I would suggest a latent growth curve analysis, as long as you have a large enough sample. Essentially you estimate the intercept and slope for each predictor and model the effects onto the outcome.

Are the values of the predictors at earlier time points are thought to impact the response? If, for instance, the impact is cummulative, you may consider using the sum of the predictor values (summed over time points) to predict the response.

Thanks Jochen, Yes, the predictors were measured at earlier time points. Running the analysis with predictor values summed over time points sounds good. However, I am interested in investigating how temporal changes in explanatory variables influence the predictor variable.

Well, then predict the response by the temporal changes. That's quite what Rafael suggested. You may need to be a bit more specific what kind of "temporal changes" you mean, like a linear trend, exponential trend, a peaking, some logistic change, or whatever... this will define what statistic from the predictors is used to predict the response (a slope, a rate, a peak-height or peak-time, time or slope of the inflection point, initial slope, limiting value, or whatever...

Thanks, Rafael. I think I will apply growth curve analysis. I have checked several tutorials about growth curve analysis.  However, in all those tutorials, participants had scores on both explanatory and dependent variables at a specific measurement time. For my data, I am a bit confused about how to input the data for the single measurement dependent variable in the 'long data' form. I have sent a sample data input. Is this correct?

Any analysis depends on amount of data, some models are not very effective when the number of variables is greater than the number of cases. 

Which stats program are you using?

SPSS. I also have basic skills in using R.

If you're comfortable using path analysis, you can do something like this: http://lavaan.ugent.be/tutorial/growth.html

And then model arrows to your single outcome variable. 

wich is your experimental design?

Basically, we had one group of students (n=30) who attended a course comprised of 15 sessions. In each session, students were measured on 4 different variables. At the end of the course, students were given an exam to measure their learning success. Thus, we have 4 independent variables (measured 15 times repeatedly) and one dependent variable (exam score, measured once). All data is continuous.

I would suggest response surface methods.

Basically it depends on your assumptions but perhaps I can suggest you either mixed effect model for cluster data assuming 15 clusters or randomized  complete blocked ANOVA by assuming 15 sessions as blocks and four variables as treatment. Response surface method maybe also alternative option.

Muhterem

What is the question your are asking with these data? What are the independent and dependent variables measured? Do you anticipate that these measurements would change over time and were they taken at regular intervals?

I suggest you use GEE which is robust to repeated measures of explanatory variables. 

What is the suitable statistical test may used to predict the relationship between four groups of dependent variables with multiple explanatory variables?