Teaching Natural Computation

This paper consists of a discussion of the potential impact on computer science education of regarding computation as a property of the natural world, rather than just a property of artifacts specifically created for the purpose of computing. Such a perspective is becoming increasingly important: new computing paradigms based on the natural computational properties of the world are being created, scientific questions are being answered using computational ideas, and philosophical debates on the nature of computation are being formed. This paper discusses how computing education might react to these developments, goes on to discuss how these ideas can help to define computer science as a discipline, and reflects on our experience at Kent in teaching these subjects

Challenges and Target Audiences

UID/LIN/03213/2013Depending on the standpoint, Computational Linguistics can be defined as a subfield of Computer Science dedicated to the processing of specific data – natural language data – or as a subfield of Linguistics concerned with formal modelling of linguistic knowledge for computation purposes. These two perspectives reflect the two main paths to this interdisciplinary field, but also the challenges posed to its teaching. Namely, focusing on, and mastering, logic reasoning and formal models, for Language and Humanities students, and acknowledging and dealing with irregularity, variation and idiosyncrasy, for Computer Science, Engineering and Technology students. This paper discusses the major obstacles and handicaps that seem to stand in the way of teaching/learning Computational Linguistics, an area with high visibility, appeal and applicability potential, aiming at raising attention to some simple but usually overseen aspects that may improve teaching/learning results.publishersversionpublishe

Mental calculation : its place in the development of numeracy

The current concerns about the standards of numeracy in primary schools, as these are manifest in different official reports (HMI, 1997; DfEE, 1998), have given a revised emphasis to mental calculation. While not completely discounting the wider aspects of mathematical achievement, the topics of space and shape, data handling and measurement are being de-emphasised (Brown et al, 2000) and mental calculation is being emphasised, with there being daily opportunities for children to develop efficient and flexible mental methods of calculating (QCA, 1999; Wilson, 1999). However, the term, mental calculation is not clearly defined (Harries and Spooner, 2000) and without conceptual clarity it may be very difficult for us to recognise, let alone understand, what pedagogical practices are needed to support the objective of increased emphasis on mental calculation. What follows is some consideration of what is meant by the term mental calculation and what this meaning implies for practice

Breaking the addition addiction: creating the conditions for knowing-to act in early algebra

We use data from a teaching experiment with a group of eight years old students to explore the potential of examining number sentences to promote relational thinking. This type of thinking requires attention to mathematical structure through consideration of relationships between terms contained in the sentence and not just on computation and comparison of the numeric values of each side. We show that children came to “know-to act” in the context of written activities and orchestrated discussions about number sentences, overcoming some of their computational habits and developing new ways to see and more flexibly approach the sentences. The results help to advance the study of young students´ emergent algebraic modes of thinking

Teaching programming with computational and informational thinking

Computers are the dominant technology of the early 21st century: pretty well all aspects of economic, social and personal life are now unthinkable without them. In turn, computer hardware is controlled by software, that is, codes written in programming languages. Programming, the construction of software, is thus a fundamental activity, in which millions of people are engaged worldwide, and the teaching of programming is long established in international secondary and higher education. Yet, going on 70 years after the first computers were built, there is no well-established pedagogy for teaching programming. There has certainly been no shortage of approaches. However, these have often been driven by fashion, an enthusiastic amateurism or a wish to follow best industrial practice, which, while appropriate for mature professionals, is poorly suited to novice programmers. Much of the difficulty lies in the very close relationship between problem solving and programming. Once a problem is well characterised it is relatively straightforward to realise a solution in software. However, teaching problem solving is, if anything, less well understood than teaching programming. Problem solving seems to be a creative, holistic, dialectical, multi-dimensional, iterative process. While there are well established techniques for analysing problems, arbitrary problems cannot be solved by rote, by mechanically applying techniques in some prescribed linear order. Furthermore, historically, approaches to teaching programming have failed to account for this complexity in problem solving, focusing strongly on programming itself and, if at all, only partially and superficially exploring problem solving. Recently, an integrated approach to problem solving and programming called Computational Thinking (CT) (Wing, 2006) has gained considerable currency. CT has the enormous advantage over prior approaches of strongly emphasising problem solving and of making explicit core techniques. Nonetheless, there is still a tendency to view CT as prescriptive rather than creative, engendering scholastic arguments about the nature and status of CT techniques. Programming at heart is concerned with processing information but many accounts of CT emphasise processing over information rather than seeing then as intimately related. In this paper, while acknowledging and building on the strengths of CT, I argue that understanding the form and structure of information should be primary in any pedagogy of programming

Synchronous Online Philosophy Courses: An Experiment in Progress

There are two main ways to teach a course online: synchronously or asynchronously. In an asynchronous course, students can log on at their convenience and do the course work. In a synchronous course, there is a requirement that all students be online at specific times, to allow for a shared course environment. In this article, the author discusses the strengths and weaknesses of synchronous online learning for the teaching of undergraduate philosophy courses. The author discusses specific strategies and technologies he uses in the teaching of online philosophy courses. In particular, the author discusses how he uses videoconferencing to create a classroom-like environment in an online class

Identifying features predictive of faculty integrating computation into physics courses

Computation is a central aspect of 21st century physics practice; it is used to model complicated systems, to simulate impossible experiments, and to analyze mountains of data. Physics departments and their faculty are increasingly recognizing the importance of teaching computation to their students. We recently completed a national survey of faculty in physics departments to understand the state of computational instruction and the factors that underlie that instruction. The data collected from the faculty responding to the survey included a variety of scales, binary questions, and numerical responses. We then used Random Forest, a supervised learning technique, to explore the factors that are most predictive of whether a faculty member decides to include computation in their physics courses. We find that experience using computation with students in their research, or lack thereof and various personal beliefs to be most predictive of a faculty member having experience teaching computation. Interestingly, we find demographic and departmental factors to be less useful factors in our model. The results of this study inform future efforts to promote greater integration of computation into the physics curriculum as well as comment on the current state of computational instruction across the United States