KEMAMPUAN PENALARAN DAN ARGUMENTASI ILMIAH SISWA SMP MELALUI PEMBELAJARAN IPA MENGGUNAKAN MODEL LEVELS OF INQUIRY BERBASIS SOSIO-SCIENTIFIC ISSUE PADA MATERI PEMANASAN GLOBAL

Tujuan penelitian ini adalah untuk memperoleh informasi perubahan kemampuan penalaran dan argumentasi ilmiah siswa setelah diterapkan model levels of inquiry berbasis Sosio-Scientific Issue pada pembelajaran IPA Materi Pemanasan Global. Tahapan Levels of Inquiry yang digunakan pada penelitian ini meliputi tiga tahap yaitu, Discovery Learning, Interactive Demonstration, dan Inquiry Lesson. Pada setiap tahapan Levels of Inquiry memiliki lima sintaks pembelajaran yaitu observation, manipulation, generalization, verification dan generalization. Metode yang digunakan adalah weak experiment dengan desain One Group Pre-test and Post-test. Subjek penelitian ini adalah siswa kelas 7 di salah satu SMP di kota Cimahi sejumlah 34 siswa. Berdasarkan analisis data, kemampuan penalaran ilmiah siswa memperoleh nilai N-gain sebesar 0,43 dengan kategori N-gain sedang. Persentase jumlah siswa berdasarkan kategori N-gain yaitu rendah 26,5%, sedang 58,8%, dan tinggi 14,7%. Pencapaian jumlah siswa dengan N-gain tertinggi tiap aspek yaitu proportional reasoning pada kategori tinggi sebesar 50,0%, control of variable pada kategori rendah sebesar 70,6%, inductive reasoning pada kategori tinggi sebesar 47,1%, correlational reasoning pada kategori sedang sebesar 41,2%, dan hypothetical deductive reasoning pada kategori sedang sebesar 50,0%. Selanjutnya, untuk kemampuan argumentasi ilmiah siswa memperoleh nilai N-gain sebesar 0,39 dengan kategori gain sedang. Persentase jumlah siswa berdasarkan kategori N-gain yaitu rendah 29,4%, sedang 61,8% dan tinggi 8,8%. Pencapaian jumlah siswa dengan N-gain tertinggi tiap aspek yaitu klaim pada kategori tinggi sebesar 50,0%, data pada kategori rendah sebesar 47,1%, warrant pada kategori rendah sebesar 38,2%, dan backing pada kategori sedang sebesar 55,9%. Berdasarkan hasil tersebut, dapat disimpulkan bahwa penerapan model Levels of Inqury berbasis Socio-scientific Issue pada pembelajaran IPA Materi Pemanasan Global dapat meningkatkan kemampuan penalaran dan argumentasi ilmiah siswa. The purpose of this study was to obtain information on changes in students' scientific reasoning and argumentative abilities after applying the level of inquiry model based on Socio-Scientific Issues in Global Warming Materials. The Levels of Inquiry stages in this study include three stages, namely, Discovery Learning, Interactive Demonstration, and Inquiry Lesson. At each stage Levels of Inquiry has five learning syntax, namely observation, manipulation, generalization, verification and generalization. The method was a weak experiment with One Group Pre-test and Post-test design. The subjects of this study were grade 7 students in one of junior high school in Cimahi with a total of 34 students. Based on data analysis, the scientific reasoning ability of students obtained an N-gain value of 0.43 with a moderate N-gain category. The percentage of students based on the N-gain category are low 26.5%, moderate 58.8%, and high 14.7%. The achievement of the number of students with the highest N-gain in every aspect are proportional reasoning in the high category by 50.0%, control of variables in the low category by 70.6%, inductive reasoning in the high category by 47.1%, correlational reasoning in the medium category by 41.2%, and hypothetical deductive reasoning in the medium category by 50.0%. Furthermore for the scientific argumentative ability, students obtain an N-gain value of 0.39 with the medium gain category. The percentage of students based on the N-gain category are low 29.4%, medium 61.8% and high 8.8%. The achievement of the number of students with the highest N-gain in every aspect are claims in the high category by 50.0%, data in the low category by 47.1%, warrant in the low category by 38.2%, and backing in the medium category by 55.9 %. Based on these results, it can be concluded that the application of the Socio-scientific Issue-based Levels of Inqury model to the learning of Science on Global Warming Material can improve students' scientific reasoning and argumentative abilities

