skip to main content

Attention:

The NSF Public Access Repository (PAR) system and access will be unavailable from 11:00 PM ET on Friday, December 13 until 2:00 AM ET on Saturday, December 14 due to maintenance. We apologize for the inconvenience.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Adaptive Learning Material Recommendation in Online Language Education
Recommending personalized learning materials for online language learning is challenging because we typically lack data about the student’s ability and the relative difficulty of learning materials. This makes it hard to recommend appropriate content that matches the student’s prior knowledge. In this paper, we propose a refined hierarchical knowledge structure to model vocabulary knowledge, which enables us to automatically organize the authentic and up-to-date learning materials collected from the internet. Based on this knowledge structure, we then introduce a hybrid approach to recommend learning materials that adapts to a student’s language level. We evaluate our work with an online Japanese learning tool and the results suggest adding adaptivity into material recommendation significantly increases student engagement.  more » « less
Award ID(s):
1657176
PAR ID:
10095418
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
ArXiv.org
ISSN:
2331-8422
Page Range / eLocation ID:
https://arxiv.org/abs/1905.10893
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ( , Proceedings of the Thirty-Third International FLAIRS Conference (FLAIRS-32))
    This paper reports the findings of an empirical study on the effects and nature of self explanation during source code comprehension learning activities in the context of learning computer programming language Java. Our study shows that self explanation helps learning and there is a strong positive correlation between the volume of self-explanation students produce and how much they learn. Furthermore, selfexplanations as an instructional strategy has no discrepancy based on student’s prior knowledge. We found that participants explain target code examples using a combination of language, code references, and mathematical expressions. This is not surprising given the nature of the target item, a computer program, but this indicates that automatically evaluating such self-explanations may require novel techniques compared to self-explanations of narrative or scientific texts. 
    more » « less
  2. ; ; ; ( , The Thirty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-32))
    This paper reports the findings of an empirical study on the effects and nature of self explanation during source code comprehension learning activities in the context of learning computer programming language Java. Our study shows that self explanation helps learning and there is a strong positive correlation between the volume of self-explanation students produce and how much they learn. Furthermore, selfexplanations as an instructional strategy has no discrepancy based on student’s prior knowledge. We found that participants explain target code examples using a combination of language, code references, and mathematical expressions. This is not surprising given the nature of the target item, a computer program, but this indicates that automatically evaluating such self-explanations may require novel techniques compared to self-explanations of narrative or scientific texts. 
    more » « less
  3. ( , In Proceedings of the Thirty-Third International FLAIRS Conference (FLAIRS-33))
    This paper reports the findings of an empirical study on the effects and nature of self explanation during source code comprehension learning activities in the context of learning computer programming language Java. Our study shows that self explanation helps learning and there is a strong positive correlation between the volume of self-explanation students produce and how much they learn. Furthermore, selfexplanations as an instructional strategy has no discrepancy based on student’s prior knowledge. We found that participants explain target code examples using a combination of language, code references, and mathematical expressions. This is not surprising given the nature of the target item, a computer program, but this indicates that automatically evaluating such self-explanations may require novel techniques compared to self-explanations of narrative or scientific texts. 
    more » « less
  4. ; ; ( , Educational Researcher)

    Language in education for children and youth from low-income communities of color, including those learning English as an additional language, has been fraught for decades with ideological entanglements, conceptual ambiguities, and empirical limitations. Meanwhile, the teacher learning challenge to implement equitable teaching practices remains largely unresolved. With an eye toward improving equitable classroom talk (ECT)—that is, meaningful participation in disciplinary practices through communal and connected language interactions—for all students from minoritized communities, we integrate research on additional language development, disciplinary practices, sociocultural classroom interactions, and teacher learning. We recommend researcher-educator collaborations (a) develop indicators of ECT, (b) use lesson videos to make ECT visible, and (c) develop and test materials to support and scale teacher learning to enact ECT.

     
    more » « less
  5. ; ; ; ; ; ( , Educational Data Mining)
    We analyze teachers’ written feedback to students in an online learning environment, specifically a setting in which high school students in Uruguay are learning English as a foreign language. How complex should teachers’ feedback be? Should it be adapted to each student’s English profi- ciency level? How does teacher feedback affect the probability of engaging the student in a conversation? To explore these questions, we conducted both parametric (multilevel modeling) and non-parametric (bootstrapping) analyses of 27,627 messages exchanged between 35 teachers and 1074 students in 2017 and 2018. Our results suggest: (1) Teach- ers should adapt their feedback complexity to their students’ English proficiency level. Students who receive feedback that is too complex or too basic for their level post 13- 15% fewer comments than those who receive adapted feed- back. (2) Feedback that includes a question is associated with higher odds-ratio (17.5-19) of engaging the student in conversation. (3) For students with low English proficiency, slow turnaround (feedback after 1 week) reduces this odds ratio by 0.7. These results have potential implications for online platforms offering foreign language learning services, in which it is crucial to give the best possible learning expe- rience while judiciously allocating teachers’ time. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email