## 2 Literature Review
It should also be mentioned that there are many other articles in the literature dealing with the problem of e-learning assessment. They focus on evaluating aspects other than the general quality of e-learning. The first example is the evaluation of mobile learning in plants in English [50]. Taiwanese students provided feedback on their preferences and requirements for digital learning materials. The analysis revealed that most of the students preferred teaching materials that included text and images optimized for mobile web pages and preferred a simple user interface. They also suggested improvements, such as improving maps, words, and images. Additionally, they recommended incorporating forum feedback and an audio guide to further improve the user experience with the system. Another example refers to learning engagement [51]. The authors used an infrared (IR) imaging sensor to obtain clear images in an online learning environment, which were used to analyze facial expressions to assess learning engagement. They developed an integrated model consisting of two sub-models utilizing the VGG-16 convolutional neural network. The first sub-model assessed the learning engagement level based on IR images captured during the learning process, while the second sub-model evaluated engagement levels based on mouse movement patterns. [PERSON] and [PERSON] [52] conducted a quantitative study on the gender perspectives of e-learning, focusing on the differences across Generations X, Y, and Z. In a study involving 611 respondents enrolled in a mandatory online course at a Business School, the authors utilized multi-group partial least-squares analysis to reveal that while gender differences in e-learning utility exist, they vary significantly across generational lines. These generational nuances might be overlooked when only gender is considered, leading to premature conclusions about the narrowing gender gap in e-learning. The study suggests that to effectively implement e-learning in traditional undergraduate programs, course development, learner support, assessment, and user characteristics must be contextualized according to gender and generational differences. Additionally, in developing countries, strengthening institutional support is essential to advancing e-learning adoption. [PERSON] et al. [53] explored the effectiveness of e-learning by focusing on the characteristics of online courses that contribute to the emotional stability of students. Their research demonstrated that, in addition to maintaining the educational process's effectiveness, the introduction of an experimental e-learning course significantly enhanced students' emotional resilience. In particular, the study found a 27% increase in the level of joy among students compared to traditional learning methods, highlighting the potential of well-designed online courses to positively impact students' emotional well-being. Another study aimed at explaining teachers' adoption of digital technology in education was carried out [54]. The authors discussed various models that explain the factors and mechanisms influencing technology use in classrooms, with a particular focus on the Technology Acceptance Model (TAM) and its various iterations. They delve into the model's effectiveness in predicting teachers' adoption of technology and provide insights and implications for both research and practical application, emphasizing the model's relevance in understanding and enhancing technology integration in educational settings. When it comes to research on the quality of e-learning conducted in Poland, it can be claimed that the literate is poor. Developing the literature review, we found a few papers that refer in some way to the evaluation of e-learning in Poland, but none of the articles directly addressed the assessment of the quality of e-learning on the example of Polish universities.