# Extracting Searching as Learning Tasks Based on IBRT Approach
## 5 Conclusions  
Studies on SAL are promising for designing advanced searching and education systems. To improve the experience of web learners, it is important to accurately present and extract SAL tasks. On the basis of learning styles and similarity metrics, this study \begin{table}
\begin{tabular}{c c} \hline \hline  & **Score** \\ \hline IBRT & 2.7 \\ BRT & 2.5 \\ BHC & 2.1 \\ LDA-Hawkes & 2.5 \\ BesItink-SVM & 2.4 \\ Cluster-SVM & 2.3 \\ QC-HTC & 1.9 \\ QCWCC & 2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluating results for the accuracy of SAL task extraction. \begin{table}
\begin{tabular}{c c} \hline \hline
**Score** & **Scoring Principles** \\ \hline
0 & Invalid \\
1 & Valid in some parts; however, most of the extraction is invalid \\
2 & Valid in some parts; however, some parts are invalid \\
3 & Valid \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The scoring principles for question 2. \begin{table}
\begin{tabular}{c c} \hline \hline  & **Score** \\ \hline IBRT & 2.3 \\ BRT & 2.0 \\ BHC & 0.9 \\ LDA-Hawkes & 1.6 \\ BestLink-SVM & 1.4 \\ Cluster-SVM & 1.4 \\ QC-HTC & 1.5 \\ QCWCC & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The evaluating results for the accuracy of the hierarchy. proposed the IBRT model to implement structured SAL task representations for learners and, thereafter, extracted tasks on the basis of that structure. To test the proposed method, we collected and analyzed experimental data from the Northeastern University UWP course. Subsequently, a series of experiments were performed against the collected datasets. In comparison to the state-of-art task extraction methods, the experimental results indicated that our proposed methods could significantly improve the performance of SAL task extraction in terms of both objective and subjective metrics. The success of the IBRT model implies that SAL task extraction studies combining both searching and learning activities are promising. In the IBRT model, searching activities can be conceptualized as a part of learning that further affect learning outcomes. The analysis and experimental results lead us to believe that the IBRT model can further explore the relationships between searching and learning activities. Our results also demonstrate the advantage of employing learning styles in SAL task extraction studies. Web learners with different learning styles have different task planning preferences. These preferences lead learners to make different task plans for the same learning tasks. As reflected in our empirical results, the learning style employed in SAL task extraction can be successful in predicting web learning tasks. Although our findings are promising, there are still some limitations of our study. First, the learning styles of a learner are highly dependent on the learning tasks. In this study, we manually analyzed and classified learning styles for UWP programs. However, methods to automatically extract learning styles may be worthy of further investigation. Second, there were limitations to the experimental data. In this study, we used the collected Northeastern University UWP coursework data as the dataset for the SAL study. This is the only dataset that contains both search activities and learning outcomes. We expect that the proposed method will be validated using large-scale datasets. Future studies should include the following aspects: (1) developing methods for automatically extracting learning styles; (2) studying the use of latent features to predict learning outcomes to help expand the dataset and further improve the performance of SAL task extraction; (3) considering the time dimension to predict SAL tasks in future studies; (4) developing task-oriented personalized recommendation systems for web learners; (5) exploring the optimization of the learning path based on SAL tasks. One of our goals is to adopt this in recommending systems for university courses, and we will further explore the SAL path on the basis of the obtained results. Conceptualization, P.L. and B.Z. ; Data curation, P.L.