Increasing Academic Success Among Disadvantaged, At-Risk Students: The U-Pace Model

2009 ◽  
Author(s):  
Jessica Barnack ◽  
Raymond Fleming ◽  
Rodney Swain ◽  
Laura Pedrick ◽  
Diane M. Reddy
Keyword(s):  
At Risk ◽  
Academic Success ◽  
At Risk Students

ENRICHMENT FOR ACADEMIC SUCCESS: HELPING AT-RISK STUDENTS

1990 ◽  
Vol 15 (6) ◽  
pp. 33-37 ◽  
Author(s):  
ARTHUR REE CAMPBELL ◽  
SANDRA M. DAVIS
Keyword(s):  
At Risk ◽  
Academic Success ◽  
At Risk Students

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 Incorporating Human–Animal Interaction Into Academic Stress Management Programs: Effects on Typical and At-Risk College Students’ Executive Function

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110116
Author(s):  
Patricia Pendry ◽  
Alexa M. Carr ◽  
Jaymie L. Vandagriff ◽  
Nancy R. Gee
Keyword(s):  
At Risk ◽  
Academic Success ◽  
At Risk Students ◽  
Academic Failure ◽  
Evidence Based ◽  
Stress Prevention ◽  
Human Animal ◽  
Human Animal Interaction ◽  
Animal Interaction ◽  
Intent To Treat

Implementation of university-based animal-assisted stress-prevention programs is increasing despite limited knowledge about impacts on students’ academic success. This randomized trial (N = 309) examined the effects of a 4-week stress-prevention program with varying levels of human–animal interaction (HAI) and evidence-based content presentations on students’ executive functioning (EF). Effects were examined while considering the moderating role of students’ risk status (N = 121), based on history of academic failure, suicidal ideation, mental health, and learning issues. Intent-to-treat analyses showed that at-risk students showed the highest levels of EF (Β = 4.74, p = .018) and metacognition (Β = 4.88, p = .013) at posttest in the condition featuring 100% HAI, effects that remained 6 weeks later (ΒGlobal EF = 4.48, p = .028; ΒMetacognition = 5.31,p = .009). Since evidence-based content presentations did not confer benefits for at-risk students’ EF, even when offered in combination with HAI, universities should consider providing at-risk students with targeted programs emphasizing exposure to HAI.

scholarly journals Not So Soft Skills: The Importance of Grit to College Student Success

2019 ◽  
Vol 26 (2) ◽  
Author(s):  
Rebekah Reysen ◽  
Patrick Perry ◽  
Matthew Reysen ◽  
R. Dewey Knight
Keyword(s):  
At Risk ◽  
Academic Success ◽  
Undergraduate Students ◽  
At Risk Students ◽  
Public Institutions ◽  
College Student Success ◽  
Future Directions ◽  
Credit Hours ◽  
Collegiate Level

According to the American College Testing organization (2012), fewer than 35% of students attending public institutions graduate within five years of enrolling. This figure increases to just over fifty percent for private attendees. Clearly, the idea of a “four-year degree” is more elusive for the majority of undergraduate students than it has ever been. These facts have led researchers to consider the factors that delay, or even prevent, graduation. The concept of “grit” (Duckworth, Peterson, Matthews, & Kelly, 2007) is defined as passion and perseverance for very long-term goals and has become a popular topic in the education literature. Duckworth et al. (2007) found that grit positively associates with academic success. The purpose of the present study was to explore the relationships between grit, academic performance, and educational attainment, as measured by number of attempted credit hours at the collegiate level. We also aimed to ascertain whether academically at-risk students (those with less than a 2.0 GPA) had lower grit scores than their non-at-risk peers. We discuss our findings in the context of potential interventions and future directions for research in this area.

Adopting Learning Analytics in a First-Year Veterinarian Professional Program: What We Could Know in Advance about Student Learning Progress

2021 ◽  
Vol 48 (6) ◽  
pp. 720-728
Author(s):  
Wenting Weng ◽  
Nicola L. Ritter ◽  
Karen Cornell ◽  
Molly Gonzales
Keyword(s):  
At Risk ◽  
Academic Success ◽  
Student Learning ◽  
Learning Analytics ◽  
Prediction Models ◽  
Early Stage ◽  
At Risk Students ◽  
Learning Performance ◽  
First Year ◽  
Actual Performance

Over the past decade, the field of education has seen stark changes in the way that data are collected and leveraged to support high-stakes decision-making. Utilizing big data as a meaningful lens to inform teaching and learning can increase academic success. Data-driven research has been conducted to understand student learning performance, such as predicting at-risk students at an early stage and recommending tailored interventions to support services. However, few studies in veterinary education have adopted Learning Analytics. This article examines the adoption of Learning Analytics by using the retrospective data from the first-year professional Doctor of Veterinary Medicine program. The article gives detailed examples of predicting six courses from week 0 (i.e., before the classes started) to week 14 in the semester of Spring 2018. The weekly models for each course showed the change of prediction results as well as the comparison between the prediction results and students’ actual performance. From the prediction models, at-risk students were successfully identified at the early stage, which would help inform instructors to pay more attention to them at this point.

Parent Perceptions of Barriers to Academic Success in a Rural Middle School

2010 ◽  
Vol 14 (1) ◽  
pp. 2156759X1001400
Author(s):  
Dana Griffin ◽  
John P. Galassi
Keyword(s):  
Middle School ◽  
At Risk ◽  
Academic Success ◽  
Parental Involvement ◽  
At Risk Students ◽  
Perceived Barriers ◽  
Academically Successful ◽  
Community Collaboration ◽  
Community Resources ◽  
Rural Middle School

In focus groups, parents of both academically successful seventh-grade students and at-risk students (i.e., failing one or more classes, numerous behavioral referrals, and/or suspensions) in a rural middle school identified perceived barriers to student success as well as school and community resources for overcoming those barriers. Qualitative analysis of the data revealed six common barrier themes for the two groups and two additional themes for parents of academically at-risk students. The results are discussed with respect to the Hoover-Dempsey and Sandler model of parental involvement and the school counselor's role in school-family-community collaboration.

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