Using MLP-F in Three Different Aberrant Behaviors in Education
Saved in:
| Title: | Using MLP-F in Three Different Aberrant Behaviors in Education |
|---|---|
| Language: | English |
| Authors: | Zhemin Zhu (ORCID |
| Source: | Journal of Educational and Behavioral Statistics. 2026 51(1):91-118. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 28 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Artificial Intelligence, Student Behavior, Testing, Cheating, Response Style (Tests), Testing Problems, Mathematical Formulas, Identification |
| DOI: | 10.3102/10769986241310527 |
| ISSN: | 1076-9986 1935-1054 |
| Abstract: | Zhu et al. proposed a person-fit method based on the neural network called machine learning person-fit method (MLP-F) and found promising improvements over some traditional methods. MLP-F relies on constructing an appropriate neural network and uses mean square error as a loss function of the neural network. The primary focus of this study is to explore the potential improvement in classifications by replacing mean squared error with cross-entropy. Additionally, the application of MLP-F requires the establishment of a large number of output nodes when dealing with numerous attributes in an exam. However, an excess of nodes in the output layer may diminish classification accuracy and escalate the demand for training data. This article introduces a novel neural network architecture designed to be more versatile and robust. The findings from the research indicate that utilizing a cross-entropy loss function and the new neural network architecture enhances the performance of MLP-F. Simulation studies, considering various aberrant behaviors, demonstrate that MLP-F is effective in identifying aberrant behaviors and particularly excels in shorter tests, showcasing its potential significance in classroom testing. |
| Abstractor: | As Provided |
| Entry Date: | 20260101 |
| Accession Number: | EJ1514382 |
| Database: | ERIC |
| Abstract: | Zhu et al. proposed a person-fit method based on the neural network called machine learning person-fit method (MLP-F) and found promising improvements over some traditional methods. MLP-F relies on constructing an appropriate neural network and uses mean square error as a loss function of the neural network. The primary focus of this study is to explore the potential improvement in classifications by replacing mean squared error with cross-entropy. Additionally, the application of MLP-F requires the establishment of a large number of output nodes when dealing with numerous attributes in an exam. However, an excess of nodes in the output layer may diminish classification accuracy and escalate the demand for training data. This article introduces a novel neural network architecture designed to be more versatile and robust. The findings from the research indicate that utilizing a cross-entropy loss function and the new neural network architecture enhances the performance of MLP-F. Simulation studies, considering various aberrant behaviors, demonstrate that MLP-F is effective in identifying aberrant behaviors and particularly excels in shorter tests, showcasing its potential significance in classroom testing. |
|---|---|
| ISSN: | 1076-9986 1935-1054 |
| DOI: | 10.3102/10769986241310527 |
