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.

EXPLORING READING SKILLS’ INSTRUCTION IN COMPULSORY ENGLISH COURSE – A CASE STUDY OF PUBLIC COLLEGE

The present study explores the reading instruction at grade- XII at a public college in Karachi to capture the reading instruction practices in compulsory English course. Using case study method involving interviews with teachers followed by observations of their reading classes an in-depth study was planned that attempted to give a holistic picture of the reading instructional practices. The teachers’ classroom practices were also interpreted in the light of the analyzed curriculum and text book being followed. The findings indicate a missed opportunity since no meaningful outcomes can be envisaged as a result of the conventional, content based and examination oriented classroom pedagogy adopted by teachers in their reading classes. The interview and classroom observation data contradicted indicating the teachers’ claims differed drastically from their actual classroom practices. This study reconfirms earlier studies and suggests several policy and pedagogical implications for education department, college administrators, English language teachers and teacher educators.

Increasing STEM Transfer Readiness Among Underrepresented Minoritized Two-Year College Students: Examining Course-Taking Patterns, Experiences, and Interventions

There is a strong need in the United States to increase the size and diversity of the domestic workforce trained in science, technology, engineering, and math (STEM). With almost half of all students that earn a baccalaureate degree enrolling in a 2-year public college at some point, the nation’s 2-year colleges provide great promise for improving the capacity of the STEM workforce for innovation and global competition while addressing the nation’s need for more equity between groups that have been historically included and those that have been economically and politically disenfranchized. Almost half of underrepresented minoritized (URM) students begin their post-secondary education at 2-year colleges yet their transfer rates within 5 years are only 16%. This study describes interventions put in place at a 2-year college to support increased transfer rates and STEM transfer readiness for URM STEM-interested students. The program studied, in place from 2017 through 2020, had an overall transfer rate of 45%. Analysis of administrative, transcript, and student survey data connects the program interventions to the existing research on STEM momentum and other research on URM STEM transfer success. Ultimately, this study identifies potential leading indicators of transfer readiness, providing much needed documentation and guidance on the efficacy and limitations of interventions to improve upward STEM transfer.

Undergraduate Research at a Teaching-Oriented College: Seniors’ Perspectives and Approaches to Consider

This study examines the relationship of undergraduate research (UGR) participation on senior students’ reported engagement, perceived gains, satisfaction with their educational experience and retention, and graduation status compared to peers that have not participated in UGR. Data were drawn from 1,472 senior students at a comprehensive, teaching-oriented public college, and collected from administration of the National Survey of Student Engagement (NSSE) from 2015 to 2019, along with institutional data. This examination uniquely investigates outcomes of UGR participation besides persistence and graduation (which are already well documented) and leverages the lens of senior students in particular. In addition, this study contributes to the literature on UGR at teaching-oriented colleges, which has been sparse most likely because there are many more opportunities for UGR at research institutions. In line with several conceptual frameworks of student engagement, data analysis revealed that relative to their peers who have not participated in UGR, UGR-participating students have higher levels of engagement, perceived gains, and overall satisfaction. UGR-participating students also continued enrollment and/or graduated at a higher rate after reaching their senior status compared to non-participating peers. The implications for teaching-oriented colleges, as well as suggestions for how these institutions can enhance their undergraduate research programming, are discussed.

PAI Curriculum Innovation In Public College To Improve Student Competency

Innovation is defined as renewal in the face of change or improvement. Change is a shift in position or situation that is likely to result in a significant increase. Practical The curriculum is changed due to several factors, for example the development of science and technology. There is a possibility that the benefits of a predetermined or expected curriculum change could go wrong. Changing the curriculum in a very short period of time is considered a failure in certain cases, but is also believed to be an attempt to achieve improvement. Curriculum reform involves several components or several factors. Curriculum change cannot be successful without being complemented by those who are supported by component systems. Changes that are partial in nature will automatically waste energy, time, funds, and energy. In addition, changes to the curriculum are more focused on the curriculum itself and ignore other aspects; such as teachers / lecturers, students, methods, media funds, etc. will potentially fail. Therefore it needs serious consideration and reasonable reasons with the desire and total involvement of the components of the education system, so as to increase competence.