Tools for Representing Problems and the Knowledge Required to Solve Them

Jonassen, David H.

doi:10.1007/11510154_5

Cited by 26 publications

(20 citation statements)

References 30 publications

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“…Faktor kendala kecakapan mahasiswa dalam memecahkan masalah fisika tidak hanya disebabkan oleh faktor proses perkuliahan, tetapi juga ditengarai oleh faktor dalam diri mahasiswa. Menurut Jonassen (2005) pemecahan masalah gagal karena pengetahuan mahasiswa yang diperlukan untuk memecahkan masalah kurang. Ditemukan kendala mahasiswa dalam memecahkan masalah pada penggunaan pendekatan fisika dan aplikasi konsep fisika (Yusuf, Prabowo, & Tjipto, 2014;Yusuf & Prabowo, 2016).…”

Section: Pendahuluanunclassified

Model Assesmen Pengetahuan Konseptual Mahasiswa Dalam Memecahkan Masalah Fisika

Yusuf

Prabowo

2019

Jambura Physics J.

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Berbagai macam model pembelajaran dikembangkan oleh para pengembang pendidikan. Namun kenyataannya kecakapan mahasiswa dalam memecahkan masalah masih rendah. Hal ini menunjukkan adanya masalah pada diri mahasiswa yang diduga adalah pengetahuan konseptual. Untuk mengkaji pengetahuan konseptual mahasiswa ini, maka perlu dikembangkan suatu model assesmen pengetahuan konseptual mahasiswa. Tujuan penelitian ini adalah untuk mengembangkan model assesmen pengetahuan mahasiswa dalam memecahkan masalah Fisika dan implementasinya. Penelitian termasuk penelitian pengembangan dengan menggunakan pendekatan kualitatif yaitu mengembangkan model assesmen pengetahuan konseptual mahasiswa dalam memecahkan masalah Fisika. Model assesmen yang telah dikembangkan akan diuji kevalidan oleh ahli yang kompeten. Proses penelitian ini akan dilaksanakan dengan beberapa tahap yaitu tahap pengembangan model instrumen pengetahuan konseptual mahasiswa, uji coba model assesmen. Uji lapangan dilaksanakan pada mahasiswa Program Studi Pendidikan Fisika Universitas Negeri Surabaya. Berdasarkan hasil Penelitian diperoleh model assesmen pengetahuan konseptual mahasiswa dalam memecahkan masalah Fisika. Hasil implementasi model assesmen dapat disimpulkan bahwa subjek dengan kategori akademik tinggi dapat mendefinisikan dan mengaplikasikan konsep dengan sangat baik, tetapi kurang baik dalam menunjukkan interelasi antara beberapa konsep pada pemecahan masalah. Sedangkan subjek dengan kategori akademik rendah dapat mendefinisikan konsep dengan baik, tetapi kurang baik dalam mengaplikasikan konsep fisika, serta tidak baik dalam menunjukkan interelasi antara beberapa konsep pada pemecahan masalah fisika.

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Section: Pendahuluanunclassified

Model Assesmen Pengetahuan Konseptual Mahasiswa Dalam Memecahkan Masalah Fisika

Yusuf

Prabowo

2019

Jambura Physics J.

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“…A smaller literature does specifically address semantic networks in applications such as professional communication (Khalifa and Liu, 2006), education (Jonassen, 2005), experimental cognitive modeling (Barsalou, 1992), modeling conceptual change in the history of science (Andersen, et al, 2006;Hyman, 2007), the use of concept mapping tools (which are essentially semantic networks without formal semantics) to express knowledge (Novak, 1998), and so on. However, so far, operationalization of the results through the computational application of the networks is uncommon.…”

Section: Value Of Semantic Network Formalismsmentioning

confidence: 99%

Designing Visual Languages for Description Logics

Gaines

2008

J of Log Lang and Inf

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Semantic networks were developed in cognitive science and artificial intelligence studies as graphical knowledge representation and inference tools emulating human thought processes. Formal analysis of the representation and inference capabilities of the networks modeled them as subsets of standard first-order logic (FOL), restricted in the operations allowed in order to ensure the tractability that seemed to characterize human reasoning capabilities. The graphical network representations were modeled as providing a visual language for the logic. Sub-sets of FOL targeted on knowledge representation came to be called description logics, and research on these logics has focused on issues of tractability of subsets with differing representation capabilities, and on the implementation of practical inference systems achieving the best possible performance. Semantic network research has kept pace with these developments, providing visual languages for knowledge entry, editing, and presenting the results of inference, that translate unambiguously to the underlying description logics. This paper discusses the design issues for such semantic network formalisms, and illustrates them through detailed examples of significant generic knowledge structures analyzed in the literature, including determinables, contrast sets, genus/differentiae, taxonomies, faceted taxonomies, cluster concepts, family resemblances, graded concepts, frames, definitions, rules, rules with exceptions, essence and state assertions, opposites and contraries, relevance, and so on. Such examples provide important test material for any visual language formalism for logic.

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“…This way of learning does not develop creative skills to solve problems independently. The improvement of programming competence can be reached by problem-based learning (Yusri, 2018): the process of problem solving problem correlates with an increase of creativity (Maker, 2004) and problem tasks are closer to real-life problems (Jonassen, 2005). However, problem solving is a less manageable process and its embedding in discipline is not a trivial task.…”

Section: Introductionmentioning

confidence: 99%

“…These tools will be considered as a type of software that allows constructing visual abstractions reproducing the concepts and objects of the real world with their relationship that can help to solve a problem (Koznov, 2012). Formed skills of working with such tools will allow students to independently produce, present and express their knowledge (Jonassen, 2005, Brilingaite, 2018. Furthermore, these tools allow you to structure the learning process by allocating time to solve each stage of a problem task.…”

Section: Introductionmentioning

confidence: 99%

Visual Modeling for Exploratory Problem Solving on Computer Science Lessons

Kostousov¹,

Simonova²

2019

Proceedings of the 16th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2019)

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The purpose of the article is to identify conditions for the effective use of visual modeling tools that can help reduce the difficulty level of solving problems during the teaching high school students programming. Visual modeling tools are a type of software that allows you to create visual abstractions that reproduce concepts and objects of the real world with their relationships, which can help in solving the problem. In this paper, we focused on preformal models based on intellect-and concept-maps. The article gives a classification and describes techniques for using various visual modeling tools to solve problem tasks. Formed skills of working with such tools will allow students to produce, represent and express independently their knowledge. In addition, these tools allow structuring the process of solving the problem, optimizing the time for solving each stage of solving a problem task. The article presents the results of comparing the learning outcomes of the control and experimental groups, which show that the proposed visual modeling tools can help in solving the programming problem tasks.

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Tools for Representing Problems and the Knowledge Required to Solve Them

Cited by 26 publications

References 30 publications

Model Assesmen Pengetahuan Konseptual Mahasiswa Dalam Memecahkan Masalah Fisika

Model Assesmen Pengetahuan Konseptual Mahasiswa Dalam Memecahkan Masalah Fisika

Designing Visual Languages for Description Logics

Visual Modeling for Exploratory Problem Solving on Computer Science Lessons

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