MEDIA PEMBELAJARAN FISIKA MENGGUNAKAN SMARTPHONE DENGAN PENDEKATAN STEM (SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS)

ABSTRAKMemanfaatkan teknologi dapat mendukung ketercapaian tujuan pembelajaran, salah satunya dapat menciptakan pembelajaran yang bervariatif dan meningkatkan motivasi belajar sehingga akan mempengaruhi hasil belajar peserta didik. Penelitian ini menggunakan desain penelitian N. Nieveen yang terdiri dari tahap pendahuluan, pengembangan, dan penilaian. Tujuan penelitian ini, untuk mengetahui validitas dan efektifitas media pembelajaran fisika materi gerak parabola agar dapat dinyatakan layak untuk digunakan. Tempat penelitian dilakukan di SMAN 3 Bondowoso pada kelas X MIPA. Hasil data yang diperoleh menggunakan instrumen tes berupa lembar instrumen validitas, soal pre-test, dan post-test. Teknik analisis data menggunakan validitas ahli, validitas audience, dan persamaan N-Gain. Hasil rata-rata validitas oleh 2 validator ahli media sebesar 90,4% dinyatakan sangat valid dan validator ahli pengguna sebesar 92,18% dinyatakan sangat valid. Hasil efektivitas pada uji lapangan sebesar 87,27% dapat dinyatakan sangat efektif dan nilai N-Gain sebesar 83,27% sehingga dalam kategori tinggi. Berdasarkan hasil penelitian tersebut maka media pembelajaran fisika menggunakan smartphone dengan pendekatan STEM (Science, Technology, Engineering, and Mathematics) layak untuk digunakan. Kata kunci: media pembelajaran; smartphone; pendekatan STEM. ABSTRACTTechnology can promote the achievement of learning goals, one of which can create varied learning and increase motivation to learn so that it will affect students' learning outcomes. This study uses N. Nieveen's research design which consists of preliminary, development and evaluation stages. The aim of this study is to determine the validity and effectiveness of physics learning materials for parabolic motion material so that it can be declared usable. The research site was carried out at SMAN 3 Bondowoso in class X Science. Data results obtained using test instruments in the form of instrument validity cards, pre-test and post-test questions. The data analysis technique uses expert validity, audience validity and the N-Gain equation. The results of the mean validity by 2 expert media validators of 90.4% were declared very valid and 92.18% of the expert user validators were declared very valid. The results of the efficiency in the field test of 87.27% can be said to be very efficient and the N-Gain value of 83.27%, which places it in the high category. Based on the results of the study, it is possible to use physics learning materials using a smartphone with a STEM (science, technology, engineering and mathematics) approach.Keywords: multimedia learning; smartphone; approach STEM.

Towards Big Industrial Data Mining Through Explainable Automated Machine Learning

Industrial systems resources are capable of producing large amount of data. These data are often in heterogeneous formats and distributed, yet they provide means to mine the information which can allow the deployment of intelligent management tools for production activities. For this purpose, it is necessary to be able to implement knowledge extraction and prediction processes using Artificial Intelligence(AI) models but the selection and configuration of intended AI models tend to be increasingly complex for a non-expert user. In this paper, we present an approach and a software platform that may allow industrial actors, who are usually not familiar with AI, to select and configure algorithms optimally adapted to their needs. Hence, the approach is essentially based on automated machine learning. The resulting platform effectively enables a better choice among the combination of AI algorithms and hyper-parameter configurations. It also makes it possible to provide features of explainability of the resulting algorithms and models, thus increasing the acceptability of these models in practicing community of the users. The proposed approach has been applied in the field of predictive maintenance. Current tests are based on the analysis of more than 360 databases from the subjected field.

Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency

Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.

Performative Framework and Case Study for Technology-Enhanced Learning Communities

This paper employs the overarching concept of communities to express the social contexts within which human creativity is exercised and learning happens. With the advent of digital technologies, these social contexts, the communities we engage in, change radically. The new landscape brought about by digital technologies is characterized by new qualities, new opportunities for action, new community affordances. The term onlife is adopted from the Onlife Manifesto and used to distinguish the new kind of communities brought about by the modern digital technologies, the onlife communities. Design principles are presented to foster such communities and support their members. These principles constitute a framework that emphasizes the concept of performativity, i.e. knowledge is based on human performance and actions done within certain social contexts, rather than development of conceptual representations. To demonstrate the use of the framework and the corresponding principles, the paper presents how they can be used to analyze, evaluate and reframe a concrete system addressing creativity and learning in the field of cultural heritage (history teaching and learning). One of the most significant results is the adoption of principles that facilitate students’ engagement in rich learning experiences moving from the role of end-user towards the role of expert-user with the support of so called maieuta-designers. The result of this process is the use of the studied software not only to consume ready-made content but the creation of new, student generated content, offering new learning opportunities to the students. As the evaluation shows, these new learning opportunities enable students to develop a deeper understanding of the topics studied.

