Knowledge tracing, the process of estimating students’ mastery over concepts from their past performance and predicting future outcomes, often relies on binary pass/fail predictions. This hinders the provision of specific feedback by failing to diagnose precise errors. We present an error-tracing model for learning programming that advances traditional knowledge tracing by employing multi-label classification to forecast exact errors students may generate. Through experiments on a real student dataset, we validate our approach and compare it to two baseline knowledge-tracing methods. We demonstrate an improved ability to predict specific errors, for first attempts and for subsequent attempts at individual problems.