# Enhanced Learning Behaviors and Ability Knowledge Tracing
## 1 Introduction  
With the growth of online education platforms such as massive open online courses (MOOCs) [1], intelligent tutoring systems [2], educational games [3], and learning management systems, a vast amount of learning process data has been generated. This has driven the development of knowledge tracing (KT) [4] methodologies, which aim to understand the changes in students' knowledge states as they progress through their learning processes. Knowledge tracing uses student's historical learning data to create models of students' learning processes, enabling personalized learning for individuals, providing teachingguidance for educators, and serving as an evaluation tool for administrators. This dynamic method of online learning systems has propelled the evolution of KT methodologies [5; 6; 7; 8], allowing for a more effective and efficient approach to education. In recent years, the integration of deep learning [9] into KT has significantly enhanced its performance, garnering substantial research attention. Deep learning models excel in capturing complex patterns and relationships within large-scale educational data, offering unprecedented insights into students' learning behaviors [10; 11; 12; 13] and knowledge acquisition processes. Despite these advancements, current KT methods [5; 7] focus on analyzing students' knowledge mastery through their exercise sequences and answers to pursue a high accuracy of mode prediction. Such approaches, while effective, often neglect the critical influence of students' overall learning performance, including factors such as exercise's difficulty, answer time, hint times, forgetting behavior, and the variability in their learning activities. These performance indicators are crucial as they provide a more comprehensive understanding of the students' learning processes and their capacity to absorb and retain knowledge. Current mainstream KT models can be categorized into traditional machine learning KT models and deep learning KT models [14]. Bayesian Knowledge Tracing (BKT) [4] is a representative model of traditional machine learning KT, utilizing the Hidden Markov Model (HMM) to separately model each knowledge skill. While BKT has made significant progress, its assumption that skills, once learned, are never forgotten oversimplifies the learning process. This simplification restricts the practical application of BKT in real-world educational contexts. Inspired by the performance of deep learning, several deep learning KT models have been developed. Deep Knowledge Tracing (DKT) [5], proposed by [PERSON] et al. in 2015, uses recurrent neural networks (RNNs) [15] or Long Short-Term Memory (LSTM) [16] to model student knowledge states through a sequence of hidden states. While DKT improved performance over BKT, it represents a learner's knowledge state of all concepts in a single hidden state, making it difficult to trace the mastery of specific concepts. Dynamic Key-Value Memory Networks (DKVMNs) [7] address this by using dynamic key-value matrices to store knowledge skills and states. In the above KT models, there is a problem in that the knowledge state of students in each learning interaction is directly determined by their answering performance in exercise. However, employing artificial intelligence methods in modeling the human learning process is challenging, as learning is influenced by learning behaviors and ability (e.g., memory, comprehension, attention, forgetting, and guessing) and individual learning ability (i.e., efficiency and interest). To better simulate the human learning process, on the basis of DKT, Learning Process-consistent Knowledge Tracing (LPKT) [17] and other models [18; 19], focusing on students' learning behavior and ability, and combining Item Response Theory (IRT) [20], we propose an Enhanced Learning Behavior and Ability Knowledge Tracing (LBAKT) model. Our model aims to offer a more holistic view of student learning by incorporating both learning behaviors and abilities.