Are your students ready for anatomy and physiology? Developing tools to identify students at risk for failure

2015 ◽  
Vol 39 (2) ◽  
pp. 108-115 ◽  
Author(s):  
Amy Gultice ◽  
Ann Witham ◽  
Robert Kallmeyer
Keyword(s):  
At Risk ◽  
Student Success ◽  
At Risk Students ◽  
Academic Preparation ◽  
Students At Risk ◽  
Science Courses ◽  
Biology Students ◽  
Anatomy And Physiology ◽  
Survey Results ◽  
Study Support

High failure rates in introductory college science courses, including anatomy and physiology, are common at institutions across the country, and determining the specific factors that contribute to this problem is challenging. To identify students at risk for failure in introductory physiology courses at our open-enrollment institution, an online pilot survey was administered to 200 biology students. The survey results revealed several predictive factors related to academic preparation and prompted a comprehensive analysis of college records of >2,000 biology students over a 5-yr period. Using these historical data, a model that was 91% successful in predicting student success in these courses was developed. The results of the present study support the use of surveys and similar models to identify at-risk students and to provide guidance in the development of evidence-based advising programs and pedagogies. This comprehensive approach may be a tangible step in improving student success for students from a wide variety of backgrounds in anatomy and physiology courses.

scholarly journals Differentiating Military-Connected and Non-Military-Connected Students: Predictors of Graduation and SAT Score

2020 ◽  
Author(s):  
Ryan Shaun Baker ◽  
Andy Berning ◽  
Sujith M. Gowda
Keyword(s):  
At Risk ◽  
Risk Prediction ◽  
Early Warning ◽  
Student Outcomes ◽  
At Risk Students ◽  
Not Given ◽  
Students At Risk ◽  
The Many ◽  
Contemporary Practice ◽  
Core Part

At-risk prediction and early warning initiatives have become a core part of contemporary practice in American high schools, with the goal of identifying students at-risk of poorer outcomes, determining which factors are associated with these risks, and developing interventions to support at-risk students’ individual needs. However, efforts along these lines have typically ignored whether a student is military-connected or not. Given the many differences between military-connected students and other students, we investigate whether models developed for non-military-connected students still function effectively for military-connected students, studying the specific cases of graduation prediction and SAT score prediction. We then identify which variables are highly different in their connections to student outcomes, between populations.

Removing Barriers

2015 ◽  
pp. 157-174
Author(s):  
S. Michael Putman ◽  
Jerrell C. Cassady ◽  
Lawrence L. Smith ◽  
Monica L. Heller
Keyword(s):  
At Risk ◽  
Local Community ◽  
Teacher Education Program ◽  
Academic Support ◽  
At Risk Students ◽  
School Failure ◽  
Support Program ◽  
Students At Risk ◽  
Local Schools ◽  
Community Outcomes

The purpose of this chapter is to articulate the success of a partnership facilitated by a PDS relationship in serving at-risk students in a collection of schools proximal to a university in the Midwest. The authors begin by describing characteristics of community partnerships, including professional development schools, which both enable and hinder schools and stakeholders when they attempt to build innovative partnerships promoting positive school and community outcomes. They then discuss how they leveraged the resources of the local community, a teacher education program, and the local schools to develop and implement an afterschool academic support program targeting students at-risk for school failure. In addition to explaining the procedural elements that were found to be useful in breaking down traditional barriers to effective partnerships (e.g., space, finance, staff, quality curriculum support), the authors present the results of their study that demonstrate student gains in both math and reading.

scholarly journals Learning How to Learn: A Student Success Course for At Risk Students

2017 ◽  
Vol 8 (3) ◽  
Author(s):  
Elizabeth R. Bowering ◽  
Joanne Mills ◽  
Allison Merritt
Keyword(s):  
At Risk ◽  
Student Success ◽  
Learning Strategies ◽  
Study Skills ◽  
At Risk Students ◽  
Cognitive Strategies ◽  
Student Success Course ◽  
Success Course ◽  
Post Test ◽  
Premier Cycle

