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ERIC Number: EJ835342
Record Type: Journal
Publication Date: 2009-May
Pages: 12
Abstractor: As Provided
Reference Count: N/A
ISSN: ISSN-0018-1560
Deep Learning Questions Can Help Selection of High Ability Candidates for Universities
Mellanby, Jane; Cortina-Borja, Mario; Stein, John
Higher Education: The International Journal of Higher Education and Educational Planning, v57 n5 p597-608 May 2009
Selection of students for places at universities mainly depends on GCSE grades and predictions of A-level grades, both of which tend to favour applicants from independent schools. We have therefore developed a new type of test that would measure candidates' "deep learning" approach since this assesses the motivation and creative thinking that we look for in university students. We recruited 526 applicants to Oxford University and gave them a short commentary test and a learning style questionnaire. Specific deep learning approach questions correlated with results in the new test, and both predicted whether the candidate subsequently obtained a place at Oxford. Furthermore high scores on one open-ended commentary question, demanding arguments in favour of a case, produced a greater than 70% chance of obtaining a first class degree at the end of their course irrespective of the candidates' type of school attended or GCSE scores. Candidates from State schools scored as well as those from Independent schools in both tests. Thus our test seemed to index candidates' potential to succeed at a highly selective university, and might usefully be added to current selection procedures for such universities.
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: United Kingdom (England)