A simple and effective remedial learning system with a fuzzy expert system

Abstract

This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate the success of the proposed system where 88 students who studied a computer-related program at a vocational high school in Taiwan participated in the experiment. The participating students came from three different types of classes. The fuzzy expert system determined the numbers of the remedial material units according to the scores of the pre-test. Based on the results of the fuzzy expert system, each student then received personalized remedial learning materials by randomly selecting problem-based learning units from a learning material repository. After reading the remedial learning materials, the students took the post-test. The experimental results reveal that the students made significant progresses after studying the remedial learning materials. Both of high-achieving students and low-achieving students made significant progresses. Moreover, all of the three types of students made significant progresses.

The design of expert system to determine the university majoring based on multiple intelligence using forward chaining method

“…The author uses the theory of multiple intelligences as a guide in identifying students' intelligence abilities. These types of multiple intelligence will be used as indicators and will be entered into the system as a knowledge base (Lin et al, 2016).…”

Modeling of Online Learning Strategies Based on Fuzzy Expert Systems and Self-Directed Learning Readiness: The Effect on Learning Outcomes

“…Determine the number of learning unit to be studied in an online learning system based on fuzzy expert systems (Lin et al, 2016):…”

“…Romero et al (2009), proposed a web-based adaptive learning system for students who share the same learning style characteristics; a personalization system of e-learning to provide a learning path based on the difficulty of learning material (Chen, 2011). Some other studies that use the principle of fuzzy logic were student learning performance assessment (Badie et al, 2014; Subbotin & Voskoglou, 2014; Pandey et al, 2015; Yadav & Singh, 2011), concept maps (Hwang, 2003; Jong et al, 2004; Panjaburees et al, 2013), self-regulated learning (Van Seters et al, 2012), learning preference assessment (Lasfeto et al, 2018), assessment of learning motivation (Srivastava et al, 2010), remedial learning systems (Srivastava et al, 2010; Hsieh et al, 2013; Lin et al, 2016), and collaborative learning (Hadjileontiadou et al, 2004). The present study examines online learning strategies based on fuzzy expert systems that use fuzzy logic theory for an efficient learning process based on deficiencies or the ability to understand concepts and then to recommend learning materials that are most suitable for facilitating students more efficient learning.…”

Fuzzy Soft Skills Assessment through Active Learning Sessions

“…Fuzzy Logic has been referred to as an inference mechanism to make decisions that involve a degree of uncertainty and imprecision (Saido et al, 2018 ; Gomathi and Rajamani, 2017 ; Ozdemir and Tekin, 2016 ; Dias and Diniz, 2013 ). A Expert System with Fuzzy Logic can be applied to improve and classify student behaviors (Lin et al, 2016 ). In the area of education, we have the following examples: Assessment of students’ difficulties to suggest strategies for learning (Gomathi and Rajamani, 2017 ; Lin et al, 2016 ; Novak and Oreski, 2016 ), assessment of cognitive skills (Saido et al, 2018 ; Chrysafiadi and Virvou, 2015 ), assessment of learning styles (Aissaoui et al, 2019 ; Alian and Shaout, 2017 ; Ozdemir et al, 2016 ), assessment of the Soft Skills communication (Ozdemir and Tekin, 2016 ) and creativity (Susnea and Vasiliu, 2016 ).…”