It is well known that university students with ineffective learning strategies and low motivation are at risk for lowered grades and stress. Given the needs of these students, Mount St. Vincent University developed the Student Success Course (SSC), a 14-week intervention that offers instruction in learning strategies, self-management, and motivation. The purpose of this study was to evaluate the effectiveness of the SSC for 100 undergraduates on academic probation. From pre- to post-test, participants reported a significant increase in cognitive strategies, study skills, and motivation as well as a significant decrease in test anxiety and procrastination (ps < .05). Over time, participants also demonstrated a significantly improved GPA (p < .0001). These results support the hypothesis that the SSC is an effective intervention, at least in the short-term, for improving learning and motivational strategies in at risk students. Il est reconnu que les étudiants d’université dont les stratégies d’apprentissage sont inefficaces et qui ont une faible motivation risquent de souffrir de stress et d’obtenir de mauvaises notes. Au vu des besoins de ces étudiants, Mount St. Vincent University a mis en place un cours pour faciliter la réussite des étudiants (Student Success Course - SSC). Il s’agit d’une intervention de 14 semaines au cours de laquelle on enseigne des stratégies d’apprentissage, de gestion autonome et de motivation. L’objectif de cette étude est d’évaluer l’efficacité de ce cours dans le cas de 100 étudiants de premier cycle placés en probation. Les participants ont rapporté, avant et après le test, une augmentation significative de leurs stratégies cognitives, de leurs compétences en matière d’apprentissage et de leur motivation, ainsi qu’une baisse importante de leur anxiété face aux examens et de leur procrastination (ps < .05). Avec le temps, les participants ont également démontré une augmentation de leur moyenne pondérée cumulative (p < .0001). Ces résultats soutiennent l’hypothèse selon laquelle le cours en question représente une intervention efficace, tout au moins à court terme, pour améliorer les stratégies d’apprentissage et de motivation chez les étudiants à risque.

scholarly journals Using Machine Learning to Identify At-risk Students in an Introductory Programming Course

2021 ◽  
Author(s):  
Cameron I. Cooper ◽  
Kamea J. Cooper
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Student Success ◽  
At Risk Students ◽  
Supervised Machine Learning ◽  
Success Rates ◽  
Data Set ◽  
Out Of Sample ◽  
Sample Test ◽  
Early Alert

Abstract Nationally, more than one-third of students enrolling in introductory computer science programming courses (CS101) do not succeed. To improve student success rates, this research team used supervised machine learning to identify students who are “at-risk” of not succeeding in CS101 at a two-year public college. The resultant predictive model accurately identifies \(\approx\)99% of “at-risk” students in an out-of-sample test data set. The programming instructor piloted the use of the model’s predictive factors as early alert triggers to intervene with individualized outreach and support across three course sections of CS101 in fall 2020. The outcome of this pilot study was a 23% increase in student success and a 7.3 percentage point decrease in DFW rate. More importantly, this study identified academic, early alert triggers for CS101. Specifically, the first two graded programs are of paramount importance for student success in the course.

Removing Barriers

2016 ◽  
pp. 1062-1078
Author(s):  
S. Michael Putman ◽  
Jerrell C. Cassady ◽  
Lawrence L. Smith ◽  
Monica L. Heller
Keyword(s):  
At Risk ◽  
Local Community ◽  
Teacher Education Program ◽  
Academic Support ◽  
At Risk Students ◽  
School Failure ◽  
Support Program ◽  
Students At Risk ◽  
Local Schools ◽  
Community Outcomes

The purpose of this chapter is to articulate the success of a partnership facilitated by a PDS relationship in serving at-risk students in a collection of schools proximal to a university in the Midwest. The authors begin by describing characteristics of community partnerships, including professional development schools, which both enable and hinder schools and stakeholders when they attempt to build innovative partnerships promoting positive school and community outcomes. They then discuss how they leveraged the resources of the local community, a teacher education program, and the local schools to develop and implement an afterschool academic support program targeting students at-risk for school failure. In addition to explaining the procedural elements that were found to be useful in breaking down traditional barriers to effective partnerships (e.g., space, finance, staff, quality curriculum support), the authors present the results of their study that demonstrate student gains in both math and reading.

