Multi-attribute decision-making methods based on Aczel-Alsina power aggregation operators for English blended teaching quality evaluation under single-valued neutrosophic sets
Restricted accessResearch articleFirst published online August, 2024
Multi-attribute decision-making methods based on Aczel-Alsina power aggregation operators for English blended teaching quality evaluation under single-valued neutrosophic sets
As a new form of teaching, blended teaching can integrate the advantages of classroom teaching and online teaching and improve the overall quality and effectiveness of English teaching under the premise of changing the orientation of teachers and students. The English blended teaching quality evaluation is a multiple attribute decision making (MADM) issue. Aczel-Alsina t-norm and t-conorm operations have the true advantage of changeability by adjusting a decision parameter. power averaging (PA) operator allowed decision attributes to strengthen each other through weight information during fusion, thereby eliminating the influence of subjective weights on the decision results. Motivate through classical Aczel-Alsina operators, PA operator and the single-valued neutrosophic sets (SVNSs), in this paper, the single-valued neutrosophic number Aczel-Alsina power averaging (SVNNAAPA) operator and the single-valued neutrosophic number Aczel-Alsina power weighted averaging (SVNNAAPWA) operator are constructed under SVNSs. The SVNNAAPWA operator is built for MADM. Eventually, an example about English blended teaching quality evaluation and some selected comparative decision analysis is used to depict the SVNNAAPWA method.
ThorneK. Blended learning: How to integrate online & traditional learning. London: Kogan Page Publishers; 2003.
2.
LiuXH, editor. The construction of college english teaching model based on blended learning. 1st International Workshop on Education Technology and Computer Science; 2009 Mar 07–08; Wuhan, PEOPLES R CHINA. LOS ALAMITOS: Ieee Computer Soc; 2009.
3.
MiaoYF. Online and offline mixed intelligent teaching assistant mode of english based on mobile information system. Mobile Information Systems.2021; 2021: 6.
4.
ShaoL. Evaluation method of it english blended teaching quality based on the data mining algorithm. Journal of Mathematics.2021; 2021: 8.
5.
WangCY. Employing blended learning to enhance learners’ english conversation: A preliminary study of teaching with hitutor. Education and Information Technologies.2021; 26(2): 2407-25.
6.
ChengJ. Research on blended teaching strategies of college english translation based on computer corpus. Wireless Communications & Mobile Computing.2022; 2022: 11.
7.
WenZLiL, Ieee, editors. A study on the application of spoc-based blended oral english teaching in higher vocational colleges. 10th International Conference on Educational and Information Technology (ICEIT); 2021 Jan 18–20; Electr Network. NEW YORK: Ieee; 2021.
8.
GaoXY. Evaluation and application of college english mixed flipping classroom teaching quality based on the fuzzy judgment model. Security and Communication Networks.2022; 2022: 9.
9.
GuJH. Blended oral english teaching based on core competence training model. Mobile Information Systems.2022; 2022: 9.
10.
HuKL. Psychological adaptability and intervention strategies of college students’ english learning under the mixed foreign language teaching environment monitoring. Journal of Environmental and Public Health.2022; 2022: 11.
11.
HuangHWangJM. Innovative research on collaborative design of blended english teaching in higher vocational colleges based on digital technology. Scientific Programming.2022; 2022: 7.
12.
NingJBanHD. Blended teaching strategies of college english translation under the background of internet. Mobile Information Systems.2022; 2022: 7.
13.
PanAQ. Construction and application of college english blended teaching system based on multidata fusion. Discrete Dynamics in Nature and Society.2022; 2022: 7.
14.
QiuNSQiuXQ. A study on the application model of blended teaching in english language teaching in colleges and universities under the ecological and internet perspectives. Journal of Environmental and Public Health.2022; 2022: 10.
15.
YanWJ. Effect of blended teaching mode in colleges and universities based on automation technology on college students’ english performance. Mobile Information Systems.2022; 2022: 10.
16.
YuLShenJ. Analysis of the correlation between academic performance and learning motivation in english course under a corpus-data-driven blended teaching model. Scientific Programming.2022; 2022: 11.
17.
ZhangYB, Ieee, editors. Application of blended teaching into the course of comprehensive english. 11th International Conference on Educational and Information Technology (ICEIT); 2022 Jan 06–08; Sichuan Normal Univ, Chengdu, PEOPLES R CHINA. NEW YORK: Ieee; 2022.
18.
MunierN. A new approach to the rank reversal phenomenon in mcdm with the simus method. Multiple Criteria Decision Making.2016; 11: 137-52.
19.
StoilovaS. An integrated multi-criteria approach for planning railway passenger transport in the case of uncertainty. Symmetry [Internet]. 2020; 12(6).
20.
StoilovaSMunierNKendraMSkrúcanýT. Multi-criteria evaluation of railway network performance in countries of the ten-t orient-east med corridor. Sustainability.2020; 12(4): 1482.
21.
