Students In Introductory Programming Courses Research Articles

Increasing Women's Participation in CS at Large Public Universities: Issues and Insights

Despite rapidly growing interest in Computer Science (CS), CS has the second lowest participation rate for women of all science and engineering degrees, according to a recent report by the US National Science Board [16]. The reasons for women's underrepresentation and their experiences may differ from university to university. Universities are very different in terms of types of students, student profiles, students' socio-economic status, race, number of students, admission requirements, and resources. For example, some universities can create gender balance in introductory courses through selective admission. However, typically, admission practices in large public universities are not able to achieve gender balance for CS majors. As a result, there are lower percentages of women in the CS major in large public universities than in selective schools. In addition, large public universities tend to have a larger number of CS students when compared to private universities. To understand women students' experiences and struggles in computer science at a large public university in the United States and to find ways to intervene in favor of gender equity in computer science, we conducted in-depth interviews with women students in introductory programming courses. In this article, we present women students' experiences, struggles, expectations and offer potential interventions based on the findings to encourage women to consider CS as their major and stay in the CS major. Our interview data shows there is a persistent effect of lack of prior programming experience and gender bias that leads to a negative experience for women students in introductory CS courses. This article presents the challenges faced by women in CS and provides their recommendations for attracting and retaining women students in CS at large public universities.

Teaching C++ programming using automated unit testing and test‐driven development—Design and efficacy study

AbstractThe best practices of agile software development have had a significant positive impact on the quality of software and time‐to‐delivery. As a result, many leading software companies employ some form of agile software development practices. Some of the most important best practices of agile software development, which have received significant attention in recent years, are automated unit testing (AUT) and test‐driven development (TDD). Both of these practices work in conjunction to provide numerous benefits. AUT leads to reduced time to test, discover bugs, fix bugs, and implement new features; wider and measurable test coverage; reproducibility, reusability, consistency, and reliability of tests; improved accuracy, regression testing, parallel testing, faster feedback cycle, reduced cost, and higher team morale. The benefits of TDD include flexible and adaptive program design, cleaner interfaces, higher code quality, maintainability, extensibility, reliability, detailed evolving specification, reduced time on bug fixes and feature implementation, reliable refactoring, and code changes, reduced cost, reduced development time, and increased programmer productivity. Unfortunately, students in introductory programming courses are generally not introduced to AUT and TDD. This leads to the development of bad programming habits and practices which become harder to change later on. By introducing the students earlier to these industry‐standard best practices, not only the motivation and interest of students in this area can be increased but also their academic success and job marketability can be enhanced. This paper presents the detailed design and efficacy study of an introductory C++ programming course designed using the principles of AUT and TDD. The paper presents the pedagogical techniques employed to build industry‐proven agile software development practices in the students. As part of the paper, all the course material including the source code for the labs and the automated unit tests are being made available to encourage people to incorporate these best practices into their curricula.

CS1 Student Grade Prediction: Unconscious Optimism vs Insecurity?

The difficulties of many students in introductory programming courses and the consequent failure and drop out make it necessary to look for motivation strategies for them to be successful. One of the strategies that is touted in the literature is self-assessment to compromise and motivate students. As we had doubts about the possibility of this strategy, we did an experiment and asked the students to predict the grades of the two tests and the two projects during a semester. Even knowing the correction grid and exercises that involve programming languages, which shows the result to the programmer, we found that the students' forecasts were not very accurate. In the first test we found that the worst students said they were going to get reasonable grades and much better than reality, while the best students thought they had worse grades than they actually had. The other moments of evaluation did not have as severe results, but forecasts continued to be inaccurate. We did tests by gender, by age, for being a freshman or not, for having taken a computer course in high school and for previous knowledge of programming languages: none of these variables proved to be as significant as the students' grades and their corresponding insecurity-fear or optimism-unconscious.

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models.

