A Rough Set Based Approach to Compute The Impact of Non-Academic Parameters on Academic Performance

Background: Our academic performance can differ due various to non academic factors namely, self motivation and mind stability where peace of mind plays a pivotal role; self confidence after any kind of set back, environment and financial pressure, family support, and capability of an individual to make a combat. These nonacademic factors may lead to stress affecting our academic performance. Objective: Inthispaper,weaimtoassess the academicfactorsaffect our academic performance. Methods: The paper is modeled on survey performed on college students and depending upon their responses related to these non-academicfactors. We have carried out the work through statistical analysis and machine learning algorithms. Density bases Clustering algorithm is being used to cluster similar kind of performance and analyze how the factors differ from each other. Moreover we also presented a comparative study between academic parameters such as attendance, interest in subject and travelling time in comparison to such non academic performances. A rough set is also being created comprising the parameterswhichgivesthebestresultamongthestudents. Results: Proposed algorithm based on RST outplayed the accuracy of current models which is existing, there is a sharp drop in RMSE value for the parameters chosen in reduct dataset rather than whole dataset. Conclusion: Our proposed model’s accuracy overshadows the current analyzing model.

Including College and Career Readiness Within a Multitiered Systems of Support Framework

Current practices of college and career readiness (CCR) emerged from within secondary school reform efforts. During a similar timeframe, evidence-based schoolwide interventions—positive behavioral interventions and supports (PBIS) and response to interventions (RTI)—were developed, first targeting elementary initiatives and then translated to secondary schools. We provide an overview of a recently established CCR framework underscoring both academic and nonacademic factors necessary for student success. To operationalize CCR approaches within secondary schools, an effort must be made to utilize existing interventions and strategies as well as data-informed efforts included within multitiered systems of support (MTSS). Therefore, we examine how CCR can be extended within secondary MTSS approaches and extend current methods by recommending measures aligning CCR elements within essential data-based decision making and fidelity of implementation tenets of MTSS. By embedding CCR within established MTSS approaches, improved post-school outcome for all students, including those with disabilities, can be achieved.

Overcoming Undermatching: Factors Associated With Degree Attainment for Academically Undermatched Students

Academic undermatching—students enrolling in colleges that are less academically selective than those for which they qualify—is a barrier hindering college degree attainment. Given the many reasons students undermatch, this phenomenon is likely to continue; yet, we know little about the characteristics that may be related to degree attainment for this population of students. Utilizing the ELS: 2002 national dataset, we developed an operationalization of undermatch and then applied this operationalization to the BPS: 04/09 national dataset ( N = 11,260). Using logistic regression methods, we identified institutional and student characteristics that are associated with degree attainment for undermatched students. Starting at a 4-year institution, completing at least precalculus, having at least one parent with a college education, being female, and being socially and academically integrated into the institution were positively associated with degree attainment. Our results emphasize the critical influence of nonacademic factors on degree attainment for undermatched students.

Engagement as predictors of performance in a single cohort of undergraduate chiropractic students

Objective: To investigate the potential association of novel academic and nonacademic factors with chiropractic student academic performance. Methods: Students enrolled into year 1 of a chiropractic master's degree (MChiro) at our college were selected for this study. Data collected included demographics, attendance, virtual learning environment use, additional learning needs, previous degree qualifications, and summative marks. Differences between students who had to take an examination more than once (resit) and nonresit students were explored using t test and χ2 analysis. Relationships between attendance and end-of-year marks were explored using regression analysis. Results: Male students outperformed female students in four of the six units and as the total year average. Students who attended <80% of classes were more likely to have a resit in one or more units (relative risk [RR] = 2.6; 95% confidence interval [CI], 1.4–4.9). Students who performed poorly (<70%) in the semester 1 unit of a course on human structure and failed the semester 1 practical assessment of a course on clinical management were significantly more likely to have one or more resit assessments in semester 2 units (RR = 3.5 [95% CI, 2.2–5.7]; RR = 3.2 [95% CI, 2.0–4.9]). Attendance and unit 105 were independent predictors of one or more resits at the end-of-year (R2 = 0.86, p < .001). Conclusion: Attendance and first semester summative marks were associated with end-of-year performance. As such, these markers of performance may be used to flag struggling students in the program.