Department of Psychology, Ajou University, Suwon 16499, Korea.
Biohealth Convergence-Open-Sharing System, Dankook University, Cheonan 31116, Korea.
Int J Environ Res Public Health. 2022 May 10;19(10):5822. doi: 10.3390/ijerph19105822.
(1) Background: A learner's cognitive load in a learning system should be effectively addressed to provide optimal learning processing because the cognitive load explains individual learning differences. However, little empirical research has been conducted into the validation of a cognitive load measurement tool (cognitive load scale, i.e., CLS) suited to online learning systems within higher education. The purpose of this study was to evaluate the psychometric properties of the CLS in an online learning system within higher education through the framework suggested by the Standards for Educational and Psychological Testing. (2) Methods: Data from 800 learners were collected from a cyber-university in South Korea. The age of students ranged from 20 to 64. The CLS was developed, including three components: extraneous cognitive load, intrinsic cognitive load, and germane cognitive load. Then, psychometric properties of the CLS were evaluated including reliability and validity. Evidence relating to content validity, construct validity, and criterion validity were collected. The response pattern of each item was evaluated on the basis of item response theory (IRT). Cronbach's α was computed for reliability. (3) Results: The CLS presented high internal consistency. A three-factor model with extraneous cognitive load, intrinsic cognitive load, and germane cognitive load was suggested by exploratory and confirmatory factor analysis. This three-factor model is consistent with the previous research into the cognitive load in an offline learning environment. Higher levels of the extraneous cognitive load and intrinsic cognitive load were related to lower levels of academic achievement in an online learning environment, but the germane cognitive load was not significantly positively associated with midterm exam scores, though it was significantly related to the final exam scores. IRT analysis showed that the item-fit statistics for all items were acceptable. Lastly, the measurement invariance was examined through differential item functioning analysis (DIF), with the results suggesting that the items did not contain measurement variance in terms of gender. (4) Conclusions: This validation study of the CLS in an online learning environment within higher education assesses psychometric properties and suggests that the CLS is valid and reliable with a three-factor model. There is a need for an evaluation tool to take into account the cognitive load among learners in online learning system because the characteristics of learners within higher education were varied. This CLS will help instructional/curriculum designers and educational instructors to provide more effective instructions and identify individual learning differences in an online learning environment within higher education.
（1）背景：学习者在学习系统中的认知负荷应得到有效处理，以提供最佳学习处理，因为认知负荷解释了个体学习差异。然而，在高等教育中，针对在线学习系统的认知负荷测量工具（认知负荷量表，即 CLS）的验证方面，实证研究很少。本研究旨在通过教育和心理测试标准建议的框架，评估高等教育中在线学习系统内的 CLS 的心理测量特性。
（2）方法：从韩国一所网络大学收集了 800 名学习者的数据。学生的年龄从 20 岁到 64 岁不等。CLS 包括三个组成部分：额外认知负荷、内在认知负荷和相关认知负荷，然后对其进行了可靠性和有效性评估。收集了与内容有效性、结构有效性和标准有效性有关的证据。基于项目反应理论（IRT）评估了每个项目的反应模式。可靠性计算了 Cronbach 的α。
（3）结果：CLS 呈现出较高的内部一致性。通过探索性和验证性因子分析，提出了一个包含额外认知负荷、内在认知负荷和相关认知负荷的三因素模型。这个三因素模型与离线学习环境中的认知负荷的先前研究一致。在在线学习环境中，较高的额外认知负荷和内在认知负荷与较低的学业成绩相关，但相关认知负荷与期中考试成绩没有显著正相关，尽管它与期末考试成绩显著相关。IRT 分析表明，所有项目的项目拟合统计数据都可以接受。最后，通过差异项目功能分析（DIF）检验了测量不变性，结果表明，这些项目在性别方面没有包含测量方差。
（4）结论：这项高等教育在线学习环境中 CLS 的验证研究评估了心理测量特性，并表明 CLS 具有三因素模型的有效性和可靠性。需要一种评估工具来考虑在线学习系统中学习者的认知负荷，因为高等教育中的学习者的特点是多样化的。这个 CLS 将有助于教学/课程设计师和教育讲师在高等教育的在线学习环境中提供更有效的指导，并识别个体学习差异。
