Learning Early Warning Guided by Course Objective Achievement via Knowledge State and Learning State Modeling

Timely and effective early warning is essential for proactive intervention to mitigate students’ learning risks. Existing studies on learning early warning primarily predict students’ knowledge mastery based on academic performance. However, they lack the assessment of course development objective achievement. According to the outcome-based education (OBE) concept, the course objective achievement serves as the foundation for comprehensive student assessment spanning knowledge acquisition, learning ability, and learning attitude. Using the course objective achievement as a guide for learning early warning can help to obtain more objective and accurate warning results. This article proposes a novel approach to learning early warning guided by course objective achievement via knowledge state and learning state modeling. This approach constructs knowledge states related to course objectives through a deep knowledge tracing model and derives the learning states comprising learning ability and learning attitude from multidimensional learning behavior data. The achievement state, fusing the knowledge and learning states, is then fed into a transformer model to capture the temporal dynamics of the achievement state and predict the achievement levels for each course objective. Based on these predictions, a four-level warning rule is employed to assess students’ learning risks. Experiments based on two real-world datasets demonstrate the effectiveness and superiority of the proposed approach. The approach provides a new research paradigm and a feasible solution to achieve accurate personalized early learning warning.