Learning Strategies Instruction

2020 ◽  
Author(s):  
Mary Grosser
Keyword(s):  
Environmental Management ◽  
Deep Learning ◽  
Learning Strategies ◽  
Transfer Of Learning ◽  
Constructivist Approach ◽  
Learning Tools ◽  
Effective Learning ◽  
Management Learning ◽  
Approach To Teaching ◽  
Strategies Instruction

Learning strategies comprise the application of overt and covert metacognitive, cognitive, affective/motivational, social, and behavioral/environmental/management learning tools to enhance the successfulness of surface and deep learning, as well as transfer of learning. The most effective learning strategies for the acquisition and manipulation of information combine the limited use of a behavioristic, teacher-directed transmission approach to teaching with a powerful cognitive and constructivist approach where students take control of their own learning and construct meaning of information.

The Analyses of Learning Strategies in English as a Second Language: Theoretical Classification and Measurement Test

2020 ◽  
Vol 4 (2) ◽  
pp. 118-129
Author(s):  
Asti Gumartifa ◽  
◽  
Indah Windra Dwie Agustiani
Keyword(s):  
Language Learning ◽  
Learning Outcomes ◽  
Learning Strategies ◽  
Learning Process ◽  
English Learners ◽  
English Language ◽  
Point Of View ◽  
Effective Learning ◽  
The Impact

Gaining English language learning effectively has been discussed all years long. Similarly, Learners have various troubles outcomes in the learning process. Creating a joyful and comfortable situation must be considered by learners. Thus, the implementation of effective learning strategies is certainly necessary for English learners. This descriptive study has two purposes: first, to introduce the classification and characterization of learning strategies such as; memory, cognitive, metacognitive, compensation, social, and affective strategies that are used by learners in the classroom and second, it provides some questionnaires item based on Strategy of Inventory for Language Learning (SILL) version 5.0 that can be used to examine the frequency of students’ learning strategies in the learning process. The summary of this study explains and discusses the researchers’ point of view on the impact of learning outcomes by learning strategies used. Finally, utilizing appropriate learning strategies are certainly beneficial for both teachers and learners to achieve the learning target effectively.

scholarly journals BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets

2021 ◽  
Vol 11 (13) ◽  
pp. 5880
Author(s):  
Paloma Tirado-Martin ◽  
Raul Sanchez-Reillo
Keyword(s):  
Deep Learning ◽  
Temporal Variations ◽  
Physical Activities ◽  
Learning Tools ◽  
Biometric Recognition ◽  
Learning Network ◽  
Deep Learning Network ◽  
Electrocardiogram Ecg

Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.

Deep learning strategies for critical heat flux detection in pool boiling

2021 ◽  
Vol 190 ◽  
pp. 116849
Author(s):  
Seyed Moein Rassoulinejad-Mousavi ◽  
Firas Al-Hindawi ◽  
Tejaswi Soori ◽  
Arif Rokoni ◽  
Hyunsoo Yoon ◽  
...  
Keyword(s):  
Heat Flux ◽  
Deep Learning ◽  
Learning Strategies ◽  
Critical Heat Flux ◽  
Pool Boiling

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

2021 ◽  
Author(s):  
Yun Zhang ◽  
Ling Wang ◽  
Xinqiao Wang ◽  
Chengyun Zhang ◽  
Jiamin Ge ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Deep Learning ◽  
Drug Discovery ◽  
Research And Development ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Chemical Reactions ◽  
Data Augmentation ◽  
Learning Method ◽  
Reaction Prediction

An effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery.

Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images

2021 ◽  
Vol 241 ◽  
pp. 114315
Author(s):  
D. Manno ◽  
G. Cipriani ◽  
G. Ciulla ◽  
V. Di Dio ◽  
S. Guarino ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Strategies ◽  
Photovoltaic Systems

Precise Agriculture: Effective Deep Learning Strategies to Detect Pest Insects

2022 ◽  
Vol 9 (2) ◽  
pp. 246-258
Author(s):  
Luca Butera ◽  
Alberto Ferrante ◽  
Mauro Jermini ◽  
Mauro Prevostini ◽  
Cesare Alippi
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Pest Insects

scholarly journals Deep Learning and Its Application to LHC Physics

2018 ◽  
Vol 68 (1) ◽  
pp. 161-181 ◽  
Author(s):  
Dan Guest ◽  
Kyle Cranmer ◽  
Daniel Whiteson
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Energy Physics ◽  
High Energy ◽  
Learning Tools ◽  
Future Prospects ◽  
Higher Dimensional ◽  
Lhc Physics ◽  
Core Concepts ◽  
Energy Physics

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

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