Effect of Self-efficacy and Emotional Engagement on Introductory Programming Students

High failure rates appear to be a norm in introductory programming courses. Many solutions have been proposed to improve the high failure rates. Surprisingly, these solutions have not lead to significant improvements in the performance of students in introductory programming courses. In this study, the relationship between self-efficacy, emotional engagement and the performance of students in introductory programming courses were examined. Enjoyment, interest, and gratification were identified as three factors contributing to emotional engagement in introductory programming courses from a review of existing literature and from focus groups. An online survey of 433 students in introductory programming courses showed that the students’ programming self-efficacy beliefs had a strong positive effect on enjoyment, while gratification and interest had a negative effect on programming performance. These findings have implications for course instructors who design and deliver introductory programming courses.

The effect of automatic assessment on novice programming: Strengths and limitations of existing systems

AbstractComputer programming is always of high concern for students in introductory programming courses. High rates of failure occur every semester due to lack of adequate skills in programming. No student can become a programmer overnight because such learning requires proper guidance as well as consistent practice with the programming exercises. The role of instructors in the development of students' learning skills is crucial in order to provide feedback on their errors and improve their knowledge accordingly. On the other hand, due to the large number of students, instructors are also overloading themselves to focus on each individual student's errors. To address these issues, researchers have developed numerous Automatic Assessment (AA) systems that not only evaluate the students' programs but also provide instant feedback on their errors as well as abridge the workload of the instructors. Due to the large pool of existing systems, it is difficult to cover each and every system in one study. Therefore, this paper provides a comprehensive overview of some of the existing systems based on the three‐analysis approaches: dynamic, static, and hybrid. Moreover, this paper aims to discuss the strengths and limitations of these systems and suggests some potential recommendations regarding the AA specifications for novice programming, which may help in standardizing these systems.

CS1 students speak

We collected advice on how to succeed at learning to program from 164 CS1 students at 3 institutions during a "saying is believing" intervention designed to encourage a growth mindset. More students gave general advice (63%) than programming-specific (23%) or attitudinal advice (34%), despite being prompted to encourage future students to develop a growth mindset toward programming. Advice categories and quotes offer educators insights into student beliefs and practices and suggest a framework for considering how best to advise students. We discuss the implications of students offering advice to other students and provide a handout of representative advice intended for distribution to students in introductory programming courses.

Another look at the behaviors of novice programmers

This paper reports on the progress of an NSF funded research project investigating the development practices of students in introductory programming courses. In previous work, we describe our extension of the BlueJ IDE to capture events associated with program development. Here we report on data collected during the Fall 2007 and Spring 2008 semesters on CS 1 students. In particular, we show that our data analysis independently confirms the results obtained in separate studies by Jadud [3, 2]. In addition we use our empirical evidence to discern some higher level "patterns" of beginning student programming behaviors including potential cheating and the impact on success of students starting projects late.

Making computer programming fun and accessible

Learning to program requires much hard work and dedication. Most students in introductory programming courses struggle to grasp programming concepts in general. Universities in the US, Canada, and elsewhere have reported withdrawal, failure, and D-grade rates approaching 50 percent in introductory computer programming courses. To address these problems, we recently designed and successfully offered a revolutionary one-semester course that integrates the use of HTML, JavaScript, and Java. This approach departs markedly from using a single general-purpose programming language such as Java or C++.

Our failing students

The high failure and low retention rates of students in introductory programming courses are of growing concern.This study looks at a group of students who are repeating an introductory programing subject, exploring issues which may affect their learning and ultimate success. Comparisons are made with a group of new students undertaking the same subject.The students were surveyed in the middle and at the end of one semester. A profile of the students, focussing on their motivation for their course selection and commitment to this course, was established. Their opinions on the usefulness of their classes, various resources, tutorial work and assignment work were sought. The needs and concerns of these students, highlighted in the surveys, are discussed and suggestions are made for the establishment of a special learning environment for failing students.