interpreting and commenting on Repeated-Measures ANOVA results

#1
Hello, everybody,
you've always been really helpful, so I've got a question to you, again :)

I am dealing with an assignment, where we have to run repeated-measures ANOVA
the assignment is as follows (I am an English Teacher, so the research is in Eng.Teach,field):

A researcher wants to find out whether the explicit or implicit instruction has a better effect on the L2 English scores of EFL learners, particularly when combined with the presence/absence of recasts.

I have found that there are following significant interactions:
• time, feedback and instruction type (F(1,73)=75,549, p<0,001);
• time, test type, feedback type, and instruction type (F(1,73)=8,128, p<0,05).
• feedback type and instruction type (F(1,73)= 18,390, p<0,05).

and now i have to conclude on what i have found. I did it, but I feel it looks very confusing.
so
If you could have a look at it and comment on it, or at least suggest some links where i can find sample reporting on ANOVA results

my conclusion as I wrote it:
The absence of recast for students receiving explicit instruction produced significantly higher scores over the January-to-June time period, whereas the recast presence in implicit instruction did not have the same significant effect: F(1,73)=75,549, p<0,001.
In addition, the effect of the interaction between all the factors (time, test type, feedback and instruction type) revealed that students receiving implicit instructions with no recast scored significantly higher on the Comprehensive test in January, whereas those receiving the explicit instruction did not show a significant difference in January, but their scores were significantly higher than those with recasts in June. The production test scores obtained in June showed significantly higher scores for the explicit teaching group with no recast than those with recast; and the production test scores obtained in January showed significantly higher scores for the implicit group with no recast. [F(1,73)=75,549, p<0,001]

thanx a million :wave: