Simple Sequencing and Selection of Learning Objects using Fuzzy Inference

2007 ◽  
Author(s):  
M. Garcia-Valdez ◽  
O. Castillo ◽  
G. Licea ◽  
A. Alanis
Keyword(s):  
Fuzzy Inference ◽  
Learning Objects ◽  
Selection Of Learning Objects ◽  
Selection Of

scholarly journals Effective Use of Learning Objects in Class Environments

2011 ◽  
pp. 493-514
Author(s):  
David Lake ◽  
Kate Lowe ◽  
Rob Phillips ◽  
Rick Cummings ◽  
Renato Schibeci
Keyword(s):  
School Environment ◽  
Teacher Selection ◽  
Learning Objects ◽  
Educational Environment ◽  
Construction Of Knowledge ◽  
Teachers And Students ◽  
The Individual ◽  
Effective Use ◽  
Selection Of Learning Objects ◽  
Selection Of

This chapter provides a model to analyse the effectiveness and efficiency of Learning Objects being used in primary and secondary schools by considering their place within that educational environment, paying particular attention to the manner in which they, like any resource, can aid or occlude productive interactions between teachers and students. It draws from a study of Australian and New Zealand schools that piloted the first release of Learning Objects from the Le@rning Federation. The chapter considers the place of Learning Objects within the overall systemic school environment, and in this environment, examines the individual classroom as the combination of tensions between the teacher’s needs, the students’ needs, and the potential available within the existing infrastructure. Within this framework, the chapter discusses the ways in which these three components interact during teacher selection of Learning Objects, students’ accession of Learning Objects in the classroom, and the use of the Learning Objects by students. It concludes by suggesting how students’ construction of knowledge can be enhanced through merging the capabilities of the resource with the needs of students and teachers.

Adaptive cognitive‐based selection of learning objects

2006 ◽  
Vol 43 (2) ◽  
pp. 121-135 ◽  
Author(s):  
Pythagoras Karampiperis ◽  
Taiyu Lin ◽  
Demetrios G. Sampson ◽  
Kinshuk
Keyword(s):  
Learning Objects ◽  
Selection Of Learning Objects ◽  
Selection Of

IMPLEMENTASI PENENTUAN ARSITEKTUR MASJID MODERN DI ACEH MENGGUNAKAN PEMODELAN SISTEM CERDAS

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Soraya Masthura Hasan ◽  
T Iqbal Faridiansyah
Keyword(s):  
Fuzzy Inference System ◽  
Architectural Design ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Selection Process ◽  
System Modeling ◽  
Inference System ◽  
Normal Limits ◽  
Roof Structure ◽  
Selection Of

Mosque architectural design is based on Islamic culture as an approach to objects and products from the Islamic community by looking at their suitability and values and basic principles of Islam that explore more creative and innovative ideas. The purpose of this system is to help the team and the community in seeing the best mosque in the top order so that the system can be used as a reference for the team and the community. The variables used in the selection of modern mosques include facilities and infrastructure, building structure, roof structure, mosque area, level of security and facilities. The system model used is a fuzzy promethee model that is used for the modern mosque selection process. Fuzzy inference assessment is used to determine the value of each variable so that the value remains at normal limits. Fuzzy values will then be included in promethee assessment aspects. The highest promethee ranking results will be made a priority for the best mosque ranking. This fuzzy inference system and promethee system can help the management team and the community in determining the selection of modern mosques in aceh in accordance with modern mosque architecture. Intelligent System Modeling System In Determining Modern Mosque Architecture in the City of Aceh, this building will be web based so that all elements of society can see the best mosque in Aceh by being assessed by all elements of modern mosque architecture.Keywords: Fuzzy inference system, Promethe, Option of  Masjid

scholarly journals PEMILIHAN INPUT MODEL ADAPTIVE FUZZY INFERENCE SYSTEM (ANFIS) BERBASIS LAGRANGE MULTIPLIER TEST DILENGKAPI GUI MATLAB (Aplikasi pada Data Harga Beras Kualitas Rendah di Indonesia Periode Januari 2013 – Februari 2019)

2019 ◽  
Vol 8 (4) ◽  
pp. 451-461
Author(s):  
Khusnul Umi Fatimah ◽  
Tarno Tarno ◽  
Abdul Hoyyi
Keyword(s):  
Lagrange Multiplier ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Lagrange Multiplier Test ◽  
Inference System ◽  
Input Selection ◽  
Lm Test ◽  
Rice Prices ◽  
Selection Of

Adaptive Neuro Fuzzy Inference System (ANFIS) is a method that uses artificial neural networks to implement fuzzy inference systems. The optimum ANFIS model is influenced by the selection of inputs, number of membership and rules. In general, the selection of ANFIS input is based on Autoregressive (AR) unit as a result of ARIMA preprocessing. Thus it requires several assumptions. In this research, an alternative selection of ANFIS input based on Lagrange Multiplier Test (LM Test) is used to test hypothesis for the addition of one input. Preprocessing is conducted to obtain the value of partial autocorrelation against Zt. The input lag variable which has the highest partial autocorrelation is the first input ANFIS. The next input selection is selected based on LM test for adding one variable. To test the performance of LM Test, an empirical study of two groups of generated data and low quality rice prices is conducted as a case study. Generating data with stationary and non-stationary criteria has a good performance because it has very good forecasting ability with MAPE out sample for each characteristic are 5.6785% and 9.4547%. In the case study using LM Test, the selected input are and  with the number of membership 2. The chosen model has very good forecasting ability with MAPE outsampel 6.4018%. Keywords : ANFIS, ANFIS Input, LM-Test, Low Quality Rice Prices, Forecasting

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