Peer assessment adds value when students provide "helpful" feedback to their peers. But, this begs the question of how we determine "helpfulness." One important aspect is whether the review detects problems in the submitted work. To recognize problem detection, researchers have employed NLP and machine-learning text classification methods. Past studies have used datasets that were narrowly focused on a small number of classes in specific academic fields. This paper reports on how well models trained on one dataset or field perform on data from classes that are unlike the classes whose data they have been trained on. Specifically we took a model developed with data from a computer science class with several programming assignments, and tried to transfer it onto an education class focused more on writing research papers. We have attempted to perform such a task on a few models including logistic regression classifier, random forest classifier, multinomial naive bayes classifier and support vector machine. We made several attempts to raise the accuracy of classification, including lemmatizing to deduct variation in data input, and active learning strategies. [For the full proceedings, see ED607784.]