Through the eyes of instructors (original) (raw)
Related papers
Variables Affecting Students' Success in CS2
Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1
When trying to understand student success in computer science, much of the attention has been focused on CS1, leaving followup courses such as CS2 less researched. Prior studies of CS2 have often taken a deductive approach by focusing on predetermined variables such as CS1 grades, the impact of different paths from CS1 to CS2, gender and race. Although this has resulted in a better insight into these variables, we wonder if there might be another way of viewing which variables affect the students' success in the course. We have therefore chosen an inductive approach to better understand what these variables might be and how they interplay. This was done by analysing 16 semi-structured interviews with students enrolled in CS2 who have another speciality than computer science. The interviews focused mainly on the students' methods for succeeding in the course, experiences of the course and programming background. Through a thematic analysis of the interviews, we found the following five main success variables for CS2: programming competence, computer literacy, opportunity to receive help, ability to help oneself and teaching. These variables can in several cases be related to the ones previously addressed, however, they can also offer a different perspective on student success in the course. CCS CONCEPTS • Social and professional topics → Computer science education.
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.
ACM SIGCSE Bulletin, 2009
This paper describes an exploratory study to identify which environmental and student factors best predict intention to persist in the computer science major. The findings can be used to make decisions about initiatives for increasing retention. Eight indices of student characteristics and perceptions were developed using the research-based Student Experience of the Major Survey: student-student interaction; student-faculty interaction; collaborative learning opportunities; pace/workload/prior experience with programming; teaching assistants; classroom climate/pedagogy; meaningful assignments; and racism/sexism. A linear regression revealed that student-student interaction was the most powerful predictor of students' intention to persist in the major beyond the introductory course. Other factors predicting intention to persist were pace/workload/prior experience and male gender. The findings suggest that computer science departments interested in increasing retention of students set structured expectations for student-student interaction in ways that integrate peer involvement as a mainstream activity rather than making it optional or extracurricular. They also suggest departments find ways to manage programming experience gaps in CS1.
Academic Perceptions of the Ideal Computer Science Student
South African Computer Journal, 2013
This paper presents the results of a case study aimed at identifying the skills that lecturers in a computer science department value in an undergraduate student, and to determine if there is a departmental construction of an ‘ideal’ student. To answer this question, a case study was undertaken in the Computer Science Department at a small university in South Africa. Participants were asked to complete a questionnaire and to take part in an interview to solicit feedback on their notion of an ‘ideal’ student. This study found that participants valued the following skills within undergraduate student: creativity; computer playfulness; planning, analytical or abstract thinking, and problem solving; introverted personality; engagement in class; working independently; self efficacy; and responsibility. It also found a strong correlation between participant’s own performance as a student and their understanding of an ‘ideal’ student. These results are then discussed within the context of ...
Instructor Perspectives on Prerequisite Courses in Computing
Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1
Recent research in computing has shown that student performance on prerequisite course content varies widely, even when students continue to progress further through the computing curriculum. Our work investigates instructors' perspectives on the purpose of prerequisite courses and whether that purpose is being fulfilled. In order to identify the range of instructor views, we interviewed twenty-one computer science instructors, at two institutions, that teach a variety of courses in their respective departments. We conducted a phenomenographic analysis on the interview transcripts, which revealed a wide variety of views on prerequisite courses. The responses shed light on various issues with prerequisite course knowledge, as well as issues around responsibility and conflicting pressures on instructors. These issues arise at the department level, as well as with individual course offerings.
