I’ve managed to make a lot of progress! I have found information on Google for implementing Logistic Regression, Naive Bayes Classifier, Support Vector Machines, Random Forests, and AdaBoost algorithms in Weka. These algorithms were the ones used in https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0171207, and I was trying to replicate its results to see if I was on the right track. Now, I just need the final step of implementing the Collaborative Filtering Recommendation algorithm and using it on the data.
I now have access to multiple data sets about student performance. I will work on them next after finishing testing these algorithms. Besides the one mentioned above, they include some Kaggle data sets, as well as the UCI Machine learning one.
I have been continuing learning about more advanced neural network topics, such as backpropagation and the form of the cost function, as well as how weights need to be randomly initialized at the beginning I am thinking about whether a neural network might be useful for this project.