担当区分：筆頭著者,　最終著者,　責任著者   出版者・発行元：Journal of Educational Data Mining  

Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge concepts and linguistic skills are hard to define, and diagnosis must be well-designed. Traditional approaches are widely applied for modeling knowledge in science or mathematics, where skills or knowledge concepts are easy to associate with each item. However, only a few works focus on defining knowledge concepts and skills using linguistic characteristics for language knowledge proficiency diagnosis. In addressing this, we propose a framework for language proficiency diagnosis based on neural networks. Specifically, we propose a series of methods based on our framework that uses different linguistic features to define skills and knowledge concepts in the context of the language learning task. Experimental results on a real-world second-language learning dataset demonstrate the effectiveness and interpretability of our framework. We also provide empirical evidence with comprehensive experiments and analysis to prove that our knowledge concept and skill definitions are reasonable and critical to the performance of our model.

DOI： 10.5281/zenodo.10948071

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記述言語：英語   掲載種別：研究論文（その他学術会議資料等）  

担当区分：筆頭著者,　最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

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記述言語：その他   掲載種別：研究論文（その他学術会議資料等）  

Language learning applications usually estimate the learner's language knowledge over time to provide personalized practice content for each learner at the optimal timing. However, accurately predicting language knowledge or linguistic skills is much more challenging than math or science knowledge, as many language tasks involve memorization and retrieval. Learners must memorize a large number of words and meanings, which are prone to be forgotten without practice. Although a few studies consider forgetting when modeling learners' language knowledge, they tend to apply traditional models, consider only partial information about forgetting, and ignore linguistic features that may significantly influence learning and forgetting. This paper focuses on modeling and predicting learners' knowledge by considering their forgetting behavior and linguistic features in language learning. Specifically, we first explore the existence of forgetting behavior and cross-effects in real-world language learning datasets through empirical studies. Based on these, we propose a model for predicting the probability of recalling a word given a learner's practice history. The model incorporates key information related to forgetting, question formats, and semantic similarities between words using the attention mechanism. Experiments on two real-world datasets show that the proposed model improves performance compared to baselines. Moreover, the results indicate that combining multiple types of forgetting information and item format improves performance. In addition, we find that incorporating semantic features, such as word embeddings, to model similarities between words in a learner's practice history and their effects on memory also improves the model.

DOI： 10.1145/3576050.3576062

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記述言語：その他   掲載種別：研究論文（学術雑誌）   出版者・発行元：Smart Learning Environments  

With the increasing use of digital learning materials in higher education, the accumulated operational log data provide a unique opportunity to analyzing student learning behaviors and their effects on student learning performance to understand how students learn with e-books. Among the students’ reading behaviors interacting with e-book systems, we find that jump-back is a frequent and informative behavior type. In this paper, we aim to understand the student’s intention for a jump-back using user learning log data on the e-book materials of a course in our university. We at first formally define the “jump-back” behaviors that can be detected from the click event stream of slide reading and then systematically study the behaviors from different perspectives on the e-book event stream data. Finally, by sampling 22 learning materials, we identify six reading activity patterns that can explain jump backs. Our analysis provides an approach to enriching the understanding of e-book learning behaviors and informs design implications for e-book systems.

DOI： 10.1186/s40561-021-00183-6

Web of Science

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開催年月日： 2024年3月

記述言語：英語  

国名：日本国  

記述言語：その他  

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記述言語：その他  

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記述言語：英語  

記述言語：日本語  

Adaptive learning path recommendation system efficiently guides learners by constructing appropriate learning sequences from a set of recommended learning materials to reach their goals. As a vital role in adaptive learning path, recommendation algorithms could be grouped into three categories: intelligent optimization, data mining and knowledge-based algorithm. This paper summarizes the strategies of relevant algorithms in the learning path recommendation, as well as their strengths and weaknesses. This paper also compares and analyzes their performance to discuss their practical application value in learning path recommendation.

DOI： 10.11527/jceeek.2018.0_258

種別：大会・シンポジウム等 

種別：学会・研究会等 

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