scholarly journals Identifying students at risk in academics

2017 ◽  
Vol 1 (1) ◽  
pp. 8-18
Author(s):  
Adam Christian Haupt ◽  
Jonathan Alt ◽  
Samuel Buttrey
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Regression Models ◽  
At Risk Students ◽  
Korean Language ◽  
Students At Risk ◽  
Content Type ◽  
Logistic Regression Models ◽  
Data Driven Approach ◽  
Defense Language Institute

Purpose This paper aims to use a data-driven approach to identify the factors and metrics that provide the best indicators of academic attrition in the Korean language program at the Defense Language Institute Foreign Language Center. Design methodology approach This research develops logistic regression models to aid in the identification of at-risk students in the Defense Language Institute’s Korean language school. Findings The results from this research demonstrates that this methodology can detect significant factors and metrics that identify students at-risk. Additionally, this research shows that school policy changes can be detected using logistic regression models and stepwise regression. Originality value This research represents a real-world application of logistic regression modeling methods applied to the problem of identifying at-risk students for the purpose of academic intervention or other negative outcomes. By using logistic regression, the authors are able to gain a greater understanding of the problem and identify statistically significant predictors of student attrition that they believe can be converted into meaningful policy change.

Assessing the Validity of College Success Indicators for the At-Risk Student: Toward Developing a Best-Practice Model

2017 ◽  
Vol 21 (2) ◽  
pp. 166-183 ◽  
Author(s):  
Leslie Tucker ◽  
Oscar McKnight
Keyword(s):  
At Risk ◽  
Academic Success ◽  
Student Success ◽  
Best Practice ◽  
Research University ◽  
At Risk Students ◽  
American College Test ◽  
Retention Data ◽  
Grade Point ◽  
Success Indicators

This study assessed the feasibility of using precollege success indicators to identify at-risk students at a large 4-year public research university in the Midwest. Retention data from students who participated in an established student success program were examined. The findings affirm that the initial admissions assessment identifying at-risk students is a feasible predictor of academic success, including high school (HS) grade point average (GPA) could predict student success over and above the variance accounted for by American College Test alone; the semester in which students are admitted is a predictor of success; first-semester college GPA can predict academic success over and above chance; there is a significant positive relationship between cognitive ability (i.e., American College Test × HS GPA) and SUCCESS; HS GPA could be used as the single best predictor of student success; and using all three variables to identify student success appears warranted. A PASS model is offered to assist in the development of interventions and success programs.

scholarly journals Using Machine Learning to Identify At-risk Students in an Introductory Programming Course at a Two-year Public College

2021 ◽  
Author(s):  
Cameron I. Cooper ◽  
Kamea J. Cooper ◽  
Cameron Collyer
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Student Success ◽  
At Risk Students ◽  
Supervised Machine Learning ◽  
Success Rates ◽  
Data Set ◽  
Public College ◽  
Out Of Sample ◽  
Early Alert

Abstract Nationally, more than one-third of students enrolling in introductory computer science programming courses (CS101) do not succeed. To improve student success rates, this research team used supervised machine learning to identify students who are “at-risk” of not succeeding in CS101 at a two-year public college. The resultant predictive model accurately identifies \(\approx\)99% of “at-risk” students in an out-of-sample test data set. The programming instructor piloted the use of the model’s predictive factors as early alert triggers to intervene with individualized outreach and support across three course sections of CS101 in fall 2020. The outcome of this pilot study was a 23% increase in student success and a 7.3 percentage point decrease in the DFW rate. More importantly, this study identified academic, early alert triggers for CS101. Specifically, the first two graded programs are of paramount importance for student success in the course.

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