Abstract
The popularization of online education, particularly the impact of the COVID-19 pandemic in the past few years, has promoted the development of online learning platforms. Consequently, the importance of optimal evaluation methods for online learning platforms has been emphasized. However, the uncertain decision-making process and complex indicators in the development of online learning platforms bring challenges to the evaluation, making it more difficult to reach a consensus. To address these issues, an online learning platform quality evaluation index system was first constructed in this paper, including system functionality, instructional Resources, social interaction, and teaching effectiveness. Secondly, regarding the fuzzy complexity of online learning platform evaluation, the concept of Pythagorean probabilistic linguistic term set (PPLTS) was proposed, effectively describing and measuring the complex information in the evaluation process of online learning platforms. On this basis, a series of aggregation methods of PPLTS were proposed, including the Pythagorean probabilistic linguistic term weighted average (PPLTWA) operator, the Pythagorean probabilistic linguistic term ordered weighted average (PPLTOWA) operator, and so on. Finally, a multi-criteria comprehensive evaluation framework in the case of PPLTS was given, and its application in the selection of online learning platforms was studied. Finally, some relevant policy suggestions were put forward to promote the development of online learning platforms. This study will not only help enrich the evaluation index system of online learning platforms, but also provide scientific evaluation method reference for researchers.
Similar content being viewed by others
Data availability statement
The data used to support the findings of the work are included within the paper.
References
Adair D, Alman SW, Budzick D, Grisham LM, Mancini ME, Thackaberry AS (2014) Many shades of MOOCs. J Online Learn Res Pract. https://doi.org/10.18278/il.3.1.5
Adams AL (2020) Online teaching resources. Public Serv Q 16(3):172–178
Almaiah MA, Alyoussef IY (2019) Analysis of the effect of course design, course content support, course assessment and instructor characteristics on the actual use of E-learning system. Ieee Access 7:171907–171922
Apolloni B, Bassis S, Rota J, Galliani GL, Gioia M, Ferrari L (2016) A neurofuzzy algorithm for learning from complex granules. Granular Comput 1:225–246
Arulogun OT, Akande ON, Akindele AT, Badmus TA (2020) Survey dataset on open and distance learning students’ intention to use social media and emerging technologies for online facilitation. Data Brief 31:105929
Biswas A, Deb N (2021) Pythagorean fuzzy Schweizer and Sklar power aggregation operators for solving multi-attribute decision-making problems. Granular Comput 6:991–1007
Buyukozkan G, Arsenyan J, Ertek G (2010) Evaluation of e-learning web sites using fuzzy axiomatic design based approach. Int J Comput Intell Syst 3(1):28–42
Chen SJ, Chen SM (2001) A new method to measure the similarity between fuzzy numbers. In: 10th IEEE International Conference on Fuzzy Systems.(Cat. No. 01CH37297) vol. 3, pp 1123–1126. IEEE.
Chen SM, Phuong BDH (2017) Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowl-Based Syst 118:204–216
Chen SM, Wang CH (2009) Fuzzy risk analysis based on ranking fuzzy numbers using α-cuts, belief features and signal/noise ratios. Expert Syst Appl 36(3):5576–5581
Chiu CM, Chiu CS, Chang HC (2007) Examining the integrated influence of fairness and quality on learners’ satisfaction and Web-based learning continuance intention. Inf Syst J 17(3):271–287
Daniel J, Cano EV, Cervera MG (2015) The future of MOOCs: adaptive learning or business model? RUSC Univ Knowl Soc J 12(1):64–73
Dong J, Wan S, Chen SM (2021) Fuzzy best-worst method based on triangular fuzzy numbers for multi-criteria decision-making. Inf Sci 547:1080–1104
Eijkman H (2008) Web 2.0 as a non-foundational network-centric learning space. Campus-Wide Inform Syst 25(2):93–104
Ejegwa PA, Jana C, Pal M (2022) Medical diagnostic process based on modified composite relation on pythagorean fuzzy multi-sets. Granul Comput 7:15–23
Herrera F, Herrera-Viedma E (2000) Choice functions and mechanisms for linguistic preference relations. Eur J Oper Res 120(1):144–161
Ic YT, Yurdakul M (2021) Development of a new trapezoidal fuzzy AHP-TOPSIS hybrid approach for manufacturing firm performance measurement. Granul Comput 6:915–929
Kang Z, He L (2018) Construction and practice of SPOC teaching mode based on MOOC. Int J Emerg Technol Learn 13(12):35
Karal H, Cebi A, Turgut YE (2011) Perceptions of students who take synchronous courses through video conferencing about distance education. Turk Online J Educ Technol TOJET 10(4):276–293
Li B, Wang X, Tan SC (2018) What makes MOOC users persist in completing MOOCs? A perspective from network externalities and human factors. Comput Hum Behav 85:385–395
Li H, Li H, Zhang S, Zhong Z, Cheng J (2019) Intelligent learning system based on personalized recommendation technology. Neural Comput Appl 31:4455–4462
Lin HF (2010) An application of fuzzy AHP for evaluating course website quality. Comput Educ 54(4):877–888
