Integrating FAHP and TOPSIS to evaluate mobile learning applications for mathematics

Başaran, Seren; Haruna, Yunusa

doi:10.1016/j.procs.2017.11.214

Cited by 18 publications

(25 citation statements)

References 7 publications

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“…Literature method is a data collection technique by studying the reference of documents or files and the collection of data on legislation, research B. TOPSIS Method TOPSIS is one of the multi criteria or alternative choice decision making methods which is an alternative that has the smallest distance from the ideal positive solution and the largest distance from the ideal negative solution from a geometric point of view with using a distance of Euclidean [11] - [14]. However, alternatives that have the smallest distance from an ideal positive solution don't have to have the largest distance from the ideal negative solution.…”

Section: Literature Methodsmentioning

confidence: 99%

Decision Support System for Exemplary Teacher Selection in Elementary School using Topsis Method

Putri¹,

Ilayaraja²,

Shankar³

et al. 2019

IJEAT

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Teacher is one of the most important components owned by school in sustaining life. It is a challenge for the management of educational institutions to be able to provide an appropriate, effective and efficient decision in data management in the competitive business world. It spurs schools to strive harder in improving the quality of the school. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a method that has a concept where the best selected alternative not only has the shortest distance from a ideal positive solution. But also has the longest distance from a ideal negative solution. The criteria used in this system vary, according to the selection of the best teachers provided by the institution. The applicant's score data that has been entered into the system will be calculated using TOPSIS method, by finding the farthest and closest distance from the positive and ideal negative solutions. Applicants with the highest v score will occupy the top of this system. Based on the results of the sample case selection shows that the results of calculations using the same system with manual calculations. This system is able to provide the best teacher recommendations.

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Section: Literature Methodsmentioning

confidence: 99%

Decision Support System for Exemplary Teacher Selection in Elementary School using Topsis Method

Putri¹,

Ilayaraja²,

Shankar³

et al. 2019

IJEAT

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show abstract

“…Some researchers have integrated the Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) into a decision-making approach for ranking alternatives from multiple attributes. In the approach, fuzzy AHP and TOPSIS were utilized to calculate the weight of evaluation attributes and ultimately ranking respectively [21]. Rajak and Shaw suggested a Fuzzy Multiple Attributes Decision Making (FMADM) methodologies, consisting of AHP and fuzzy TOPSIS, for accessing and selecting M-medical APPs.…”

Section: Related Literaturementioning

confidence: 99%

“…Before evaluating the alternatives, it is necessary to conduct a scientific and effective assessment indicator system. In the application of FMAGDM, the assessment criteria are usually determined by literature analysis and expert group discussion [7,21,40].…”

Section: The Assessment Indicator Systemmentioning

confidence: 99%

“…We discussed with 100 users of M-medical APPs to construct an applicable evaluation system. According to common indicators of published studies on APP evaluation [7,20,21,41], and users' special needs for mobile medical services, we finally determined the assessment indicator system. The system consists of 8 evaluation indicators, where response time and price are cost attributes.…”

Section: The Assessment Indicator Systemmentioning

confidence: 99%

“…To verify the effectiveness and superiority of proposed method, the ranking of the alternatives calculated by UML-TOPSIS, UML-TOPSIS-EW and UMLDM-EW are compared with that of UMLDM (see Figure 6a). Similar to UMLDM, TOPSIS [7,21] is utilized for individual alternatives ranking, while other processes remain unchanged, which is named ULM-TOPSIS, see Figure 6b. Both ULM-TOPSIS-EW (see Figure 6c) and UMLDM-EW (see Figure 6d) are group decision-making algorithms, in which the corresponding attribute weight of each decision-maker is determined through the entropy weight method, according to ULM-TOPSIS and UMLDM, respectively.…”

Section: Comparison Analysismentioning

confidence: 99%

See 2 more Smart Citations

Uncertain Multiplicative Language Decision Method Based on Group Compromise Framework for Evaluation of Mobile Medical APPs in China

Zhang

Ding

2020

IJERPH

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The mobile medical application (M-medical APP) can optimize medical service process and reduce health management costs for users, which has become an important complementary form of traditional medical services. To assist users including patients choose the ideal M-medical APP, we proposed a novel multiple attribute group decision making algorithm based on group compromise framework, which need not determine the weight of decision-maker. The algorithm utilized an uncertain multiplicative linguistic variable to measure the individual original preference to express the real evaluation information as much as possible. The attribute weight was calculated by maximizing the differences among alternatives. It determined the individual alternatives ranking according to the net flow of each alternative. By solved the 0–1 optimal model with the objective of minimizing the differences between individual ranking, the ultimate group compromise ranking was obtained. Then we took 10 well-known M-medical APPs in Chinese as an example, we summarized service categories provided for users and constructed the assessment system consisting of 8 indexes considering the service quality users are concerned with. Finally, the effectiveness and superiority of the proposed method and the consistency of ranking results were verified, through comparing the group ranking results of 3 similar algorithms. The experiments show that group compromise ranking is sensitive to attribute weight.

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A New Direction in TOPSIS Method for Evaluation of Alternates: R-TOPSIS

Kumar

Singh

2021

Lecture Notes in Civil Engineering

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Integrating FAHP and TOPSIS to evaluate mobile learning applications for mathematics

Cited by 18 publications

References 7 publications

Decision Support System for Exemplary Teacher Selection in Elementary School using Topsis Method

Decision Support System for Exemplary Teacher Selection in Elementary School using Topsis Method

Uncertain Multiplicative Language Decision Method Based on Group Compromise Framework for Evaluation of Mobile Medical APPs in China

A New Direction in TOPSIS Method for Evaluation of Alternates: R-TOPSIS

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