Predicting Task Planning Ability for Learners Engaged in Searching as Learning Based on Tree-Structured Long Short-Term Memory Networks

Abstract

The growing utilization of web-based search engines for learning purposes has led to increased studies on searching as learning (SAL). In order to achieve the desired learning outcomes, web learners have to carefully plan their learning objectives. Previous SAL research has proposed the significant influence of task planning quality on learning outcomes. Therefore, accurately predicting web-based learners’ task planning abilities, particularly in the context of SAL, is of paramount importance for both web-based search engines and recommendation systems. To solve this problem, this paper proposes a method for predicting the ability of task planning for web learners. Specifically, we first introduced a tree-based representation method to capture how learners plan their learning tasks. Subsequently, we proposed a method based on the deep learning technique to accurately predict the SAL task planning ability for web learners. Experimental results indicate that, compared to baseline approaches, our proposed method can provide a more effective representation of learners’ task planning and deliver more accurate predictions of learners’ task planning abilities in SAL.