Predictors Of Student Success Research Articles

Can coaching bridge the gap for incoming Latinx graduate students?

ABSTRACT Attrition rates in the first two years of graduate school are high and ∼50% higher for underrepresented students. Here we evaluate an online group coaching intervention using a controlled and an experimental group to determine its impact on skills that are likely predictors of student success, namely the Hallmark of Success indices developed by the National Institutes of Health (NIH). Our intervention includes eight online coaching sessions (four prior to the first year of graduate school and four during the first year of graduate school). Coaching group sessions, led by certified coaches, address Resilience, Risk and Protective Factors, Accountability, Leadership, Teamwork, Professional Networking, and Self-Assessment. We evaluated the change in Hallmark of Success indices with a pre- and post-questionnaire of 44 items used to assess students’ self-evaluation in nine areas. Students in the experimental coaching group showed a significant increase in the Hallmark of Success indices in seven out of nine areas of student performance between the pre- and post-questionnaires compared to the null model of no changes between time periods. In addition, in all of the nine indices of success, we noted significant differences between the pre- and post-surveys for the experimental group. Our experimental design shows that our cost-effective coaching model improves student performance as perceived by the students.

Predictors of Student Success in College-Level General Chemistry

Success in college-level general chemistry is important because it is a required course for many different STEM majors and student success in early STEM classes correlates with retention of students in STEM. Identifying factors associated with success and failure can give educators a better understanding of the warning signs to look out for in struggling students. This paper compares factors and behaviors with final grades in general chemistry courses at Binghamton University, including adjectives selected from a word bank (“valued,” “supported,” etc.), resources used, and opinion on group work as it relates to learning. Correlations were found between student adjective selection and final grade, resource use and final grade, and perspective on group work and final grade.

Unveiling the Best-fit Model: A Comparative Analysis of Classification Methods in Predicting Student Success

To reduce failure and personalize instruction, educators work to predict student achievement. For this objective, this study compared several categorization techniques. The study investigated techniques employing datasets from Portuguese schools, even though various circumstances make it difficult to gather full data and achieve high accuracy. Upon evaluating the various algorithms, including Random Forest and Decision Trees, the study determined that Random Forest was the most successful model, attaining a 94.55% accuracy rate. This demonstrates how machine learning—more especially, Random Forest—could forecast student achievement. The study opens the door for applying these techniques to early interventions and personalized learning. But more work needs to be done, such as creating publicly accessible educational datasets and investigating different strategies like regression algorithms to manage the nuances of grading systems more effectively.

Drug Information Quizzes as Predictors of Student Success in Integrated Pharmacotherapy Courses

ObjectiveTo determine if drug quizzes (DQs) can serve as predictors of student success in pharmacy coursework. MethodsDe-identified student exam and DQ data from 2 courses of the pharmacy curriculum were evaluated over 3 years. One-way analysis of variance, Spearman’s rank correlation analysis, and Mann–Whitney U test were utilized to determine significant changes in student performance on exams and DQs over 3 years. ResultsOver 3 years, significant changes in exam performance were accompanied by significant changes in student performance on the respective DQ. A significant positive correlation was observed between student performance on DQ and their scores on respective major examinations in 22 of 24 datasets. Finally, students failing an exam had significantly lower DQ scores compared to students passing an exam in most analyzed datasets across 3 years. ConclusionDrug quizzes can serve as a predictor of student success or failure in pharmacy courses.

Predicting Student Success in Co-remediated General Education Mathematics Courses at a Large Urban Public University

Placement and support of students in first-year mathematics courses at institutions of higher education has long been a consequential issue. In a bid to address it, many systems and institutions of higher learning have elected to implement a co-remediation framework in place of pre-remediation, due in large part to the prohibitive cost of the latter, both in terms of financial resource, as well as student academic progress. Accompanying this evolution has been the expansion of the introductory mathematics curriculum beyond algebra to include statistics and quantitative reasoning. The present study discusses three distinct introductory mathematics courses at a large urban public M.S. granting institution in the U.S., with the goal of identifying the characteristics that correlate with success in each. Conditional probability and non-completion rate analyses were implemented to compare student performance in each course. Predictive models were then trained and validated, building insight concerning the differential relationships of demographic and academic covariates with course completion.

