<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>As society requires a deeper understanding and demand for the actual abilities of students in higher education institutions, traditional assessment tests no longer meet the current needs. This paper first divides assessment techniques into two main categories from an application perspective: assessment of student learning performance and in-depth cognitive diagnosis. Students are automatically provided with appropriate learning content based on their ability level and learning style, providing them with accurate and timely feedback. Secondly, a new fuzzy inference model is proposed to determine students’ student outcomes by addressing the obvious shortcomings of the fuzzy sets usually used for student outcome assessment. Finally, the validity and usefulness of its assessment model are verified by the student learning performance on a real data set. The results show that the fuzzy inference assessment model designed in this paper can obtain an assessment accuracy of 85.8% for the learner’s learning outcomes, which has a good assessment effect. And the fuzzy inference assessment model also retains the greatest advantage of linear fitting regression, which reflects the correlation between the parameters of students’ learning behaviors and the final learning outcomes. The assessment method based on the fuzzy inference model predicts learners’ learning risks and provides learning interventions in advance for smart learning, and also provides new ideas for deepening education reform.</p>
</abstract>

As society requires a deeper understanding and demand for the actual abilities of students in higher education institutions, traditional assessment tests no longer meet the current needs. This paper first divides assessment techniques into two main categories from an application perspective: assessment of student learning performance and in-depth cognitive diagnosis. Students are automatically provided with appropriate learning content based on their ability level and learning style, providing them with accurate and timely feedback. Secondly, a new fuzzy inference model is proposed to determine students’ student outcomes by addressing the obvious shortcomings of the fuzzy sets usually used for student outcome assessment. Finally, the validity and usefulness of its assessment model are verified by the student learning performance on a real data set. The results show that the fuzzy inference assessment model designed in this paper can obtain an assessment accuracy of 85.8% for the learner’s learning outcomes, which has a good assessment effect. And the fuzzy inference assessment model also retains the greatest advantage of linear fitting regression, which reflects the correlation between the parameters of students’ learning behaviors and the final learning outcomes. The assessment method based on the fuzzy inference model predicts learners’ learning risks and provides learning interventions in advance for smart learning, and also provides new ideas for deepening education reform.

A study on the measurement and standardized assessment model of student learning outcomes in vocational institutions

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

As society requires a deeper understanding and demand for the actual abilities of students in higher education institutions, traditional assessment tests no...

A study on the measurement and standardized assessment model of student learning outcomes in vocational institutions

Pubblicato online: 30 set 2023

Pagine: -

Ricevuto: 21 ott 2022

Accettato: 10 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00400

Parole chiave
Fuzzy inference model, Cognitive diagnosis, Student outcome assessment, Fuzzy set, Data set.

© 2023 Minxiao Zhao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A study on the measurement and standardized assessment model of student learning outcomes in vocational institutions

Pubblicato online: 30 set 2023

Pagine: -

Ricevuto: 21 ott 2022

Accettato: 10 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00400

Parole chiaveFuzzy inference model, Cognitive diagnosis, Student outcome assessment, Fuzzy set, Data set.

© 2023 Minxiao Zhao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Fuzzy inference model, Cognitive diagnosis, Student outcome assessment, Fuzzy set, Data set.