scholarly journals Active Learning Models in Indonesia

2019 ◽  
Author(s):  
Hilda Jasri ◽  
Juju Masunah
Keyword(s):  
Active Learning ◽  
Learning Models

Introducing AIDE: a Software Suite for Annotating Images with Deep and Active Learning Assistance

2021 ◽  
Author(s):  
Benjamin Kellenberger ◽  
Devis Tuia ◽  
Dan Morris
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Expert Knowledge ◽  
Semantic Segmentation ◽  
Third Party ◽  
Learning Models ◽  
Web Browser ◽  
Web Based ◽  
Model Training ◽  
Bounding Boxes

<p>Ecological research like wildlife censuses increasingly relies on data on the scale of Terabytes. For example, modern camera trap datasets contain millions of images that require prohibitive amounts of manual labour to be annotated with species, bounding boxes, and the like. Machine learning, especially deep learning [3], could greatly accelerate this task through automated predictions, but involves expansive coding and expert knowledge.</p><p>In this abstract we present AIDE, the Annotation Interface for Data-driven Ecology [2]. In a first instance, AIDE is a web-based annotation suite for image labelling with support for concurrent access and scalability, up to the cloud. In a second instance, it tightly integrates deep learning models into the annotation process through active learning [7], where models learn from user-provided labels and in turn select the most relevant images for review from the large pool of unlabelled ones (Fig. 1). The result is a system where users only need to label what is required, which saves time and decreases errors due to fatigue.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.0402be60f60062057601161/sdaolpUECMynit/12UGE&app=m&a=0&c=131251398e575ac9974634bd0861fadc&ct=x&pn=gnp.elif&d=1" alt=""></p><p><em>Fig. 1: AIDE offers concurrent web image labelling support and uses annotations and deep learning models in an active learning loop.</em></p><p>AIDE includes a comprehensive set of built-in models, such as ResNet [1] for image classification, Faster R-CNN [5] and RetinaNet [4] for object detection, and U-Net [6] for semantic segmentation. All models can be customised and used without having to write a single line of code. Furthermore, AIDE accepts any third-party model with minimal implementation requirements. To complete the package, AIDE offers both user annotation and model prediction evaluation, access control, customisable model training, and more, all through the web browser.</p><p>AIDE is fully open source and available under https://github.com/microsoft/aerial_wildlife_detection.</p><p> </p><p><strong>References</strong></p>

Motivational Active Learning in Blended and Virtual Learning Scenarios

2018 ◽  
pp. 935-957
Author(s):  
Johanna Pirker ◽  
Maria Riffnaller-Schiefer ◽  
Lisa Maria Tomes ◽  
Christian Gütl
Keyword(s):  
Active Learning ◽  
Learning Strategies ◽  
Relevant Literature ◽  
Virtual Learning ◽  
Learning Approaches ◽  
Learning Models ◽  
Engagement Strategies ◽  
Active And Collaborative Learning ◽  
Learning Success ◽  
Learning Scenarios

The way people learn has changed over the last years. New pedagogical theories show that engaging and active learning approaches are particularly successful in improving conceptual understanding and enhancing the students' learning success and motivation. The Motivational Active Learning approach combines engagement strategies based on active and collaborative learning models with gamification. While many active learning models rely on in-class setups and active and personal interactions between students and between instructors, MAL was designed to integrate active learning in different settings. Our research project focuses on enhanced learning strategies with MAL in different computer-supported scenarios. This chapter outlines the potential of the pedagogical model MAL (Motivational Active Learning) in the context of blended and virtual learning scenarios; it also summarizes relevant literature and discusses implications and future work.

Persuasion-based recommender system ensambling matrix factorisation and active learning models

2020 ◽  
Author(s):  
Rubén Sánchez-Corcuera ◽  
Diego Casado-Mansilla ◽  
Cruz E. Borges ◽  
Diego López-de-Ipiña
Keyword(s):  
Active Learning ◽  
Recommender System ◽  
Learning Models ◽  
Matrix Factorisation

scholarly journals Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1932
Author(s):  
Ramyar Saeedi ◽  
Keyvan Sasani ◽  
Assefaw H. Gebremedhin
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Mobile Health ◽  
Health Monitoring ◽  
Learning Models ◽  
Healthcare Applications ◽  
User Data ◽  
Adaptation Cost ◽  
Mobile Health Monitoring ◽  
Machine Learning Models

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85 % (for the first dataset) and 92 % (for the second dataset) by labeling only 15 % of unlabeled data.

scholarly journals Active learning for the power factor prediction in diamond-like thermoelectric materials

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Ye Sheng ◽  
Yasong Wu ◽  
Jiong Yang ◽  
Wencong Lu ◽  
Pierre Villars ◽  
...  
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Transport Properties ◽  
Gradient Boosting ◽  
Learning Models ◽  
Material Development ◽  
Materials Genome ◽  
P Type ◽  
Type Power ◽  
Machine Learning Models

