A new expert system for learning management systems evaluation based on neutrosophic sets

Abstract

There has been a sudden increase in the usage of Learning Management Systems applications to support learner's learning process in higher education. Many studies in learning management system evaluation are implemented under complete information, while the real environment has uncertainty aspects. As these systems were described by development organizations with uncertainty terms such as vague, imprecise, ambiguity and inconsistent, that is why traditional evaluation methods may not be effective. This paper suggests neutrosophic logic as a better option to simulate human thinking than fuzzy logic because unlike fuzzy logic, it is able to handle indeterminacy of information which expresses the percentage of unknown parameters. As previous studies suggested neutrosophic decision maker and neutrosophic expert systems as future work in ecommerce and e‐learning applications, this paper presents neutrosphic expert system for learning management systems evaluation. Information for building and validating the neutrosophic expert system is collected from five experts using surveys, and then analysis is done by using Fuzzytech 5.54d software. Finally, the comparison between fuzzy expert system and neutrosophic expert system results show that the neutrosophic logic is capable of representing uncertainty in human thinking for evaluating Learning Management Systems.

Quality Evaluation Fuzzy Method of Automated Control Systems on the LMS Example

“…Some researchers use recency, frequency, and monetary as linguistic variables [27]. Another research [28] concerns with other dimensions of system quality of LMS such as usability, reliability, accessibility, efficiency, error tolerance, learnability, memorability, user satisfaction, fault tolerance, maturity, recoverability, navigability, robustness, understandability.…”

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

“…Many scholars have studied certain measurements for SVNS; the measurements include similarities (Karaaslan, ; Ye & Fu, ; Ye & Zhang, ) and entropies (Aydoğdu, ; Biswas, Pramanik, & Giri, ). In addition, some studies have applied SVNS to multiple criteria decision making (M. Radwan, Senousy, & M. Riad, ; Sun, Hu, & Chen, ; Yang, Hu, Sun, & Chen, ; Ye, ).…”

“…Many scholars have studied certain measurements for SVNS; the measurements include similarities (Karaaslan, 2018;Ye & Fu, 2016;Ye & Zhang, 2014) and entropies (Aydoğdu, 2015;Biswas, Pramanik, & Giri, 2014). In addition, some studies have applied SVNS to multiple criteria decision making (M. Radwan, Senousy, & M. Riad, 2016;Yang, Hu, Sun, & Chen, 2018;Ye, 2017). FS, IFS, and SVNS have attracted many researchers because they are more simple and more effective than the rough set (Roy, Chatterjee, Bandyopadhyay, & Kar, 2018) and other extensions of FS, such as fuzzy soft set (Das, Ghosh, Kar, & Pal, 2017), neutrosophic soft set (Das, Kumar, Kar, & Pal, 2017), hesitant fuzzy soft set (Das, Malakar, Kar, & Pal, 2017), hesitant FS Sun, Hu, Zhou, & Chen, 2018), and picture hesitant FS ).…”

Analyzing of process capability indices based on neutrosophic sets

“…One of the fuzzy extension methods named NSs that are expressed with truth, indeterminacy and falsity membership functions which are all independent from each other and this give an ability to represent the uncertainty more effectively and clearer. Independency of truth-membership (TM) and indeterminacy-membership (IM) functions from each other and expressing the fact that an individual does not have full control of the issue with the IM function has an important place in modeling uncertainty problems (Radwan et al 2016 ). NSs provide an effective approach on PCA since they have more advantages compared to other fuzzy set extensions in terms of both ease of application and flexibility.…”