Extracting Searching as Learning Tasks Based on IBRT Approach

Li, Pengfei; Zhang, Bin; Zhang, Yin

doi:10.3390/app12125879

Cited by 2 publications

(3 citation statements)

References 50 publications

(71 reference statements)

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“…In [40], P Li et al built a search process dataset to verify the performance of their search task extraction method. The search task in this dataset consists of 5 subtasks.…”

Section: G Experimental Results On the Second Datasetmentioning

confidence: 99%

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A Probabilistic Model Toward How People Search to Build Outcomes

et al. 2023

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Although increased attention is being given to understanding how people search to build task outcomes, a formal model of the relation between how people search and how people build task outcomes is still lacking. This paper proposes a unified probabilistic model of how people search to build outcomes. The model involves 3 types of searcher behaviors (i.e., query submission, document selection, and information transformation) to model the effect of the information collected during search, and uses the item response theory to capture the ternary relations between the ability to transform information, the information collected, and the probability of successfully building task outcomes. We evaluate the proposed model in the task of identifying searchers' proficiencies under the assumption that high proficiency searchers would have high abilities to transform information. The results obtained high accuracies and F1 scores, which could reflect the effectiveness of the proposed model. The model contributes to the formal understanding of how people search to build task outcomes, and provides new possibilities for personalized and session-based information retrieval research.INDEX TERMS Item response theory, searching as learning, outcomes.

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“…In [40], P Li et al built a search process dataset to verify the performance of their search task extraction method. The search task in this dataset consists of 5 subtasks.…”

Section: G Experimental Results On the Second Datasetmentioning

confidence: 99%

“…The search task, the setups of our proposed model, and the experimental results are introduced in detail from Section V.A to Section V.F. The second dataset was from [40], and the results are presented in section V.G. In Section V.H, we present a comparative experiment.…”

Section: Experimental Evaluationsmentioning

confidence: 99%

A Probabilistic Model Toward How People Search to Build Outcomes

et al. 2023

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“…Studies have proposed that SAL combined with thoughtful task planning can lead to enhanced learning outcomes [7,8]. At the beginning of the SAL process, learners often only possess a vague understanding of the learning object, meaning their knowledge structures are insufficient to precisely articulate what they seek to learn.…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Dong,

Zhang

et al. 2023

Applied Sciences

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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.

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Extracting Searching as Learning Tasks Based on IBRT Approach

Cited by 2 publications

References 50 publications

A Probabilistic Model Toward How People Search to Build Outcomes

A Probabilistic Model Toward How People Search to Build Outcomes

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

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