Using a Student Model to Improve Explanation in an ITS

This paper presents the use of a student model to improve the explanations provided by an intelligent tu- toring system,namely SimpleQuestl,in the domain of electronics.The method of overlay modelling is adopted to build the student model.The diagnosis is based on the comparison of the behavinurs of the student and the ex- pert.The student model is consulted by the “explainer” and “debugging” procedures in order to re-order the sequence of the explanation.

Are the total fertility rates of men and women different at below-replacement levels?An answer obtained from the G7 countries

A child is born to a father and a mother.This fact,however,is yet to be recognized by demography,in which fertility refers to women’s natural ability to give birth.The main reason for the absence of men is that data on births are more often available for women than for men.But in the last few decades,data availability has greatly improved.Recent studies show that total fertility rates(TFRs)of men can be calculated for most countries in the world and that the difference between the TFRs of men and women can be quite large.For low-fertility countries,nonetheless,these studies show little difference between the TFRs of men and women,giving rise to the question:Is men’s fertility worth further investigation?To avoid ambiguity in describing a particular difference as small or big,this paper provides a formula for probabilistic TFRs.Using hypothesis test on probabilistic TFRs,we can say that the difference between the TFRs of men and women is statistically significant for all the G7 countries,except for France.To explain the differences between the TFRs of men and women,this study uses models of stable populations and concludes that the one-sex stable population models cannot explain the results whereas a two-sex joint stable population model can do so.By using the two-sex population model,we can explain why the TFR of men in France is almost the same as that of women,and why it is lower than that of women in the other six G7 countries.More specifically,by using the model,we can help explain 76%of the variance in the difference between the TFRs of men and women.Future studies may be able to show that men’s TFR is significantly lower than women’s in other countries too and explain why it is so.The above findings,however,require closer attention and further investigation,because low fertility could lead to socioeconomic problems.Beyond TFRs,extending fertility studies from women to men as well,that is,conducting fertility studies on both women and men,will fundamentally improve our knowledge about fertility age patterns,trends,determinants,policies and other related issues.