StoilovaSMunierN. A novel fuzzy simus multicriteria decision-making method. An application in railway passenger transport planning. Symmetry [Internet]. 2021; 13(3): 483.
22.
WiȩckowskiJKizielewiczBShekhovtsovASałabunW. Rancom: A novel approach to identifying criteria relevance based on inaccuracy expert judgments. Engineering Applications of Artificial Intelligence.2023; 122: 106114.
23.
LinkovIVargheseAJamilSSeagerTPKikerGBridgesT, editors. Multi-criteria decision analysis: A framework for structuring remedial decisions at contaminated sites. NATO Advanced Research Workshop on Comparative Risk Assessment and Environmental Decision Making; 2002 Oct 13–16; Rome, ITALY. DORDRECHT: Springer; 2004.
24.
PusicDLusicZ. Multi-criteria decision analysis for nautical anchorage selection. Journal of Marine Science and Engineering.2023; 11(4): 21.
25.
SahaAVilluriVGKBhardwajAKumarS. A multi-criteria decision analysis (mcda) approach for landslide susceptibility mapping of a part of darjeeling district in north-east himalaya, india. Applied Sciences-Basel.2023; 13(8): 23.
26.
WieckowskiJWatrobskiJKizielewiczBSalabunW. Complex sensitivity analysis in multi-criteria decision analysis: An application to the selection of an electric car. Journal of Cleaner Production.2023; 390: 15.
27.
BhuyanMJDekaNSaikiaA. Micro-spatial flood risk assessment in nagaon district, assam (india) using gis-based multi-criteria decision analysis (mcda) and analytical hierarchy process (ahp). Risk Analysis. 2023; 16.
28.
IwehCDSemassouGCAhouansouRH. Optimum site selection of hybrid solar photovoltaic (pv)-hydro power plants in off grid locations in cameroon using the multi-criteria decision analysis (mcda). Energy Sources Part a-Recovery Utilization and Environmental Effects.2023; 45(3): 8076-91.
29.
ZadehLA. Fuzzy sets. Information and Control.1965; 8(3): 338-53.
30.
AtanassovKT. Intuitionistic fuzzy sets. Fuzzy Sets and Systems.1986; 20(1): 87-96.
31.
SmarandacheF. Neutrosophy: Neutrosophic probability, set, and logic: Analytic synthesis & synthetic analysis: American Research Press; 1998.
32.
MukherjeeADasR. Neutrosophic bipolar vague soft set and its application to decision making problems. Neutrosophic Sets and Systems.2020; 32: 410-24.
33.
MajumderPDasSDasRTripathyBC. Identification of the most significant risk factor of covid-19 in economy using cosine similarity measure under svpns-environment. Neutrosophic Sets and Systems.2021; 46: 112-27.
34.
DasSDasRPramanikS. Single valued bipolar pentapartitioned neutrosophic set and its application in madm strategy. Neutrosophic Sets and Systems.2022; 49: 145-63.
35.
DasRMukherjeeATripathyBC. Application of neutrosophic similarity measures in covid-19. Annals of Data Science.2022; 9(1): 55-70.
36.
DasSDasRTripathyBC. Topology on rough pentapartitioned neutrosophic set. Iraqi Journal of Science.2022; 63(6): 2630-40.
37.
PramanikS. Single-valued neutrosophic set: An overview. In: Rezaei N, editor. Transdisciplinarity. Cham: Springer International Publishing; 2022. pp. 563-608.
38.
BiswasPPramanikSGiriBC. Entropy based grey relational analysis method for multi-attribute decision making under single valued neutrosophic assessments. Neutrosophic Sets and Systems.2014; 2: 102-10.
39.
AczélJAlsinaC. Characterizations of some classes of quasilinear functions with applications to triangular norms and to synthesizing judgements. Aequationes Mathematicae.1982; 25(1): 313-15.
40.
AshrafSAhmadSNaeemMRiazMAlamMAStevicZ. Novel edas methodology based on single-valued neutrosophic aczel-alsina aggregation information and their application in complex decision-making. Complexity.2022; 2022: 1-18.
41.
YagerRR. The power average operator. IEEE Transactions on Systems, Man, and Cybernetics-Part A.2001; 31(6): 724-31.
42.
WangHSmarandacheFZhangYQSunderramanR. Single valued neutrosophic sets. Multispace Multistruct.2010; (4): 410-13.
43.
PengJJWangJQWangJZhangHYChenXH. Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems. International Journal of Systems Science.2016; 47(10): 2342-58.
44.
YongRYeJDuSGZhuAQZhangYY. Aczel-alsina weighted aggregation operators of simplified neutrosophic numbers and its application in multiple attribute decision making. Cmes-Computer Modeling in Engineering & Sciences.2022; 132(2): 569-84.
45.
GuoQLiuJFGuoL. Research on online and offline blended teaching of higher vocational english under the background of “internet plus”. Basic & Clinical Pharmacology & Toxicology.2020; 127: 73-73.
46.
