1,693 research outputs found
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
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