PENGEMBANGAN BAHAN AJAR MOBILE APPLICATION BERBASIS SEAMLESS LEARNING DI SEKOLAH DASAR

The learning process will run when there is two-way communication between teachers and students. Teaching materials are used as a support for teachers to make it easier to convey material and for students to understand the material during the learning process. Based on the results of observations and interviews that have been conducted at SDN 2 Gadingkembar in learning activities, practical teaching materials are needed that can be accessed anywhere at any time without time limits, to help students understand the material in IPAS learning. This research aims to develop seamless learning-based mobile application teaching materials in elementary schools. This teaching material development uses the Lee & Owens development model by taking qualitative and quantitative types of data. Mobile application teaching materials based on seamless learning in elementary schools that have been developed will be validated by teaching material experts and material experts. The data collection techniques used are observation, interviews, questionnaires and documentation. The subject of this research is grade IV students of SDN 2 Gadingkembar. The results of the teaching material expert validation test get a percentage score of 89% and for the material validation test get a percentage score of 85%. While the results of the teacher user response obtained 94% while the results of student responses obtained 100%. Based on the results of this study, it can be concluded that teaching materials for mobile applications based on seamless learning in elementary schools show valid criteria and can be used in the IPAS learning process

Learned multi-stability in mechanical networks

We contrast the distinct frameworks of materials design and physical learning in creating elastic networks with desired stable states. In design, the desired states are specified in advance and material parameters can be optimized on a computer with this knowledge. In learning, the material physically experiences the desired stable states in sequence, changing the material so as to stabilize each additional state. We show that while designed states are stable in networks of linear Hookean springs, sequential learning requires specific non-linear elasticity. We find that such non-linearity stabilizes states in which strain is zero in some springs and large in others, thus playing the role of Bayesian priors used in sparse statistical regression. Our model shows how specific material properties allow continuous learning of new functions through deployment of the material itself

The effects of the design and development of a chemistry curriculum reform on teachers’ professional growth: a case study

A curriculum innovation requires new learning material for students and a preparation program for teachers, in which teacher learning is a key ingredient. In this paper we describe how three experienced teachers, involved in the development and subsequent classroom enactment of student learning material for context-based chemistry education, professionalized. For data collection a questionnaire, three interviews and discussion transcripts were used. Our results show that: (a) teachers, cooperating in a network under supervision of an expert, can develop innovative learning material; (b) the development of learning material can be seen as a powerful program to prepare teachers for an innovation; and (c) teachers’ knowledge increased in all five pedagogical content knowledge domains during the development and class enactment phases

Development Of Learning Material Of Pakem-Plus For Mathematics Lesson At Elementary School

Active, creative, effective, and enjoy full learning (PAKEM) needs to be supported by good teachers’ understanding about both of content and choosing context. In Aceh Province having Islamic educational concept, it is rather difficult to find teachers who have both good knowledge and religious concept. To solve that problem, this research develops some learning material for PAKEM to grow optimally potential students, teachers, and culture (including the Islamic culture) and to improve the education quality, which is writer called PAKEM-Plus. Learning material developed consist of teacher’s guidebook, lesson plan, student’s worksheet, and classroom assessment for mathematics at grade 5 which satisfy validity and practicality criteria. This is a developmental research to develop a learning material. The result of this research is a learning material has satisfied validity and practicality criteria. Key word: Active, creative, effective, and enjoy full learning (PAKEM), Islamic cultur

PENGEMBANGAN MEDIA PEMBELAJARAN SIKLUS HIDROLOGI PADA PEMBELAJARAN IPAS BERBASIS AUDIO-VISUAL KELAS IV SEKOLAH DASAR

Based on the results of observations and interviews that have been conducted with grade IV teachers of SDN Rejoagung 3 Ngoro Jombang, an analysis of student needs was obtained, namely the need for concrete learning media to support learning, especially in the hydrological cycle material. This aims to make it easier for students to understand the material and be more active in following learning. The purpose of the study is to develop audio-visual based hydrological cycle learning media in IPAS learning using the ADDIE model (Analyze, Design, Development, Implementation, Evaluation). The subjects of this study were all grade IV students of SDN Rejoagung 3 Ngoro Jombang, of which there were 18 people. As for the analysis techniques used, there are two data analysis techniques, namely in the form of descriptive qualitative and quantitative descriptive data analysis techniques. The results of material expert validation get a score of 92.5% and the results of media expert validation get 87.5% which is included in the category of very valid for implementation. Then the results of obtaining student response questionnaires get a score of 97% from 18 students. The results of the teacher's response received a score of 95%. Based on the results that have been obtained that this learning media can and is feasible to be applied in grade IV Elementary School on water cycle material. However, there are shortcomings in this learning media, namely media that is designed with a large enough shape and requires electric current in its use, so it is less effective if used without using an electric current connection. The material available is very limited, which is only for water cycle material. It is hoped that this learning media can be used well, and become a reference for teachers, students, principals or even the general public, and can be improved even better for further research

Learning Material-Aware Local Descriptors for 3D Shapes

Material understanding is critical for design, geometric modeling, and analysis of functional objects. We enable material-aware 3D shape analysis by employing a projective convolutional neural network architecture to learn material- aware descriptors from view-based representations of 3D points for point-wise material classification or material- aware retrieval. Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical mate- rials, posing a challenge for learning methods. To address this challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material labels. We focus on furniture models which exhibit interesting structure and material variabil- ity. In addition, we also contribute a high-quality expert- labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We further apply a mesh-aware con- ditional random field, which incorporates rotational and reflective symmetries, to smooth our local material predic- tions across neighboring surface patches. We demonstrate the effectiveness of our learned descriptors for automatic texturing, material-aware retrieval, and physical simulation. The dataset and code will be publicly available.Comment: 3DV 201

Do not forget: Full memory in memory-based learning of word pronunciation

Memory-based learning, keeping full memory of learning material, appears a viable approach to learning NLP tasks, and is often superior in generalisation accuracy to eager learning approaches that abstract from learning material. Here we investigate three partial memory-based learning approaches which remove from memory specific task instance types estimated to be exceptional. The three approaches each implement one heuristic function for estimating exceptionality of instance types: (i) typicality, (ii) class prediction strength, and (iii) friendly-neighbourhood size. Experiments are performed with the memory-based learning algorithm IB1-IG trained on English word pronunciation. We find that removing instance types with low prediction strength (ii) is the only tested method which does not seriously harm generalisation accuracy. We conclude that keeping full memory of types rather than tokens, and excluding minority ambiguities appear to be the only performance-preserving optimisations of memory-based learning.Comment: uses conll98, epsf, and ipamacs (WSU IPA