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An Online Lecture Concentration According to Online Learning Behavior, Learner Characteristics, Learning Satisfaction, and Teacher-student Interaction of University Students

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KMID : 0928020210250010057
±Ç¼ÒÈñ ( Kwon So-Hi ) - Kyungpook National University College of Nursing

±è½Å¿µ ( Kim Shin-Young ) - Kyungpook National University College of Nursing
±èÁöÇö ( Kim Ji-Hyun ) - Kyungpook National University College of Nursing
±èÀººó ( Kim Eun-Bin ) - Kyungpook National University College of Nursing
Á¤¿¬¿ì ( Jeong Yeon-Woo ) - Kyungpook National University College of Nursing
±Ç³ª¿µ ( Kwon Na-Young ) - Kyungpook National University College of Nursing
±ÇÇý¿µ ( Kwon Hye-Young ) - Kyungpook National University College of Nursing
¹Ú½ÃÀº ( Park Si-Eun ) - Kyungpook National University College of Nursing
º¯Çý¿ø ( Byun Hye-Won ) - Kyungpook National University College of Nursing
¹Ú½¾ ( Park Seurk ) - Kyungpook National University College of Nursing

Abstract

Purpose: Because of the COVID-19 pandemic in 2020, colleges had no choice but to introduce online classes without sufficient preparation. As a result, various problems emerged in college students¡¯ learning experience. To establish a strategy for improving online education quality, the necessity to understand university students¡¯ learning behavior, learner characteristics, learning satisfaction, and the impact of professor-student interaction on online lecture concentricity.
Methods: This study is a cross-sectional survey conducted on college students from nine universities who experienced online lectures in the first semester of 2020. Online lecture learning behavior, learner characteristics, learning satisfaction, and professor-student interaction were analyzed using t-test, ANOVA, and Pearson¡¯s correlation coefficient to explore the difference in online lecture concentration.

Results: The concentration in-class lectures and online lectures averaged 7.04 and 5.73, respectively. According to the analysis, online learning concentration was a significant positive correlation by learner characteristics (r=.40, p<.001), professor-student interaction (r=.30, p<.001), online lecture evaluation (r=.34, p<.001).

Conclusion: Therefore, it is suggested that universities should establish guidance strategies related to lecture time zones, measures for managing and supervising learners, reflecting online lecture evaluation results, and strategies for improving student interaction.
KeyWords
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Learning behavior, Lecture concentration, Personal satisfaction, Teacher-student interaction
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