Reflections on 'What do we Think we are Doing' 20-year Most Influential Paper award talk

My 1996 paper [1] challenged the VL community to ask What do we think we are doing? It might now be called a Systematic Literature Review, although formal procedures for SLR were not developed until later [5]. It made a textual analysis of publications in which authors described a cognitive rationale for VL research, observing that many relied on insights from folk psychology, from introspection, or speculative computer analogies to the brain. This was a study of metacognition – beliefs about one’s cognitive ability that shape the mental strategies we choose. In the case of programming language designers, the choices being shaped were not their own problem-solving strategies (something we all do), but the design rationale for new languages (which will affect others).Hitachi Lt

Graph Algorithm Animation with Grrr

We discuss geometric positioning, highlighting of visited nodes and user defined highlighting that form the algorithm animation facilities in the Grrr graph rewriting programming language. The main purpose of animation was initially for the debugging and profiling of Grrr code, but recently it has been extended for the purpose of teaching algorithms to undergraduate students. The animation is restricted to graph based algorithms such as graph drawing, list manipulation or more traditional graph theory. The visual nature of the Grrr system allows much animation to be gained for free, with no extra user effort beyond the coding of the algorithm, but we also discuss user defined animations, where custom algorithm visualisations can be explicitly defined for teaching and demonstration purposes

Objektum orientált programozás tanítása vizualizációs eszközökkel

Ez a cikk bemutatja az algoritmus - vizualizáció t, mint segédeszközt az objektum orientált programozás tanítása során. A z algoritmus - vizualizáció elméleti bevezetése és né hány oktatási vonatkozású eredmény közlése után, a z írás pél dát mutat két jól használható v izualizációs eszközre : a BlueJ - re és a Jeli ot ra , végül értékeli az oka t

Algoritmus-vizualizáció a programozásoktatásban

Ez a cikk bemutatja az algoritmus-vizualizáció elméletét és eddig oktatási vonatkozású eredményeit, majd példát mutat egy jól használható algoritmus-vizualizációs eszközre, végül értékeli azt. A cikk bemutatja továbbá, hogyan lehet felhasználni oktatóként és tanulóként az algoritmus-vizualizációs eszközöket, konkrétan a programozási tételek tanítása és tanulása közben, illetve értékeli a tanulási és tanítási módszert. Két eszközt fog példaként felhozni: a Jeliot-ot és a TRAKLA2-t

Algorithm visualization in programming education

This paper introduces the theory of algorithm visualization and its education-related results obtained so far, then an algorithm visualization tool is going to be presented as an example, which we will finally evaluate. This article illustrates furthermore how algorithm visualization tools can be used by teachers and students during the teaching and learning process of programming, and equally evaluates teaching and learning methods. Two tools will be introduced: Jeliot and TRAKLA2

Knowledge Testing in Algorithms – an Experimental Study

With the appearance of INTERNET technologies the developers of algorithm animation systems have shifted to build on-line system with the advantages of platform-independence and open accessibility over earlier ones. As a result, there is ongoing research in the re-design and re-evaluation of AAS in order to transform them in task-oriented environments for design of algorithms in on-line mode. The experimental study reported in the present paper contributes in this research

Investigating the Effectiveness of Active Interaction Tools on Student Learning

In this project, we investigate the effectiveness of active interaction animation tools for learning. We limit our scope to a particular computer science course that teaches graph algorithms on an undergraduate level. More specifically, we evaluate student understanding of basic graph algorithms when two kinds of interactive animation tools are used by the students to learn the algorithms: active interaction and passive interaction. We hypothesize that animations which engage students in active interaction are more effective and more beneficial to learning and comprehension than the animations which do not explicitly engage students in active interaction. We conduct an experiment and study the effects of these two kinds of interactive animation on learning effectiveness