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This work investigates how students, given a partial software design consisting of a class diagram and high-level use cases, translate the use cases into pseudocode. We gathered pseudocode solutions in March 2023 from intermediate undergraduate students in their fourth programming course (covering a combination of data structures, object-oriented programming, and some discrete mathematics) at a four-year public university in the United States. We are interested in pseudocode as a way of helping students move from static information about an object-oriented problem to a design that captures dynamic behaviour that can then be implemented in code.

Student 'copying' is often considered negatively as thoughts of plagiarism come to mind. Previously, we investigated the ways that instructors expect to use copying and imitation positively in their teaching. In this paper, we follow up that study by focusing on the student perspective and explore the ways in which students see copying and imitating as positive tools in learning to program (both at an introductory level and through more advanced learning of algorithms, etc.).In a qualitative research study, using semi-structured interviews, students were asked about how they use copying positively - their goals when they copy, how they go about it, how they view the experience of copying, and results beyond simply fulfilling their immediate goals.When comparing student results with the previous study, it was noted that there is some level of agreement between instructors and students about how copying can be useful for learning to program. There is also some degree of mismatch between instructor and student views. Students did not report imitating instructors' approaches to learning and sometimes were unsure about whether they were supposed to copy the materials that they were given. This leads to some teaching suggestions in terms of instructors being more explicit in their attitude to this type of 'legitimate' copying and imitation.

Students' "copying" is often considered negatively. In this paper, we explore the ways in which copying and imitation are used positively by computing instructors in their teaching. We found that instructors expect their students to use these strategies in many different contexts and at many different levels.

We expand upon previous research that looked at the question: \Can graduating students design software systems?" Specifically we want to examine students' understanding of the phenomenon\produce a design." What does this instruction mean to them? In order to investigate student understandings, we examined their designs using a phenomenographic approach. Our outcome space includes six understandings: (0) the design a layman might produce; (1) a design with some formal notation; (2) a design that uses formal notations to express the static relationships among the parts; (3) a design that uses formal notation to express sequential (dynamic) information, but does not relate that to the static system parts; (4) a design that includes and relates multiple artifacts, both static and dynamic; and (5) a design that relaxes the notations and includes only essential artifacts. The last understanding was found only in our expert's design, and we do not expect it from undergraduates. 

We report preliminary results from an ongoing investigation of how computing professionals perceive and value selfdirected learning, based on a qualitative analysis of interviews with ten computing professionals. The professionals expect that future colleagues will have a well-developed ability to learn on their own. They indicate that professionals use a range of resources, strategies, and collaborators to help them learn. They find their work-related self-directed learning enjoyable, expressing a sense of confidence and pride; yet they often also find it to be a stressful never-ending process. Copyright 2012 ACM.

Threshold concepts can be used to both organize disciplinary knowledge and explain why students have difficulties at certain points in the curriculum. Threshold concepts transform a student's view of the discipline; before being learned, they can block a student's progress. In this paper, we propose that in computing, skills, in addition to concepts, can sometimes be thresholds. Some students report finding skills more difficult than concepts. We discuss some computing skills that may be thresholds and compare threshold skills and threshold concepts.

Programming is a core subject within Computer Science curricula and many also consider it a particularly difficult subject to learn. There have been many studies and suggestions on what causes these difficulties and what can be done to improve the situation.

This thesis builds on previous work, trying to understand what difficulties students have when learning to program. The included papers cover several areas encountered when trying to learn programming.

In Paper I we study how students use annotations during problem solving. The results show that students who annotate more also tend to be more successful. However, the results also indicate that there might be a cultural bias towards the use of annotations.

Not only do students have problems with programming, they also have problems with designing software. Even graduating students fail to a large extent on simple design tasks. Our results in Paper II show that the majority of the students do not go beyond restating the problem when asked to design a system.

