Board games as a teaching tool for technology classes in Compulsory Secondary Education

Aquest treball estudia la tècnica coneguda com game-based learning, és a dir, l’ús dels jocs com a eina didàctica. Primer que res, es fa recerca sobre els treballs ja existents i es veu que, tot i haver-hi articles sobre game-based learning, és difícil trobar-ne de relacionats amb la tecnologia, més enllà d’ensenyar a programar. A continuació, es revisen els continguts curriculars i les competències de secundària i es relacionen amb alguns jocs de taula ja existents, dels quals es detallen breument les regles de joc. Es veu que hi ha continguts curriculars, pels quals es difícil trobar un joc que hi encaixi. A més a més, es desenvolupa la idea d’un nou joc de taula, basat en el ja existent Party & Co., per treballar alguns dels continguts curriculars pels quals no s’ha trobat cap joc existent que s’hi escaigui. Finalment, s’explica una experiència duta a terme durant el període de pràctiques en el centre escolar al curs de 3r d’ESO. Es disposava de tres grups i en tots tres es va seguir la mateixa programació: classe introductòria expositiva, una sessió de muntatge de robots LEGO, 4 sessions de programació i un petit test. En un dels tres grups, però, es va fer una classe prèvia extra on es va jugar a un joc de taula anomenat RoboRally. Els objectius eren dobles: que aprenguessin la importància de l’algorísmica i que s’ho passessin bé. Els resultats mostren que aquest grup va treballar més i millor. En el treball s’analitzen els resultats obtinguts

Visualization designs for constraint logic programming

We address the design and implementation of visual paradigms for observing the execution of constraint logic programs, aiming at debugging, tuning and optimization, and teaching. We focus on the display of data in CLP executions, where representation for constrained variables and for the constrains themselves are seeked. Two tools, VIFID and TRIFID, exemplifying the devised depictions, have been implemented, and are used to showcase the usefulness of the visualizations developed

Computer science in Dutch secondary education: independent or integrated?

Nowadays, in Dutch secondary education, computer science is integrated within school subjects. About ten years ago computer science was considered an independent subject, but in the mid-1980s this idea changed. In our study we investigated whether the objectives of teaching computer science as an independent subject are met when computer science is integrated within school subjects. The main problem was that there was no formal curriculum of computer science as an independent subject. Therefore we interviewed 13 experts in the field of computer science and then compared this formal curriculum with the operational (integrated) curriculum, which is still in the development stage. It appears that most of the components of the formal curriculum are being covered by the operational curriculum, and we therefore concluded that these curricula are equivalent, although there may be differences in the level of teaching. In our opinion the best approach to computer science is to combine the independent and the integrated approaches

A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed

Explanatory debugging: Supporting end-user debugging of machine-learned programs

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a “program” that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on “explanatory debugging”, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions