Research Article
Alfiya R. Masalimova, Elmira R. Khairullina, Natalya I. Lapidus, Natalia A. Orekhovskaya, Marina R. Zheltukhina, Ekaterina A. Baranova
CONT ED TECHNOLOGY, Volume 14, Issue 3, Article No: ep371
ABSTRACT
Smartphones are mobile technology cutting-edge. Daily, the amount of time spent on a phone increases. Excessive smartphone use and addiction have developed into big social issues. Addiction to smartphones is a negative and pathological concept that is assessed by a set of subjective and behavioral symptoms including fixation, loss of control, and withdrawal symptoms. Teachers in STEM fields have a higher degree of involvement with their students in the use of digital tools. STEM teacher candidates must demonstrate an understanding of how to incorporate technology successfully into classroom activities. Determine the incidence of smartphone addiction among prospective STEM educators to justify future awareness training. The association between pre-service STEM instructors’ smartphone addiction was studied. The research included 242 persons, 180 of whom were females and 62 males. The SAI is self-administered and scored independently for each dimension. Data is analyzed using machine learning techniques. Cluster analysis is used to analyze the inventory’s dimensions. The k-means technique is used for cluster analysis. The library’s SHAP (SHapley additive explanations) approach was used to evaluate the classification result and assess the impact of attributes on the classification result. According to the findings, the highest level was judged to be 30 participants. Approximately 3%4 participants are deemed moderate (high and very high). Also, 48 people are at a low level. In terms of the overall group, it is modest. Being in the lowest cluster is linked to 100+ uses, whereas being in the highest cluster is linked to 6-10 uses. The exact degree of smartphone use linked to smartphone addiction is unknown. Females inversely correlate with the highest and lowest clusters.
Keywords: smartphone addiction, pre-service teachers, machine learning algorithm, cluster analysis
Research Article
Min Young Doo
CONT ED TECHNOLOGY, Volume 14, Issue 1, Article No: ep331
ABSTRACT
This study investigated flipped learners’ perceptions, perceived usefulness, intention to register for flipped learning classes, and learning engagement using cluster analysis with a sample of 306 undergraduate students in flipped classes. The students were classified into five clusters based on their level of social influence and cognitive instrumental processes using the technology acceptance model 2. There were significant differences among the five clusters in perceived usefulness, intention to register for flipped learning classes, and learning engagement. The perception of and preference for flipped learning also varied by cluster. The flipped learning feature that most participants commented on was online pre-learning sessions. The advantages of flipped learning that have to do with online pre-learning sessions include facilitating understanding of lectures, providing review opportunities for learning, flexible learning time, and individualized learning. These advantages indicate that providing students with instructionally sound pre-learning sessions leads to successful flipped learning. The research findings also suggest that flipped class instructors and school administrators utilize cluster analysis with meaningful variables to provide students with effective and tailored learning support.
Keywords: flipped learning, perceptions of flipped learning, perceived usefulness, learning engagement, registration intention, cluster analysis