Perceived benefits and barriers of a prototype early alert system to detect engagement and support ‘at-risk’ students: The teacher perspective

2020 ◽  
Vol 156 ◽  
pp. 103954
Author(s):  
Amara Atif ◽  
Deborah Richards ◽  
Danny Liu ◽  
Ayse Aysin Bilgin
Keyword(s):  
At Risk ◽  
At Risk Students ◽  
Perceived Benefits ◽  
Alert System ◽  
Teacher Perspective ◽  
Early Alert

Early Alert System for Detection of At-Risk Students

2018 ◽  
Author(s):  
Rejitha Ravikumar ◽  
Fatma Aljanahi ◽  
Amala Rajan ◽  
Vishwesh Akre
Keyword(s):  
At Risk ◽  
At Risk Students ◽  
Alert System ◽  
Early Alert

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.

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.

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.

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.

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.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document