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

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.

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

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.

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

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.

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

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.

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

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.

Volcanic Risk Mitigation that Could Have Been Derailed but Wasn’t: Pinatubo, Philippines 1991

This is the story of a successful risk mitigation effort at Mount Pinatubo in 1991 that could easily have failed. The counterfactuals are the myriad of ways that the effort could have failed but didn’t. Forecasts for a large, VEI 6 eruption were the basis of 10, 20, 30 and, during the climactic eruption, even 40 km radius evacuations. Let’s use the metaphor of a train headed for the destination of successful mitigation, but that could have easily have been derailed or slowed and shunted off to a siding. Among the possible nodes of derailment: capability and trust between responding institutions; external distractions, both natural and man-made; early alert; scientific judgment of whether, when, and how big an eruption will occur; stochastic or unpredictable factors that can make even the best scientific judgment moot; optimal balance between caution and decisive actions, by scientists and civil defense alike; and effective communication between all parties. Potential derailments are detailed at each of these nodes for Pinatubo.

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

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.

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

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.