10th International Congress on Information and Communication Technology

Authors - Elrasheed Ismail Mohommoud Zayid, Ahmad Mohammad Aldaleel, Omar Abdullah Omar Alshehri
Abstract - Machine learning classifiers are the first candidate methodology that could be used to assess the digital innovation across a set of teachers. This study aims to collect, build, represent, and discuss a reliable digital innovation skills (DIS) dataset by recruiting teachers chosen from the teachers who work in Bisha Province, Saudi Arabia. The study processed a rich data sample and made it accessible and shareable for the researchers' open use. DIS assessment addressed the problems and helped design a suitable innovation training module for the local community teachers. The total dataset comprises 400 conveniently collected data points, and each data point represents a complete record of teachers among the DSTs of Bisha Province. The research fields are prepared and set as fifty questionnaire questions, which distributed across the DSTs community in the area using social networks. Each question represents a single input or output feature for the classification model. Before running the ML models, the input variables are encoded serially from F0 to F49, and based on an explanatory test performed using LazyPredictools, only the positively contributing features are used. The extensive dataset, which is kept in the Mendeley Data repository, has a great deal of possibilities for reuse in sensitivity analysis, policymaking, and additional study. The decision tree, extra tree, and extreme gradient boosting (XGB) classifiers are examples of the recruited algorithms for evaluating DISs. The authors believe that this a wealthy kind of innovative respiratory dataset with its classification features will become a valuable mining source for interested researchers.