Designing indonesian teacher engagement index (itei) applications based on android

Manalu, Sonya Rapinta; Sasmoko,; Permai, Syarifah Diana; Widhoyoko, Samuel Anindyo; Indrianti, Yasinta

doi:10.1088/1742-6596/978/1/012005

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…The ITEI was developed since 2015 through a grant by the Indonesian Ministry of Research and Higher Education (Kementerian Riset dan Pendidikan Tinggi, Kemenristekdikti) partnered with the Directorate General of Teachers and Educational Manpower (Direktorat Jenderal Guru dan Tenaga Kependidikan), the Indonesian Ministry of Education and Culture (Kementerian Pendidikan dan Kebudayaan Republik Indonesia), and the Executive Board of Indonesian Teachers Association (Pengurus Besar Persatuan Guru Republik Indonesia, PB-PGRI). A few applications were developed to facilitate data gathering and engagement evaluation of Indonesian teachers, such as a mobile application [5] and a website, http://www.itei.me. The teacher engagement data gathered by ITEI researchers and PB-PGRI on teachers from various regions around Indonesia has reached 10,642 validated data and around 2,000 unvalidated data.…”

Section: Related Workmentioning

confidence: 99%

Building an Artificial Neural Network with Backpropagation Algorithm to Determine Teacher Engagement Based on the Indonesian Teacher Engagement Index and Presenting the Data in a Web-Based GIS

Buddhtha¹,

Natasha²,

Irwansyah³

et al. 2019

IJCIS

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Teacher engagement is a newly-emerged concept in the field of Indonesian teacher education. To support this concept, we designed an artificial neural network (ANN) using backpropagation, stochastic learning, and steepest gradient descent algorithms to determine teacher engagement based on the Indonesian Teacher Engagement Index (ITEI). The resulting ANN may be used in a data-gathering website for teachers to use for self-evaluation and self-intervention. The optimal architecture for the ANN has 44 input nodes, 26 first hidden layer nodes, 20 second hidden layer nodes, and 7 output nodes, with a learning rate of 0.05 and trained over 5000 iterations. The sample data used for training was gathered by ITEI researchers and the Executive Board of Indonesian Teachers Association (Pengurus Besar Persatuan Guru Republik Indonesia, PB-PGRI) and includes data of teachers from all around Indonesia. The maximum accuracy of this ANN was 97.98%. The sample data were then used to create an executive information system presented in the form of a map created using ArcGIS Pro software.

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Section: Related Workmentioning

confidence: 99%

Building an Artificial Neural Network with Backpropagation Algorithm to Determine Teacher Engagement Based on the Indonesian Teacher Engagement Index and Presenting the Data in a Web-Based GIS

Buddhtha¹,

Natasha²,

Irwansyah³

et al. 2019

IJCIS

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“…By leveraging the latest technological advances, ITEI has been developed in the form of android applications that can be accessed by all teachers in Indonesia wherever they are and whenever (Manalu et al 2018). This application was developed with the aim to become an application that is able to support government policy, especially in developing teacher competence in Indonesia so that positive impact for the progress of education in Indonesia .…”

Section: Introductionmentioning

confidence: 99%

Real Time System for Indonesian Teacher Engagement Index Application

Sasmoko¹,

Indriati²,

Kristin³

et al. 2020

Proceedings of the 1st Progress in Social Science, Humanities and Education Research Symposium (PSSHERS 2019)

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Technological developments in education must have a positive impact on improving teacher quality and competence in Indonesia so that teachers are able to run their profession optimally. When teachers are able to play an optimal role then the teacher will provide a significant added value for improving the quality of education in Indonesia. The Indonesian Teacher Engagement Index application was developed to capture the teacher's need for self-assessment. And to improve this application then ITEI Apps created one scheme using real time system. The research method used is exploratory research. The result of the research is real time system design which is an additional scheme as an effort to continue to maximize the value and function of this application for the teacher as the user.

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Designing Determining Teacher Engagement Based on The Indonesian Teacher Engagement Index Using Artificial Neural Network

Sasmoko

Moniaga

Indrianti

et al. 2019

2019 International Conference on Information and Communications Technology (ICOIACT)

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Designing indonesian teacher engagement index (itei) applications based on android

Cited by 3 publications

References 8 publications

Building an Artificial Neural Network with Backpropagation Algorithm to Determine Teacher Engagement Based on the Indonesian Teacher Engagement Index and Presenting the Data in a Web-Based GIS

Building an Artificial Neural Network with Backpropagation Algorithm to Determine Teacher Engagement Based on the Indonesian Teacher Engagement Index and Presenting the Data in a Web-Based GIS

Real Time System for Indonesian Teacher Engagement Index Application

Designing Determining Teacher Engagement Based on The Indonesian Teacher Engagement Index Using Artificial Neural Network

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