Investigating factors of student learning in introductory courses (original) (raw)
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When do students learn?: investigating factors in introductory courses
Journal of Computing Sciences in Colleges, 2012
This work investigates which introductory topics are the most difficult to teach and sheds some light on factors that improve student learning. Unlike past studies [1], this uses the results of a survey of instructors of introductory computer science courses (commonly called" CS1" and" CS2"). The survey asked about the instructional time spent and importance placed on a range on commonly taught concepts. For each concept, the survey also asked instructors to rate their students' level of mastery at the course's end. Using ...
A study to identify predictors of achievement in an introductory computer science course
Proceedings of the 2003 SIGMIS conference on Computer personnel research Freedom in Philadelphia--leveraging differences and diversity in the IT workforce - SIGMIS CPR '03, 2003
In the study reported on here, 65 prospective computer or information science majors (47 male, 18 female) worked through a tutorial on the basics of Perl. All actions were recorded and time-stamped, allowing us to investigate the relationship between six factors that we believed would predict performance in an introductory computer science (CS) course (as measured by course grade) and how much students would learn from the tutorial (as measured by gain score from pre-test to post-test). These factors are: preparation (SAT score, number of previous CS courses taken, and pre-test score), time spent on the tutorial as a whole and on individual sections, amount and type of experimentation, programming accuracy and/or proficiency, approach to materials that involve mathematical formalisms, and approach to learning highly unfamiliar material (string manipulation procedures). Gender differences with respect to these factors were also investigated.
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...
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...
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.
Interacting factors that predict success and failure in a CS1 course
ACM Sigcse Bulletin, 2004
The factors that contribute to success and failure in introductory programming courses continue to be a topic of lively debate, with recent conference panels and papers devoted to the subject (e.g. . Most work in this area has concentrated on the ability of single factors (e.g. gender, math background, etc.) to predict success, with the exception of Wilson et al. , which used a general linear model to gauge the effect of combined factors. In Rountree et al. we presented the results of a survey of our introductory programming class that considered factors (such as student expectations of success, among other things) in isolation. In this paper, we reassess the data from that survey by using a decision tree classifier to identify combinations of factors that interact to predict success or failure more strongly than single, isolated factors.
1997
The objective of this study was to find the answers to two primary research questions: "Do students learn programming languages better when they are offered in a particular order, such as 4th generation languages before 3rd generation languages?"; and "Do students learn programming languages better when they are taken in separate semesters as opposed to simultaneously?" Students from nine introductory programming classes over two semesters at a large Midwestern university were used as subjects for this experiment; 275 students responded to a survey at the end of the semester. Subject responses were divided into three groups, depending on the class being rated. These classes were: introduction to Visual Programming, introduction to COBOL programming, and introduction to C programming. To test hypotheses, linear regression was used, running the data against two separate dependent variables, grade and comfort factor. Mixed results were found for the different types of classes. (AEF)
Observations of student competency in a CS1 course
2005
Two issues of related interest are investigated in this paper. The first issue is associated with the statement that "Learning to program is a key objective in most introductory computing courses, yet many computing educators have voiced concern over whether their students are learning the necessary programming skills in those courses" (McCracken et al. 2001). The second issue considers which task CS1 students find more difficult: code generation or code comprehension. To investigate this, we analysed our CS1 course results in terms of laboratory exercises, comprehension, generation, factual/conceptual, and multiple-choice exam questions. Contrary to our initial expectations, the code comprehension and generation skills of our students appear to be tracking each other.
Approaches to learning in computer programming students and their effect on success
Proceedings of the 28th HERDSA Annual Conference: Higher Eduation in a Changing World (HERDSA 2005), 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. This multi-national, multi-institutional study investigates the ...