Linguistic Skill Modeling for Second Language Acquisition
| Autor: | Brian Zylich, Andrew S. Lan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Recall
Context (language use) 02 engineering and technology 010501 environmental sciences 01 natural sciences Second-language acquisition Linguistics Memorization Second language ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing TUTOR computer Word (computer architecture) 0105 earth and related environmental sciences computer.programming_language Meaning (linguistics) |
| Zdroj: | LAK |
| DOI: | 10.1145/3448139.3448153 |
| Popis: | To adapt materials for an individual learner, intelligent tutoring systems must estimate their knowledge or abilities. Depending on the content taught by the tutor, there have historically been different approaches to student modeling. Unlike common skill-based models used by math and science tutors, second language acquisition (SLA) tutors use memory-based models since there are many tasks involving memorization and retrieval, such as learning the meaning of a word in a second language. Based on estimated memory strengths provided by these memory-based models, SLA tutors are able to identify the optimal timing and content of retrieval practices for each learner to improve retention. In this work, we seek to determine whether skill-based models can be combined with memory-based models to improve student modeling and especially retrieval practice performance for SLA. In order to define skills in the context of SLA, we develop methods that can automatically extract multiple types of linguistic features from words. Using these features as skills, we apply skill-based models to a real-world SLA dataset. Our main findings are as follows. First, incorporating lexical features to represent individual words as skills in skill-based models outperforms existing memory-based models in terms of recall probability prediction. Second, incorporating additional morphological and syntactic features of each word via multiple-skill tagging of each word further improves the skill-based models. Third, incorporating semantic features, like word embeddings, to model similarities between words in a learner’s practice history and their effects on memory also improves the models and appears to be a promising direction for future research. |
| Databáze: | OpenAIRE |
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