Why+do+students+drop+out?

Several models have contributed to the body of research (Gravel, 2012; Park and Choi, 2009). Tinto's Retention Model (1975) has been widely used and has a strong evidence base (Tinto, 1993, as cited in Berge and Huang, 2004; Gravel, 2012; Martinez, 2003). Tinto's model does not necessarily explain the inter-relationships in off-campus and online courses and so newer models for retaining students have been developed (Berge and Huang, 2004). Park's framework (below) is the latest iteration. It refines and adapts Rovai's model (2003 as cited in Park & Choi, 2009) by categorizing the reasons that students have for dropping out of their online course and also capturing the inter-relationships between these factors and placing less emphasis on learner skills (Park & Choi, 2009). Park's (2007) headings: learner characteristics, external factors and internal factors will be the preferred terminology for this project.
 * Frameworks for describing attrition**

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 * Which factors are more influential on a students' decision to drop out? **

Park and Choi (2009) investigated which parts of this model were more significant and found that it is the internal factors: "Learners are more likely to drop out of an online course when they perceive that the organisation does not support their learning and the course is not related to their own lives" (Park & Choi, 2009, p 214). The learners' connection to their institution is personal. It is more than just as a consumer of a content delivery. Services and institutions that find ways to encourage social and academic integration have lower drop-out rates (Martinez, 2003; Gravel, 2012).

External factors also play a role in attrition and Park and Choi (2009) believe the effects of these can be mitigated by course design that actively considers these effects and offers individualised support via the internal factors.

Levy (2004) considered learner characteristics and student satisfaction and found the latter was the better predictor for dropping out. Levy's (2004) confirmed a lack of evidence for learner characteristics and learner skills as being significant predictors of attrition. Interestingly, Levy (2004) noted that the drop-out rates were higher for students who were first-time enrollees that for those who were in the later stages of their qualification and suggested that this may be due to their unfamiliarity with the expectations of an online course.

For personal, circumstantial and institutional reasons many students will drop out of their online course. These frameworks encourage institutions to look at the factors under their control and modify them to facilitate a better relationship with learners (Berge and Huang, 2004).

Strategies that reduce attrition