Getting stuck is something that most learners experience at one time or another. In Paper III we investigate how successful students handle these situations. The results show that the students use a large number of different strategies to get unstuck and continue their learning. Many of the strategies involve social interaction with peers and others.

In Papers IV, V, and VI we study what students experience as being key and threshold concepts in Computer Science. The results show that understanding particular concepts indeed affect the students greatly, changing the way they look at Computer Science, their peers, and themselves.

The two last papers, Papers VII and VIII, investigate how researchers, teachers and students view concurrency. Most researchers/teachers claim that students have difficulties because of non-determinism, not understanding synchronization, etc. According to our results the students themselves do not seem to think that concurrency is significantly more difficult than any other subject. Actually most of them find concurrency to be both easy to understand and fun.

Programmering har en central roll i datavetenskapliga utbildningar. Många anser att programmering är svårt att lära sig. Ett stort antal studier har undersökt vad som orsakar dessa svårigheter och hur det är möjligt att övervinna dem. Denna avhandling är en del av denna forskning. Artiklarna i avhandlingen undersöker vilka problem som studenterna stöter på under sina programmeringsstudier.

Artikel 1 beskriver hur studenter använder sig av annoteringar vid problemlösning. Resultaten visar att studenter som gör många annoteringar tenderar att prestera bättre. Resultaten antyder också att det kan finnas kulturella skillnader i hur ofta annoteringar används.

Studenter har inte bara problem vid programmering, de har också problem med att utforma programvarusystem. Även sistaårsstudenter misslyckas till stor del att utforma lösningar för relativt enkla system. Resultaten i Artikel II visar att majoriteten av studenterna inte kommer längre än en omformulering av problemet.

Att inte förstå ett koncept eller en specifik detalj är något som alla studenter stöter på då och då. I Artikel III undersöker vi hur framgångsrika studenter hanterar en sådan situation. Resultaten visar att studenterna använder sig av ett stort antal olika strategier för att få en förståelse för konceptet/detaljen. Många av de redovisade strategierna bygger på en social interaktion med andra.

Artiklarna IV, V och VI utforskar vad studenterna uppfattar som nyckelkoncept inom datavetenskap och hur förståelsen av dessa koncept påverkar dem. Resultaten visar att förståelsen av vissa specifika koncept kan göra att studenterna ändrar hur de ser på datavetenskap, kollegor och sig själva.

I artiklarna VII och VIII undersöker vi hur forskare, lärare och studenter ser på de problem studenter har vid jämlöpande programmering. De flesta forskare och lärare hävdar att studenterna har problem med att förstå icke-determinism, synkronisering, etc. Våra resultat visar dock att studenterna inte själva tycks anse att jämlöpande programmering är signifikant svårare än andra ämnen. Tvärtom, de flesta anser att jämlöpande programmering är både lätt att förstå och roligt.

We interviewed eight students to better understand what kind of difficulties students have when learning concurrent programming. According to these interviews students does not consider concurrency to be radically more difficult than other Computer Science subjects - something that is in contrast to many research papers. Instead the students found concurrency to be an interesting and fun subject that they considered to be approximately equal in difficulty to other subjects. For some, the added complexity only acted as inspiring challenge. © 2011 Author.

In this paper we present background and early results from an investigation of how computing students learn computer science topics through informal means, that is, outside of organized classes. We provide some overall perspective by discussing the variety of research areas that fall under the general \informal learning" name. From there we propose specific research questions that concern what the students learn, what resources they bring to bear, what strategies they employ, and how they evaluate their progress. Preliminary results indicate that students primarily learn specific technologies, and that both their motivation and evaluation are closely tied to projects (at school, work, or home). 

We examine the transformations experienced by students during their study of computing. These transformations led to changes in the students' perceptions of computer science, in their sense of identity as computer scientists, their behavior and their confidence. The changes are caused by learning or using particular concepts, and often associated with writing computer programs, learning new programming languages, or interacting with peers.