WuF, Ieee, editors. Discussion on mixed teaching mode of college english under the background of internet. 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE); 2020 Dec 25–27; Harbin, PEOPLES R CHINA. NEW YORK: Ieee; 2020.
47.
YaoY. Research on “mixed” english teaching model under mooc platform. Basic & Clinical Pharmacology & Toxicology.2020; 127: 204-04.
48.
HuangH. Research on improving the teaching effectiveness of crew english course in higher vocational education based on blended teaching. Basic & Clinical Pharmacology & Toxicology.2021; 128: 192-92.
49.
LiPZhangHTsaiSB. A new online and offline blended teaching system of college english based on computer internet technology. Mathematical Problems in Engineering.2021; 2021: 12.
50.
LiXLQinSXLuoFC. Exploration on the construction of online plus offline blended mode of college english teaching. Basic & Clinical Pharmacology & Toxicology.2021; 128: 97-97.
51.
WangWP. Blended learning-based english teaching mode among new english majors. Agro Food Industry Hi-Tech.2017; 28(1): 516-19.
52.
XuJZhangHLManXP, editors. A brief study of the application of blended learning in higher vocational college english teaching. International Conference on Advanced Education, Psychology and Sports Science (AEPSS); 2017 Jun 26–28; Guangzhou, PEOPLES R CHINA. LONDON: Francis Acad Press; 2017.
53.
ZhouCY. Empirical study on the effectiveness of teaching model of college english writing within blended learning mode. Educational Sciences-Theory & Practice.2018; 18(5): 1060-76.
54.
LiGC, editor. A study on the blended-teaching mode combining moocs and flipping classroom in college english teaching. 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM); 2017 Apr 28–30; Shenyang, PEOPLES R CHINA. PARIS: Atlantis Press; 2017.
55.
WangS, editor. The composition of blended learning model and its application in english classroom teaching. 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI); 2017 Nov 03–05; Shenyang, PEOPLES R CHINA. PARIS: Atlantis Press; 2017.
56.
HuiYL. Evaluation of blended oral english teaching based on the mixed model of spoc and deep learning. Scientific Programming.2021; 2021: 9.
57.
QiuXY. Blended teaching mode of higher vocational english based on mooc plus spoc. Wireless Communications & Mobile Computing.2022; 2022: 9.
58.
GargH, Algorithms for possibility linguistic single-valued neutrosophic decision-making based on copras and aggregation operators with new information measures. Measurement.2019; 138: 278-90.
59.
Ul HaqRSSaeedMMateenNSiddiquiFNaqviMYiJB, et al. Sustainable material selection with crisp and ambiguous data using single-valued neutrosophic-merec-marcos framework. Applied Soft Computing.2022; 128: 109546.
HezamIMMishraARRaniPSahaASmarandacheFPamucarD. An integrated decision support framework using single-valued neutrosophic-maswip-copras for sustainability assessment of bioenergy production technologies. Expert Systems with Applications.2023; 211: 118674.
62.
SalabunWUrbaniakK, editors. A new coefficient of rankings similarity in decision-making problems. 20th Annual International Conference on Computational Science (ICCS); 2020 Jun 03–05; Amsterdam, NETHERLANDS. CHAM: Springer International Publishing Ag; 2020.
63.
SałabunWWa̧tróbskiJShekhovtsovA. Are mcda methods benchmarkable? A comparative study of topsis, vikor, copras, and promethee ii methods. Symmetry.2020; 12(9): 1549.
64.
GouXXiaoPHuangDDengF. Probabilistic double hierarchy linguistic alternative queuing method for real economy development evaluation under the perspective of economic financialization. Economic Research-Ekonomska Istrazivanja.2021; 34(1): 3225-44.
65.
GouXXuZLiaoHHerreraF. Probabilistic double hierarchy linguistic term set and its use in designing an improved vikor method: The application in smart healthcare. Journal of the Operational Research Society.2021; 72(12): 2611-30.
66.
LinMWXuZSZhaiYLYaoZQ. Multi-attribute group decision-making under probabilistic uncertain linguistic environment. Journal of the Operational Research Society.2018; 69(2): 157-70.
67.
XieWYRenZLXuZSWangH. The consensus of probabilistic uncertain linguistic preference relations and the application on the virtual reality industry. Knowledge-Based Systems.2018; 162: 14-28.
68.
BashirZAliJRashidT. Consensus-based robust decision making methods under a novel study of probabilistic uncertain linguistic information and their application in forex investment. Artificial Intelligence Review.2021; 54(3): 2091-132.
69.
JanaCMuhiuddinGPalM. Some dombi aggregation of q-rung orthopair fuzzy numbers in multiple-attribute decision making. International Journal of Intelligent Systems.2019; 34(12): 3220-40.
70.
KrishankumarRRavichandranKSKarSCavallaroFZavadskasEKMardaniA. Scientific decision framework for evaluation of renewable energy sources under q-rung orthopair fuzzy set with partially known weight information. Sustainability.2019; 11(15): 21.
