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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