The first aim of this research was to determine the relationship between students’ background and educational and interview scores on entry, and their motivation to learn as measured by the MES-UC in week three of the physiotherapy program. Of note, for this sample, there was no relationship between academic entry scores and scoring on the MES-UC, with the exception of scoring for the disengagement dimension where there was a positive relationship between academic entry scores and disengagement from learning. Given the small sample size, this may be a chance finding although disengagement from learning in first year students was also previously noted in study of a larger sample of physiotherapy students from the same university [27]. The authors postulate that the transition into the higher education learning environment as well as possible alternate aspirations for some high achieving students, including progression into the medical program, may be possible explanations for this finding.
The admissions interview correlated with three of four global scores including a positive relationship with booster thoughts and behaviours and a negative relationship with disengagement and the global behavioural score representing ‘guzzlers’. Applicants selected for interview have undertaken academic screening and have reached a threshold of academic performance deemed appropriate to complete academic tasks within the physiotherapy program. Thus, students enter with similar academic capabilities. Differentiating students that may be more motivated to learn and progress through the program, is much more difficult to determine on entry but the link between admissions interview and MES-UC does confirm that for this sample, the interview may be targeting alternate factors outside of cognitive ability. Previous research has shown a relationship between the admissions interview for this program and performance in clinical placements in Years 2–4, stronger than academic scores on entry [29]. Determining any link between academic motivation and performance though the program, including clinical performance, may be useful to determine the value of monitoring student motivation in future cohorts. Monitoring of students was highlighted as a key institutional activity to improve study success, in a report into student dropout and completion in higher education in Europe [2].
The second aim of the study was to determine any relationships between the dimensions of academic motivation and student performance, taking into consideration gender and age. Although gender differences in achievement at university have previously been identified in the literature [29,30,31,32], anxiety and task management were the only motivation dimensions to show any significant gender differences, with females scoring higher in both areas. Of note there was no link between either of these motivation dimensions and student performance so although they may have affected motivation to learn, they did not influence subsequent outcomes in first year. Anxiety towards learning may bring about enhanced task management to avoid failure [33], thus the two dimensions may have worked together to ensure satisfactory academic outcomes.
Significant relationships were found between self-belief and results in three out of four semester one courses and three out of five semester two courses. There was no relationship between the other 10 motivation factors and student performance. Self-belief, as represented on the MES-UC, denotes a ‘students’ belief and confidence in their ability to understand or to do well in their university/college studies, to meet challenges they face, and to perform to the best of their ability’ [31]. This definition of self-belief is congruous with ‘self-efficacy’, where students make cognitive judgements of their capabilities [34]. Zajacova et al. [35] further termed self-efficacy in the academic context as ‘academic self-efficacy’, referring to a student’s confidence in their ability to complete a particular learning activity or task. Motivation to learn and self-efficacy have an integrated or co-dependent relationship as determined by contemporary motivation theories. In the expectancy value theory of motivation, individuals are more likely to engage in tasks where they have higher self-efficacy or belief about their actions and the likely outcomes that will follow [13]. In Bandura’s social cognitive theory [11, 36, 37], the perceived importance of the task is central to motivation with self-efficacy underpinning a person’s beliefs about their personal competence. Pajares [20] noted that a person’s efficacy beliefs are linked to their effort, perseverance and resilience when completing tasks and further highlighted the link between self-efficacy and emotional reactions with decreased self-efficacy leading to stress, depression and/or reduced problem-solving abilities. Likewise, Zajacova et al. [35] found academic self-efficacy and stress to be negatively correlated. Thus, tapping into a student’s self-efficacy or self-belief may be the key to both improving performance and decreasing stress. Targeting students with lowered self-belief on entry into higher education and providing appropriate intervention, may result in improved student outcomes including retention.
It is important for universities to understand how student self-efficacy interacts with institutional characteristics as this may ultimately influence retention rates. Self-belief scoring was linked to performance in certain first year courses comparative to other courses. Two out of three of the courses where there was no link between self-belief scoring and student performance, were not delivered by the physiotherapy program, with the third course since undergoing substantial changes due to student feedback on curriculum provided through traditional course review processes. This may indicate that measuring academic motivation may be useful to assist curriculum review and feedback. Curriculum development based on motivation theory needs further investigation though Turner [38] identified the role of developing experiences through the higher education journey based around control, success and improvement, to foster self-belief in students.
The initial transition into university has been highlighted as a key period to provide intervention, with a review of Australian higher education students from eight institutions, undertaken mid-year, revealing that just over a third of first year students reported having difficulty getting motivated to study [39]. Similarly, a review of psychosocial adjustment of first year college students in the U.S. noted a significant decline in psychological, cognitive and affective well-being in first semester [25]. The decline plateaued in second semester with the researchers noting that identification and intervention in first semester was paramount. It appears that the key time to measure and implement any intervention to enhance motivation to learn is within the first six months of first year.
The authors acknowledge the limitations of this study, including the small sample size of study participants from one cohort of physiotherapy students from a Western Australian university. Further, this study involved the use of a self-reporting instrument applied at one time point, to determine motivation to learn, based on a framework developed by A.J Martin, supported by contemporary models of motivation theory [19]. A preliminary proxy longitudinal study determined the validity of this instrument for the population tested [27].
The literature points towards context and institution-specific research as being the key to understanding the complex construct of student motivation [8, 40]. The value of lessons learned from a local study to produce benefits through localised translation into practice, cannot be underestimated. Thus, this study reported on findings from investigating motivation to learn, specific to a physiotherapy program, considering the social context and interplay between a student’s motivation including their academic self-efficacy and the role of localised curriculum, specific to the learner. It is important to note that although the sample size for this study was not large, moderate effect sizes were shown in the correlation findings. Further research will review students’ change in motivation over time, as measured by the MES-UC, as well as relationships between academic motivation and performance throughout Years 2–4 of the program, including clinical performance. This may assist with planning the timing of any proposed intervention to enhance academic motivation during the degree program.