Information system support in construction industry with semantic web technologies and/or autonomous reasoning agents

Information technology support is hard to find for the early design phases of the architectural design process. Many of the existing issues in such design decision support tools appear to be caused by a mismatch between the ways in which designers think and the ways in which information systems aim to give support. We therefore started an investigation of existing theories of design thinking, compared to the way in which design decision support systems provide information to the designer. We identify two main strategies towards information system support in the early design phase: (1) applications for making design try-outs, and (2) applications as autonomous reasoning agents. We outline preview implementations for both approaches and indicate to what extent these strategies can be used to improve information system support for the architectural designer

A Unified Checklist for Observational and Experimental Research in Software Engineering (Version 1)

Current checklists for empirical software engineering cover either experimental research or case study research but ignore the many commonalities that exist across all kinds of empirical research. Identifying these commonalities, and explaining why they exist, would enhance our understanding of empirical research in general and of the differences between experimental and case study research in particular. In this report we design a unified checklist for empirical research, and identify commonalities and differences between experimental and case study research. We design the unified checklist as a specialization of the general engineering cycle, which itself is a special case of the rational choice cycle. We then compare the resulting empirical research cycle with two checklists for experimental research, and with one checklist for case study research. The resulting checklist identifies important questions to be answered in experimental and case study research design and reports. The checklist provides insights in two different types of empirical research design and their relationships. Its limitations are that it ignores other research methods such as meta-research or surveys. It has been tested so far only in our own research designs and in teaching empirical methods. Future work includes expanding the comparison with other methods and application in more cases, by others than ourselves

Building an Expert System for Evaluation of Commercial Cloud Services

Commercial Cloud services have been increasingly supplied to customers in industry. To facilitate customers' decision makings like cost-benefit analysis or Cloud provider selection, evaluation of those Cloud services are becoming more and more crucial. However, compared with evaluation of traditional computing systems, more challenges will inevitably appear when evaluating rapidly-changing and user-uncontrollable commercial Cloud services. This paper proposes an expert system for Cloud evaluation that addresses emerging evaluation challenges in the context of Cloud Computing. Based on the knowledge and data accumulated by exploring the existing evaluation work, this expert system has been conceptually validated to be able to give suggestions and guidelines for implementing new evaluation experiments. As such, users can conveniently obtain evaluation experiences by using this expert system, which is essentially able to make existing efforts in Cloud services evaluation reusable and sustainable.Comment: 8 page, Proceedings of the 2012 International Conference on Cloud and Service Computing (CSC 2012), pp. 168-175, Shanghai, China, November 22-24, 201

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

A literature review of expert problem solving using analogy

We consider software project cost estimation from a problem solving perspective. Taking a cognitive psychological approach, we argue that the algorithmic basis for CBR tools is not representative of human problem solving and this mismatch could account for inconsistent results. We describe the fundamentals of problem solving, focusing on experts solving ill-defined problems. This is supplemented by a systematic literature review of empirical studies of expert problem solving of non-trivial problems. We identified twelve studies. These studies suggest that analogical reasoning plays an important role in problem solving, but that CBR tools do not model this in a biologically plausible way. For example, the ability to induce structure and therefore find deeper analogies is widely seen as the hallmark of an expert. However, CBR tools fail to provide support for this type of reasoning for prediction. We conclude this mismatch between experts’ cognitive processes and software tools contributes to the erratic performance of analogy-based prediction

