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Monday, July 10 • 17:00 - 17:30
3002 Generic Early Warning Signals for Critical Transitions: An Assessment of the Signals' Utility as a Predictive Management Tool

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Complex systems range from business entities to the climate. Complex systems have tipping points at which a small perturbation can trigger a critical transition leading to an emergence at an alternate stable state. Although there are differences in the nature of complex systems, their behaviors exhibit universal characteristics as they near tipping points. Among such characteristics are common generic early warning signals that precede critical transitions. The signals include: critical slowing down in which the rate of recovery from perturbations decreases over time; an increase in the variance of the state variable; an increase in the skewness of the state variable; an increase in the autocorrelations of the state variable; flickering between different states; and characteristic spatial patterns, such as an increase in spatial correlations over time. Presence of such signals has significant management implications, as the identification of the signals prior to the tipping point could allow management to identify intervention points. Despite the applications of the generic early warning signals in various fields, such as studies on fisheries, semiconductor research, and studies on epileptic seizures, a review of literature did not identify any applications in the area of managing student program withdrawal at the undergraduate level in distance universities, hence the research gap. This area could benefit from the application of generic early warning signals because the program withdrawal rate amongst distance university students is higher than the program withdrawal rate at face-to-face conventional universities. The program withdrawal problem presents an existential crisis for distance universities especially since, in some jurisdictions, public funding is dependent on the volume of students who persist and complete their courses. The proposed generic early warning signals for critical transition are not without controversy. Some researchers have argued that the identification of variables to which the generic signals were applied can only be accomplished post hoc, and that in some of the analyzed case studies the generic early warning signals remained absent despite the critical transitions. This is referred to as false negatives. Literature also suggests that the risk of false positives exists, where the signals are identified despite the absence of a critical transition. This research assessed the generic early warning signals through an intensive case study of undergraduate program student withdrawal at a Canadian distance university. The university is non-cohort based due to its system of continuous course enrolment where students can enrol in a course at the beginning of every month. The university’s student population therefore consists largely of adult learners given its convenient system of asynchronous distance learning which allows students to pursue individualized study. The assessment of the signals was achieved through the comparison of the incidences of generic early warning signals among students who withdrew or simply became inactive in their undergraduate program of study, the true positives, to the incidences of the generic early warning signals among graduates, the false positives. Research findings showed support for the signal pertaining to the rise in flickering which is represented in the increase in the student’s non-pass rates prior to withdrawing from a program; moderate support for the signals of critical slowing down as reflected in the increase in the time a student spends in a course; and moderate support for the signals on increase in autocorrelation and increase in variance in the grade variable. The findings did not support the signal on the increase in skewness of the grade variable. The assessment of the signals suggests that the signals, with the exception with the increase of skewness, could be utilized as a predictive management tool and potentially add one more tool in addressing the student program withdrawal problem.

Monday July 10, 2017 17:00 - 17:30 CEST
3rd Floor, Room SR 121, Institut für Computertechnik,TU Wien Gußhausstraße 27-29, 1040 Wien, Austria

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