High dropout rates in university-level computer science programmes have been consistently reported as a problem in previous works. Reasons for dropping out of CS studies are a multitude, and whether the event is a tragedy depends on the reason and the party involved. This is especially so in the context of the Finnish tertiary education model, where universities receive much of their funding based on the number of graduates, rather than on students' enrollment, attendance, or progress during studies. In this work, we report on the results of a survey sent to 416 (former) students of a Finnish computer science bachelor's programme who, according to student registry data, were likely to have dropped out of their studies. We received responses from 76 invitees and analysed them regarding the factors they self-reported as contributing to the cessation of their studies. Our results indicate that the most significant causes of dropping out are finding employment during the studies, failure to integrate well into the studies, 'personal reasons', the implementation of the studies, and a lack of support from the university. Very few students report issues relating to the atmosphere in their studies or student life (e.g., bullying, racism, harassment), parental leave, or mandatory national service (i.e., conscription or the non-military civil service alternative).

The confidence gap, that is, the difference in self-assessed confidence between genders regarding the ability to succeed in a given task, has been previously reported and studied in the context of computer science education. In this paper, we investigate the prevalence of the confidence gap during subsequent introductory and advanced Python programming courses - attended by a range of students from CS majors to non-university students taking the courses as free MOOCs - from a variety of perspectives, including student perceptions of what constitutes as 'good' or a 'bad' grade, what they predict as their own grade at the start of the course, and previous exposure to programming in various contexts. Our results provide both an updated snapshot into the evolving confidence gap, and additional data about students' beliefs on how well they are likely to perform in university-level programming courses targeted at first-year students.

Having students peer review each other's exercises is a common task in modern computing classrooms. In large classes, peer review might even partly replace traditional instructor-led review -- and prior work has found some indications that the quality of peer reviews can be close to that of instructor reviews. In this work, we explore the difference between instructor and peer reviews of student-created programming exercises. One task in an introductory programming course was to have students design their own programming exercises -- including an exercise description, model solution, and test cases -- which were then reviewed by peers. After the course, we had two instructors review the same student-created exercises. We compare the scores given by the instructors and the students to analyze potential differences. Our results suggest that agreement between instructors and students as measured by inter-rater reliability is low, although differences between instructor and student review score distributions are not statistically significant. Additionally, instructors have more fluctuation in their reviews compared to students. Due to the rising popularity of peer reviews, more research is needed to examine to what extent they could complement traditional instructor-led review of exercises.

An indubitable way to put learning at scale in practice is to implement Massive Open Online Courses, or MOOCs. When a wide-enough portfolio of them is available, new applications arise. For instance, university admissions in Finland, where this study was conducted, have traditionally been based on students' grades in high school studies, an entrance examination, or a combination of both. A minority of students have been accepted through an open university admission path where students can get a study right if they complete enough university course credits with a high enough grade in a given time frame. In this work, we report results from a multi-university project in which the open university admission path has been expanded. All the universities in the Digital Education For All (DEFA) project remarkably expanded their portfolio of MOOCs that were offered both openly and for free, and the new admission path was simultaneously actively marketed and modified. In our analysis, we focus on examining whether the project increased computer science enrolments in the participating universities and how students accepted through the project perform in their studies compared to their peers accepted through other, traditional intake paths.

Crowdsourcing is a method of collecting services, ideas, materials or other artefacts from a relatively large and open group of people. Crowdsourcing has been used in computer science education to alleviate the teachers’ workload in creating course content, and as a learning and revision method for students through its use in educational systems. Tools that utilize crowdsourcing can act as a great way for students to further familiarize themselves with the course concepts, all while creating new content for their peers and future course iterations. In my research, I focus on investigating the effects of computing education systems that use crowdsoucing on students’ learning, and the types of quality assurance methods required to use the artefacts students produce with these tools.

The Digital Education For All project (DEFA) is a joint collaboration of five universities to open first-year computer science courses online and for free to audiences outside of universities. Additionally, students who complete enough courses through the project can apply for a study right at any of the participating universities.

The term Confidence Gap refers to the phenomenon of men being more confident in their ability to succeed in their studies and elsewhere. It is an acknowledged phenomenon both in Computer Science as well as STEM subjects at large, likely influencing students' career path choices and selection of study major. In this work, we analyze data from multiple introductory programming courses. We do this by looking at the interaction of (1) students' performance measured in terms of completed assignments, (2) self-reported confidence in the ability to succeed in the programming course, (3) major, and (4) gender. Aligned with prior research, we observe the existence of the Confidence Gap. At the same time, men and women who chose Computer Science as their major are more confident in their ability to succeed in their first programming course than their counterparts in other subjects.

Crowdsourcing refers to the act of using the crowd to create content or to collect feedback on some particular tasks or ideas. Within computer science education, crowdsourcing has been used -- for example -- to create rehearsal questions and programming assignments. As a part of their computer science education, students often learn relational databases as well as working with the databases using SQL statements. In this article, we describe a system for practicing SQL statements. The system uses teacher-provided topics and assignments, augmented with crowdsourced assignments and reviews. We study how students use the system, what sort of feedback students provide to the teacher-generated and crowdsourced assignments, and how practice affects the feedback. Our results suggest that students rate assignments highly, and there are only minor differences between assignments generated by students and assignments generated by the instructor.

When learning programming, students learn the syntax of a programming language, the semantics underlying the syntax, and practice applying the language in solving programming problems. Research has suggested that simply the syntax may be hard to learn. In this article, we study difficulty of learning the syntax of a programming language. We have constructed a tool that provides students code that they write character-by-character. When writing, the tool automatically highlights each character in code that is incorrectly typed, and through the highlight-based feedback directs students into writing correct syntax. We conducted a randomized controlled trial in an introductory programming course organized in Java. One half of the population had the tool in the course material immediately before programming exercises where the practiced syntax was used, while the other half of the course population did not have the tool, thus approaching the exercises in a traditional way. Our results imply that isolated syntax writing practice may not be a meaningful addition to the arsenal used for teaching programming, at least when the programming course utilizes a large set of small programming exercises. We encourage researchers to replicate our work in contexts where syntax seems to be an issue.

Small automatically assessed programming assignments are an often used resource for learning programming. Creating sufficiently large amounts of such assignments is, however, time consuming. As a consequence, offering large quantities of practice assignments to students is not always possible. CrowdSorcerer is an embeddable open-source system that students and teachers alike can use for creating and evaluating small automatically assessed programming assignments. While creating programming assignments, the students also write simple input-output -tests, and are gently introduced to the basics of testing. Students can also evaluate the assignments of others and provide feedback on them, which exposes them to code written by others early in their education. In this article we both describe the CrowdSorcerer system and our experiences in using the system in a large undergraduate programming course. Moreover, we discuss the motivation for crowdsourcing course assignments and present some usage statistics.