Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing

Recently, knowledge tracing models have been applied in educational data mining such as the Self-attention knowledge tracing model(SAKT), which models the relationship between exercises and Knowledge concepts(Kcs). However, relation modeling in traditional Knowledge tracing models only considers the static question-knowledge relationship and knowledge-knowledge relationship and treats these relationships with equal importance. This kind of relation modeling is difficult to avoid the influence of subjective labeling and considers the relationship between exercises and KCs, or KCs and KCs separately. In this work, a novel knowledge tracing model, named Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing(NGFKT), is proposed to reduce the impact of the subjective labeling by calibrating the skill relation matrix and the Q-matrix and apply the Graph Convolutional Network(GCN) to model the heterogeneous interactions between students, exercises, and skills. Specifically, the skill relation matrix and Q-matrix are generated by the Knowledge Relation Importance Rank Calibration method(KRIRC). Then the calibrated skill relation matrix, Q-matrix, and the heterogeneous interactions are treated as the input of the GCN to generate the exercise embedding and skill embedding. Next, the exercise embedding, skill embedding, item difficulty, and contingency table are incorporated to generate an exercise relation matrix as the inputs of the Position-Relation-Forgetting attention mechanism. Finally, the Position-Relation-Forgetting attention mechanism is applied to make the predictions. Experiments are conducted on the two public educational datasets and results indicate that the NGFKT model outperforms all baseline models in terms of AUC, ACC, and Performance Stability(PS).Comment: 11 pages, 3 figure

Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness

Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise-rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, namely the Knowledge-Graph-Exercise Representativeness and Informativeness Framework, to address these two issues. The framework consists of four intricate components and a novel cognitive diagnosis model called the Neural Attentive cognitive diagnosis model. These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component. The informativeness component evaluates the informational value of each question and identifies the candidate question set that exhibits the highest exercise informativeness. Furthermore, the skill embeddings are employed as input for the knowledge importance component. This component transforms a one-dimensional knowledge graph into a multi-dimensional one through four class relations and calculates skill importance weights based on novelty and popularity. Subsequently, the exercise representativeness component incorporates exercise weight knowledge coverage to select questions from the candidate question set for the tested question set. Lastly, the cognitive diagnosis model leverages exercise representation and skill importance weights to predict student performance on the test set and estimate their knowledge state. To evaluate the effectiveness of our selection strategy, extensive experiments were conducted on two publicly available educational datasets. The experimental results demonstrate that our framework can recommend appropriate exercises to students, leading to improved student performance.Comment: 31 pages, 6 figure

Reverse k Nearest Neighbor Search over Trajectories

GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. In this paper, we study a new kind of query-Reverse k Nearest Neighbor Search over Trajectories (RkNNT), which can be used for route planning and capacity estimation. Given a set of existing routes DR, a set of passenger transitions DT, and a query route Q, a RkNNT query returns all transitions that take Q as one of its k nearest travel routes. To solve the problem, we first develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data are available for answering a RkNNT query. Then we introduce a filter refinement framework for processing RkNNT queries using the proposed indexes. Next, we show how to use RkNNT to solve the optimal route planning problem MaxRkNNT (MinRkNNT), which is to search for the optimal route from a start location to an end location that could attract the maximum (or minimum) number of passengers based on a pre-defined travel distance threshold. Experiments on real datasets demonstrate the efficiency and scalability of our approaches. To the best of our best knowledge, this is the first work to study the RkNNT problem for route planning.Comment: 12 page