Matti Tedre

Ethnocomputing: ICT in cultural and social context

Although culture has recently been recognized as one factor in interface design, CS and engineering are generally thought to be culturally neutral. The approach presented here recognizes society and culture in computational concepts and applications.

The development of computer science: a sociocultural perspective

Computer science is a broad discipline, and computer scientists often disagree about the content, form, and practices of the discipline. The processes through which computer scientists create, maintain, and modify knowledge in computer science---processes which often are eclectic and anarchistic---are well researched, but knowledge of those processes is generally not considered to be a part of computer science. On the contrary, I argue that understanding of how computer science works is an important part of the knowledge of an educated computer scientist. In this paper I discuss some characteristics of computer science that are central to understanding how computer science works.

The long quest for computational thinking

Computational thinking (CT) is a popular phrase that refers to a collection of computational ideas and habits of mind that people in computing disciplines acquire through their work in designing programs, software, simulations, and computations performed by machinery. Recently a computational thinking for K-12 movement has spawned initiatives across the education sector, and educational reforms are under way in many countries. However, modern CT initiatives should be well aware of the broad and deep history of computational thinking, or risk repeating already refuted claims, past mistakes, and already solved problems, or losing some of the richest and most ambitious ideas in CT. This paper presents an overview of three important historical currents from which CT has developed: evolution of computings disciplinary ways of thinking and practicing, educational research and efforts in computing, and emergence of computational science and digitalization of society. The paper examines a number of threats to CT initiatives: lack of ambition, dogmatism, knowing versus doing, exaggerated claims, narrow views of computing, overemphasis on formulation, and lost sight of computational models.

The Science of Computing: Shaping a Discipline

The identity of computing has been fiercely debated throughout its short history. Why is it still so hard to define computing as an academic discipline? Is computing a scientific, mathematical, or engineering discipline? By describing the mathematical, engineering, and scientific traditions of computing, The Science of Computing: Shaping a Discipline presents a rich picture of computing from the viewpoints of the fields champions. The book helps readers understand the debates about computing as a discipline. It explains the context of computings central debates and portrays a broad perspective of the discipline. The book first looks at computing as a formal, theoretical discipline that is in many ways similar to mathematics, yet different in crucial ways. It traces a number of discussions about the theoretical nature of computing from the fields intellectual origins in mathematical logic to modern views of the role of theory in computing. The book then explores the debates about computing as an engineering discipline, from the central technical innovations to the birth of the modern technical paradigm of computing to computings arrival as a new technical profession to software engineering gradually becoming an academic discipline. It presents arguments for and against the view of computing as engineering within the context of software production and analyzes the clash between the theoretical and practical mindsets. The book concludes with the view of computing as a science in its own rightnot just as a tool for other sciences. It covers the early identity debates of computing, various views of computing as a science, and some famous characterizations of the discipline. It also addresses the experimental computer science debate, the view of computing as a natural science, and the algorithmization of sciences.

ICT4D: a computer science perspective

The term ICT4D refers to the opportunities of Information and Communication Technology (ICT) as an agent of development. Research in that field is often focused on evaluating the feasibility of existing technologies, mostly of Western or Far East Asian origin, in the context of developing regions. A computer science perspective is complementary to that agenda. The computer science perspective focuses on exploring the resources, or inputs, of a particular context and on basing the design of a technical intervention on the available resources, so that the output makes a difference in the development context. The modus operandi of computer science, construction, interacts with evaluation and exploration practices. An analysis of a contextualized information technology curriculum of Tumaini University in southern Tanzania shows the potential of the computer science perspective for designing meaningful information and communication technology for a developing region.

Computing as a Science: A Survey of Competing Viewpoints

Since the birth of computing as an academic discipline, the disciplinary identity of computing has been debated fiercely. The most heated question has concerned the scientific status of computing. Some consider computing to be a natural science and some consider it to be an experimental science. Others argue that computing is bad science, whereas some say that computing is not a science at all. This survey article presents viewpoints for and against computing as a science. Those viewpoints are analyzed against basic positions in the philosophy of science. The article aims at giving the reader an overview, background, and a historical and theoretical frame of reference for understanding and interpreting some central questions in the debates about the disciplinary identity of computer science. The article argues that much of the discussion about the scientific nature of computing is misguided due to a deep conceptual uncertainty about science in general as well as computing in particular.

Three traditions of computing: what educators should know

Educators in the computing fields are often familiar with the characterization of computing as a combination of theoretical, scientific, and engineering traditions. That distinction is often used to guide the work and disciplinary self-identity of computing professionals. But the distinction is, by no means, an easy one. The three traditions of computing are based on different principles, they have different aims, they employ different methods, and their products are very different. Educators in the field of computing should be aware of the fundamental differences between the traditions of computing so that they can offer their students a truthful and balanced view about computing branches. In this article the three traditions of computing are presented and some of their underlying assumptions, principles, application areas, restrictions, and weaknesses are portrayed. Also, some of the landmark arguments in the debates about the identity of computing disciplines are discussed.

