Research Article
An Online Big-Data Driven Design of Reading and Writing Test
@INPROCEEDINGS{10.1007/978-3-031-65126-7_29, author={Yuwei Sun and Yongcheng Wen and Yazhen Zhu}, title={An Online Big-Data Driven Design of Reading and Writing Test}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 19th EAI International Conference, QShine 2023, Shenzhen, China, October 8 -- 9, 2023, Proceedings, Part I}, proceedings_a={QSHINE}, year={2024}, month={8}, keywords={Learning-oriented test Online big-data driven task Reliability \& Validity of the test}, doi={10.1007/978-3-031-65126-7_29} }- Yuwei Sun
Yongcheng Wen
Yazhen Zhu
Year: 2024
An Online Big-Data Driven Design of Reading and Writing Test
QSHINE
Springer
DOI: 10.1007/978-3-031-65126-7_29
Abstract
This paper presents an online big-data driven design of reading and writing tests, incorporating empirical data analysis. The study aims to investigate the nature of reading and writing abilities, their corresponding relationship, and the impact of background variables on learning-oriented test performance. The counterpart was administered through an online platform, where data are collected for assessing the performance of students. The objectives of our work are to provide insights into the test design, delivery, and feedback mechanisms, and to conduct a statistical evaluation of the test’s reliability, validity, and correlations. The findings contribute to our understanding of reading and writing assessment in an online context, while also highlighting the implications of background variables on test performance.
