Development of Deeper Learning Cycle-Project Based Learning Model Based on Resource Sharing in Artificial Neural Network Courses
Zulham Sitorus1, Ganefri, Refdinal2
1Zulham Sitorus, Engineering Faculty of Postgraduate Program Technology and Technical Education of Doctoral Program State University of Padang.
2Ganefri, Engineering Faculty of Postgraduate Program Technology and Technical Education of Doctoral Program State University of Padang. Refdinal, Engineering Faculty of Postgraduate Program Technology and Technical Education of Doctoral Program State University of Padang.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1698-1702 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6244018520/2020©BEIESP | DOI: 10.35940/ijrte.E6244.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The aim of this study was to determine the development of Deeper Sharing Learning Cycle-PjBL model based on Resource Sharing in the application of information literacy to prepare the learning process and find out how students’ learning abilities. Deeper Learning Cycle-PjBL Model based on Resource Sharing consists of Resource Sharing applications, model books, teaching module books, learning tools, the use of lecturer applications and the use of valid, practical and effective student applications. The method used in this research is the Borg and Gall research and development model with 10 stages which are then simplified into 5 stages of development. The validity of the model is analyzed using Aiken’V while the practicality of the practicality of the model is measured by the user. The effectiveness of the model is measured by using the model and measuring learning outcomes. Respondents were used in the experimental class and control class with 33 students and were analyzed by t-test. Research in the development of the model through scientific studies, and analysis is done by testing the Confirmatory Factor Analysis using the Lisrel application. The results of the construct test on the syntax after testing meet the criteria for goodness-of-fit-models with the value P.Value = 0.54750, while for the RSMEA value = 0.000, thus for the value of x2 / df ≤ 2, thus the Deeper Learning Cycle-PjBL Model is based Resource Sharing is declared valid.
Keywords: DELC, PjBL, Resource Sharing, Artificial Neural Network.
Scope of the Article: Cloud Resources Utilization in IoT.