MEMS accelerometer: proof of concept for geotechnical engineering testing

Geotechnical engineering materials are inherently variable, which leads to many simplifications when trying to model their behavior. The materials must always be characterized prior to any design work so that the engineer knows which direction he must progress to have a reliable design. Although subsurface characterization techniques and geotechnical design steadily improve, they are by no means infallible. The combination of geotechnical subsurface characterization along with geophysical techniques for improved design and construction monitoring has begun to surface as a viable alternative to the standard techniques in geotechnical engineering. This is important because there is a lack of Quality Control/Quality Assurance during the construction stage of a project, which further compounds the problems inherent from the complexity of the subsurface. Geophysical techniques based on elastic wave propagation provide an excellent combination of characterization and monitoring for the observation of geotechnical engineering projects. Elastic wave propagation provides coverage between traditional boreholes and it helps infer changes in the state of stresses. Unfortunately, sensors for this type of monitoring have typically been expensive, and the use of elastic wave propagation for characterization and monitoring has just begun to become to be implemented. The application of elastic wave tomography needs an inexpensive set of sensors to help justify its inclusion in the broad area of construction monitoring and characterization systems. This set of inexpensive sensors has arrived on the market developed from Miniature Electro-Mechanical Systems (MEMs) technology. This research developed the Analog Devices’ ADXL250 MEMS accelerometer to determine its limitations and its range of applications. In addition, a packaging system developed to allow for a broader range of applications in geotechnical engineering. Once the sensor was fully calibrated, a long-term goal for the research was to utilize the instrument in a laboratory experiment to obtain a tomographic image of the state of stress within a model. While the sensor was utilized in a model in this study, the final reasoning for its use within the model was simply to show its capabilities and areas of application. Simple velocity distributions are given as well as inferences made about the driving factors for these behaviors

Logical Learning Through a Hybrid Neural Network with Auxiliary Inputs

The human reasoning process is seldom a one-way process from an input leading to an output. Instead, it often involves a systematic deduction by ruling out other possible outcomes as a self-checking mechanism. In this paper, we describe the design of a hybrid neural network for logical learning that is similar to the human reasoning through the introduction of an auxiliary input, namely the indicators, that act as the hints to suggest logical outcomes. We generate these indicators by digging into the hidden information buried underneath the original training data for direct or indirect suggestions. We used the MNIST data to demonstrate the design and use of these indicators in a convolutional neural network. We trained a series of such hybrid neural networks with variations of the indicators. Our results show that these hybrid neural networks are very robust in generating logical outcomes with inherently higher prediction accuracy than the direct use of the original input and output in apparent models. Such improved predictability with reassured logical confidence is obtained through the exhaustion of all possible indicators to rule out all illogical outcomes, which is not available in the apparent models. Our logical learning process can effectively cope with the unknown unknowns using a full exploitation of all existing knowledge available for learning. The design and implementation of the hints, namely the indicators, become an essential part of artificial intelligence for logical learning. We also introduce an ongoing application setup for this hybrid neural network in an autonomous grasping robot, namely as_DeepClaw, aiming at learning an optimized grasping pose through logical learning.Comment: 11 pages, 9 figures, 4 table

Design thinking support: information systems versus reasoning

Numerous attempts have been made to conceive and implement appropriate information systems to support architectural designers in their creative design thinking processes. These information systems aim at providing support in very diverse ways: enabling designers to make diverse kinds of visual representations of a design, enabling them to make complex calculations and simulations which take into account numerous relevant parameters in the design context, providing them with loads of information and knowledge from all over the world, and so forth. Notwithstanding the continued efforts to develop these information systems, they still fail to provide essential support in the core creative activities of architectural designers. In order to understand why an appropriately effective support from information systems is so hard to realize, we started to look into the nature of design thinking and on how reasoning processes are at play in this design thinking. This investigation suggests that creative designing rests on a cyclic combination of abductive, deductive and inductive reasoning processes. Because traditional information systems typically target only one of these reasoning processes at a time, this could explain the limited applicability and usefulness of these systems. As research in information technology is increasingly targeting the combination of these reasoning modes, improvements may be within reach for design thinking support by information systems