Salient Factors in Predicting Student Success, Including Course Modality

Salient Factors in Predicting Student Success, Including Course Modality

Unveiling the Disparity: Investigating Factors Impacting Graduation Rates in Science Degrees and Predicting Student Success through Machine Learning

Unveiling the Disparity: Investigating Factors Impacting Graduation Rates in Science Degrees and Predicting Student Success through Machine Learning

Predicting Student Success Using Big Data and Machine Learning Algorithms

The prediction of student performance, allows teachers to track student results to react and make decisions that affect their learning and performance, given the importance of monitoring students to fight against academic failure. We realized a system of the prediction of academic success and failure of the students, which is the overall result and the goal of the educational system. We used the personal information of the students, the academic evaluation, the activities of the students in VLE, ​​Psychological, the student environment, and we added practical work and homework, mini projects, and the number of student absences which gives a vision of the quality of the student. Then we applied the methods of artificial intelligence and educational Data mining such as KNN, C4.5 and SVM for the prediction of the academic success of students, but these methods are not sufficient given the progressive number of students, specialties, learning methods and the diversity of data sources as well as student data processing time. To solve this problem, Big Data technology was used to distribute the processing in order to minimize the execution time without losing the efficiency of the algorithms used. In this system we cleaned the data and then applied the property selection algorithms to find the useful properties in order to improve the algorithm prediction rate and also to reduce the execution time. Finally, we stored the data in HDFS and we applied the classification algorithms for the prediction of student success using MAPREDUCE. We compared the results before and after the use of big data and we found that the results after the use of Big Data are very good at execution time and we arrived at a recognition rate of 87.32% by the SVM algorithm.

School Counselors’ Perceptions Regarding Importance of and Ability to Implement Transition Services for Students With Disabilities

The purpose of this study was to investigate school counselors’ perception of and ability to implement the 20 predictors of post-school success for students with disabilities. Results indicated statistical differences between grade levels in their perceived ability to implement transition services. We offer specific recommendations for how school counselors can provide support in alignment with the 20 special education predictors of improved postschool success for students with disabilities.

Predictors of student pharmacist success on an advanced pharmacy practice experience-readiness performance-based assessment

IntroductionThe study objective was determining the relationship of certain predictor variables with student performance on an advanced pharmacy practice experience (APPE)-readiness performance-based assessment (PBA) in the third professional year (P3). MethodsThis retrospective study aimed to identify if student predictors and/or curricular-related predictor variables correlate with student performance on a patient interview and documentation activity (PIDA), an APPE-readiness assessment. Student predictors included Pharmacy College Admission Test score, pre-pharmacy cumulative grade point average (GPA), end of first professional year (P1) GPA, end of second professional year GPA, end of fall P3 GPA, and elective Pharmacists' Patient Care Process (PPCP) score. Curricular-related predictor variables comprised specific activities with emphasis on patient care skill development in P1 through P3 years. Adjusted and unadjusted linear regression models assessed correlations between PIDA score and student predictors as well as curricular variables. ResultsStudents from the Classes of 2018 through 2020 at Southern Illinois University Edwardsville School of Pharmacy (N = 231) were included. Two student predictors, P3 GPA and PPCP elective score, showed statistically significant correlations with PIDA performance. One curriculum-related variable, P3 activity with an emphasis on medication reconciliation, demonstrated a statistically significant correlation with student performance on the PIDA. ConclusionsStudent performance on a cumulative PBA may be predictive from prior curricular learning experiences that assess similar patient care skills. The predictive impact of the elective PPCP score on a PBA needs additional research. Recent implementation of a new curriculum may lead to identification of other predictors of student success on PBAs.

Effectiveness of the blended design of a first-year biology course

ABSTRACT Growing class sizes and the rapid advancement of information technology have prompted institutions to implement blended learning in their academic offering. There are few reports in the literature on a holistic assessment of the effectiveness of the instructional design of blended learning courses for large classes. We defined effectiveness from a pedagogical and outcomes perspective and interrogated the design of a large first-year biology course with the aim of optimising the blend to benefit all students, but specifically the at-risk and ‘murky middle’ (MM) students. This was achieved by investigating patterns of student engagement with learning opportunities over time and its relationship with performance. The results show that participation in face-to-face tutorial classes was the stronger predictor of student success followed by online formative assessments. Students did not engage meaningfully with optional activities, such as virtual classrooms and pre-reading for lectures, and their engagement with compulsory learning opportunities declined during the semester, with the MM slipping towards those at risk of failing. We conclude that students need carefully chosen compulsory activities that are complementary in design and purpose, while optional activities are best suited for enrichment purposes.

Grit as a Key Factor in the Ability of Students to Achieve Productive Global Field Research.

