decision variable
Grouping is like a sieve, which helps us to filter out the miscellaneous factors that may affect the experimental results. Only in this way can we see the influence of intervention measures on students more accurately and make the experimental results more reliable!
Compare the experimental group and the control group.
Grouping can also make us see the effect of intervention more clearly. For example, we can divide students into different groups according to gender, age and grades. Then compare the control group and the experimental group and easily find out the difference between the two groups!
Improve experimental efficiency
Grouping is like a magician of time, which allows us to carry out experimental intervention and measurement on more students at the same time and make full use of every minute!