Knowledge Tracing (KT) aims to model students' knowledge states based on their historical learning sequence, playing a critical role in online education platforms.As the performance of sequence-based KT methods continues to improve, their increasing model complexity and lack of transparency have become significant limitations.In contrast, educational theory-driven KT methods incorporate educationally meaningful features (such as question difficulty or time spent on questions) to enhance interpretability and performance.However, these models typically adopt simpler structures to reduce complexity and avoid overfitting, which limits their ability to effectively capture the sequential characteristics of learning compared to sequence-based methods.To address these limitations, this paper aims to integrate the strengths of both types of methods by proposing an Interpretable KT approach with Difficulty-Aware Attention and Selective State Space Model (ASIKT).Specifically, leveraging educational context, we design a difficulty-enhanced attention mechanism to model students' knowledge retrieval process

Event Argument Extraction (EAE) aims to identify and extract key information such as entities, times, and locations related to specific events from text and serves as a fundamental task for many NLP applications. Recent researches have utilized large language models (LLMs) for EAE, effectively addressing the resource-intensive nature of annotating training datasets for this task. However, when performing EAE on longer texts (document-level EAE), the presence of descriptions unrelated to the events within document-level EAE can lead LLMs to identify incorrect arguments. To address this issue, we propose Simple-Hard Refining: a novel prompt framework that segments EAE into straightforward and complex extraction tasks. Based on the complexity of inference, we divide EAE task into simple-argument extraction and hard-argument extraction. By utilizing a chain of prompt to perform simple and hard argument extraction sequentially, noise introduced by irrelevant description for simple-argument extraction can be effectively alleviated. Furthermore, we explore the potential of LLMs to furnish dependable explanations for their extraction outcomes. We design an explanation-based prompting method that involves a three-step explanation process: relevant sentence extraction, argument role semantic analysis, and argument role entity localization. This method further enhances the extraction accuracy at each stage of the framework. Our experiments demonstrate that our method achieves state-of-the-art performance, surpassing various baselines that utilize LLMs for the EAE task. Ablation studies further verify the effectiveness of each stage of our framework and show the ability of our proposed approach to effectively mitigate noise. Our work contributes to the structured extraction of event argument information using LLMs.

Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors. However, most current KT methods focus on improving overall AUC, leaving ample room for optimization in modeling sequences of excessive or insufficient lengths. As sequences get longer, computational costs will increase exponentially. Therefore, KT methods usually truncate sequences to an acceptable length, which makes it difficult for models on online service systems to capture complete historical practice behaviors of students with too long sequences. Conversely, modeling students with short practice sequences using most KT methods may result in overfitting due to limited observation samples. To address the above limitations, we propose a model called Sequence-Flexible Knowledge Tracing (SFKT). Specifically, to flexibly handle long sequences, SFKT introduces a total-term encoder to effectively model complete historical practice behaviors of students at an affordable computational cost. Additionally, to improve the prediction accuracy of students with short practice sequences, we introduce a contrastive learning task and data augmentation schema to improve the generality of modeling short sequences by constructing more learning objectives. Extensive experimental results show that SFKT achieves significant improvements over multiple benchmarks, demonstrating the value of exploring the modeling of sequences of excessive or insufficient lengths. Our code is available at https://github.com/zmy-9/SFKT.

As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, existing KT methods focus on predicting students' responses to practices. However, purely predicting student responses without imposing specific constraints on hidden concept mastery values does not guarantee the accuracy of these intermediate values as concept mastery values. To address this issue, we propose a principled approach called Counterfactual Monotonic Knowledge Tracing (CMKT), which builds on the implicit paradigm described above by using a counterfactual assumption to constrain the evolution of students' mastery of knowledge concepts. Specifically, CMKT first assesses students' knowledge concept mastery value based on their historical practice sequences. Then, CMKT sets the answer of the most recent practice as the opposite of the actual answer and, based on this counterfactual answer, assesses the student's corresponding counterfactual knowledge mastery value. During the model training process, CMKT constrains the update of the student's knowledge states by ensuring that the two types of knowledge mastery values of students satisfy a fundamental educational theory, the monotonicity theory, to provide specific semantics for the assessed mastery values by the model. Finally, extensive experiments on five datasets demonstrate the superiority of CMKT over baseline models.

