Evaluating knowledge representations for program characterization

Knowledge representation attempts to organize the knowledge of a context in order for automated systems to utilize it to solve complex problems. Among several difficult problems, one worth mentioning is called code-generation, which is undecidable due to its complexity. A technique to mitigate this problem is to represent the knowledge and use an automatic reasoning system to infer an acceptable solution. This article evaluates knowledge representations for program characterization for the context of code-generation systems. The experimental results prove that program Numerical Features as knowledge representation can achieve 85% near to the best possible results. Furthermore, such results demonstrate that an automatic code-generating system, which uses this knowledge representation is capable to obtain performance better than others codegenerating systems

Reinventing discovery learning: a field-wide research program

© 2017, Springer Science+Business Media B.V., part of Springer Nature. Whereas some educational designers believe that students should learn new concepts through explorative problem solving within dedicated environments that constrain key parameters of their search and then support their progressive appropriation of empowering disciplinary forms, others are critical of the ultimate efficacy of this discovery-based pedagogical philosophy, citing an inherent structural challenge of students constructing historically achieved conceptual structures from their ingenuous notions. This special issue presents six educational research projects that, while adhering to principles of discovery-based learning, are motivated by complementary philosophical stances and theoretical constructs. The editorial introduction frames the set of projects as collectively exemplifying the viability and breadth of discovery-based learning, even as these projects: (a) put to work a span of design heuristics, such as productive failure, surfacing implicit know-how, playing epistemic games, problem posing, or participatory simulation activities; (b) vary in their target content and skills, including building electric circuits, solving algebra problems, driving safely in traffic jams, and performing martial-arts maneuvers; and (c) employ different media, such as interactive computer-based modules for constructing models of scientific phenomena or mathematical problem situations, networked classroom collective “video games,” and intercorporeal master–student training practices. The authors of these papers consider the potential generativity of their design heuristics across domains and contexts