A Mentoring Program for Remedial Students

2002 ◽  
Vol 4 (3) ◽  
pp. 207-226 ◽  
Author(s):  
Ralph Pagan ◽  
Runae Edwards-Wilson
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Positive Impact ◽  
At Risk Students ◽  
Grade Point Averages ◽  
Academic Probation ◽  
Mentoring Program ◽  
Grade Point ◽  
Remedial Students ◽  
Academic Standing

The effectiveness of a mentoring program for 53 at-risk students was investigated. The investigation followed the similar research models as those previously implemented in higher education settings whereby undergraduate and graduate peers, in good academic standing, served as mentors to students in academic jeopardy. The grade point averages and attrition of a cohort of students on academic probation or warning was recorded during two consecutive semesters. A mentoring intervention was instituted during the second semester. The results indicated that the mentoring intervention had a positive impact on retention and grade point averages for this student cohort.

scholarly journals Style Over Substance in Interventions for At-Risk Students: The Impact of Intrusiveness

2000 ◽  
Vol 20 (2) ◽  
pp. 5-15 ◽  
Author(s):  
Anthony Molina ◽  
Robert Abelman
Keyword(s):  
At Risk ◽  
Student Performance ◽  
At Risk Students ◽  
Grade Point Averages ◽  
Academic Probation ◽  
Retention Rates ◽  
Success Strategies ◽  
Grade Point ◽  
Academic Dismissal ◽  
The Impact

Academic advisors charged with developing and implementing student success strategies should ask: To what extent is the process of intervention, rather than the nature of any specific intervention, responsible for an influx in at-risk student performance and persistence? Students in varying degrees of academic probation were randomly assigned to one of three intervention strategies that incorporated controlled content but divergent levels of intrusiveness. The most intrusive intervention resulted in higher cumulative grade-point averages and higher retention rates for all students. Students with the highest risk of academic dismissal were the most responsive to the most intrusive intervention.

Style Over Substance Revisited: A Longitudinal Analysis of Intrusive Intervention

2001 ◽  
Vol 21 (1-2) ◽  
pp. 32-39 ◽  
Author(s):  
Robert Abelman ◽  
Anthony Molina
Keyword(s):  
At Risk ◽  
Student Performance ◽  
Academic Advising ◽  
At Risk Students ◽  
Grade Point Averages ◽  
Retention Rates ◽  
Grade Point ◽  
Intervention Process ◽  
Intervention Content ◽  
Long Term Impact

In a recent report, the authors showed that the academic intervention process, rather than the specific intervention content, was responsible for a short-term influx in at-risk student performance and persistence. Students in varying degrees of academic probation were randomly assigned to one of three intervention strategies that incorporated controlled content but divergent levels of intrusiveness. Results showed that the most intrusive intervention produced higher cumulative grade-point averages and retention rates for all at-risk students. This follow-up study on the long-term impact of these one-time interventions confirms results regarding performance and persistence: Some intrusion is better than none in academic advising.

Redesigning Prediction Algorithms for At-Risk Students in Higher Education

2019 ◽  
pp. 232-250 ◽  
Author(s):  
Dennis Foung
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Logistic Regression ◽  
Academic Writing ◽  
Industry 4.0 ◽  
Classification Tree ◽  
At Risk Students ◽  
Higher Education Administrators ◽  
Writing Courses ◽  
Education Administrators

Use of algorithms and data mining approaches are not new to Industry 4.0. However, these may not be common for students and educators in higher education. This chapter compares various classification techniques: classification tree, logistic regression, and artificial neural networks (ANN). The comparison focuses on each method's accuracy, algorithm, and practicality in higher education. This study made use of a dataset from two academic writing courses in a university in Hong Kong with more than 5,000 records. Results suggest that classification trees and logistic regression can be easily used in the higher education context, but ANN may not be applicable in higher educational settings. The research team suggests that higher education administrators take this research forward and design platforms to realize these classification algorithms to predict at-risk students.

Academic Achievement and Demographics of International Undergraduates

2016 ◽  
pp. 233-259
Author(s):  
Dulce Amor L. Dorado ◽  
Barry Fass-Holmes
Keyword(s):  
Academic Achievement ◽  
Community College ◽  
West Coast ◽  
Native Language ◽  
Public University ◽  
Grade Point Averages ◽  
First Year ◽  
Grade Point ◽  
Academic Standing ◽  
First Time

Are international undergraduates whose native language is not English less prepared to succeed academically at an American four-year institution after transferring from an American community college than ones who are first-time freshmen (NFRS) or exchange visitors (EAPR)? This question's answer was no at an American West Coast public university where five cohorts of international transfer undergraduates (TRAN) earned mean first-year grade point averages (GPA) between B- and B. Less than 12% of these students earned GPAs below C, and less than 15% were in bad academic standing (probation, subject to disqualification, or dismissed). In comparison, five parallel cohorts of NFRS and EAPR earned mean first-year GPAs averaging between B and B+ to A-. Less than 10% earned GPAs below C or were in bad academic standing. Thus, a minority of this university's international undergraduates struggled academically regardless of whether they were TRAN, NFRS, or EAPR.

scholarly journals An Early Warning System to Detect At-Risk Students in Online Higher Education

2020 ◽  
Vol 10 (13) ◽  
pp. 4427 ◽  
Author(s):  
David Bañeres ◽  
M. Elena Rodríguez ◽  
Ana Elena Guerrero-Roldán ◽  
Abdulkadir Karadeniz
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Predictive Model ◽  
Early Warning ◽  
Early Warning System ◽  
Time Lag ◽  
At Risk Students ◽  
Warning System ◽  
Online Higher Education ◽  
Depth Analysis

Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.

Style Over Substance Reconsidered: Intrusive Intervention and At-Risk Students with Learning Disabilities

2002 ◽  
Vol 22 (2) ◽  
pp. 66-77 ◽  
Author(s):  
Robert Abelman ◽  
Anthony Molina
Keyword(s):  
At Risk ◽  
Learning Disabilities ◽  
Student Performance ◽  
At Risk Students ◽  
Retention Rates ◽  
Students With Learning Disabilities ◽  
Grade Point ◽  
Probationary Students ◽  
The Individual ◽  
The Impact

In two recent publications, we reported that the academic intervention process, not the specific intervention content, was responsible for a short-and long-term influx in at-risk student performance (grade-point average) and persistence (retention). All at-risk students who participated in the most intrusive of three interventions had higher cumulative grade-point averages and retention rates than those who received less intrusive interventions. In this post hoc analysis, we looked at probationary students with learning disabilities and found that they are only responsive to the individual attention and personalized accommodation provided under a highly intrusive model, and the impact is temporary.

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.

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