## 5 Discussion  
### Single Criterion Analysis  
We first analyzed the results in Tables 4 and 5, which reflect the performance of the attributes of the two courses on the six criteria. We first analyzed the three criteria \(c_{1}\), \(c_{2}\), and \(c_{3}\), which are the proportion of positive, neutral, and negative reviews among all learners' comments on an attribute and directly reflect learners' satisfaction with the attribute. For an attribute, the higher \(c_{1}\) is, or the lower \(c_{2}\) and \(c_{3}\) are, the higher the learner's satisfaction is; otherwise, the lower the satisfaction is. It is not difficult to find that for the two programming courses, there is little difference in the attribute satisfaction of learners for between the two courses. The two attributes that learners are most satisfied with are \"Teaching level\" and \"Learning threshold\". The majority of the learners believed that the teachers in both courses possess a commendable level of expertise, and the courses were tailored to cater to beginners. The attributes that the learners felt performed well are \"Teachers' expressive ability\", \"Teaching methods\", \"Course organization\" and \"Learning experience\". It means that the teachers of these two courses have good expression ability, can basically tell the course content clearly, and adopt appropriate teaching methods. The schedule of the course is reasonable, and the key and difficult points are prominent. The learner's listening experience was good. For \"Supporting material\", Java language performs well, while C language performs moderately. For C, it may be necessary to provide more high-quality supporting material. The satisfaction of the \"Platform\" and \"Professional knowledge\" of the two courses is general, which means that learners are dissatisfied with the services provided by the MOOC platform to a certain extent, and the amount and depth of professional knowledge provided by the courses may need to be improved. Finally, learners expressed dissatisfaction with the \"Exercise\" aspect of both courses, indicating that there is ample room for improvement in terms of exercise difficulty and quantity. We then analyzed \(c_{4}\). It is the proportion of non-emotional reviews for an attribute. As previously mentioned in Section 1, although non-emotional reviews do not directly reflect satisfaction or dissatisfaction, the attributes that reviewers mentioned in non-emotional reviews are the attributes that they thought might be of importance to future learners. It is evident that \"Professional knowledge\" is a relatively crucial attribute. A possible reason for this could be that in technical courses, learners tend to prioritize the quantity and quality of professional knowledge imparted by the instructor. Additionally, the expression of professional knowledge in reviews tends to utilize more non-emotional descriptions. This does not imply that other attributes are less significant; however, they may be more easily expressed through emotional reviews. For \(c_{5}\), it is the average of the number of reviews involving each word in an attribute. A higher value of \(c_{5}\) means that people pay more attention to the attribute word in this attribute and, accordingly, pay more attention to this attribute. Learners of both courses pay significant attention to attributes such as \"Teaching level\", \"Learning threshold\", \"Course organization\", and \"Teacher's expressive ability\". These four attributes directly influence the learning effectiveness of students. Hence, this finding aligns with our intuitive understanding. \"Professional knowledge\" and \"Platform\" are of much interest in C language but not so much in Java. This divergence might be attributed to the widespread perception of C as a programming language closer to hardware, distinguished by its low-level and intricate syntax and structure. Consequently, learners often require a deeper level of proficiency and educational guidance to comprehend and utilize it effectively.