Growth in demand for employees with programming proficiency necessitates a workforce that is correctly and efficiently trained in programming fundamentals. Previous research has found correlations between intermediate programmers’ program-development habits and their success in computer science courses, but to date, these approaches have not worked well when predicting the success of students in their first course. This research project is an examination of the Normalized Programming State Model’s applicability to novice programmers, after modifying it to potentially improve its ability to detect flaws in the programs these students write. We compared the adapted model’s predictive power with that of the previous implementation of the model...
Identifying and mitigating the difficulties experienced by novice programmers is an active area of ...
The fundamental concepts of programming are essential to any Computer Science course yet, these conc...
This paper presents a new approach to automatically detect- ing lower-performing or “at-risk” studen...
The high failure rates of many programming courses means there is a need to identify struggling stud...
Research over the past fifty years into predictors of programming performance has yielded little imp...
As the technology sector grows, the need for computer programmers is increasing. This has led to eff...
Research over the past fifty years into predictors of programming performance has yielded little imp...
Learning to program is difficult and can result in high drop out and failure rates. Numerous researc...
This thesis details a longitudinal study on factors that influence introductory programming success...
© 2018 Association for Computing Machinery. Recent data-driven research has produced metrics for qua...
This thesis investigates factors that can be used to predict the success or failure of students taki...
This paper identifies novice programmer activities and their implications for the programming outcom...
This thesis describes a longitudinal study on factors which predict academic success in introductory...
This paper presents a Support Vector Machine predictive model to determine if prior programming know...
Abstract Predicting Student Success in an Introductory Programming Course at an Urban Midwestern Com...
Identifying and mitigating the difficulties experienced by novice programmers is an active area of ...
The fundamental concepts of programming are essential to any Computer Science course yet, these conc...
This paper presents a new approach to automatically detect- ing lower-performing or “at-risk” studen...
The high failure rates of many programming courses means there is a need to identify struggling stud...
Research over the past fifty years into predictors of programming performance has yielded little imp...
As the technology sector grows, the need for computer programmers is increasing. This has led to eff...
Research over the past fifty years into predictors of programming performance has yielded little imp...
Learning to program is difficult and can result in high drop out and failure rates. Numerous researc...
This thesis details a longitudinal study on factors that influence introductory programming success...
© 2018 Association for Computing Machinery. Recent data-driven research has produced metrics for qua...
This thesis investigates factors that can be used to predict the success or failure of students taki...
This paper identifies novice programmer activities and their implications for the programming outcom...
This thesis describes a longitudinal study on factors which predict academic success in introductory...
This paper presents a Support Vector Machine predictive model to determine if prior programming know...
Abstract Predicting Student Success in an Introductory Programming Course at an Urban Midwestern Com...
Identifying and mitigating the difficulties experienced by novice programmers is an active area of ...
The fundamental concepts of programming are essential to any Computer Science course yet, these conc...
This paper presents a new approach to automatically detect- ing lower-performing or “at-risk” studen...