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Rashid-Doubell, Fiza; Cuculescu-Santana, Mirela (2012)
Publisher: Northumbria University
Languages: English
Types: Article
Subjects: B900, X300, X200

Classified by OpenAIRE into

mesheuropmc: education
Many elements have been identified as contributors of academic success amongst medical students but to group these components in order to develop guidelines for intervention strategies is atypical. One such tool which could allow this possibility is the Effective Lifelong Learning Inventory (ELLI) developed by the University of Bristol. ELLI is an online self-assessment instrument which identifies and measures the dimensions of learner development. It comprises of 90 key questions used to measure the seven dimensions of learning power: changing and learning; meaning making; critical curiosity; creativity; learning relationships; strategic awareness and resilience.\ud \ud This study used ELLI to explore learning dimensions as potential drivers for academic success. A small cohort of thirty-three first year postgraduate medical students consented and completed the first ELLI before starting formal classes. Only eighteen of these completed it a second time, 45 days later. The data from the ELLI questionnaires were analysed both for the whole cohort and separately for each academic performance group (defined using grade point averages).\ud \ud The results showed that the students obtained the highest scores for the meaning making or changing and learning dimensions, and the lowest scores for creativity or resilience. After a period of postgraduate study, only the successful students displayed significant improvements in the mean ELLI scores, with increases for all ELLI dimensions apart from resilience. Those who were less successful made declines in more than one dimension.\ud \ud It was concluded that ELLI is an effective instrument for identifying key learning dispositions and it is proposed that an intervention could be developed in the future to improve academic achievement.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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