Programming: predicting student success early in CS1. a re-validation and replication study
Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, 2018
This paper describes a large, multi-institutional revalidation study conducted in the academic year 2015-16. Six hundred and ninetytwo students participated in this study, from 11 institutions (ten institutions in Ireland and one in Denmark). The primary goal was to validate and further develop an existing computational prediction model called Predict Student Success (PreSS). In doing so, this study addressed a call from the 2015 ITiCSE working group (the second "Grand Challenge"), to "systematically analyse and verify previous studies using data from multiple contexts to tease out tacit factors that contribute to previously observed outcomes". PreSS was developed and validated in a longitudinal study conducted over a three year period (twelve years previous from 2004-06). PreSS could predict with near 80% accuracy, how a student would likely perform on an introductory programming module. Notably this could be achieved at a very early stage in the module. This paper describes a revalidation of the original PreSS model on a significantly larger multi-institutional data set twelve years after its initial development and looks at recent research on additional factors that may improve the model. The work involved the development of a fully automated end-to-end tool, which can predict student success early in CS1, with an accuracy of 71%. This paper describes, in detail the PreSS model, recent research, pilot studies and the re-validation and replication study of the PreSS model. CCS CONCEPTS • Social and professional topics → Computer science education; CS1;
International Journal of Advanced Computer Science and Applications, 2020
The reliance on science and technology by both countries and corporate entities is increasingly evident as the evolving trend of digitization not only pervades every facet of life but also assumes a dominant role. Correspondingly, the significance of producing competent computer science and information technology (IT) graduates becomes highly imperative. Already, in most developed and developing countries, there has been an increasing demand for these competencies such as network engineers, programmers, and other IT-related specialists. Although these competencies are equally valuable, programming skills constitute the core of the strength of every other IT-related competence. Nevertheless, programming is reported in the literature to be one of the most difficult courses to students. Moreover, the level of performance in programming is said to be significantly low with an attendant high rate of students' dropout. There is a concerted research effort toward addressing the challenge of poor academic performance by attempting to answer the question of what factors affect academic performance in general. However, there is scanty literature on the factors that affect the ability to understand the concept of programming in specific. This paper, therefore, reports a case study investigation of students' perception of the effect of cognitive factors as the determinant of success in computer programming. The findings showed that performance in introductory programming is impacted by a range of interrelated cognitive factors including self-efficacy and the love for technology.
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/the-relationship-between-computer-science-instructional-practices-and-retention-a-multi-level-study https://www.ijert.org/research/the-relationship-between-computer-science-instructional-practices-and-retention-a-multi-level-study-IJERTV10IS070084.pdf Many computer science (CS) departments want to increase student retention in their courses. Understanding the factors that influence the probability of students continuing to enroll in CS courses is a critical step in increasing retention. Prior research on CS retention has mainly focused on variables like prior programming experience and students' personality traits, all of which are outside the control of undergraduate instructors. This research looks at factors that are under the influence of teachers, specifically instructional practices that have a direct effect on students' classroom experiences. Over the course of four semesters, participants were recruited from 25 parts of 14 different courses.Although adjusting for students' mastery of CS concepts and status as a CS major, a multi-level model was used to assess the effects of individual and class-average perceptions of cooperative learning and instructor directedness on the likelihood of subsequent enrollment in a CS course. The average rating of cooperative learning within a course segment was negatively correlated with retention, according to the findings. Students' individual impressions of instructional activities were not associated with retention. Greater mastery of CS concepts and considering or having declared a CS major were linked to a higher likelihood of taking potential CS courses, which is consistent with previous study.The findings' implications are explored.
Many factors can be attributed to the high rate of failure in computer programming courses. This paper studies the background of students, their fields of study and learning approaches applied to the study of programming courses. It is worth considering as a major factor and necessary to research into the causes of failure of students in programming courses from the learner perspective. Programming courses form part of the core concentration areas for students especially studying Information Technology (IT) and Computer Science as well as those other fields of study sandwiched with IT in an undergraduate degree programs. Through the use of questionnaires, interviews and focused group, a survey was conducted using one hundred (100) students at the middle and end of the semester. The responses from the three groups of students were compared. Their opinions to the usefulness of their background, field of study and learning approaches toward programming courses were investigated. The needs and concerns about these key factors are highlighted in the survey and discussed thereby leading to the inferences made and then proposed recommendations on the learning approach in relation to the background and field of study of students in computer programming courses in order to improve understanding of programming by students, hence, reducing failure rates.
2018
Across the United States, enrollment in high school computer science (CS) courses is increasing. These increases, however, are not spread evenly across race and gender. CS remains largely an elective class, and fewer than three-fourths of the states allow it to count towards graduation. The Chicago Public Schools has sought to ensure access for all students by recently enacting computer science as a high school graduation requirement. The primary class that fulfills the graduation requirement is Exploring Computer Science (ECS), a high school introductory course and professional development program designed to foster deep engagement through equitable inquiry around CS concepts. The number of students taking CS in the district increased significantly and these increases are distributed equitably across demographic characteristics. With ECS serving as a core class, it becomes critical to ensure success for all students independent of demographic characteristics, as success in the cour...