Automated assessment of complex assignments is crucial for scaling up learning of complex skills such as critical thinking. To address this challenge, one previous work has applied supervised machine learning to automate the assessment by learning from examples of graded assignments by humans. However, in the previous work, only simple lexical features, such as words or n-grams, have been used. In this paper, we propose to use topics as features for this task, which are more interpretable than those simple lexical features and can also address polysemy and synonymy of lexical semantics. The topics can be learned automatically from the student assignment data by using a probabilistic topic model. We propose and study multiple approaches to construct topical features and to combine topical features with simple lexical features. We evaluate the proposed methods using clinical case assignments performed by veterinary medicine students. The experimental results show that topical features are generally very effective and can substantially improve performance when added on top of the lexical features. However, their effectiveness is highly sensitive to how the topics are constructed and a combination of topics constructed using multiple views of the text data works the best. Our results also show that combining the prediction results of using different types of topical features and of topical and lexical features is more effective than pooling all features together to form a larger feature space.