Motivation for Studying in Medical School and Its Implication in Achieving Learning Excellence - Essay Example

Motivation for Studying in Medical School and its implication in achieving learning excellence Contents Contents Introduction 2 Researchquestions 2 Participants 3 Methodology 4 Analysis 5 Behaviours indicating motivation types 5 Correlations with personality types 7 Conclusions 9 References 10 Abstract This paper uses quantitative analysis of questionnaire data gathered online from a sample of first year medical students to explore and test the hypothesis that intrinsically motivated students are more successful in course assessments. It is concluded that broadly indicative trends suggest that relationships exist between personality type, motivation and performance, but that the relationships may be masked by unconsidered factors. Areas for development of this study are also highlighted with consideration of where attention could best be focussed. Introduction Motivation is a personal drive that produces the arousal, direction, and persistence of behaviour. Many theories have attempted to define and explain motivation as a basic drive to minimise physical pain and maximise pleasure, the pursuit of a state of being, an ideal or altruistic concept amongst other drives. Motivation is important to teaching and learning because it lays a crucial role in the student’s approach to learning, as understanding their motivation can potentially predict levels of interest and effort placed on the process of learning and consequently academic results. Stipek (1988) proposed a variety of reasons for lack of motivation and behaviours associated with high academic achievement; he specifically found that encouraging intrinsic motivation helped to improve learning outcomes. Intrinsic motivation, however, often requires more effort to generate than extrinsic motivation. To decide if it is worth this extra effort, the degree to which improvements occur has to be quantified to see if results compare favourably to more direct motivational methods, such as high-stakes examinations. Personality profiling may also offer an additional advantage given the widening participation agenda, as different types of motivation may be shown as more effective for students at risk of failure. This research may consequently lead to suggesting alterations in teaching practice and support mechanisms which enhance the learning process for specific types of students. Research questions In order to limit the scope of this study to a reasonable level, the following questions were devised: 1. What student behaviours indicate intrinsic and extrinsic motivation? 2. To what extent can these behaviours be predicted by personality type? 3. How can both of these factors be related to final examination performance? Participants Following ethical approval, 120 first-year medical students from a single institution were invited to complete an online survey. Response anonymity was ensured through use of a third-party website which collected responses without requesting information which could be used to identify individuals. Ritchie & Lewis stress the need to demonstrate any potential reciprocity when seeking participants so as to ensure a positive relationship with research in general and to make the immediate proposition “more of an exchange” and therefore more appealing to participants (2003. p.64). However, as this study was concerned with motivation it was decided that encouraging participation by offering a direct reward was unsuitable (and arguably limits the definition of voluntary participation) and so a post-study debrief and workshop was organised and offered to all students in the population regardless of participation in the study. The response rate of 25.8% (31 respondents) demonstrates a broadly representative opportunity sample of the population, suggesting that generalization to the population is worthwhile – particularly if personality type profiling is used. Gender was split as evenly as possible in an odd numbered list with 16 female and 15 male respondents. 7 of the 31 were from a non-English speaking background. Ages ranged from 18 to 45, with an average just over 22. This basic background data illustrates a sample with breadth of representation which also demonstrates a profile similar to the population from which it was drawn, hence suggesting that the population is sufficiently represented for generalisations to be drawn should statistical significance be demonstrated. Methodology Questionnaire data has been consistently useful in gaining insight to perceptions of an issue, and provided that questions are suitably formed and tested can provide time- and cost-efficient means of gaining a large body of conveniently analysable data (Bell, 2005). This is of particular relevance compared to, for example, interview data which can be overly intrusive when participant introspection is required (ibid). At this scale of study, interview data might only have been possible for a very small number of participants and so offered little generalisability of results. While some researchers argue for a specific minimum number of participants in order to successfully investigate one topic using interview methods (e.g. McCracken, 1988, argues for a minimum of eight participants), Arksey and Knight (1999) stress that the main aim of sampling is satisfied by collecting data and making constant comparisons until no unique information is being presented, akin to the principles of grounded theory. By examining qualitative comments from participants in the ‘free text’ response options after the first week of responses, it became apparent that new responses were supporting existing judgements rather than offering new perspectives and so it could be argued that a broad representation of views is covered in analysis. Analysis was facilitated through the SPSS software suite, where standard measures of correlation and regression were used to test for relationships between variables. Qualitative responses were coded into similar groups manually due to the manageable number of participants to form categories which could be used in extensions of this project as it was decided that qualitative analysis software offered few advantages given the small quantity of qualitative data to be analysed. Analysis Behaviours indicating motivation types When asked to state their main motivation for studying a specific module, responses were varied between behaviours which suggested intrinsic or extrinsic motivation. The provided responses focussed on desire to pass (11), desire to learn (5), interest in subject (4), desire for better medical practise (4), and fear of failure (4). Free response options also indicated prime motivators were perceived to be for success over and above passing (2) and a little of everything (1). These responses suggest that the options presented successfully captured the range of necessary responses, as free text options could reasonably be categorised into existing responses and simply added more detail (or failed to identify a prime motivator). When asked to state their main motivation for not studying a specific module, responses again varied across the scale. A dislike for the topic was not selected by any respondents, indicating that it would not be worth considering as a prime motivator for future questionnaires. Respondents instead chose the perceived difficulty of the topic (12) or teaching style (6), judging the material irrelevant to future aims (3), and lack of interest (2). A larger number of free-text responses suggests that this question should be adapted in future revisions of this study to allow for the increased range of responses. However, some of these