The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence. However, the KT task suffers from data sparsity, which makes it challenging to learn robust representations for students with few practice records and increases the risk of model overfitting. Therefore, in this paper, we propose a Cognition-Mode Aware Variational Representation Learning Framework (CMVF) that can be directly applied to existing KT methods. Our framework uses a probabilistic model to generate a distribution for each student, accounting for uncertainty in those with limited practice records, and estimate the student’s distribution via variational inference (VI). In addition, we also introduce a cognition-mode aware multinomial distribution as prior knowledge that constrains the posterior student distributions learning, so as to ensure that students with similar cognition modes have similar distributions, avoiding overwhelming personalization for students with few practice records. At last, extensive experimental results confirm that CMVF can effectively aid existing KT methods in learning more robust student representations. Our code is available at https://github.com/zmy-9/CMVF.

With the increasing demands of personalized learning, knowledge tracing has become important which traces students' knowledge states based on their historical practices. Factor analysis methods mainly use two kinds of factors which are separately related to students and questions to model students' knowledge states. These methods use the total number of attempts of students to model students' learning progress and hardly highlight the impact of the most recent relevant practices. Besides, current factor analysis methods ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism. More specifically, we propose a novel student-related factor which records the most recent attempts on relevant concepts of students to highlight the impact of recent exercises. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information including questions' relation and difficulty level. We also add a regularization term about questions' difficulty level to restrict pre-trained question representations to fine-tuning during the process of predicting students' performance. Moreover, we apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction in different practice records. At last, we conduct experiments on several real-world datasets and results show that MF-DAKT can outperform existing knowledge tracing methods. We also conduct several studies to validate the effects of each component of MF-DAKT.

User preference profile is generally significant to marketing strategy decisions as well as user experience improvement for mobile telecom operators. To establish preference profile, perators create a hierarchical taxonomy of preference and classify records of user browsing history on mobile internet by the taxonomy to measure user preference. However, the incompleteness of recorded browsing history makes it nontrivial to observe all the users' preferences. To complete missing preferences, recommendation based methodology is commonly exploited. Although taxonomy contains the semantic relationships between preferences, there are merely a few works that explored them for recommendation. We extend these works by clearly defining the relation types and learning relation strengths among preferences in the taxonomy, on which we propose a Taxonomy-induced Matrix Factorization (TMF) model. We perform experiments on a large dataset of user browsing data from a Chinese telecom operator. The results show that our proposed model outperforms the standard matrix factorization model. In addition, the relations learned by TMF are detailed analyzed to show their inherent effects for the inference improvement.

Churn prediction, which is to identify who are prone to abandon the subscription, is of high significance for the operators to retain the potential churners. It should be noted that, in practice, the earlier the churners are identified, the more effective strategies the operators can develop to retain them. While the earlier prediction of customer churn in telecommunication industry has not been well investigated and the predicting accuracy of previous work degrades unacceptably when the interval between observed attributes and predicted labels is prolonged. In this paper, from a different perspective, we study the effectiveness to find the churners as early as possible with the accuracy being high enough, which we define as Early Churn Prediction. The predictive performance of the proposed model, which takes time series attributes and influence of churning contacts in social network into consideration, is investigated. We evaluate the method using a 12-month-long dataset collected by one of the largest operators in China. The results show that our model significantly outperforms the previous work especially when the prediction interval is larger than 3 months.

This paper proposes a new composition method to represent semantic compositionality of sentences. Using the unfolding recursive autoencoders, we build sentence representing trees from the original sentences of words. We utilize trained word embeddings and sentence parser to train the model, and we can build sentence representing trees from the trained model. We further propose to use dynamic average pooling to pool the trees and get fix-size vector representation for sentences. The fix-size vector representation after dynamic average pooling can then be used to represent sentences. We verify the validity of sentence representations by using them to classify sentence paraphrase. Experiment shows that compared to the baseline representation, using proposed representations together with sub-vector Euclidean distance feature, the classification performance can be improved by 1.39% for testing accuracy, which proves the proposed method can better represent semantic compositionality of sentences.