A Design of a Simple Yet Effective Exercise Recommendation System in K-12 Online Learning

Huang, Shuyan; Liu, Qiongqiong; Chen, Jiahao; Hu, Xiangen; Liu, Zitao; Luo, Weiqi

doi:10.1007/978-3-031-11647-6_36

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…As a result, a variety of contemporary machine learning methods [1,25,27,30,32,38] have been developed to track learner skill level from historical data. Such methods demonstrate strong empirical success and thus have been leveraged to adaptively recommend exercises to learners [15,37] or even generate new exercises based on skill level [8]. This adaptivity can be viewed as a complementary piece to the problem of exercise retrieval directed language learning that we study in this paper: learner initiated personalization can leverage existing tools from adaptivity to ensure exercises are both relevant and at the right skill level.…”

Section: Related Workmentioning

confidence: 99%

“…and adaptively serve exercises to learners [8,15,37]. Adaptivity, while a critical part of any personalized education system, is a passive form of personalization from the learner's point of view: While exercises are tailored to an estimate of the learner's skill level, this customization occurs behind the scenes, with no opportunity for learners to take initiative in shaping the learning process.…”

Section: Introductionmentioning

confidence: 99%

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Large language model augmented exercise retrieval for personalized language learning

Xu,

Monroe,

Bicknell

2024

Proceedings of the 14th Learning Analytics and Knowledge Conference

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We study the problem of zero-shot exercise retrieval in the context of online language learning, to give learners the ability to explicitly request personalized exercises via natural language. Using realworld data collected from language learners, we observe that vector similarity approaches poorly capture the relationship between exercise content and the language that learners use to express what they want to learn. This semantic gap between queries and content dramatically reduces the effectiveness of general-purpose retrieval models pretrained on large scale information retrieval datasets like MS MARCO [2]. We leverage the generative capabilities of large language models to bridge the gap by synthesizing hypothetical exercises based on the learner's input, which are then used to search for relevant exercises. Our approach, which we call mHyER, overcomes three challenges: (1) lack of relevance labels for training, (2) unrestricted learner input content, and (3) low semantic similarity between input and retrieval candidates. mHyER outperforms several strong baselines on two novel benchmarks created from crowdsourced data and publicly available data.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Large language model augmented exercise retrieval for personalized language learning

Xu,

Monroe,

Bicknell

2024

Proceedings of the 14th Learning Analytics and Knowledge Conference

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“…For example, Chen et al [28] proposed a simple and effective solution for building a practical teacher recommendation system for one-to-one online courses. Huang et al [29] proposed a simple but effective method for recommending high-quality and varied student exercises. Chang et al [30] proposed a "keyword cloud" learning interest/difficulty reminder system based on learners' videoviewing logs and subtitles to promote self-directed learning.…”

Section: Introductionmentioning

confidence: 99%

A Course Teacher Recommendation Method Based on an Improved Weighted Bipartite Graph and Slope One

2022

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The quality of course teaching is directly related to education quality. Many scholars have attempted to identify the associations between course-teaching quality and teachers' characteristics, such as educational background, degree, professional title, age, teaching age, job burnout, and academic research. However, because these characteristics are mostly evolvable, research findings are inconsistent. Therefore, we attempted to identify the association between teaching styles that reflect teachers' stable psychological quality, Technological Pedagogical Content Knowledge (TPACK), and teaching quality. To this end, we first collected data from three different disciplines at a university using the constructed teaching quality, TPACK, and course difficulty questionnaires, together with the TSTI scale proposed by Grigorenko and Sternberg. We constructed three matrices with different sparsities as experimental datasets using teachers with the teaching style and PTACK attributes, courses with the course difficulty attribute, and teaching quality. We then constructed a weighted bipartite graph with the teachers and courses in the matrix as nodes and the teaching quality divided by course difficulty as the weights of the edges. We proposed an improved Slope One algorithm based on a weighted bipartite graph to scientifically predict teachers' teaching quality in untaught courses. Finally, we constructed a TOP-N recommendation model for course teachers that combined teaching style and TPACK features to achieve accurate recommendations for course teachers. The experiments show that our proposed solution is feasible and that the algorithmic model is effective. Therefore, we developed a scientific method to improve the quality of university course teaching.

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Modeling Portraits of Students and Exercises for Exercise Recommendation

Gao

Zhao

et al. 2023

Lecture Notes in Computer Science

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A Design of a Simple Yet Effective Exercise Recommendation System in K-12 Online Learning

Cited by 4 publications

References 16 publications

Large language model augmented exercise retrieval for personalized language learning

Large language model augmented exercise retrieval for personalized language learning

A Course Teacher Recommendation Method Based on an Improved Weighted Bipartite Graph and Slope One

Modeling Portraits of Students and Exercises for Exercise Recommendation

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