Native Language Identification (NLI) is concerned with predicting the native language of an author writing in a second language. We investigate NLI for Arabic, with a focus on the types of linguistic information given that Arabic is morphologically rich. We use the Arabic Learner Corpus (ALC) foro training and testing along with a linear SVM. We explore lexical, morpho-syntactic, and syntactic features. Results show that the best single type of information is character n-grams ranging from 2 to 6. Using this model, we achieve an accuracy of 61.84%, thus outperforming previous results (Ionesco, 2015) by 11.74% even though we use an additional 2 L1s. However, when using prefix and suffix sequences, we reach an accuracy of 53.95%, showing that an approximation of unlexicalized features still reaches solid results.