Chakraborty Montosh, Reddy Y Anil Kumar, Ghoshal Joy A, Amudharaj D, Tripathi Mukesh
Department of Biochemistry, All India Institute of Medical Sciences, Guntur, Andhra Pradesh, India.
Department of Anatomy, All India Institute of Medical Sciences, Guntur, Andhra Pradesh, India.
J Educ Health Promot. 2021 Aug 31;10:302. doi: 10.4103/jehp.jehp_1125_20. eCollection 2021.
COVID-19 lockdown has mandated the medical colleges to start academics using electronic mode. Synchronous e-learning was started by our institute to replicate traditional classes in line with the routine academic schedule. the objective of this study attempted to assess the e-learning readiness of the students of our institute.
A cross-sectional descriptive study was planned using the model proposed by Oketch . with local modifications. The questionnaire was designed in Google Forms and mailed to respond using Likert scale. The nonparametric data collected from the total 84 respondents were analyzed for validity and reliability of the questionnaire, mean values to know the readiness (mean = 3.4), and one-step multiple regression to know the predictors.
The mean eLR (e-learning readiness) as evaluated from attitudinal readiness (Mean = 3.6), culture readiness (Mean = 2.3), material and technological readiness (Mean = 3.7), and mental health readiness (Mean = 2.4) is 3.03 (60.6% with = 84). Multiple regression analysis revealed that all the variables except MHR can significantly predict e-learning readiness linearly ( < 0.05).
The institute is ready for e-learning in terms of AR and MTR (mean values >3.4). CR and MHR still need a lot of improvisation to make it acceptable for e-learning. The model could explain 54.9% readiness level with CR as the most important predictor. More than 73% ( = 84) of the respondents have acknowledged the present form of online classes to be the best available option in COVID-19 lockdown and most of them are adapted to e-classes in the institute.
新冠疫情封锁要求医学院校采用电子模式开展学术活动。我们学院启动了同步电子学习，以按照常规学术日程复制传统课程。本研究旨在评估我院学生对电子学习的准备情况。
采用奥凯奇提出的模型并进行了局部修改，计划开展一项横断面描述性研究。问卷在谷歌表单中设计，并通过李克特量表邮寄给受访者作答。对从84名受访者收集的非参数数据进行分析，以评估问卷的有效性和可靠性、了解准备情况的平均值（均值 = 3.4）以及了解预测因素的一步多元回归分析。
从态度准备情况（均值 = 3.6）、文化准备情况（均值 = 2.3）、材料与技术准备情况（均值 = 3.7）和心理健康准备情况（均值 = 2.4）评估得出的平均电子学习准备度为3.03（n = 84时为60.6%）。多元回归分析显示，除心理健康准备度外，所有变量均可显著线性预测电子学习准备度（P < 0.05）。
就态度准备情况和材料与技术准备情况而言（均值 > 3.4），学院已做好电子学习的准备。文化准备情况和心理健康准备情况仍需大幅改进，以使其适用于电子学习。该模型可以解释54.9%的准备水平，其中文化准备情况是最重要的预测因素。超过73%（n = 84）的受访者认可当前形式的在线课程是新冠疫情封锁期间最佳的可用选择，并且他们中的大多数人已适应学院的电子课程。
