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This paper aims at explaining the outcomes of information technology education for international students using anthropological theories of cultural schemas. Even though computer science and engineering are usually assumed to be culture-independent, the practice in classrooms seems to indicate that learning patterns depend on culture. The situation in a college in Finland is evaluated based on study records, surveys on learning experiences, learning logs by students and classroom observations. Reasons for the educational outcome are discussed theoretically using cultural anthropology and the socio-cultural theory of learning. The universality of Western scientific thinking is questioned, and a need to recognise different cultural schemas in the thinking of international students is acknowledged. The study concludes that a view of human cognition as social and embodied gives new insights to information technology education when there is a great diversity of student backgrounds and their culturally produced mental schemas. (Contains 2 figures and 2 tables.)

Computer science is becoming increasingly important in our society. Meta skills, such as problem solving and logical and algorithmic thinking, are emphasized in every field, not only in the natural sciences. Still, largely due to gaps in tuition, common misunderstandings exist about the true nature of computer science. These are especially problematic for high school students, who need to have a realistic view of what studying computer science would be like in order to make advised decisions about their future careers. The principal objective of Finnish high schools is to prepare young students for further studies and life in general; it can therefore be seen as something of a paradox that these schools are not obliged to provide their students with tuition in computer science. In this paper we describe the project "DASK" (DAtakunskap i SKolan, Computer Science in Schools), which aims at introducing computer science to Swedish-speaking high school students by providing university level computer science courses on the web. The purpose is to give a realistic view of computer science, at the same time removing any potential misunderstandings about its true nature. The "DASK"-curriculum includes web-based versions of the five basic courses taught at the university with no trade-offs in either contents or difficulty level. The main focus is on student activity, emphasizing the importance of continuous work throughout the courses. We have formulated the "ActiWe" (Active on the Web) principle, a main guideline applied when designing, developing and giving the courses. Instructors at the university are responsible for the courses in most ways (acting as the course lecturer, maintaining the material, checking assignments, giving feedback etc.), but each high school has a contact teacher who supports the students locally. "DASK" was initiated in 2002, and has been popular from the very beginning; feedback from both students and teachers has been positive. In a recently conducted survey (January 2005), many of the teachers stated that their schools would not be able to provide any computer science-related courses on their own, thus indicating a definite need for the "DASK"-courses. In addition, the high number of participants registering for the first course each year implies that there is a genuine interest for computer science among high school students. Our experience shows that the greatest challenges for both students and university instructors are time-related: in most schools, the "DASK"-courses are not given time in the schedule and are thus not part of the actual tuition; this leads to already stressful periods becoming even more burdensome for the students. Giving the "DASK"-courses has also proved to be time-consuming for the university instructors; much time and effort has to be invested in maintaining the material, but particularly in giving individual feedback for assignments to all students on a weekly basis. A future challenge is thus to improve the time management of both students and instructors. (Contains 2 figures and 1 table.)

Purpose: Digital learning has become a global trend. Partly or fully automatic learning systems are integrated into education in schools and universities on a previously unseen scale. Learning systems have a lot of potential for re-education, life-long learning and for increasing access to educational resources. Learning systems create massive amounts of data about learning behaviour. Analysing that data for educational decision making has become an important track of research. The purpose of this paper is to analyse data from an intermediate-level computer science course, which was taught to 141 students in spring 2018 at University of Turku, Department of Future Technologies, Finland. Design/methodology/approach: The available variables included number of submissions, submission times, variables of groupwork and final grades. Associations between these variables were looked at to reveal patterns in students' learning behaviour. Findings: It was found that time usage differs per different grades so that students with grade 4 out of 5 used most time. Also, it was found that studying at night is connected to weaker learning outcomes than studying during daytime. Several issues in relation to groupwork were revealed. For example, associations were found between prior skills, preference for individual vs groupwork, and course learning outcomes. Research limitations/implications: The research was limited by the domain of available variables, which is a common limitation in learning analytics research. Practical implications: The practical implications include important ideas for future research and interventions in digital learning. Social implications: The importance of research on soft skills, social skills and collaboration is highlighted. Originality/value: The paper points a number of important ideas for future research. One important observation is that some research questions in learning analytics need qualitative approaches, which need to be added to the toolbox of learning analytics research. [ABSTRACT FROM AUTHOR]

The diversity and interdisciplinarity of computer science and the multiplicity of its uses in other sciences make it hard to define computer science and to prescribe how computer science should be carried out. The diversity of computer science also causes friction between computer scientists from different branches. Computer science curricula, as they stand, have been criticized for being unable to offer computer scientists proper methodological training or a deep understanding of different research traditions. At the Department of Computer Science and Statistics at the University of Joensuu we decided to include in our curriculum a course that offers our students an awareness of epistemological and methodological issues in computer science, and we wanted to design the course to be meaningful for practicing computer scientists. In this article the needs and aims of our course on the philosophy of computer science are discussed, and the structure and arrangements--the whys, whats, and hows--of that course are explained. The course, which is given entirely on-line, was designed for advanced graduate or postgraduate computer science students from two Finnish universities: the University of Joensuu and the University of Kuopio. The course has four relatively broad themes, and all those themes are tied to the students' everyday work or their own research topics. I have prepared course readings about each of those four themes. The course readings describe, in a compact and simple form, the cruces of the topics that are discussed in the course. The electronic version of the course readings includes hyperlinks to a large number of articles that are available on-line. The course readings are publicly available on the course home page, and they are licensed under the creative commons license. The first theme in the course is centered around a fundamental question--What is computer science? Students are introduced to the disciplinary history of computer science, to a number of characterizations of computer science made by the pioneers of the discipline, and to some methodological and epistemological viewpoints on computer science. The second theme is centered around the question--What is science? Students are introduced to, for instance, the concepts of pure and applied science, "hard" and "soft" sciences, the aims of science, the scientific method, scientific reasoning, the formation of scientific concepts and theories, and the Science Wars. The third theme concerns the division of computer science into its theoretical, engineering, and empirical traditions. The lecture notes introduce the students to descriptions of computer science that emphasize the mathematical tradition over other traditions and to descriptions that emphasize engineering or empirical traditions. The fourth theme is the philosophy of science. Throughout the course terminology of the philosophy of science is used, and the students are introduced to a number of central issues in the philosophy of science, to some of the most notable schools in the philosophy of science, and to some critical views of science. This course is aimed at providing a broad understanding of the different traditions of computer science, of the methodological differences between the branches of computer science, of the strengths and limitations of the different traditions in computer science, and of how the philosophy of science can be of help to computer scientists. In the course, critical reading and well-argumented writing are encouraged. The students learn that there are many problems that do not have clear-cut answers; they learn that there are many open problems where multiple incompatible, yet credible viewpoints can be defended. The students also learn to articulate their own positions, to defend those positions, to comment and criticize other positions, and to reflect and rethink their positions according to criticism. The students also get the chance to think about the intellectual foundations of their own work and their own research studies. [ABSTRACT FROM PUBLISHER]