**4. Shifting focus from equality to equity**

Traditional US medical students have an average age of 24 and enroll in a medical program directly after completing college level pre-medical courses in science and math [25]. Similarly, in the UK, traditional medical students have an average age of 18–19 and have completed their schooling with necessary prerequisites [26]. With the move towards widening access and subsequent admission of non-traditional medical students, researchers have challenged the effectiveness of traditional curriculum to meet the diverse needs of 21st century student cohort [27]. The time-based curriculum assumes that all matriculating medical students have a homogenous level of knowledge, skills, and experiences, and therefore are able to progress through the continuum with a consistent pace. However, with the reforms in the admissions process and the reduced emphasis on standardized exam scores, the assumption of a "level playing field" seems to be no longer valid [28]. There is a greater need to shift from the one-size-fits-all approach of the traditional curriculum to individualization of learning experiences for medical students [29]. Recognition of these needs has led to the popularity of competency-based medical education (CBME).

CBME shifts the focus from knowledge-based standardized exams to holistic development of knowledge, skills and behaviors required to be a competent physician. The core elements of CBME include time variability, focus on outcomes, entrustability and professional identity formation [30]. Although CBME is time and resource intensive, several institutions have recognized the value of using core Entrustable Professional Activities (EPAs) and competency-based milestones in developing competent physicians while also honoring the existing multifariousness of medical student cohorts. In addition to providing tailored learning experiences, the greatest value of CBME lies within the opportunities for individualized formative feedback and real-time remediation for students [31].

Although CBME is a giant step towards equitable medical education, the challenges to its implementation lie in its trivia. Curriculum experts have stressed that there is no universal approach to CBME [32]. Institutions must set competency standards based on their unique contexts and implement their curriculum using several iterative cycles of planning and evaluation. Successful implementation of CBME lies in the ability to track assessment data of individual medical students in order to be able to assess their entrustability and progression. CBME researchers have promoted the use of learning analytics to integrate assessment data from multiple sources and provide visual representations of student progress [33]. Use of artificial intelligence in tracking student progress could be invaluable in providing individualized support to a 21st century medical student.

Therefore, a competency-based curriculum could support students' need for autonomy and competence and promote their progression and success.

### **5. Exploring student academic success factors**

The ability to predict academic performance of medical students has been a significant topic of discourse among medical educators. Historically, academic achievements of students prior to medical school such as Medical College Admissions Test (MCAT) and Grade Point Averages (GPA) have been found to predictive of their academic performance and progression [34]. Similarly, the UK Clinical Aptitude Test (UKCAT) has also been found to be a predictor of student academic success [35]. These factors have been used as screening criteria for admissions or for identifying the need for additional academic support. With the introduction of non-cognitive measures in the admissions criteria, institutions began to explore traits or skills associated with motivation, attitude, and mindset as predictors of student academic success. There have been debates around the individual and/or collective roles of factors such as grit, perseverance, selfefficacy, and self-regulation in determining academic success [36]. Researchers have also explored the prospects of using these factors to identify students at-risk of experiencing academic difficulties in medical school [37].

Grit is defined as "perseverance and passion for long-term goals". Consistency of perseverance and effort has been positively related to academic performance and success [38]. This concept was further elaborated by the notion of academic psychological capital (PsyCap) and its impact on academic achievements. In addition to grit, the core constructs of PsyCap include positive psychological resources such as hope, efficacy, resilience, and optimism. All these factors have been shown to impact a student's response to challenges and adversity in pursuit of successful academic outcomes [39].

Metacognition or "thinking about thinking" has been another factor commonly researched as a predictor of high performance. Metacognition involves students' knowledge about cognitive strategies and their regulation of these strategies before, during and after learning events [40]. Researchers have attempted to compare metacognitive skills between high and low performing students to identify patterns that determine success [41]. Furthermore, the influence of self-regulation on learning has also been explored. Self-regulated learning builds on the concept of metacognition and also considers the influence of social and motivational factors on learning [42]. Self-regulated learning is generally described as a cyclical process, often triggered by the formulating of goals and the subsequent employment of strategies to achieve, and monitor advancement towards those goals, followed by engagement in reflection and the formulation of new learning goals. Among medical students a positive correlation has been identified between self-regulated

#### *How to Support Student Academic Success DOI: http://dx.doi.org/10.5772/intechopen.100061*

learning and academic achievement [43]. Additionally, academic self-efficacy which is defined as learner's judgments about their ability to successfully attain educational goals has also been associated with academic performance [44]. Researchers have found associations between academic self-efficacy of students and their ability to self-regulate [45].

Essentially, the above non-cognitive factors have been found to have some degree of relationship with academic performance and success. Medical schools have used self-reported psychometric inventories to assess these factors in students. Some examples of self-reported inventories include Learning and Study Strategies Inventory (LASSI), Motivated Strategies for Learning Questionnaire (MSLQ ), Academic Self-Efficacy Scale (AES) etc. [44, 46]. The psychometric data from the inventories have been combined with qualitative data from reflective journals, group discussions and interviews to assess these factors [47]. This data has been typically used to identify students who may be potentially at-risk of poor academic performance either to inform the admissions process or to direct remediation efforts.

However, the utilization of these factors as predictors of success among medical students, could impede the progress towards widening access. The reason for this argument is twofold. Firstly, self-efficacy is impacted by prior knowledge, experiences, and social support systems [48]. A diverse group of medical students with diverse levels of prior knowledge and experiences may not have uniform levels of self-efficacy. Secondly, literature has highlighted the domain-specificity of self-regulation and self-efficacy. Academic self-efficacy levels among students can vary depending on the context. It is found to be directly related to their "need for cognition", i.e., their inclination to enjoy tasks involving higher mental activity [49]. Besides motivation, persistence and effort, academic self-efficacy is also impacted by knowledge and regulation of metacognitive strategies [50]. The ability of students to regulate their own learning is dependent upon the specificity and complexity of the task [51]. Task-specific metacognitive strategies can be developed over time with practice and feedback. Therefore, the challenges to student development and progress could also be secondary to the learning environment including teaching and assessment practices [52]. Furthermore, reactive remediation approaches that are based on identification of "at-risk" students could have a negative impact on students' self-efficacy [53]. A learning environment that scaffolds the development of metacognition and self-regulation into the curriculum is vital to student success.

Therefore, shifting the focus from predicting outcomes to supporting student development might be beneficial in promoting student autonomy and sense of belonging.
