# Improving Web Readability Using Video Content: A Relevance-Based Approach
## 6 Discussion  
The results of this study highlight the effectiveness of the proposed NLP-based cosine similarity approach for evaluating the relevance of video content in enhancing webpage readability. The findings demonstrate that the automated scores strongly align with user ratings (with a correlation coefficient of r = 0.87), validating the reliability of the methodology for assessing video-text alignment. The following key insights were obtained.  
Relevance Distribution: The automated analysis indicated that 40% of the videos were highly relevant, showing strong alignment with the webpage text. Another 36% were moderately relevant, offering partial support to the content, while 24% were categorized as irrelevant, highlighting areas for improvement in selecting videos for educational websites. This distribution emphasizes the critical need for curating multimedia elements that effectively support a webpage's educational goals.  
User Study Validation: When comparing the results of the user study with the automated scores, the average difference was only five percent. Such a small margin of error shows that cosine similarity is very effective in capturing the user's perception of the relevance of videos, even though this measure is quite simple. However, some of these differences suggest that there is a need to employ more complex approaches to deal with more complex semantic relations.  
Error Analysis: The deviations observed suggest this approach has some limitations. The automated system sometimes provided lower ratings to videos with more general or related topics, but the users found these useful. In contrast, there were videos that included technical terms that obtained a high level of cosine similarity but low user ratings due to language differences.  
Implications for Practice: The results of this study will be helpful for web designers, educators, and content producers. With the help of automated relevance scoring, developers are able to further filter the videos that are most relevant to the page content and therefore increase its readability and interactivity. Moreover, the research creates a framework for evaluating multimedia integration and contributes to the current knowledge of educational technology.  
Limitations and Future Directions: However, the proposed methodology has some limitations. Cosine similarity may not be able to reveal semantic relations or contextual  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Relevance Category** & **Number of Videos** & **Mean Automated Score** & **Mean User Rating** & **Mean Error Rate (\%)** & **Key Observations** \\ \hline Highly Relevant & & & & & Strong agreement between automated scores and user ratings. \\ (Score \textgreater{} 0.7) & 20 & 0.78 & 4.6 & 4\% & Minor discrepancies due to technical jargon. \\ \hline Moderately Relevant & & & & & Partial alignment was observed; \\ (Score 0.5–0.7) & 18 & 0.62 & 3.7 & 5\% & broader topics were rated higher by users than by automated scores. \\ \hline Irrelevant (Score \textless{} 0.5) & 12 & 0.42 & 2.4 & 6\% & Users occasionally rated tangentially relevant videos higher for perceived educational value. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error rate analysis.  
differences. Furthermore, other variables such as content topics and the characteristics of the audience are likely to influence their perceptions of relevance, and these were not considered in this study. Future research may benefit from incorporating deep learning models into a semantic analysis to enhance the sensitivity of relevance scoring for varied content types.