title: Attitudes to e-learning, learning style and achievement in learning neuroanatomy by medical students.
abstract: BACKGROUND
  Two main learning approaches adopted by students have been identified by research: deep (seeking for meaning motivated by interest in the subject matter) and surface (rote-learning motivated by fear of failure). There is evidence that learning approach is influenced by learning environment (e.g. Trigwell et al. 1999). Online courses pose the challenge of designing software that will encourage the more desirable approach to learning.
  
  
  OBJECTIVE
  The aims were to evaluate how successful an online course is at encouraging deep approach to learning, which factors might influence the approach adopted towards it, and whether the approach adopted is related to academic performance.
  
  
  METHODS
  Using 205 second-year pre-clinical medical students, we compared their approach to learning, as measured by Biggs et al. (2001) 2F-SPQ-R, for a computer-aided learning (CAL) course in Neuroanatomy with that for their studies in general. We then examined student attitudes towards the CAL course and the ratings of the course Web pages in terms of the learning approach they encourage (done by 18 independent raters).
  
  
  RESULTS
  The students reported using significantly less deep approach to learning for the CAL course. However, their approach for the course was not related to results on a neuroanatomy assessment based on it. Enjoyment of the course, assessment of the amount of information in it as appropriate, and ease of understanding the course were all associated with a deeper approach. The only agreement between the raters of the CAL course was for some pages that included patient case studies, which were unanimously given a very high deep rating. Assessment marks for questions referring to these pages were higher than for the rest of the assessment.
  
  
  CONCLUSIONS
  The study suggests that maximizing the use of clinical relevance should increase the interest and enjoyableness of the course and thereby aid deep learning and retention of information.
authors.affiliation: University of Oxford, Oxford, UK.
authors.forename: Elena
authors.initials: E
authors.lastname: Svirko
authors.forename: Jane
authors.initials: J
authors.lastname: Mellanby
mesh_terms: D001292:Attitude to Computers; D003194:Computer-Assisted Instruction / Q000379:methods*; D004504:Education, Medical, Undergraduate / Q000379:methods*; D004739:England; D005260:Female; D007722:Health Knowledge, Attitudes, Practice; D006801:Humans; D020407:Internet; D007858:Learning; D008297:Male; D009445:Neuroanatomy / Q000193:education*; D012647:Self-Assessment; D013337:Students, Medical / Q000523:psychology*; D011795:Surveys and Questionnaires; D014495:Universities