An interpretable prediction method for university student academic crisis warning

Zhai, Mingyu; Wang, Sutong; Wang, Yanzhang; Wang, Dujuan

doi:10.1007/s40747-021-00383-0

Cited by 28 publications

(15 citation statements)

References 24 publications

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“…Advances in Multimedia change the on-campus training into a simulated enterprise training, whether it is the clothing worn by the students or the management method, the unified management of the enterprise is adopted, and then a real enterprise production environment is simulated to help students carry out practical operations [14]. Through the above methods, students can feel the real job position, and it is conducive to improving the teaching quality and effectiveness of colleges and universities and also greatly narrows the gap between colleges and universities and enterprises, and facilitating the integration of higher education institutions and enterprises [15]. Teachers occupy a very important position in the teaching process.…”

Section: Introductionmentioning

confidence: 99%

A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms

Min

2022

Advances in Multimedia

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In order to improve the talent training effect of electrical courses, this paper proposes a talent training model for electrical courses considering diverse constraint models and knowledge recognition algorithms. In order to obtain better performance of traditional deep learning models, it is usually necessary to increase the parameter scale of traditional deep learning models. Pretrained language models can be trained unsupervised directly using unlabeled corpora to learn vector representations of words without using labeled datasets. In addition, this paper uses the knowledge base and alias dictionary to build a knowledge graph and constructs a teaching model for electrical courses considering diverse constraint models and knowledge recognition algorithms. Through the research, it can be seen that the experimental teaching model of electrical courses proposed in this paper considering diverse constraint models and knowledge recognition algorithms has a very good effect on talent training.

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Section: Introductionmentioning

confidence: 99%

A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms

Min

2022

Advances in Multimedia

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“…(2) Multi-dimensional and multi-source meteorological and hydrological data will be considered in future studies to provide more accurate estimations for water availability, referring to more advanced prediction models, e.g. interpretable prediction model [47]. (3) Further discussion on this topic will be held with more uncertain factors, e.g.…”

Section: Discussionmentioning

confidence: 99%

Robust programming for basin-level water allocation with uncertain water availability and policy-driven scenario analysis

Yao

Hou

2021

Complex Intell. Syst.

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Uncertainties from hydrological and meteorological environments constantly pose disturbances to water sustainability. Programming under such uncertainties aims at finding solutions to this risky condition. From the sight of uncertain water availability, this paper builds a water life cycle model to reduce the risks of inappropriate estimations of water availability within a river basin and incorporates the results in robust programming. Then, a policy-driven scenario analysis is conducted to provide managerial implications in terms of ongoing water-saving policies. With Min–Tuo river basin as the case, we finally reach the conclusions that: (1) Equity is a necessity when considering the water allocation in a river basin, which enables a more sustainable mode of local water use. (2) Local citizens’ willingness to follow the policies is a key to relieve the water pressure, while the progress of water-saving techniques could add to its effectiveness.

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“…It is commonly used to allocate benefits between collaborators. The SHAP value of a feature is the marginal contribution of that feature to the feature mix [52][53][54], i.e., the impact of that feature on the final output value is calculated. SHAP is widely used.…”

Section: Xgboost-based Shap Modelmentioning

confidence: 99%

Novel Feature-Based Difficulty Prediction Method for Mathematics Items Using XGBoost-Based SHAP Model

Yi,

Sun,

2024

Mathematics

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The level of difficulty of mathematical test items is a critical aspect for evaluating test quality and educational outcomes. Accurately predicting item difficulty during test creation is thus significantly important for producing effective test papers. This study used more than ten years of content and score data from China’s Henan Provincial College Entrance Examination in Mathematics as an evaluation criterion for test difficulty, and all data were obtained from the Henan Provincial Department of Education. Based on the framework established by the National Center for Education Statistics (NCES) for test item assessment methodology, this paper proposes a new framework containing eight features considering the uniqueness of mathematics. Next, this paper proposes an XGBoost-based SHAP model for analyzing the difficulty of mathematics tests. By coupling the XGBoost method with the SHAP method, the model not only evaluates the difficulty of mathematics tests but also analyzes the contribution of specific features to item difficulty, thereby increasing transparency and mitigating the “black box” nature of machine learning models. The model has a high prediction accuracy of 0.99 for the training set and 0.806 for the test set. With the model, we found that parameter-level features and reasoning-level features are significant factors influencing the difficulty of subjective items in the exam. In addition, we divided senior secondary mathematics knowledge into nine units based on Chinese curriculum standards and found significant differences in the distribution of the eight features across these different knowledge units, which can help teachers place different emphasis on different units during the teaching process. In summary, our proposed approach significantly improves the accuracy of item difficulty prediction, which is crucial for intelligent educational applications such as knowledge tracking, automatic test item generation, and intelligent paper generation. These results provide tools that are better aligned with and responsive to students’ learning needs, thus effectively informing educational practice.

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An interpretable prediction method for university student academic crisis warning

Cited by 28 publications

References 24 publications

A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms

A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms

Robust programming for basin-level water allocation with uncertain water availability and policy-driven scenario analysis

Novel Feature-Based Difficulty Prediction Method for Mathematics Items Using XGBoost-Based SHAP Model

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