Quick Links
Archives
How    to    cite    this    Article:     Vijaya    Ramanathan,    Pananghat    A.    Kumar,    Anand    Ramanathan.     A    STUDY    ON    PREDICTING    ACADEMIC PERFORMANCE     OF     THE     FIRST     YEAR     MEDICAL     UNDERGRADUATE     STUDENTS.      Int     J     Anatomy     Res     2016;4(4):3215-3220.     DOI: 10.16965/ijar.2016.441.
Type of Article: Original Research DOI: http://dx.doi.org/10.16965/ijar.2016.441 Page No.:  3215-3220
A STUDY ON PREDICTING ACADEMIC PERFORMANCE OF THE FIRST YEAR MEDICAL UNDERGRADUATE STUDENTS Vijaya Ramanathan * 1 , Pananghat A. Kumar 2 , Anand Ramanathan 3 . *1 Department of Anatomy, Meenakshi Medical College and Research Institute, Enathur, Kanchipuram, Tamilnadu, India. 2 Department of Anatomy and Co-ordinator, Clinical Simulation Laboratory, PSG Institute of Medical Sciences and Research, Coimbatore, Tamilnadu, India. 3  Oral & Maxillofacial Clinical Sciences, Oral Cancer Research and Coordinating Centre, Faculty of  Dentistry, University of Malaya, Kuala Lumpur, Malaysia. Corresponding    author:     Dr.Vijaya    Ramanathan,    Department    of    Anatomy,    Meenakshi    Medical    College    and    Research    Institute,    Enathur,    Kanchipuram, Tamilnadu, India. E-Mail:  viji_vairavan@yahoo.com ABSTRACT Background:   Students   in   many   Asian   countries   are   qualified   to   seek   admission   to   undergraduate   medical   degree   soon   after   they   leave   the   secondary schools.   Selection   criteria   vary   in   different   countries.   In   an   effort   to   standardise   the   admission   procedures   Medical   Council   of   India(MCI)   has   introduced National   Eligibility   Cum   Entrance   Test   (NEET)   as   the   entry   requirement   for   the   undergraduate   medical   course   from   this   year.   In   addition   to   the   scholastic success guided by cognitive factors, non-cognitive factors could also have a direct positive relationship to student’s academic performance. Materials   and   Methods:    One   hundred   and   fifty   two,   first   year   medical   undergraduate   students   were   briefed   about   this   project.   The   students   filled   a questionnaire   containing   70   items   distributed   in   3   sections   (i)   Preliminary   interview   (ii)   Learning   strategy   survey   and   (iii)   Approaches   to   study   skills inventory (ASSIST). The scores were analysed statistically and any correlation between parameters were identified Results:    Majority   of         students   in   this   cohort   were   well   motivated.   The   number   of   students   with   inadequate      learning   strategies   were   more,   compared   to those with well equipped strategies. The deep and strategic approach had moderately high scores compared to surface apathetic approach. Conclusion:       Small         number   of   students   were   not   motivated   and   needed   to   be   guided   in   this   aspect.   Many   students   needed   monitoring   especially   in   their learning and test taking strategies. Moreover surface apathetic approach (fear factor) required to be eliminated effectively by faculty members. KEY   WORDS:    academic   performance,   undergraduate   medical   students,   National   Eligibility   Cum   Entrance   Test   (NEET),   cognitive   factors,   non   cognitive factors, learning strategies,  deep approach, strategic approach, surface apathetic approach, motivation. References 1 . James   D,   Chilvers   C.   Academic   and   non-academic   predictors   of   success   on   the   Nottingham   Undergraduate   medical   course   1970-1995.   Med   Educ. 2001;35:1056-1064. 2 . Siu   E,   Reiter   HI.   Overview:   What’s   worked   and   what   hasn’t   as   a   guide   towards   predictive   admissions   tool   development.   Adv   Health   Sci   Educ   Theory Pract. 2009;14:759-75. 3 . West C, Sadoski M. Do study strategies predict academic performance in medical school? Med Educ. 2011;45:696-703. 4 . Faisal   A.   Latif   Al-Nasir,   Robertson.A.S   Can   Selection   Assessments   Predict   Students’   Achievements   in   the   Premedical   Year?   :   A   Study   at   Arabian   Gulf University. Education for Health.2001;14(2):277-286. 5 . Farrokhi-   Khajeh-Pasha.   Y,   Nedjat   S,   Mohammadi   A,   Rad   EM,   Majdzadeh   R,   Monajemi   F,   Jamali   E,   Yazdani   S.   The   validity   of   Iran’s   national   university entrance examination (Konkoor) for predicting medical students’ academic performance. BMC Medical Education. 2012;12:60. 6 . Zuniga   D,   Mena   B,   Oliva   R,   et   al.   