  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.
