Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve assessment questions. Unfortunately, how to make the best use of both product and process data to sequentially model learning behaviors is still under investigation. This study proposes a novel deep learning-based approach for enhanced learner modeling that can sequentially predict learners' future learning performance (i.e., item responses) based on modeling their history learning behaviors. The evaluation results show that the proposed model outperforms another popular deep learning-based learner model, and process data learning of the model contributes to improved prediction performance. In addition, the model can be used to discover the mapping of items to skills from scratch without prior expert knowledge. Our study showcases how product and process data can be modelled under the same framework for enhanced learner modeling. It offers a novel approach for learning evaluation in the context of computer-based assessments.