## 4 Results
### Recommendation Process on Coursera
#### 4.3.2 Feature Extraction of Course Attributes and Learner Clustering  
Although course attribute preferences can be obtained, there may be a certain correlation between the 11 course attributes, as shown in Table 2. It is necessary to reduce the dimensions of the course attributes before clustering the learners. According to Step 2 shown in Figure 1, principal component analysis was applied. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**User\_id** & **Materials** & **Price** & **Duration** & **Weeks** & **Assistants** & **Clicks** & **Views** & **Registrations** & **Rating Volume** & **Reviews** & **Rating** \\ \hline
8164 & 54 & 319 & 13 & 4 & 1 & 562407 & 894006 & 281359 & 12905 & 2554 & 5 \\ \hline
10269 & 61 & 331 & 12 & 4 & 2 & 182944 & 486581 & 67486 & 3152 & 611 & 5 \\ \hline
14654 & 64 & 237 & 10 & 5 & 2 & 145783 & 719196 & 114781 & 3423 & 917 & 5 \\ \hline
15908 & 78 & 434 & 17 & 4 & 2 & 140287 & 76550 & 45004 & 1834 & 392 & 5 \\ \hline
26307 & 98 & 390 & 19 & 6 & 2 & 236303 & 816381 & 156626 & 7623 & 1023 & 5 \\ \hline
28185 & 86 & 284 & 19 & 6 & 1 & 85564 & 142817 & 73284 & 1847 & 383 & 5 \\ \hline
47728 & 58 & 311 & 17 & 4 & 2 & 500932 & 1697855 & 201391 & 18999 & 2604 & 5 \\ \hline
60377 & 71 & 336 & 15 & 4 & 2 & 238566 & 2433070 & 437778 & 34573 & 6151 & 5 \\ \hline
88789 & 69 & 364 & 9 & 5 & 1 & 80350 & 51049 & 38149 & 1058 & 288 & 5 \\ \hline
90744 & 74 & 299 & 16 & 5 & 1 & 156001 & 306587 & 102930 & 3985 & 878 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A subset of data on learners’ course attribute preferences with memory weight. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**User\_id** & **Indigenous-** & **Visual-Elements-** & **Fundamentals-** & **Global-** & **Moral-** & **Philosophy** & **Terrorism** & **Indo-** & **Modem-** & **Artif-deas** & **Craft-of-** \\ \multicolumn{1}{c}{**Design**} & & **Design** & & & & & & & \\ \hline
8164 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
10269 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
14654 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
15998 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
26307 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
28185 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
47728 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
60377 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
88789 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
90744 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A subset of learners’ course rating preferences with memory weight. The results of the KMO and Bartlett tests for course attribute preferences are presented in Table 6. The KMO measure was 0.877, which is greater than 0.8. The Bartlett spherical test was significant, at \(p<0.05\), indicating that the data were suitable for principal component analysis [30]. After dimension reduction and combing course attribute preferences with multidimensional capabilities that matched course traits, learners were clustered using the K-means clustering algorithm. This involved evaluating the clustering results by minimizing the distance between samples within each cluster. The smaller the sum of the squared distances between the class centers of each class and the sample points within the class, the more similar the class members. As the number of clusters increased, the error sum decreased until it reached a critical value and remained relatively stable; therefore, this point could be considered the optimal number of clusters. In this study, the elbow method, a heuristic used in determining the number of clusters in a dataset, was applied to find the appropriate inflection point quickly and effectively [31]. \begin{table}
\begin{tabular}{c c} \hline \hline
**KMO Sampling Suitability Quantity** & **0.877** \\ \hline [PERSON]’s sphericity test & approximate chi-square & 29,090.65 \\ \cline{2-2}  & degrees of freedom & 276 \\ \cline{2-2}  & significance & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: KMO and Bartlett test.