Proposing a recommender system through mining English reading comprehension errors of engineering students

This paper presents a sentence reading recommender system through mining engineering students´ English reading comprehension error patterns. Students were first trained to analyze sentences in the steps of Cumulative Sentence Analysis (CSA). The errors produced by the students during their reading process were used as the database for data mining. The results of data mining showed that distinctive error patterns and association rules were identified for different groups of students. Based on the association rules of the error patterns, suitable sentences for the students to practice were recommended.