scholarly journals AN ACTIVE LEARNING APPROACH FOR A DESIGN THINKING COURSE

2020 ◽  
Author(s):  
Francesca Mattioli ◽  
Lucia Rampino
Keyword(s):  
Active Learning ◽  
Design Thinking ◽  
Learning Approach

Peningkatan Hasil Belajar Fisika Melalui Pendekatan Numbered Head Together Dengan Pemberian Reward di SMP Negeri 5 Kota Jambi

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Delismar Delismar
Keyword(s):  
Action Research ◽  
Active Learning ◽  
Learning Outcomes ◽  
Learning Process ◽  
Learning Outcome ◽  
Class Action ◽  
Learning Approach ◽  
Physics Learning ◽  
Lecture Method ◽  
Student Observation

In classical learning approach, conventional lecture method is commonly used by teachers in implementing learning process in classes.  The teacher becomes the main source of learning.  The current student’s habit that tends to be passive and individualistic resulted in a passive and monotone learning.      To overcome these problems, I was interested to implement the model of numbered heads together in learning Physics in the Class VII B of SMP Negeri 5 Kota Jambi. The purpose of this learning approach is to enable students to develop cooperative skill and more active learning of physics and to improve learning results. This research is a class action research, which were performed in two cycles.  All students’ activities in the class were observed and recorded in observation sheet, consisting of teacher observation sheet and student observation sheet. To find out the learning outcomes, formative test was performed using a written instrument form.  The results show the increase of students’ discipline, cooperation, liveliness, timeliness in learning Physics.  In addition, the learning model also increases the students’ learning outcomes. The average learning results increased to 75.38 (increase 3.25 points).  To conclude, the implementation of Number Head Together increase students’ discipline, cooperation, activities, and timeliness.  The model also increase the Physics learning outcome of student in SMP Negeri 5 Kota  Jambi.

An Active Learning Approach to Teaching Social Determinants of Health

2020 ◽  
Vol 6 (4) ◽  
pp. 266-273
Author(s):  
Jeanita W. Richardson
Keyword(s):  
Active Learning ◽  
Learning Style ◽  
Social Determinants ◽  
Learning Approach ◽  
Learning Style Preferences ◽  
Open Discussion ◽  
Approach To Teaching ◽  
The Individual ◽  
Style Preferences ◽  
The Impact

This active learning exercise is designed to deconstruct the impact of social determinants through the assumption of randomly selected personas. As an active learning exercise, it provides opportunities for discussion, problem solving, writing, and synthesis, while incorporating multiple learning style preferences. Part 1 involves assessing the individual social determinants at work. Part 2 involves exploring ways said determinants can enhance community health through collaboration. Assumption of personas unlike one’s own facilitates an open discussion of social position and ranges of factors influential to health without potentially evoking a sense of defensiveness associated with personal privilege (or the lack thereof).

A Temporal Dependency Based Multi-modal Active Learning Approach for Audiovisual Event Detection

2017 ◽  
Vol 48 (2) ◽  
pp. 709-732 ◽  
Author(s):  
Patrick Thiam ◽  
Sascha Meudt ◽  
Günther Palm ◽  
Friedhelm Schwenker
Keyword(s):  
Active Learning ◽  
Event Detection ◽  
Learning Approach

scholarly journals An active learning approach for clustering single-cell RNA-seq data

2021 ◽  
Author(s):  
Xiang Lin ◽  
Haoran Liu ◽  
Zhi Wei ◽  
Senjuti Basu Roy ◽  
Nan Gao
Keyword(s):  
Active Learning ◽  
Single Cell ◽  
Learning Approach ◽  
Rna Seq

An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization

2021 ◽  
Vol 143 (8) ◽  
Author(s):  
Opeoluwa Owoyele ◽  
Pinaki Pal ◽  
Alvaro Vidal Torreira
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Design Optimization ◽  
Fuel Injection ◽  
A Priori ◽  
Computational Cost ◽  
Learning Approach ◽  
Data Generation ◽  
Compression Ignition Engine ◽  
Hyperparameter Selection

AbstractThe use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

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