Abstract The Materials Genome Initiative requires the crossing of material calculations, machine learning, and experiments to accelerate the material development process. In recent years, data-based methods have been applied to the thermoelectric field, mostly on the transport properties. In this work, we combined data-driven machine learning and first-principles automated calculations into an active learning loop, in order to predict the p-type power factors (PFs) of diamond-like pnictides and chalcogenides. Our active learning loop contains two procedures (1) based on a high-throughput theoretical database, machine learning methods are employed to select potential candidates and (2) computational verification is applied to these candidates about their transport properties. The verification data will be added into the database to improve the extrapolation abilities of the machine learning models. Different strategies of selecting candidates have been tested, finally the Gradient Boosting Regression model of Query by Committee strategy has the highest extrapolation accuracy (the Pearson R = 0.95 on untrained systems). Based on the prediction from the machine learning models, binary pnictides, vacancy, and small atom-containing chalcogenides are predicted to have large PFs. The bonding analysis reveals that the alterations of anionic bonding networks due to small atoms are beneficial to the PFs in these compounds.

scholarly journals Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning

2019 ◽  
Vol 10 (35) ◽  
pp. 8154-8163 ◽  
Author(s):  
Yao Zhang ◽  
Alpha A. Lee
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Supervised Learning ◽  
Molecular Properties ◽  
Learning Models ◽  
Molecular Properties Prediction ◽  
Design Experiments ◽  
Machine Learning Models

We report a statistically principled method to quantify the uncertainty of machine learning models for molecular properties prediction. We show that this uncertainty estimate can be used to judiciously design experiments.

Pembelajaran Terpadu dan Urgensinya dalam Pengembangan Karakter Anak Sekolah Dasar

2019 ◽  
Vol 17 (2) ◽  
pp. 1-12
Author(s):  
Elizar Elizar
Keyword(s):  
Elementary School ◽  
Active Learning ◽  
Prospective Teachers ◽  
Young Generation ◽  
Integrated Learning ◽  
Learning Models ◽  
Learning Program ◽  
Learning Evaluation ◽  
Learning Programs

Integrated learning models is a learning that is associated with an increased ability and characters of student in elementary school. School as one of the means and facitating role in shaping the young generation and motivate the studens to give to become savvy genaration, independent, creative, and innovative.To anticipate the occurrence of inefficiency in implementing the learning program should be planned with the best. Prior to the implementation of a learning program conducted there are some things that must be considered, planning before setting up learning programs need, designing and organizing learning evaluation, is used as an effort to obtain information to assess the success of a program. One of some efforts that could be done to improve students’ skill, as prospective teachers, in implementing an active learning was that by implementing integrated learning. It covered modelling a lecturer as a model in implementing integrated learning in the class and it integratef active learning models in fragmented, connected, sequenced, shared, webbed, threaded, integrated, immersed, and networked

An Integral Approach to Active Learning in Mathematics

2016 ◽  
pp. 177-194
Author(s):  
Chris L. Yuen ◽  
Veronika Bohac Clarke
Keyword(s):  
Theoretical Model ◽  
Active Learning ◽  
Learning Process ◽  
Teaching And Learning ◽  
Integral Approach ◽  
Learning Models ◽  
Multiple Perspectives ◽  
Repair Strategy ◽  
Multi Level

In this chapter we examine the notion of “active learning” through Wilber's Integral AQAL Model and through two learning models based on AQAL. Our examination of Edwards' integral learning and Renert and Davis' five stages of mathematics, results in a multi-perspective, multi-level notion of “active learning”. We demonstrate, through the development of a rubric to gauge students' “activeness”, the complexity of what is involved in the teaching and learning process when one becomes mindful of the perspectives and levels (AQAL) that are present for every student. Several episodes of learning are used to show how each theoretical model applies, and an extended episode, which illustrates a student's repair strategy on a mathematically erroneous concept, is used to illustrate the analysis of the extent of active learning. The chapter concludes with a discussion of how the rubric of active learning, along with the four continua, can help teachers be mindful of the multiple perspectives that influence learning.

scholarly journals Penerapan Metode Artikulasi Sebagai Upaya Peningkatan Pemahaman Siswa:

2019 ◽  
Vol 1 (1) ◽  
pp. 17-30
Author(s):  
Feri Ferdian ◽  
Zaenal Arifin
Keyword(s):  
Action Research ◽  
Active Learning ◽  
Learning Outcomes ◽  
Learning Process ◽  
Learning Strategy ◽  
Learning Models ◽  
Post Test ◽  
Power Point ◽  
Active Learning Strategy

This study is a classroom action research (CAR) about the application of articulation methods in improving the understanding of class X IPA 2 students of MA Al Mahrusiyah Lirboyo Kota Kediri. This study involved 34 students consisting of 34 women. In this study, the meeting was held for 4 times, 2 meetings applied an active learning strategy for the articulation model, one meeting held a pre-test and once again held a post-test. Each meeting for each cycle is explored with planning so that each research researcher prepares: 1. Learning Implementation Plan (RPP) using articulation methods, 2. LKS, and 3. Preparing learning methods. The results of the findings of the research conducted increased learning outcomes from cycle I to cycle II, this is also because the role of the teacher performs the learning process with articulation learning models with power point learning media.

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