AKTIVITAS DAN PEMECAHAN MASALAH MATEMATIKA MENGGUNAKAN MODEL PROBLEM BASED LEARNING DI SMK

Vocational High School is a formal institution that creates more independent, creative, innovative, skilled, and responsible students in solving a problem. Graduates from vocational education are expected to be more competent in solving problems in the world of work. Students are also required to be more active in solving a problem. The research objective was to determine student activity and analyze mathematical problem-solving abilities in implementing mathematics learning using the Problem Based Learning model. The method used is to use a qualitative design. In this method, student activity is measure using the observation sheet, and the problem-solving ability test is carried out by following the steps of Polya's theory. The results showed that the overall student activity reached the excellent category. The general average problem-solving ability obtained an ideal classification, with the movement and moderate mathematical problem-solving ability of students achieving a reasonable variety and positively impacting all vocational students

Integrating Generalizations with Exemplar-Based Reasoning

Knowledge represented as generalizations is insufficient for problem solving in many domains,such as legal reasoning, because of a gap between the language of case-descriptions and the language in which generalizations are expressed, and because of the graded structure of domain categories. Exemplar-based representation addresses these problems, but accurate assessment of similarity between an exemplar of a category and a new case requires reasoning both with general domain theory and with the explanation of the exemplar's membership in the category. G R E B E is a system that integrates generalizations and exemplars in a cooperative manner. Exemplar-based explanations are used to bridge the gap between case-descriptions and generalizations, and domain theory in the form of general rules and specific explanations is used to explain the equivalence of new cases to exemplars

Solving Tree Problems with Category Theory

Artificial Intelligence (AI) has long pursued models, theories, and techniques to imbue machines with human-like general intelligence. Yet even the currently predominant data-driven approaches in AI seem to be lacking humans' unique ability to solve wide ranges of problems. This situation begs the question of the existence of principles that underlie general problem-solving capabilities. We approach this question through the mathematical formulation of analogies across different problems and solutions. We focus in particular on problems that could be represented as tree-like structures. Most importantly, we adopt a category-theoretic approach in formalising tree problems as categories, and in proving the existence of equivalences across apparently unrelated problem domains. We prove the existence of a functor between the category of tree problems and the category of solutions. We also provide a weaker version of the functor by quantifying equivalences of problem categories using a metric on tree problems.Comment: 10 pages, 4 figures, International Conference on Artificial General Intelligence (AGI) 201

Assessing schematic knowledge of introductory probability theory

[Abstract]: The ability to identify schematic knowledge is an important goal for both assessment and instruction. In the current paper, schematic knowledge of statistical probability theory is explored from the declarative-procedural framework using multiple methods of assessment. A sample of 90 undergraduate introductory statistics students was required to classify 10 pairs of probability problems as similar or different; to identify whether 15 problems contained sufficient, irrelevant, or missing information (text-edit); and to solve 10 additional problems. The complexity of the schema on which the problems were based was also manipulated. Detailed analyses compared text-editing and solution accuracy as a function of text-editing category and schema complexity. Results showed that text-editing tends to be easier than solution and differentially sensitive to schema complexity. While text-editing and classification were correlated with solution, only text-editing problems with missing information uniquely predicted success. In light of previous research these results suggest that text-editing is suitable for supplementing the assessment of schematic knowledge in development

The Design of the Fifth Answer Set Programming Competition

Answer Set Programming (ASP) is a well-established paradigm of declarative programming that has been developed in the field of logic programming and nonmonotonic reasoning. Advances in ASP solving technology are customarily assessed in competition events, as it happens for other closely-related problem-solving technologies like SAT/SMT, QBF, Planning and Scheduling. ASP Competitions are (usually) biennial events; however, the Fifth ASP Competition departs from tradition, in order to join the FLoC Olympic Games at the Vienna Summer of Logic 2014, which is expected to be the largest event in the history of logic. This edition of the ASP Competition series is jointly organized by the University of Calabria (Italy), the Aalto University (Finland), and the University of Genova (Italy), and is affiliated with the 30th International Conference on Logic Programming (ICLP 2014). It features a completely re-designed setup, with novelties involving the design of tracks, the scoring schema, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. Benchmark domains are taken from past editions, and best system packages submitted in 2013 are compared with new versions and solvers. To appear in Theory and Practice of Logic Programming (TPLP).Comment: 10 page

Supporting Collaborative Learning in Videoconferencing using Collaboration Scripts and Content Schemes

Studies have shown that videoconferences are an effective medium for facilitating communication between parties who are separated by distance. Furthermore, studies reveal that videoconferences are effective when used for distance learning, particularly due to their ability to facilitate complex collaborative learning tasks. However, as in face-to-face communication, learners benefit when they receive additional support for such learning tasks. This article provides an overview of two empirical studies to illustrate more general insights regarding some effective and less effective ways to support collaborative learning with videoconferencing. The focus is on content schemes as content-specific support and task-specific support as collaboration scripts. Based on the results of the two studies, conclusions can be drawn about support measures that promote learning. Conclusions can also be reached about the need for employing both content schemes and collaboration scripts to provide learners with the most benefit.Studien haben gezeigt, dass Videokonferenzen ein effektives Medium für die verteilte Kommunikation sind. Ebenso zeigten erste Studien, dass sich Videokonferenzen auch in Telelernumgebungen einsetzen lassen, insbesondere weil sie komplexe kooperative Lernaufgaben ermöglichen. Lernende profitieren jedoch in solchen Lernaufgaben – ähnlich wie face to face – von zusätzlicher Unterstützung. In diesem Beitrag werden zwei empirische Studien dargestellt, die weiterführende Erkenntnisse hinsichtlich effektiver und weniger effektiver Arten der Unterstützung kollaborativen Lernens in Videokonferenzen erbringen sollen. Der Schwerpunkt liegt dabei auf Wissensschemata als Methode inhaltlicher Unterstützung, und aufgabenspezifischer Unterstützung in Form von Kooperationsskripts. Ausgehend von den Ergebnissen dieser zwei Studien werden Folgerungen über lernförderliche Merkmale der Unterstützungsmaßnahmen formuliert. Befunde weisen auf die Notwendigkeit Wissensschemata und Kooperationsskripts kombiniert anzuwenden hin, um für die Lernenden den größtmöglichen Nutzen zu erreichen

Conceptual and socio-cognitive support for collaborative learning in videoconferencing environments

Studies have shown that videoconferences are an effective medium for facilitating communication between parties who are separated by distance. Furthermore, studies reveal that videoconferences are effective when used for distance learning, particularly when learners are engaged in complex collaborative learning tasks. However, as in face-to-face communication, learners benefit most when they receive additional support for such learning tasks. This article provides an overview of three empirical studies to illustrate more general insights regarding some of the more and less effective ways of supporting collaborative learning with videoconferencing. The focus is on conceptual support, such as structural visualization and socio-cognitive support, such as scripts. Based on the results of the three studies, conclusions can be drawn about the conceptual and socio-cognitive support measures that promote learning. Conclusions can also be reached about the need for employing both conceptual and socio-cognitive support to provide learners with the most benefit