Global fieldwork is an invaluable educational experience for students who aspire to pursue careers as global public health professionals and cancer experts. Student-led research projects can be mutually beneficial for students and host institutions by providing opportunities for bilateral learning, sharing resources, building databases, and ultimately creating uniquely informed multi-cultural health research relevant to global communities. The USA-host country partnerships can be delicate, requiring tactful approaches to the investment in the careers of students and the field projects. The US and host institutions must therefore be selective in determining which students have the privilege of participating in global field work. This paper examines the importance of grit as a character trait contributing to the success of student-led global health research projects. Grit has been explored at length as a predictor of student success in domestic educational experiences, yet is underrepresented in the context of global education, field training, and evaluation of research and learning outcomes. This manuscript utilizes testimonials of three public health graduate students recently returned from summer cancer epidemiology education training fellowships to explore the role that grit played in completion of their independent research projects. Ultimately, this paper discusses ways to identify grit in student applicants and to foster an improved capacity for grit before, during, and after their field experiences. We share the experiences with an aim of providing participant perspectives that may be used by educators, students, and administrators at US and international partner institutions to inform global research, experiential learning, and educational and training programs.

The Use Of Student Achievement Data In Teacher Evaluation: How Should It Be Used?

Student achievement data is widely recognized as a predictor of student success in education. The student achievement data is often used to determine if students are learning, understanding, processing, and retaining information. However, teachers are often unfairly evaluated based on student data, and little attention is given to student achievement outside of the classroom as well as how students without fair access to equitable resources perform. The student achievement data is also used to determine if schools are underperforming, and the data is also tied to whether or not students are proficient in subject content. These evaluations can often be tied to a teacher’s career path. This article looks to explore if teachers were more involved in the design of these evaluations, whether or not they would be a more fair assessment of teacher and student achievement. As teachers are experts in classroom environment, would teachers not be more qualified to develop fair evaluation tools to understand student and teacher achievement? Keywords: Teacher Evaluation, Student Achievement, Achievement Data

Are There Predictors of Success for Students With Disabilities Pursuing Postsecondary Education?

Students with disabilities are entering college in increasing numbers. Despite the increase, college persistence and completion remains a troublesome hurdle. Evidence-based practices and predictors have been identified for secondary-level students with disabilities; however, a parallel classification does not exist for postsecondary education. The purpose of this systematic review was to determine whether there are evidence-based predictors of college success with regard to retention, academic achievement, and graduation. Results indicated that although there are an insufficient number of studies to validate a core set of evidence-based practices at this time, there are a series of student-related practices that positively predict grade point average, retention, and graduation that warrant future investigation.

Predicting Student Success in Online Physical Education

ABSTRACT Currently, limited data are available on student retention rates and attrition factors in online physical education (OLPE) courses. Several early OLPE studies as well as the 2007 NASPE Initial Guidelines for Online Physical Education have suggested that certain prescreening efforts be in place prior to student enrollment in OLPE; however, at present no such empirically sound and theoretically based screening instruments exist. The purpose of the study is to identify online student cognitive characteristics and environmental factors associated with success and/or failure within college online health-related fitness (HRF) courses. Students (N = 821) enrolled in a 16-week online HRF course at a university during the Fall 2017 participated in the study. Data were collected at the beginning of the course with two previously validated research instruments, the Educational Success Prediction Instrument Version-2 (ESPRI-V2) and the Distance Learning Survey (DLS). Results reveled a relationship between course completion and six variables: GPA, class standing, hours worked outside of school, achievement, organization and study environment). The full model containing all predictors was statistically significant (χ 2 (6, N = 821) = 94.296, p <.001), indicating that the model was able to distinguish between students who completed and did not complete the online course. This analysis provides an initial understanding of the unique student characteristics affecting OLPE course completion.

Examining the Predictive Validity of the Health Sciences Reasoning Test (HSRT) in a Nontraditional Graduate OT Program

Abstract Date Presented 03/28/20 The purpose of this study was to determine whether the HSRT predicts student success. The data was analyzed using an ordinal logistic regression and a Spearman’s rho correlation coefficient. Statistical analyses revealed that the HSRT is not a valid predictor of student success, and there is also a weak relationship between the HSRT and student success. The HSRT can be further studied with other admission tools to determine what most accurately measures student success in a graduate OT program. Primary Author and Speaker: Kathryn Kendrick Additional Authors and Speakers: Joanna Such, Aileen Burke Contributing Authors: Catherine Cavaliere, Pamela Story

Predicting Student Success on the National Board for Certification in Occupational Therapy® Examination.

