This study aimed to describe seven indicators of students’ verbal linguistic intelligence in reading subject. It used a qualitative research method. The subjects of this study were 30 students consisted of 9 male and 21 female students. They took the reading subject in the second semester of the first year. They were given a test of verbal-linguistic intelligence. Seven students were selected to be interviewed because they have verbal-linguistic intelligence and good communication. To find out the validity of the data, the researchers used triangulation of the test results and the results of interviews and triangulation of the second researcher and research assistants. Furthermore, the data were analyzed using the content analysis method which consisted of three steps, they were data reduction, data presentation, and conclusion drawing/verification. The results of the study show that there were seven indicators of verbal-linguistic intelligence of students in reading subject, first, having excellent initial knowledge in mentioning words, second, enjoying wordplay with Scrabble, third, entertaining themselves and other students by playing tongue twisters, fourth, explaining the meaning of the words written and discussed, fifth, having difficulties in mathematics lesson, sixth, their conversation refers to something they have read and heard, and the last, having the ability to write poetry based on personal experience.

One of the ways to enhance and improve the quality of learning delivery is through the use of technology, particularly the Internet, which facilitates faster and easier access to information. This research aims to explore the degree to which factors such as digital literacy, metacognitive awareness, meaningful learning, habits of using smartphones, and personal learning competence are related to one another. Both the relationship between metacognitive awareness and personal learning competence, as well as the relationship between smartphone habits and personal learning competence, are moderated by a moderating variable known as the fear of missing out. Fear of missing out is a moderating variable. Structural equation modeling, specifically partial least squares, was employed to analyze data from 597 engineering students. SmartPLS version 4 was the tool used for this analysis. The study found that the moderating variable, fear of missing out, significantly impacts metacognitive awareness, learning personal competence, and smartphone habits, making it a crucial factor to investigate. This result is significant because it is a variable that influences the learning that students go through for their education and because it is an extremely important thing to investigate.