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Simple random negative sampling is a technique used to enhance decision-making in sequential models with numerous potential negative instances, like recommender systems. However, it ignores the patterns that can be discovered in complex sequences to select the most informative negative samples. In this paper, we address this challenge by introducing a Neighborhood-Aware Negative Sampling (NANS) technique in the context of student knowledge modeling (KM) and behavior modeling (BM). In the education domain, KM quantifies student knowledge based on past performance, while BM focuses on behaviors like student preferences of questions. With the vast number of problems to choose from and the intricate relationship between student knowledge and behavior, selecting the proper negative samples becomes a notable challenge in this problem. NANS, along with our proposed multi-objective, multi-task sequential model for KM and BM, NANS-KoBeM frames the simultaneous modeling of student knowledge and question selection as a multi-task learning problem with dual objectives: predicting students’ performance and their question selections. 

Knowledge Tracing (KT) focuses on quantifying student knowledge according to the student's past performance. While KT models focus on modeling student knowledge, they miss the behavioral aspect of learning, such as the types of learning materials that the students choose to learn from. This is mainly because traditional knowledge tracing (KT) models only consider assessed activities, like solving questions. Recently, there has been a growing interest in multi-type KT which considers both assessed and non-assessed activities (like video lectures). Since multi-type KT models include different learning material types, they present a new opportunity to investigate student behavior, as in the choice of the learning material type, along with student knowledge. We argue that student knowledge can affect their behavior, and student interest in learning materials may affect their knowledge. In this paper, we model the relationship between students' knowledge states and their choice of learning activities. To this end, we propose Pareto-TAMKOT which frames the simultaneous learning of student knowledge and behavior as a multi-task learning problem. It employs a transition-aware multi-activity KT method for two objectives: modeling student knowledge and student behavior. Pareto-TAMKOT uses the Pareto Multi-task learning algorithm (Pareto MTL) to solve this multi-objective optimization problem. We evaluate Pareto-TAMKOT on one real-world dataset, demonstrating the benefit of approaching student knowledge and behavior modeling as a multi-task learning problem. 

Marked temporal point process models (MTPPs) aim to model event sequences and event markers (associated features) in continuous time. These models have been applied to various application domains where capturing event dynamics in continuous time is beneficial, such as education systems, social networks, and recommender systems. However, current MTPPs suffer from two major limitations, i.e., inefficient representation of event dynamic’s influence on marker distribution and losing fine-grained representation of historical marker distributions in the modeling. Motivated by these limitations, we propose a novel model calledMarked Point Processes withMemory-EnhancedNeural Networks (MoMENt) that can capture the bidirectional interrelations between markers and event dynamics while providing fine-grained marker representations. Specifically, MoMENt is constructed of two concurrent networks: Recurrent Activity Updater (RAU) to capture model event dynamics and Memory-Enhanced Marker Updater (MEMU) to represent markers. Both RAU and MEMU components are designed to update each other at every step to model the bidirectional influence of markers and event dynamics. To obtain a fine-grained representation of maker distributions, MEMU is devised with external memories that model detailed marker-level features with latent component vectors. Our extensive experiments on six real-world user interaction datasets demonstrate that MoMENt can accurately represent users’ activity dynamics, boosting time, type, and marker predictions, as well as recommendation performance up to 76.5%, 65.6%, 77.2%, and 57.7%, respectively, compared to baseline approaches. Furthermore, our case studies show the effectiveness of MoMENt in providing meaningful and fine-grained interpretations of user-system relations over time, e.g., how user choices influence their future preferences in the recommendation domain.