responses evidently fit into existing categories – a lack of cohesion within the module topics and the range of topics could have been indicated by teaching style, although the point relates more to organisation rather than delivery. This sentiment was shared in 3 of the 7 responses, indicating that organisation of the module could have been a useful category. The remaining four free-text responses were not specific to the module and referred instead to personal issues such as lack of general motivation, procrastination, general boredom, etc. To test for agreement between these behaviours, responses were categories into families showing intrinsic or extrinsic motivation. Intrinsic motivators to study a module encompassed desire to learn the topic and interest in the topic. Desire to pass and fear of failure were categorised as extrinsic motivators as they were externally conferred. While it was originally conceived to indicate intrinsic motivation in the sense of bettering oneself, desire to have the knowledge for better medical practice begged the question of motivation to improve one’s medical practice, this question possibly indicated either intrinsic or extrinsic factors. Without triangulation through interview data or follow-up questions, this cannot be known for sure and so was not considered in this grouping. As a result, eight participants were judged to be intrinsically motivated to study a module while 18 were extrinsically motivated. Regarding motivators to not study a module, dislike or lack of interest in the topic were classed as intrinsic demotivators while irrelevance or difficulty were classed as extrinsic. Free text responses were judged to be sufficiently complex and vague that categorising them into either intrinsic or extrinsic would be a values judgement which might not accurately reflect the intended meaning, and so were not considered in analysis unless they clearly indicated one category or the other (e.g. laziness or procrastination were categorised as intrinsic). As a result, 7 respondents were judged to have been intrinsically demotivated while 22 were extrinsically demotivated. By removing from analysis all respondents who could not be categorised into intrinsic or extrinsic for both questions, 25 statistical comparisons could be made. Using Pearson R correlation, intrinsic motivation to study weakly correlated negatively with intrinsic motivation to not study (-.185). Likewise, extrinsic motivation to study a module failed to predict extrinsic motivation to not study. The low p-value scores, indicating a 37% probability that the results could have been obtained by mere chance, further suggest that increased response opportunities would be necessary to adequately decide from student behaviours whether they were primarily intrinsically or extrinsically motivated. This data does, however, suggest that differences might exist between motivations to do something or motivations to not do something – students may sign up for courses for primarily intrinsic reasons, but avoid courses based on extrinsic factors. The data gathered here offers some broad support for this idea. Correlating intrinsic motivation indicating actions with exam performance produced extremely weak correlations (0.28) at insignificant levels (.884 p-value), although this may be more related to a lack of information than a lack of relationship. Relationships were positive, though still not within traditional limits of significance, for intrinsic motivation and time spent studying, indicating that those students who gave intrinsic reasons for studying were also slightly more likely to study for an increased period (.270 correlation at 18.2% significance, which Muijs [2004] categorises as on the border of indicating a weak positive relationship between the 2 variables). The relationship was predictably stronger, though not by much, between time spent studying and final exam mark (.368 correlation at 7.7% significance). This may offer an additional explanation for the weak correlation between intrinsic motivation and exam performance, as exam performance was not as strongly related to effort as assumed in the hypothesis that increased motivation leads to increased performance (via increased effort). Correlations with personality types Lacking firm indication of motivation behaviours, research question 2 cannot be explored in significant detail given current data. As such, exam marks were used as the main focus for comparison with personality types. Introversion or extroversion as measured in the questionnaire had an extremely weak and insignificant relationship with exam scores, producing a p-value of .973 (indicating a 97.3% chance that any correlation between the features is a pure coincidence). Likewise for distinctions between thinking and feeling responses (80% chance of coincidence, weak relationship in favour of feeling), and judging and perceiving responses (87% chance of coincidence, weak relationship in favour of judging). The only slightly statistically significant correlation is between exam performance and sensing/intuitive traits. This occurs at 22.4% significance, beyond traditional limits of 1%, 5% and 10% as described by Muijs (2004), but much more substantial than other relationships in this study. This suggests a weak relationship (.233 Pearson R value) between intuitive traits and improved examination performance. As with the data analysed in the previous section, this suggests that traits are worth exploring but – to achieve statistical significance – a much wider range of marks and traits have to be collected. Conclusions This paper has found that more extensive data collection regarding student behaviours is necessary to predict their primary source of motivation. However, working from personality type data using MBTI profiles there is a suggestion that learners with intuitive traits can be expected to perform higher for the specific module in question and that some traits merit further examination. More extensive, possibly triangulated, study is necessary to fully understand this topic. While the findings of this paper lack statistical significance at traditional levels, Muijs (2004) stresses the importance of considering indicative trends. From this perspective, this study has shown that such a future larger scale study is likely to be worthwhile in this area and, to this end, has provided a useful pilot to highlight areas of strength and opportunities for improvement to the current questionnaire specifically and the questionnaire methodology in general. The weak relationship between time spent studying and exam performance also suggests that intrinsic motivation may be more evident as an influence on lower level exams where rote learning is an advantage, but at higher levels of education other factors (such as intelligence) play a more significant role and effort may not be as significant a filter as previously thought. There may also be valuing in assessing effort by more qualitative means, such as intensity of study rather than time. References Arksey, H. and Knight, P. (1999). Interviewing for Social Scientists. London: Sage Publications. Bell, J. (2005). Doing Your Research Project: A Guide for First-Time Researchers in Education, Health, and Social Science. 4th Ed. Berkshire: Open University Press. McCracken, G. (1988). The Long Interview. London: Sage Publications. Muijs, D. (2004). Doing Quantitative Research in Education with SPSS. London: Sage Publications. Ritchie, J. & Lewis, J. [eds]. (2003). Qualitative Research Practice: A Guide for Social Science Students and Researchers. London: Sage Publications. Stipek, D. (1988). Motivation to learn: From theory to practice. Englewood Cliffs, NJ: Prentice Hall. Read More