Artificial Intelligence Education System Based on Differential Evolution Algorithm to Optimize SVM

The artificial intelligence education system promotes the rooting of artificial intelligence in the education field and accelerates its entry into the era of intelligent education. This article focuses on the development of the artificial intelligence education system and proposes an artificial intelligence education system based on differential evolution algorithm optimization support vector machine. First, the processing of educational demand information data is automated, then a differential evolution algorithm is built to optimize the support vector machine model, and the model is used to implement various educational tasks to achieve automated education. The test results show that the model classification accuracy, classification recall rate, classification accuracy rate, and F1-score value are 4 items. Performances have been improved to improve the efficiency of education work and provide a reference for exploring the application and practice of artificial intelligence in education.

Money matters (especially if you are good at math): Numeracy, verbal intelligence, education, and income in satisfaction judgments

Objective numeracy, the ability to understand and use mathematical concepts, has been related to superior decisions and life outcomes. Unknown is whether it relates to greater satisfaction in life. We investigated numeracy’s relations with income satisfaction and overall life satisfaction in a diverse sample of 5,525 American adults. First, more numerate individuals had higher incomes; for every one point higher on the eight-item numeracy test, individuals reported $4,062 more in annual income, controlling for education and verbal intelligence. Combined, numeracy, education, and verbal intelligence explained 25% of the variance in income while Big-5 personality traits explained less than 4%. Further, the higher incomes associated with greater numeracy were related to more positive life evaluations (income and life satisfaction). Second, extant research also has indicated that the highly numerate compare numbers more than the less numerate. Consistent with numeracy-related income comparisons, numeracy moderated the relation between income and life evaluations, meaning that the same income was valued differently by those better and worse at math. Specifically, among those with lower incomes, the highly numerate were less satisfied than the less numerate; this effect reversed among those with higher incomes as if the highly numerate were aware of and made comparisons to others’ incomes. Further, no clear income satiation point was seen among those highest in numeracy, and satiation among the least numerate appeared to occur at a point below $50,000. Third, both education and verbal intelligence related to income evaluations in similar ways, and numeracy’s relations held when controlling for these other relations. Although causal claims cannot be made from cross-sectional data, these novel results indicate that numeracy may be an important factor underlying life evaluations and especially for evaluations concerning numbers such as incomes. Finally, this study adds to our understanding of education and intelligence effects in life satisfaction and happiness.