scholarly journals The effect experiential learning model based concrete-pictorial-abstract (EL-CPA) on mathematics attitude of deaf students

2020 ◽  
Vol 1657 ◽  
pp. 012070
Author(s):  
L O Amril ◽  
Darhim ◽  
D Juandi
2014 ◽  
Vol 7 (1) ◽  
pp. 81-101 ◽  
Author(s):  
Kyungmin Park ◽  
◽  
Doobong Kang ◽  
Seonjeong Kim ◽  
Suehyun Park ◽  
...  

Vidya Karya ◽  
2021 ◽  
Vol 36 (1) ◽  
pp. 25
Author(s):  
Yudi Supiyanto ◽  
Heny Sulistyaningrum ◽  
Henny Sri Astuty

One learning model that prioritizes direct experience is experiential learning. Experiential learning orientates learning to direct experience. This follows the objectives of microteaching courses. The purposes of the study were:  1. Developing a microteaching learning model based on experiential learning through models and groups to improve qualified teaching skills/practical and effective, 2. Developing supporting the administration of microteaching learning model based on experiential learning through the role of models and groups to improve qualified teaching skills /practical, and effective.  This research used a developmental study using Plomp model, which consisted of five stages. Furthermore, to assess the quality of microteaching learning models based on experiential learning, Nieveen criteria was used to fulfil the practicality and effectiveness. The study results were based on the practical aspects of management, lecturer and student activities on learning using microteaching learning models based on experiential learning through the role of models and groups with an overall average of 3.5 with very good indicators. It had very practical implementation. The result of the effectiveness data analysis from student learning outcomes, lecturer response questionnaire, and student response questionnaire to microteaching learning, had good average learning outcomes. The effectiveness data analysis from student learning outcomes, lecturer response questionnaire, and student response questionnaire to microteaching learning model based on experiential learning through the role of models and groups had good average learning outcomes.Keywords: Experiential Learning; Micro Teaching; Teaching Skills


Author(s):  
Mohammad Fahmi Nugraha

The environmental problems at this time, especially the diversity of bat cave dwellers in the karst of Cibalong, Tasikmalaya should be given the special attention by all of the society elements, especially by the educators who must act real and solve the problems to give the view of knowledge to the community and the students in understanding the importance of bats which is considered as a pest and it is associated with mystical things. One of the effort is looking for and implementing  some of learning model based on the local wisdom to change and establish the scientific thinking of the sociaety and the students to analyze the presence of bat in term of the survival of the ecosystem. It is expected that bats and their habitats in Karst of Cibalong, Tasikmalaya can be preserved.


2021 ◽  
Author(s):  
Junjie Shi ◽  
Jiang Bian ◽  
Jakob Richter ◽  
Kuan-Hsun Chen ◽  
Jörg Rahnenführer ◽  
...  

AbstractThe predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is often indispensable. Normally such tuning requires the dedicated machine learning model to be trained and evaluated on centralized data to obtain a performance estimate. However, in a distributed machine learning scenario, it is not always possible to collect all the data from all nodes due to privacy concerns or storage limitations. Moreover, if data has to be transferred through low bandwidth connections it reduces the time available for tuning. Model-Based Optimization (MBO) is one state-of-the-art method for tuning hyper-parameters but the application on distributed machine learning models or federated learning lacks research. This work proposes a framework $$\textit{MODES}$$ MODES that allows to deploy MBO on resource-constrained distributed embedded systems. Each node trains an individual model based on its local data. The goal is to optimize the combined prediction accuracy. The presented framework offers two optimization modes: (1) $$\textit{MODES}$$ MODES -B considers the whole ensemble as a single black box and optimizes the hyper-parameters of each individual model jointly, and (2) $$\textit{MODES}$$ MODES -I considers all models as clones of the same black box which allows it to efficiently parallelize the optimization in a distributed setting. We evaluate $$\textit{MODES}$$ MODES by conducting experiments on the optimization for the hyper-parameters of a random forest and a multi-layer perceptron. The experimental results demonstrate that, with an improvement in terms of mean accuracy ($$\textit{MODES}$$ MODES -B), run-time efficiency ($$\textit{MODES}$$ MODES -I), and statistical stability for both modes, $$\textit{MODES}$$ MODES outperforms the baseline, i.e., carry out tuning with MBO on each node individually with its local sub-data set.


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