Evaluating Online Assessment Strategies: A Systematic Review of Reliability and Validity in E-Learning Environments
Authors/Creators
- 1. College of Education, Zhejiang Normal University, China
- 2. College of Software Engineering, Nankai University, China
Description
This article offers a meticulous exploration of the effectiveness of online assessment methods within digital learning spheres. This systematic review, formulated around the critical question of the reliability and validity of these methods, delves into a comprehensive analysis of 16 meticulously chosen studies from an initial pool of 1500. The research emphasizes the increasing significance of technological evolution in reshaping educational assessment, focusing on adapting digital tools like AI analytics and adaptive testing. Central to the findings is the contextual importance of online assessments in diverse learning environments, influenced by technological infrastructure and student demographics. The review critically examines the challenges in ensuring consistent and accurate evaluations across varying digital platforms, stressing the need for reliable and valid assessments that resonate with real-world skills. Furthermore, the article underscores the critical role of educators in this digital transition, highlighting the necessity for ongoing professional development and community building among teaching professionals. It advocates for inclusive and adaptable assessment strategies that cater to a wide range of learners, including those with special educational needs. Conclusively, the review synthesizes these insights to emphasize the multifaceted nature of online assessment. It calls for a balanced approach that integrates technology's benefits and challenges. The article concludes with a call for continued research and tailored, context-specific assessment strategies, providing valuable guidance for educators, policymakers, and researchers committed to enhancing the quality and equity of digital education.
Files
6 (12) 1-18.pdf
Files
(1.1 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:905a4902d3b14fd730055bb21808728d
|
1.1 MB | Preview Download |