Structural Model of Metacognition in Online Learning during the Covid-19 Pandemic

During the Covid-19 pandemic crisis, the conventional learning process through face-to-face interactions between lecturers and students was suspended due to the implementation of the social restrictions policy by the government to control and reduce the spread of the coronavirus. At that time, the learning process was carried out online, the interaction between lecturers and students through internet communication media by utilizing applications via e-Iearning, google meetings, zoom, and so on. This study aims to develop a measurement model of self-efficacy, digital literacy, and metacognition in higher education and to develop a metacognitive structural model of undergraduate students in The Electrical Engineering Department (EED) Faculty of Engineering (FE) Unesa, and the structural model of metacognition of EED-FE Unesa students in online learning during the Covid-19 pandemic. The research design uses predictive methods to assess self-efficacy and digital literacy on metacognition in online learning. The instrument is validated by experts in the fields of vocational education, learning technology, and educational evaluation. Data collection techniques using survey methods with online communication. The research population is students of the EED-FE Unesa. The research sample was collected through stratified purposive sampling. The data analysis technique is a quantitative research approach that uses a structural equation model (SEM). The SEM design uses the analysis of two models, and there were the measurement model and the structural model. There are three latent variables of the measurement model, i.e., digital self-efficacy, digital literacy, and metacognition. The results showed that the measurement model was able to produce convergent validity, meaning that the items to measure the construct were valid. This means that the indicator variable is correlated with the latent variable. The structural model of metacognition fit with data.