Approach for Predicting the Knowledge Points of Math Questions

In order to provide high-quality learning services,various online systems should possess the fundamental ability to predict the knowledge points and units to which a given test question belongs.The existing methods typically rely on manual labeling or traditional machine learning methods.Manual labeling methods have high time costs and high demands for human resources,while traditional machine learning methods only focus on the shallow features of the topics,ignoring the deep semantic relationship between the topic text and the knowledge point units.These two methods have relatively large limitations in practical applications.This paper proposes a convolutional neural network method combined with multiple features to predict the knowledge point units.We construct a binary classification dataset in the three grades of primary mathematics.Considering the supplementary role of Pinyin to Chinese text and the unique identification characteristics of Unicode encoding for characters,we obtain the Pinyin representation and the Unicode encoding representation of the original Chinese text.Then,we put the three representation methods into the convolutional neural network for training,obtain three kinds of semantic vectors,fuse them,and finally obtain higher-dimensional fusion features.Our experimental results demonstrate that our approach achieves good performance in predicting the knowledge units of test questions.

A Knowledge Graph based Software Engineering Curriculum Design Method

New theories,methodologies,and technologies have been continuously invented and widely applied in modern software development,along with many new tools and best practices that are of remarkable significance in the software industry.In Software Engineering(SE)programs of universities,it is quite difficult for their curricula to chase after the fast-evolving technology trend.As a consequence,there have been significant challenges in designing an evolvable SE curriculum.In this paper,we present a knowledge graph based curriculum design method for SE programs.Knowledge Points(KPs)are organized into a multi-layer and multi-dimensionally annotated knowledge graph called SEKG,and five principles are applied to partition the SEKG into a set of inter-related courses.Metrics for evaluating the quality of an SE curriculum are briefly discussed.This method can not only help design a systematic curriculum from existing software engineering KPs but also facilitate curriculum evolution to adapt to technology trends.

Research on identification of potential knowledge growth points in information science

The scientific system’s complexity makes it impossible to solve social problems by a single discipline independently,and interdisciplinary knowledge cooperation and innovation become an indispensable research mode of modern science.Identifying the potential interdisciplinary knowledge association is the key to promoting interdisciplinary cooperation.In this paper,based on analyzing the growth points of science,"knowledge growth point"is defined as the growth point of science that produces new knowledge,and its fundamental attributes and evaluation indexes have been analyzed.In contrast,the"interdisciplinary knowledge growth point"is defined as the introduction of related interdisciplinary concepts,theories,techniques,and methods,to conduct integrated research of key knowledge points of active disciplines,to generate growth point of innovative knowledge,and analyze its related research status.The identification of"potential interdisciplinary knowledge growth points"is helpful to promote knowledge innovation.Therefore,it is intended to analyze the identification methods of the generation of key knowledge nodes of the element of disciplines and interdisciplinary related knowledge,and explore quantitative and qualitative consultation to identify potential interdisciplinary knowledge growth points.