Distributing Authorship at a Localised Scale: Developing public engagement in sound mapping

Sound maps, particularly the web-based examples that have proliferated since the early 2000s, have proven compelling and valuable as means of conveying diverse perspectives of urban, rural and wilderness sound environments, while opening the creative process of mapping through field recording to non-expert user groups. As such, sound maps hold the promise of broad public engagement with everyday sonic experience and spatial typologies. Yet this straightforward participatory aim is prone to complication in terms of participatory frameworks and scale of analysis. Drawing on a catalogue of sound maps by the author, this article problematises the participatory norms of sound mapping and, in tandem, calls for a more nuanced approach to scale than typically seen to date in sound maps based on geospatial mapping APIs. A sound mapping workshop in Lisbon with a multidisciplinary participant group provided the opportunity to ‘re-prototype’ sound maps at the scale of a local neighbourhood using multimodal means of representation; the results highlighted questions of form, scale, representation, authorship and purpose in sound mapping and demonstrated its continuing potential as a participatory practice.

Digital (R)Evolution: Open-Source Softwares for Orthodontics

Among the innovations that have changed modern orthodontics, the introduction of new digital technologies in daily clinical practice has had a major impact, in particular the use of 3D models of dental arches. The possibility for direct 3D capture of arches using intraoral scanners has brought many clinicians closer to the digital world. The digital revolution of orthodontic practice requires both hardware components and dedicated software for the analysis of STL models and all other files generated by the digital workflow. However, there are some negative aspects, including the need for the clinician and technicians to learn how to use new software. In this context, we can distinguish two main software types: dedicated software (i.e., developed by orthodontic companies) and open-source software. Dedicated software tend to have a much more user-friendly interface, and be easier to use and more intuitive, due to being designed and developed for a non-expert user, but very high rental or purchase costs are an issue. Therefore, younger clinicians with more extensive digital skills have begun to look with increasing interest at open-source software. The aim of the present study was to present and discuss some of the best-known open-source software for analysis of 3D models and the creation of orthodontic devices: Blue Sky Plan, MeshMixer, ViewBox, and Blender.

Transmission Quality Classification with Use of Fusion of Neural Network and Genetic Algorithm in Pay&Require Multi-Agent Managed Network

Modern computer systems practically cannot function without a computer network. New concepts of data transmission are emerging, e.g., programmable networks. However, the development of computer networks entails the need for development in one more aspect, i.e., the quality of the data transmission through the network. The data transmission quality can be described using parameters, i.e., delay, bandwidth, packet loss ratio and jitter. On the basis of the obtained values, specialists are able to state how measured parameters impact on the overall quality of the provided service. Unfortunately, for a non-expert user, understanding of these parameters can be too complex. Hence, the problem of translation of the parameters describing the transmission quality appears understandable to the user. This article presents the concept of using Machine Learning (ML) to solve the above-mentioned problem, i.e., a dynamic classification of the measured parameters describing the transmission quality in a certain scale. Thanks to this approach, describing the quality will become less complex and more understandable for the user. To date, some studies have been conducted. Therefore, it was decided to use different approaches, i.e., fusion of a neural network (NN) and a genetic algorithm (GA). GA’s were choosen for the selection of weights replacing the classic gradient descent algorithm. For learning purposes, 100 samples were obtained, each of which was described by four features and the label, which describes the quality. In the reasearch carried out so far, single classifiers and ensemble learning have been used. The current result compared to the previous ones is better. A relatively high quality of the classification was obtained when we have used 10-fold stratified cross-validation, i.e., SEN = 95% (overall accuracy). The incorrect classification was 5/100, which is a better result compared to previous studies.

User-Tailored Orthosis Design for 3D Printing with PLACTIVE: A Quick Methodology

This paper proposes a methodology for user-tailored orthosis design for 3D printing that aims to give a non-expert, user-oriented tool that easily generates a customized orthosis. Additionally, this work aims to verify the biocompatibility of the PLACTIVE TM (PLACTIVE AN1TM, nano-additive concentration 1%, Copper 3D, Santiago, Chile) filament after extrusion to check its feasibility for 3D printed orthoses. A forefinger and a thumb orthosis were successfully designed applying the proposed methodology. The results showed that the proposed methodology is able to generate simple and practical orthoses through a fairly easy and intuitive procedure. Furthermore, experimental tests showed that the biocompatibility of the PLACTIVE TM filament is not affected after extrusion process, suggesting that it is a feasible material for 3D-printed orthoses.