# Extracting Searching as Learning Tasks Based on IBRT Approach
## 1 Introduction  
Search engines are the most used tools for accessing online information and are increasingly being used for learning purposes, such as when making travel plans and finding properties to purchase. From the aspect of searching as learning (SAL), searching is conceptualized as a series of activities for learning, such as planning tasks, issuing queries, analyzing web sources, piecing information together, and evaluating and using information [1; 2]. Unlike traditional studies on searching as a learning tool, which only focus on accurate search results, SAL focuses on learning activities and outcomes during the search process. Studies have determined that tasks are interleaved during SAL processes. Web learners must recursively plan learning tasks, formulate search queries, scan search results, and adapt their knowledge structure to complete learning tasks [3; 4]. Therefore, accurate task extraction is important for information retrieval systems to support and foster a large variety of more sophisticated search strategies. Studies have shown that task extraction can help systems discover related and interesting tasks [5; 6], improve learners' engagement [7], and improve ranking performance [8]. Moreover, the accurate extraction of tasks also helps to accurately locate users' learning statures in their task space, as well as further recommend and help users better finish their learning tasks [9]. In view of this importance, SAL task extraction has received considerable attention in recent years [10; 11; 12]. While some of these studies focused on task extraction, SAL task extraction is still a very challenging problem because systems struggle to quantify the concurrent learning during searching, each task can be defined at different levels of granularity, and learners have different learning styles [13]. Studies of constructivist epistemology have determined that, during the learning process, each learner must (i) identify the knowledge gap, (ii) plan search tasks, (iii) generate queries on the basis of their knowledge structure and learningstyle, and (iv) analyze and synthesize search results to improve the knowledge structure, recursively performing (i) until they complete the learning goal. From this perspective, learners with different learning styles demonstrate different planning preferences when developing search tasks, and these planning preferences lead to different issued queries. From the perspective of information retrieval system studies, the research and development of task extraction methods are always based on the query-query or query-results relationship. These methods may work well for search tasks such as look-up, but not necessarily for SAL tasks, as these require the searcher to reflect, learn, and analyze the results [14, 15]. We are motivated by the need for an accurate representation and extraction of SAL tasks and propose an efficient improved Bayesian rose tree (IBRT) model. Most existing hierarchical task extraction methods cannot capture learning styles and SAL factors. In contrast, our proposed method can accurately represent tasks by capturing SAL factors and improve performance by employing learning styles. To test our method, we collected and analyzed students' development processes from the UWP programming course at Northeastern University, where 46 students learned by searching the web to complete UWP programming assignments. Collecting students' periodic learning outcomes is difficult in real time, and manual observation of searching processes may introduce privacy concerns. Therefore, we designed the experimental environment based on two methods for collecting student trace data: (i) we developed an IDE plugin that can automatically collect snapshots; (ii) we developed an automated approach that can be applied to search behavior captured by a Chrome browser plugin. These methods help provide insight for a better understanding of how novice programmers go about developing search tasks.