways. With attrition rates significantly higher than in face-to-face programs, the development of models to explain online retention is considered imperative. This presentation will focus on the relationship between student characteristics and online behaviors, and retention. Participants will be presented with a methodology that can be used in their own programs to help understand factors influencing retention and ways to detect at risk students.

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Phil Ice's: Student Retention in Online Programs

  1. 1. Student Retention In Online Programs Phil Ice, Ed.D. SLN SOL Summit Syracuse, 2010
  2. 2. The State of Online Learning  approximately 4 million students taking online courses with 12.9% growth rate  outpacing face-to-face by 6 to 1  US Department of Education metastudy – online is more effective than face-to-face  dropout rates much higher – some studies show up to 7 times greater
  3. 3. Retention  a concern in higher education since the late 1800’s  GPA, SAT, ACT, ect. traditional predictors of retention  Tinto, Astin, Braxton and others have demonstrated the role of social integration  measures of social integration well defined in the face-to-face setting – not in online
  4. 4. social presence cognitive presence teaching presence
  5. 5. Social Presence  the ability of participants in a community of inquiry to project themselves socially and emotionally -- as ‘real’ people  the degree to which participants in computer mediated communication feel socially and emotionally connected
  6. 6. Social Presence - Elements  affective expression (expressing emotion, self-projection)  open communication (learning climate, risk free expression)  group cohesion (group identity, collaboration)
  7. 7. APUS Study  American Public University System  Approximately 60,000 students  100% online  monthly course starts  CoI is the end of course survey  eight semesters of data collection  CoI survey items regressed on retention
  8. 8. Findings  21 of the 34 items were found to be significant predictors  21.1% of variance accounted for  two items accounted for 20.2% of variance accounted for using forward entry:  Q16: Online or web-based communication is an excellent medium for social interaction – 18%  Q15: I was able to form distinct impressions of some course participants – 2.2%
  9. 9. Subsequent Research  Inclusion of Transfer Credit, Age, Gender, Ethnicity, GPA, Last Course Grade, Military / Civilian Status, Program, Course Duration, Time Since Last Course  42.7% of variance accounted for  19% for two previous CoI items  15.3% for Transfer Credit  4.6% for Last Course Grade
  10. 10. Conclusions  students perceptions of adequacy of the online medium for social interaction may be significant for retention  technology may be a prime determinant of students perception of adequacy  Institutional investment in social networking and rich interactive technologies may significantly impact retention  Transfer Credit may be an indicator that students have acquired skills – more research needed
  11. 11. Moving Forward  Transactional data needed to understand what is occurring in the LMS / other learning environments  Event layer data extraction for LMS  Semantic mapping using Common Library for materials generated within courses
  12. 12. The Technology Fix  Occasional and poor connectivity are problems  PLE’s to overcome low media richness  Implementing RIA’s  AIR / Flex POC

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