Know Your Discipline: Teaching the Philosophy of Computer Science

Background Computer science is a relatively young discipline. Its birth can be traced to the 1940s, when wider academic interest in automatic computing was triggered by the construction of the first fully electronic, digital, Turing-complete computer, ENIAC, in 1945 and the concomitant birth of the stored-program paradigm (see, e.g., Aspray, 2000). It still took some 20 years for computer science to achieve a disciplinary identity distinct from fields such as mathematics, electrical engineering, physics, and logic (cf. Atchison et al., 1968; Rice & Rosen, 2004). Throughout the short history of electronic digital computing, there has been a great variety of approaches, definitions, and outlooks on computing as a discipline. Arguments about the content of the field, its methods, and its aims have sometimes been fierce, and the rapid pace of extension of the field has made it even harder to define computer science (see Tedre, 2006, pp. 255-351). Over the last 60 years, researchers in the fields of computing have brought together a variety of scientific disciplines and research methodologies. The resulting science--computer science--offers a variety of ways for explaining phenomena; most notably it offers computational models and algorithms. The increased investments in research efforts in computer science have been paralleled by the growth of the number of computing-centered fields, such as computer engineering, scientific computation, electrical engineering, decision support systems, architectural design, and software engineering. Although interdisciplinarity has made the development of computer science possible in the first place (cf. Bowles, 1996; Puchta, 1996; Williams, 1985, p. 209), it also poses a very real challenge to computer scientists. Firstly, it is not certain what kinds of topics should be considered to be computer science proper. The attempts to describe computer science are invariably either very narrow and applicable to only some subfields of computer science (e.g., Dijkstra, 1974), or so broad that they do not exclude much (e.g., Newell, Perlis, & Simon, 1967). Secondly, it is very difficult to come up with an overarching set of rules of how computer science research should ideally be done. The subjects that computer scientists study include, for instance, programs, logic, formulae, people, complexity, machines, usability, and systems. An overarching set of rules for computer science research should cover research in fields such as software engineering, complexity theory, usability, the psychology of programming, management information systems, virtual reality, and architectural design. It is uncertain if an overarching, all-inclusive definition of computer science is possible, and if such definition is even necessary. It is important for computer scientists to understand the challenges (and possibilities) that the vast diversity of computer science research can cause. Many disputes about how computer scientists should work have their roots in different conceptions about what computer science actually is (cf. Denning et al., 1989). Many misunderstandings and controversies between scientists from different branches of computer science might be avoided by their understanding the research traditions within which people in those branches work. Even more importantly, computer scientists must know that the same approaches cannot be used with the whole variety of subjects that computer scientists study. Mathematical and computational models are precise and unambiguous, yet they are confined to the abstract world of mathematics and they fail to capture the richness of physical and social reality. Narratives and ethnographies are rich in dimension and sensitive to detail, yet equivocal and context-dependent. Narratives have little use in deriving formulae, and formal proofs have little explanatory power regarding usability. It has been argued that there are three particularly lucid traditions in computer science: the theoretical tradition, the empirical tradition, and the engineering tradition (cf. …

In search of contextual teaching of progranlvhng in a tanzanian secondary school

Teaching programming in non-Western surroundings reveals the cultural roots and dependencies of Computer Science. Both the concepts and the teaching methods of the discipline needed to be rethought in a teaching experiment carried out in Kidugala, Tanzania, among secondary school students. Following the idea of contextualized Computer Science, called ethnocomputing, we used culturally relevant entry points to teach the basics of programming. HIV/AIDS was chosen as the topic of an Internet site, to be designed and implemented by novice programmers, using the Java language. Analyzed by action research, our experiences indicate a significant motivation among the students to learn programming skills in order to be able to deal with a taboo-like topic on a neutral platform - a computer. The experiment suggests that a culturally relevant entry point, combined with problem-based learning, could challenge novice programmers also in Western societies; a side-effect of studying Computer Science education in a less developed country.

How learning analytics can early predict under-achieving students in a blended medical education course

Abstract Aim: Learning analytics (LA) is an emerging discipline that aims at analyzing students’ online data in order to improve the learning process and optimize learning environments. It has yet un-explored potential in the field of medical education, which can be particularly helpful in the early prediction and identification of under-achieving students. The aim of this study was to identify quantitative markers collected from students’ online activities that may correlate with students’ final performance and to investigate the possibility of predicting the potential risk of a student failing or dropping out of a course. Methods: This study included 133 students enrolled in a blended medical course where they were free to use the learning management system at their will. We extracted their online activity data using database queries and Moodle plugins. Data included logins, views, forums, time, formative assessment, and communications at different points of time. Five engagement indicators were also calculated which would reflect self-regulation and engagement. Students who scored below 5% over the passing mark were considered to be potentially at risk of under-achieving. Results: At the end of the course, we were able to predict the final grade with 63.5% accuracy, and identify 53.9% of at-risk students. Using a binary logistic model improved prediction to 80.8%. Using data recorded until the mid-course, prediction accuracy was 42.3%. The most important predictors were factors reflecting engagement of the students and the consistency of using the online resources. Conclusions: The analysis of students’ online activities in a blended medical education course by means of LA techniques can help early predict underachieving students, and can be used as an early warning sign for timely intervention.