A predictive tool to support academic and practice outcomes for occupational therapy students is needed for use in advising students. To determine whether characteristics of academic and professional behavior across cohorts could indicate the likelihood of student success in passing the National Board for Certification in Occupational Therapy® examination. Causal-comparative research. Master of occupational therapy students (N = 315; eight cohorts). Demographics, college entrance exams, grade point average, program benchmarks (i.e., specific course grades, fieldwork grades, Occupational Therapy Knowledge Exam scores), licensure exam. Baseline and in-program characteristics were used to determine success; for each unit increase in the success score, the odds of passing the exam were 4.11 (95% confidence interval [2.23, 7.60]). Case studies that have used this success score suggest that additional resources and targeted interventions could be allocated to help students at highest risk of not passing the licensure exam. Using a predictive tool to support student success can enable more effective academic advising throughout an occupational therapy program and result in positive outcomes on the licensure exam.

POE: Understanding Innovative Learning Places and Their Impact on Student Academic Engagement—Index 6–8 ‘Alpha’ Survey Developments

New evidence builds upon the Student Engagement IndexTM and Teacher Engagement IndexTM research (Scott-Webber, Konyndyk, &amp;amp; French, 2019; Scott-Webber, Konyndyk, French, &amp;amp; French, 2018; Scott-Webber, Konyndyk, French, Lembke, &amp;amp; Kinney, 2017) determining post-occupancy answers to, &amp;ldquo;Can we demonstrate that the design of the built environment for grades 6&amp;ndash;8 impacts student academic engagement levels post-occupancy?&amp;rdquo; The early studies used respondents from grades 9&amp;ndash;12. This one is from users in grades 6&amp;ndash;8 (&amp;lsquo;alpha&amp;rsquo; pilot). All studies were conducted in the USA as convenience samples. Engagement performance is a high predictor of student success across multiple domains and learning/work experiences. Specifically, &amp;ldquo;Research that shows that engagement, the time and energy students devote to educationally purposeful activities, is the best single predictor of their learning and personal development&amp;rdquo; (Anonymous, NSSE, 2010, p. 2), and thus our research focus. From both the students and educators perspectives, design of the built space impacts engagement performance (p &amp;lt; .0001).

Predictors of Student Success in Canadian Polytechnics and CEGEPs

Student success in post-secondary education is an ongoing concern, however, research has focused on relatively homogeneous university samples. Moreover, Canadian research on predictors of student success is limited. Following recent trends, we examined non-cognitive, personal qualities, rather than cognitive predictors (e.g., IQ), of student success. Relying on a psychosocial model, we examined age, gender, perceived stress, maternal education, identity style, perseverance, and student engagement as predictors of student success in a multi-site sample of students attending a CEGEP in Quebec (N = 239; Mage = 18.6 years; 68.2% female) and a polytechnic school in Ontario (N = 209; Mage = 20.6 years; 71.3% female). Maternal education and perseverance emerged as significant predictors in both samples. Links between informational identity and cognitive engagement and student success differed by location. Our findings suggest the need to focus on student perseverance, and to consider identity and cognitive engagement dependent on the educational context.

Nelson-Denny Reading Test Scores as a Predictor of Student Success in Osteopathic Medical Education

Reading skills are crucial in medical school, where students are expected to absorb an onslaught of new and complex material. Studies on reading assessment in osteopathic medical education are lacking. To address gaps in the literature related to reading assessment and to investigate the correlation of the Nelson-Denny Reading Test with various performance indicators in osteopathic medical education. The West Virginia School of Osteopathic Medicine administered the Nelson-Denny Reading Test to first- and second-year students between 2015 and 2017. Raw scores were translated into the percentile rank, scale score, grade equivalent score, and stanine score based on guidelines supplied with the Nelson-Denny Reading Test. These translated scores were compared with Medical College Admission Test (MCAT) scores, first- and second-year performance on course examinations, Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) Level 1 scores, and scores provided in a 2002 study by Haught and Walls. A total of 623 students took the first-year Nelson-Denny Reading Test, and 408 took both the first- and second-year Nelson-Denny Reading Test. Findings showed a large correlation between the Nelson-Denny Reading Test and the verbal reasoning section (r=0.56 for the class of 2020 and 0.46 for the class of 2021) of the old MCAT (before 2015) and the reasoning skills section (r=0.42 for the class of 2020 and 0.49 for the class of 2021) of the new MCAT (released in 2015). There were no correlations with first- and second-year course examination scores or COMLEX-USA Level 1 scores. The Nelson-Denny Reading Test scores reported by Haught and Walls for medical students and health professional students were slightly higher than those found for osteopathic medical students in this study. The reasoning skills section of the new MCAT could serve as a good proxy for a reading test. There were no correlations between the Nelson-Denny Reading Test and performance in the first 2 years of medical school or COMLEX-USA Level 1 performance. Further research can strengthen the findings and determine whether correlations exist with clinical performance.