Lin YL, Lin HW, Hung TT (2015) Value hierarchy for massive open online courses. Comput Hum Behav 53:408–418
Liu Q, Liu Q, Yang L, Wang G (2018) A multi-granularity collective behavior analysis approach for online social networks. Granul Comput 3:333–343
Marks RB, Sibley SD, Arbaugh JB (2005) A structural equation model of predictors for effective online learning. J Manag Educ 29(4):531–563
Maslov I, Nikou S, Hansen P (2021) Exploring user experience of learning management system. Int J Inform Learn Technol 38(4):344–363
Mastan IA, Sensuse DI, Suryono RR, Kautsarina K (2022) Evaluation of distance learning system (e-learning): a systematic literature review. J Teknoinfo 16(1):132–137
Miranda P, Isaias P, Pifano S (2015) Model for the evaluation of MOOC platforms. In: ICERI2015 Proceedings. IATED, pp 1199–1208
Mohammadi H (2015) Investigating users’ perspectives on e-learning: an integration of TAM and IS success model. Comput Hum Behav 45:359–374
Palmer SR, Holt DM (2009) Examining student satisfaction with wholly online learning. J Comput Assist Learn 25(2):101–113
Pang Q, Wang H, Xu Z (2016) Probabilistic linguistic term sets in multi-attribute group decision making. Inf Sci 369:128–143
Park SY (2009) An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. J Educ Technol Soc 12(3):150–162
Pellas N, Kazanidis I (2015) On the value of Second Life for students’ engagement in blended and online courses: a comparative study from the Higher Education in Greece. Educ Inf Technol 20:445–466
Qi C, Liu S (2021) Evaluating on-line courses via reviews mining. IEEE Access 9:35439–35451
Rodriguez RM, Martinez L, Herrera F (2011) Hesitant fuzzy linguistic term sets for decision making. IEEE Trans Fuzzy Syst 20(1):109–119
Rong L, Wang L, Liu P, Zhu B (2021) Evaluation of MOOCs based on multigranular unbalanced hesitant fuzzy linguistic MABAC method. Int J Intell Syst 36(10):5670–5713
Saaty TL (1988) What is the analytic hierarchy process? Springer, Berlin Heidelberg, pp 109–121
Sun Z, Anbarasan M, Praveen Kumar DJCI (2021) Design of online intelligent English teaching platform based on artificial intelligence techniques. Comput Intell 37(3):1166–1180
Terras MM, Ramsay J (2015) Massive open online courses (MOOCs): insights and challenges from a psychological perspective. Br J Educ Technol 46(3):472–487
Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25(6):529–539
Tzeng GH, Chiang CH, Li CW (2007) Evaluating intertwined effects in e-learning programs: a novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Syst Appl 32(4):1028–1044
Wang R (2019) Massive open online course platform blended English teaching method based on model-view-controller framework. Int J Emerg Technol Learn 14(16):188
Xiao J, Wang M, Jiang B, Li J (2018) A personalized recommendation system with combinational algorithm for online learning. J Ambient Intell Humaniz Comput 9:667–677
Xu Z (2012) Linguistic decision making. Springer, Berlin Heidelberg
Yager RR (2013) Pythagorean membership grades in multicriteria decision making. IEEE Trans Fuzzy Syst 22(4):958–965
Yang C, Huan S, Yang Y (2020) Application of big data technology in blended teaching of college students: a case study on rain classroom. Int J Emerg Technol Learn (IJET) 15(11):4–16
Ye ZX, Luo R (2021) Evaluating online courses: how learners perceive language MOOCs. Lect Notes Comput Sci 12511:334–343
Yepes-Baldó M, Romeo M, Martín C, García MÁ, Monzó G, Besolí A (2016) Quality indicators: developing “MOOCs” in the European higher education area. Educ Media Int 53(3):184–197
Yousef AMF, Chatti MA, Schroeder U, Wosnitza M (2014) What drives a successful MOOC? An empirical examination of criteria to assure design quality of MOOCs. In: 2014 IEEE 14th International Conference on Advanced Learning Technologies. IEEE, pp. 44–48
Zadeh LA (1965) Fuzzy sets. Inf Control 18:338–353
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci 8(3):199–249
Zeng SZ, Gu JX (2023) Coordination evaluation and dynamic adjustment mechanism of China’s green development at inter-provincial level. Ecol Ind 153:110419
Zeng SZ, Hu YJ, Llopis-Albert C (2023) Stakeholder-inclusive multi-criteria development of smart cities. J Bus Res 154:113281
Zhang N, Su WH, Zhang CK, Zeng SZ (2022) Evaluation and selection model of community group purchase platform based on WEPLPA-CPT-EDAS method. Comput Ind Eng 172:108573
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
JG and DL writing—original draft. WC methodology, visualization. SZ writing—review & editing.
Corresponding author
Ethics declarations
Conflict of interest
We certify that there is no conflict of interest with any individual or organization or organization for the work.
Ethical approval
The work does not contain any studies with human participants or animals performed by the author.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gu, J., Chen, W., Luo, D. et al. A multi-criteria comprehensive evaluation framework of online learning platform based on Pythagorean probabilistic linguistic information. Granul. Comput. 8, 1701–1714 (2023). https://doi.org/10.1007/s41066-023-00392-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41066-023-00392-z