Student diagnosis in practice; bridging a gap

Ragnemalm, Eva L.

doi:10.1007/bf01099757

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…This study focuses on presentation of pre-generated learning content designed with respect to different learning styles for different student models. The student models can be generated for three different purposes, and different types of data are required (Ragnemalm, 1996): Style-based approaches rely on gathering data about learning styles, motivation-based approaches rely on gathering data to increase student motivation (Martens, Gulikers, & Bastiaens, 2004), and knowledge-based approaches rely on gathering data about students' knowledge levels to set difficulty levels for questions and to select related learning content to compensate for students' weak points. This study is based on capturing learning styles of students with the help of student models.…”

Section: Methodsmentioning

confidence: 99%

Effect of Bayesian Student Modeling on Academic Achievement in Foreign Language Teaching (University Level English Preparatory School Example)

Aslan¹,

Öztürk²,

İnceoğlu³

2014

ESTP

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Considering the increasing importance of adaptive approaches in CALL systems, this study implemented a machine learning based student modeling middleware with Bayesian networks. The profiling approach of the student modeling system is based on Felder and Silverman's Learning Styles Model and Felder and Soloman's Index of Learning Styles Questionnaire. The questionnaire was adapted to Turkish for this experimental study conducted with respect to the visual/verbal and active/reflective dimensions of the model. A topic in EFL was chosen for the learning content design, which was also carried into the digital domain and remastered as separate learning scenes for different learning styles. Computer software was also implemented to carry out the experimental learning processes. A quasi-experimental pre-test, post-test design was conducted with 46 volunteers, with 23 students assigned each to a control and an experimental group to compare academic achievement between student-based learning and conventional computer-based learning. No significant difference was found in academic achievement between the control and experimental groups after the experimental treatment. The diagnostic performance of the proposed student modeling system was also compared with performances from similar studies. This student modeling system had a successful prediction rate of 41% on the visual/verbal dimension and 54% on the active/reflective dimension, respectively.

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Section: Methodsmentioning

confidence: 99%

Effect of Bayesian Student Modeling on Academic Achievement in Foreign Language Teaching (University Level English Preparatory School Example)

Aslan¹,

Öztürk²,

İnceoğlu³

2014

ESTP

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“…Report. e student diagnostic report provides assessment for individual students, which could help teachers identify areas of students' strengths and weaknesses [20,22,24]. Traditional deep learning method can only give a vague mastery description of each knowledge.…”

Section: Student Diagnosismentioning

confidence: 99%

HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing

Tong¹,

Wang²,

Zhou³

et al. 2020

Preprint

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Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. Given learners' exercise records, a knowledge tracing model can trace their hidden knowledge state dynamically. In recent years, many deep learning models have been applied to tackle the KT task, which has shown promising results. However, they still have limitations. Most existing methods simplify the exercising records as knowledge sequence, which fails to explore rich information existed in exercise texts. Besides, the latent hierarchical graph nature of exercises and knowledge remain unexplored. us, in this paper, we propose a hierarchical graph knowledge tracing model framework (HGKT) which can leverage the advantages of hierarchical exercise graph and sequence model to enhance the ability of knowledge tracing. Besides, we introduce the concept of problem schema to be er represent a group of similar exercises and propose a hierarchical graph neural network to learn representations of problem schemas. Moreover, in the sequence model, we employ two a ention mechanisms to highlight important historical states of students. In the testing stage, we present a K&S diagnosis matrix that could trace the transition of mastery of knowledge and problem schema, which can more easily be applied to di erent applications. Finally, we conduct extensive experiments to evaluate the model on a large scale real-world dataset.e results prove the e ectiveness of our model and the diversity of its application scenarios.

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