## 3 Materials and Methodology
### Preprocessing the Categorized Signals
#### 3.3.7 Combined Preprocessing for Hybrid Model  
Since the hybrid model uses both XGBoost (for explicit signals) and GRUs (for implicit signals), preprocessing must align with the requirements of each algorithm. Based on the suitability of the data, both signals were preprocessed by corresponding models and the final data were updated as described in Table 12. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Learner ID** & **Initial Module** & **Time Spent (Minutes)** & **Click Count** & **Engagement Trend** & **Revisit Count** & **Padded Sequence** & **Expanded Remarks** \\ \hline
1 & Data Structures & 50 & 30 & 2 & 3 & [50, 30, 2, 3, 0, 0] & Moderate engagement with consistent interaction \\ \hline
2 & Data Structures & 25 & 20 & 1 & 1 & [25, 20, 1, 1, 0, 0] & Average engagement pattern \\ \hline
3 & Algorithms & 60 & 50 & 3 & 5 & [60, 50, 3, 5, 0, 0] & Highly engaged and revisits frequently \\ \hline
4 & Binary Trees & 15 & 10 & 1 & 1 & [15, 10, 1, 1, 0, 0] & Low engagement, requires motivation \\ \hline
5 & Graph Algorithms & 45 & 28 & 2 & 2 & [45, 28, 2, 2, 0, 0] & Moderate engagement with consistent interaction \\ \hline
6 & SQL Basics & 20 & 15 & 1 & 1 & [20, 15, 1, 1, 0, 0] & Average engagement pattern \\ \hline
7 & Testing & 55 & 40 & 3 & 3 & [55, 40, 3, 3, 0, 0] & Highly engaged and revisits frequently \\ \hline
8 & Networking & 35 & 25 & 2 & 2 & [35, 25, 2, 2, 0, 0] & Moderate engagement with consistent interaction \\ \hline
9 & Machine Learning & 10 & 8 & 1 & 1 & [10, 8, 1, 1, 0, 0] & Low engagement, requires motivation \\ \hline
10 & Data Analytics & 65 & 45 & 3 & 5 & [65, 45, 3, 5, 0, 0] & Highly engaged and revisits frequently \\ \hline \end{tabular}
\end{table} 
Table 11: Sample data after preprocessing implicit signals. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline  & & & \multicolumn{6}{c}{**Explicit Signals**} & \multicolumn{6}{c}{**Implicit Signals**} \\ \hline
**Learner** & **Initial** & **Pre-Test** & **Post-Test** & **Improvement** & **Satisfaction** & **Completion** & **Time Spent** & **Click** & **Trend** & **Reries** & **Knowledge** \\
**ID** & **Module** & **Score (\%)** & **Score (\%)** & **Score (\%)** & **(\%)** & **Rating (1-5)** & **(I = Completed)** & **(Minutes)** & **Count** & **(Low = 1,** **Rob** & **Reries** & **Knowledge** \\ \hline
1 & Data & & & & & & & & & & & & \\ Structures & 70 & 85 & 15 & 5 & 1 & 50 & 30 & 2 & 3 & Valid \\ \hline
2 & Data & & & & & & & & & & & \\ Structures & 60 & 80 & 20 & 4 & 1 & 25 & 20 & 1 & 1 & Invalid \\ \hline
3 & Algorithms & 55 & 65 & 10 & 5 & 1 & 60 & 50 & 3 & 5 & Valid \\ \hline
4 & Binary Trees & 72 & 90 & 18 & 3 & 0 & 15 & 10 & 1 & 1 & Invalid \\
5 & Graph & & & & & & & & & & \\ Algorithms & 65 & 77 & 12 & 4 & 1 & 45 & 28 & 2 & 2 & Valid \\ \hline
6 & SQL Basics & 50 & 58 & 8 & 3 & 0 & 20 & 15 & 1 & 1 & Invalid \\ \hline
7 & Testing & 60 & 75 & 15 & 5 & 1 & 55 & 40 & 3 & 3 & Valid \\ \hline
8 & Networking & 55 & 65 & 10 & 4 & 1 & 35 & 25 & 2 & 2 & Valid \\ \hline
9 & Machine & 70 & 95 & 25 & 5 & 1 & 10 & 8 & 1 & 1 & Valid \\ \hline
10 & Data & & & & & & & & & & \\ Analytics & 62 & 80 & 18 & 4 & 1 & 65 & 45 & 3 & 5 & Invalid \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Data after preprocessing both explicit and implicit signals. Insights from the dataset:  
* Learners 1, 7, 9, and 10 achieved the highest combined effectiveness, driven by strong engagement and explicit improvements. These learners benefit from advanced and exploratory learning paths. * Learners 2, 5, and 8 demonstrated steady combined effectiveness despite some engagement gaps. Personalized resources can further boost their performance. * Learners 3, 4, and 6 showed lower combined effectiveness due to limited explicit improvement or low engagement. These learners need targeted interventions:  
* Learner 3: Needs foundational reinforcement despite high engagement.