Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions. We propose the use of text mining to extract semantic, syntactic, and frequency-based features from course content. In addition, we classify intended learning outcomes according to their higher- or lower-order thinking skills. A learning parameter is then formulated to model the impact of these cognitive levels (that are expected for each course) on student performance. These features are then embedded and represented as graphs. Past academic achievements are then fused with the above features for grade prediction. We validate the performance of the above approach via datasets corresponding to three engineering departments collected from a university. Results obtained highlight that the proposed technique generates meaningful feature representations and outperforms existing methods for grade prediction. [For the full proceedings, see ED623995.]