Predicting Student Performance In A Beginning Computer Science Class (Piaget, Personality, Cognitive Style)

Pupose of the Study. The purpose of this study was to determine factors which effectively predict success in a first course for computer science majors. A secondary goal was to provide a model of the successful computer science student in order to improve teaching and learning in the classroom; Procedures. The sample consisted of 58 students enrolled in all three sections of Computer Science I, during Spring semester, 1985. Student characteristics selected included age, sex, previous high school and college grades, number of high school and college mathematics classes, number of hours worked, and whether the job was computer-related or involved programming. A measure of Piagetian cognitive development developed by Kurtz, the Group Embedded Figures Test (GEFT) and the Myers-Briggs Personality Index (MBTI) were administered early in the semester. These measures were correlated with the student\u27s letter grade in the class using both Chi Square and Pearson\u27s Product Moment Coefficient statistical tests; Findings. Significant relationships were found between grade and the students\u27 previous college grades and the number of high school mathematics classes (p \u3c .05). The correlation between grade, and both number of hours worked and working as a programmer, approached significance (p \u3c .10). Both the Group Embedded Figures Test (p \u3c .01) and the measure of Piagetian Intellectual Development stages (p \u3c .05) were also significantly correlated with grade in this rigorous Pascal programming class; While there was no relationship between the personality type and grade, the Myers-Briggs results provided an interesting profile of the computer science major. On the Extroversion-Introversion, Sensing-Intuitive, and Thinking-Feeling indices, the students were considerably more introverted, intuitive and thinking than the population as a whole, though they were close to national norms on the Perception-Judging index. While computer science students were somewhat like engineering students, they more strongly resembled chess players, when these results were compared with other studies

How Early Is Early Enough: Correlating Student Performance With Final Grades

Student retention is one of the greatest challenges facing computer science programs. Difficulties in an introductory programming class often snowball, resulting in poor student performance or dropping the major completely. In this paper, we present an analysis of 197 students over 6 semesters from 11 sections of an introductory programming class at a private four-year liberal arts university in the southeastern United States. The goal of this research was to find the earliest point in the assessment sequence which could predict final grade outcomes. Accordingly, we measured the degree of correlation between student performance on quizzes, labs, programs, and tests compared to final course grade. Overall, the results show a strong positive correlation for all four assessment modalities. These results hold significance for educators and researchers insofar as the body of computing education research is extended by evaluating the relative effectiveness of early semester subsets of each of the four categories of student data to model class outcomes

Predicting Student Failure in an Introductory Programming Course with Multiple Back-Propagation

One of the most challenging tasks in computer science and similar courses consists of both teaching and learning computer programming. Usually this requires a great deal of work, dedication, and motivation from both teachers and students. Accordingly, ever since the first programming languages emerged, the problems inherent to programming teaching and learning have been studied and investigated. The theme is very serious, not only for the important concepts underlying computer science courses but also for reducing the lack of motivation, failure, and abandonment that result from students frustration. Therefore, early identification of potential problems and immediate response is a fundamental aspect to avoid student’s failure and reduce dropout rates. In this paper, we propose a machine-learning (neural network) predictive model of student failure based on the student profile, which is built throughout programming classes by continuously monitoring and evaluating student activities. The resulting model allows teachers to early identify students that are more likely to fail, allowing them to devote more time to those students and try novel strategies to improve their programming skills

An Empirical Investigation of the Relationship Between Success in Mathematics and Visual Programming Courses

Many universities do not have prerequisites for the introductory computer visual programming course. Therefore, faculty and students do not have any means of predicting the student’s performance in this course. This research addresses this issue. Past research and accepted theory are presented to show the cognitive requirements for success in a first procedural programming course to be similar to those required for success in a mathematics course. Such research is lacking for visual programming. This research shows similar correlations between math courses and visual programming courses. Significant positive correlations were found between grades from Freshmen mathematics courses, ACT math scores, SAT math scores and grades from a Sophomore introductory visual programming course. This indicates that students who perform well in Freshman level Math courses, possess the cognitive characteristics required to perform equally well in Sophomore level visual programming classes. We can predict that students who perform well in math courses will perform equally well in a visual programming course

Predicting Grades

To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. Existing grade prediction systems focus on maximizing the accuracy of the prediction while overseeing the importance of issuing timely and personalized predictions. This paper proposes an algorithm that predicts the final grade of each student in a class. It issues a prediction for each student individually, when the expected accuracy of the prediction is sufficient. The algorithm learns online what is the optimal prediction and time to issue a prediction based on past history of students' performance in a course. We derive a confidence estimate for the prediction accuracy and demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 UCLA undergraduate students who have taken an introductory digital signal processing over the past 7 years. We demonstrate that for 85% of the students we can predict with 76% accuracy whether they are going do well or poorly in the class after the 4th course week. Using data obtained from a pilot course, our methodology suggests that it is effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student, thereby enabling timely interventions by the instructor (at the student or class level) when necessary.Comment: 15 pages, 15 figure

Methods of Learning in a Microprocessor Applications Course

Occupational and Adult Educatio

Exploring Machine Learning Methods to Automatically Identify Students in Need of Assistance

ABSTRACT Methods for automatically identifying students in need of assistance have been studied for decades. Initially, the work was based on somewhat static factors such as students' educational background and results from various questionnaires, while more recently, constantly accumulating data such as progress with course assignments and behavior in lectures has gained attention. We contribute to this work with results on early detection of students in need of assistance, and provide a starting point for using machine learning techniques on naturally accumulating programming process data. When combining source code snapshot data that is recorded from students' programming process with machine learning methods, we are able to detect high-and low-performing students with high accuracy already after the very first week of an introductory programming course. Comparison of our results to the prominent methods for predicting students' performance using source code snapshot data is also provided. This early information on students' performance is beneficial from multiple viewpoints. Instructors can target their guidance to struggling students early on, and provide more challenging assignments for high-performing students. Moreover, students that perform poorly in the introductory programming course, but who nevertheless pass, can be monitored more closely in their future studies

Programming Process, Patterns and Behaviors: Insights from Keystroke Analysis of CS1 Students

With all the experiences and knowledge, I take programming as granted. But learning to program is still difficult for a lot of introductory programming students. This is also one of the major reasons for a high attrition rate in CS1 courses. If instructors were able to identify struggling students then effective interventions can be taken to help them. This thesis is a research done on programming process data that can be collected non-intrusively from CS1 students when they are programming. The data and their findings can be leveraged in understanding students’ thought process, detecting patterns and identifying behaviors that could possibly help instructors to identify struggling students, help them and design better courses