Modelling   the   academic   performance   of   medical   students   in   basic   sciences   and   preclinical   courses:   a   longitudinal study. Rev Med Chil.2009;37(10):1291-300. 7 . Bastias.   G,   Villarroe.   l   L,   Zuniga   D,   et   al.   Academic   performance   of   medical   students:   a   predictable   result?   Revista   Medica   de   Chile.2000;128(6):671- 678. 8 . Eva   KW,   Reiter   HI,   Trinh   H,Wasi   P,   Rosenfeld   J,   Norman   GR.   Predictive   validity   of   the   multiple   mini-interview   for   selecting   medical   trainees.   Medical Education.2009;43:767-775. 9 . Hojat   M,   Robeson   M,   Damjanov   I,   Veloski   JJ,   Glaser   K,   Gonnella   JS   .   Students’   Psychosocial   Characteristics   as   Predictors   of   Academic   Performance   in Medical School. Academic Medicine.1993;68:635-637. 1 0 . Heming   TA,   Nandagopal   S.   Comparitive   Difficulties   with   Non-Scientific   General   Vocabulary   and   Scientific/Medical   Terminology   in   English   as   a   Second Language (ESL) Medical Students. Sultan Qaboos University Med J.   2012;12:485-492. 1 1 . Tait. H & Entwistle, N. J Identifying students at risk through ineffective study strategies. Higher Education, 1996;31:99-118. 1 2 . Eamonn    Ferguson,    David    James,    Laura    Madeley.    Factors    associated    with    success    in    medical    school:    Systematic    review    of    the    literature.    BMJ. 2002;324:952-957. 1 3 . Tait.   H,   Entwistle   N.   J.,   McCune   V.   ASSIST:   a   reconceptualisation   of   the   Approaches   to   Studying   Inventory.   In   C.   Rust   (ed.)   Improving   students   as learners. Oxford: Oxford Brookes University, The Oxford Centre for Staff and Learning Development. 1998. 1 4 . Marton. F, Säljö. R. On qualitative differences in learning. I. Outcome and process. British Journal of Educational Psychology, 1976;46:4-11. 1 5 . Ramsden.   P,         Entwistle.   N.   J.   Effects   of   academic   departments   on   students’   approaches   to   learning.   British   Journal   of   Educational   Psychology, 1981;51:368-383. 1 6 . Mcmanus   IC,   Smithers   E,   Partridge   P,   et   al.   A   levels   and   intelligence   as   predictors   of   medical   careers   in   UK   doctors:   a   20   year   prospective   study. BMJ.2003;327:139-142. 1 7 . Hamdy   H,   Prasad   K,   Anderson   MB,      Scherpbier   A,   Williams   R,   Zwiesrstra   R,   Cuddihy   H.   BEME   systematic   review:   Predictive   values   of   measurements obtained in medical schools and future performance in medical practice. Med Teach.2006;28:103-116. 1 8 . White   C.B,   Dey   E.L,   Fantone   J.S,   Analysis   of   factors   that   predict   clinical   performance   in   medical   school.   Advances   in   Health   Science   Education.2009; 14:455-464. 1 9 . Dixon    D.    Relation    between    variables    of    preadmission,    medical    school    performance,    and    COMLEX-USA    levels    1    ans    2    performance.    JAOA. 2004;104(8):332-337. 2 0 . De Angelis S. Non cognitive predictors of academic performance. Going beyond traditional measures. J Allied Health. 2003;32:52-57. 2 1 . Hoschl C, Kozeny J. Predicting academic performance of medical students: the first three years. Am J Psychiatry.1997;154(6 Suppl):87-92. 2 2 .  Norrish MIK, Kumar PA, Heming TA. Interim identification of “at risk” students: A predictive model. J Contemp Med Edu. 2014;2(4):199-203. 2 3 . Hays RB, Lawson M, Gray C. Problems presented by medical students seeking support: A possible intervention framework. Med Teach 2011;33:161-4. 2 4 . Sami   Shaban,   Michelle   Mclean.   Predicting   performance   at   medical   school:         Can   we   identify   at   risk   students?   Advances   in   medical   education   and     Practice.2011;2:139-148. 2 5 . Prunuske A, Skildum A. Just- in-time remediation of medical students during  the preclinical years. Med Sci Educ.2014;24:103-9. 2 6 . Yates   J.   Development   of   a   ‘toolkit’   to   identify   medical   students   at   risk   of   failure   to   thrive   on   the   course:   an   exploratory   retrospective   case   study.BMC Medical Education.2011;11:95. 2 7 . Griff  ER,  Matter SF. Early identification of at- risk students using a personal response system. Br. J Educ Technol. 2008; 39: 1124-30.





HOME ABOUT US EDITORIAL BOARD AUTHOR GUIDELINES SPECIAL SERVICES CONTACT US HOME ABOUT US EDITORIAL BOARD AUTHOR GUIDELINES SPECIAL SERVICES CONTACT US
Volume 4 |Issue 4.3 |  2016 Date of Publication:  31 December 2016
DOWNLOAD PDF
TABLE OF CONTENTS