A study to identify predictors of achievement in an introductory computer science course (original) (raw)

Predicting and Improving Performance on Introductory Programming Courses (CS1)

2019

This thesis describes a longitudinal study on factors which predict academic success in introductory programming at undergraduate level, including the development of these factors into a fully automated web based system (which predicts students who are at risk of not succeeding early in the introductory programming module) and interventions to address attrition rates on introductory programming courses (CS1). Numerous studies have developed models for predicting success in CS1, however there is little evidence on their ability to generalise or on their use beyond early investigations. In addition, they are seldom followed up with interventions, after struggling students have been identified. The approach overcomes this by providing a web-based real time system, with a prediction model at its core that has been longitudinally developed and revalidated, with recommendations for interventions which educators could implement to support struggling students that have been identified. This t...

Investigating factors of student learning in introductory courses

Proceeding of the 44th ACM technical symposium on Computer science education - SIGCSE '13, 2013

Instructors of the introductory computer science courses, commonly called "CS1" and "CS2", face a large number of choices when designing their classes. Instructors have available to them a multitude of ways to explain each topic as well as course-wide choices such as objects-first or objects-late or using a functional or procedural language. Understanding how these options can affect student learning would help simplify these decisions. Unfortunately, just comparing how well students perform may not be accurate as it ignores the many confounding factors that could also have made a difference. To get beyond that problem, this study investigates underlying factors that affect student learning.

Investigation of tutorials in a core computing subject

1998

Abstract Tutorials for large subjects entail a considerable inveshnent of teachingresources. What are we tryingto acconipIishin this enterprise? Studentsin a second-yearcore computingsubjec~ Infcmnation Systems3 (IS3), were surveyedto determinetheir perceptions and desires regarding the tutorial componentof the subjecL Surveyresults were matchedto final exam score. Students' level of pwticipationin tie tutorialwas the most significant factorin determiningoutcomeon the exam.

Approaches to learning in computer programming students and their effect on success

2005

Within education research there has been sustained interest in developing models that can predict, or alternatively explain, student success. In computing education, attempts have been made to predict success in programming courses. Models previously used in this area have included a range of demographic, cognitive and social factors. These models emphasise presage factors. Biggs' 3P general model of student learning, by comparison, measures attitudinal factors.

Strategies for encouraging individual achievement in introductory computer science courses

ACM SIGCSE Bulletin, 2000

Students in introductory computer science courses often vary widely in background and ability. As a result, some students are bored by the pace of presentation, while others struggle to keep up. This paper describes our experience using open-ended assignments and programming contests to capture the interest of our strongest students without adversely affecting the educational experience for the other students in the class. This approach has been markedly successful, particularly for highly motivated students, who are often able to work well beyond the level of the class. The paper also includes a survey of student reactions to the various extra-credit opportunities, which indicates that many student value this component of the class even if they do not participate directly in these activities.

A study on student performance in first year CS courses

2010

Novice students often find it difficult to learn programming. Consequently this leads to high failure and high dropout rates. We must ask ourselves if these problems are caused by specific programming issues or if there are other courses that struggle with the same problem. This paper presents a study that involved Computer Science freshmen. The study tried to evaluate the connection of academic results between the first programming course and other first year courses. Subsequently the idea was to encourage teachers to dialogue about teaching and learning strategies concerning courses where the students' results are related, in order to share problems and solutions. Students were also submitted to a learning style test, in order to identify the influence of its dimensions in the obtained results.

Evolution of an introductory computer science course: the long haul

University requirements for the material covered in introductory computer science courses have evolved over the years, and those courses must therefore evolve as well. In this paper, we discuss the 7-year evolution of such a course at the U.S. Air Force Academy. In 1995, the main thrust of the course was to develop students' programming skills to support later programming activities, even for those students not majoring in computer science. Although some general survey topics were covered, programming skill development was the main goal of the course. Since that time, the course has evolved significantly into a course that covers general computer science and Information Technology (IT) topics in greater depth and breadth, with a continuing but greatly reduced programming component. During that 7-year period, we changed programming languages for the course, significantly changed the way in which we evaluated programming ability, incorporated graphics into the course, conducted an...

Results from repeated evaluation of an online tutor on introductory Computer Science

2011 Frontiers in Education Conference (FIE), 2011

We analyzed the data collected over 7 semesters by a single Computer Science software tutor to study the differences between the sexes and races on their prior self-confidence, prior preparedness and their assessment of the tutor. We found that when there was a statistically significant difference in the prior selfconfidence of male and female students, female students had lower prior self-confidence than male students, in spite of the fact that there was no significant difference in the prior preparedness of male and female students. The prior self-confidence of female students in Computer Science may be improving with increasing enrollment. Whenever there was a statistically significant difference among racial groups, positively stereotyped racial groups were better prepared and had higher prior selfconfidence than the traditionally under-represented racial groups. Whenever there was a statistically significant difference between the sexes in the assessment of the tutor, female students assessed the tutor more favorably than male students. When there was a statistically significant difference between racial groups, under-represented racial groups assessed the tutor more favorably than positively stereotyped racial groups. When there was a statistically significant difference in how developer's students assessed the tutor versus how other adopters' students assessed it, assessment by developer's students was more positive than that by students of other adopters.