A multi-criteria comprehensive evaluation framework of online learning platform based on Pythagorean probabilistic linguistic information

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.