###### Abstract  
Question difficulty and student ability are important factors that affect students' correct answers. Because existing knowledge-tracking models fail to consider these factors, they cannot accurately predict the results of students' answers. In order to explore question difficulty and student ability information more accurately and to improve the accuracy of model prediction, this paper proposes a multi-task knowledge-tracking model (MTLKT) with a novel representative approach to question difficulty and student ability. The model first used the idea of multi-task learning to share underlying information and parameters, to jointly train, and to obtain an information difficulty representation vector consisting of skill difficulty and question difficulty. Then, combined with student learning process, a performance bias function was introduced to improve the attention mechanism and obtain a vector for student current knowledge state and a vector for question-solving performance, thus obtaining a vector for student ability information representation. Finally, the above vectors were concatenated and input into the model as a new representative embedding vector. The experimental results of three real-world data sets showed that our model had great improvement in the evaluation criteria of AUC and ACC and had a better predictive performance than the existing advanced knowledge-tracking models. knowledge tracking; multi-task learning; attention mechanism; education data mining; deep learning +
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above influencing factors in isolation or insufficiently makes it difficult to model students accurately. For example, the pre-trained PEBG model [3] extracts high-level information, such as the relationship between questions and skills, the relationship between skills and skills, and the difficulty of questions, but does not consider the change in student ability. The CKT [4] model based on convolutional neural networks takes into account the differences in students' a priori knowledge, as well as learning ability, and takes a student's answer performance over a period of time as his knowledge mastery level. However, the model ignores the differences caused by the different difficulties of different questions. The context-aware AKT knowledge-tracking model [5] uses a monotonic attention mechanism to calculate the attentional weights between questions and obtains a student's knowledge state through weighted aggregation, but the modeling process does not take into account the impact of answer performance on student ability and is not sufficiently considered. Although these models perform well in prediction, they ignore certain factors and have great potential for improvement in prediction accuracy. It is a major challenge for current research to consider the above factors in the modeling process, to dig deeper into the difficulty information of questions, and to extract information about student ability accurately. Therefore, this paper proposes a multi-task knowledge-tracking model (MTLKT) based on a novel question difficulty and student ability representation method. We specifically build a question information difficulty extraction module, which uses the idea of multi-task learning to share the underlying information and parameters, to jointly train, and to obtain an information difficulty representation vector composed of skill difficulty and question difficulty. We build a student ability extraction module. With the introduction of a performance bias function to enhance the weight of questions with good performance and weaken the weight of questions with poor performance, our study reflects the influence of students' specific response performances on student current state and ability to improve the attention mechanism and obtain a student current knowledge state vector and a question-solving performance vector. Once summed, we can obtain a vector representing student ability information.