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    A review of knowledge tracking
    LIU Heng-yu, ZHANG Tian-cheng, WU Pei-wen, YU Ge
    Journal of East China Normal University(Natural Sc    2019, 2019 (5): 1-15.   DOI: 10.3969/j.issn.1000-5641.2019.05.001
    Abstract1005)   HTML396)    PDF (1936KB)(1279)      
    In the field of education, scientifically and purposefully tracking the progression of student knowledge is a topic of great significance. With a student's historical learning trajectory and a model for the interaction process between students and exercises, knowledge tracking can automatically track the progression of a student's learning at each stage. This provides a technical basis for predicting student performance and achieving personalized guidance and adaptive learning. This paper first introduces the background of knowledge tracking and summarizes the pedagogy and data mining theory involved in knowledge tracking. Then, the paper summarizes the research status of knowledge tracking based on probability graphs, matrix factorization, and deep learning; we use these tools to classify the tracking methods according to different characteristics. Finally, the paper analyzes and compares the latest knowledge tracking technologies, and looks ahead to the future direction of ongoing research.
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    A survey on coreference resolution
    CHEN Yuan-zhe, KUANG Jun, LIU Ting-ting, GAO Ming, ZHOU Ao-ying
    Journal of East China Normal University(Natural Sc    2019, 2019 (5): 16-35.   DOI: 10.3969/j.issn.1000-5641.2019.05.002
    Abstract591)   HTML255)    PDF (671KB)(623)      
    Coreference resolution is the task of finding all expressions that point to the same entity in a text; this technique is widely used for text summarization, machine translation, question answering systems, and knowledge graphs. As a classic problem in natural language processing, it is considered NP-Hard. This paper first introduces the basic concepts of coreference resolution, analyzes some confusing concepts related thereto, and discusses the research significance and difficulties of the technique. Then, we summarize research advances in coreference resolution, divide them into stages from a technical standpoint, introduce the representative approaches for each stage, and discuss the advantages and disadvantages of various methods. The summarized approaches are five-fold:rule-based, machine learning, global optimization, knowledge base, and deep learning. Next, we introduce benchmark conferences for the problem of coreference resolution; in this context, we explain and compare their corpus and common evaluation metrics. Finally, this paper highlights the open problems for coreference resolution, and discusses trends and directions of future research.
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    A review of machine reading comprehension for automatic QA
    YANG Kang, HANG Ding-jiang, GAO Ming
    Journal of East China Normal University(Natural Sc    2019, 2019 (5): 36-52.   DOI: 10.3969/j.issn.1000-5641.2019.05.003
    Abstract498)   HTML250)    PDF (1811KB)(466)      
    Artificial Intelligence (AI) is affecting every industry. Applying AI to education accelerates the structural reform of education and transforms traditional education into intelligent adaptive education. The automatic Question Answer system, based on deep learning, not only helps students to answer questions and acquire knowledge in real-time, but can also quickly gather student behavioral data and accelerate personalization of the educational process. Machine reading comprehension is the core module of an automatic Question Answer system, and it is an important technology to understand student problems, document content, and acquire knowledge quickly. With the revival of deep learning and the availability of large-scale reading comprehension datasets, a number of neural network-based machine reading models have been proposed over the past few years. The purpose of this review is three-fold:to introduce and review progress in machine reading comprehension; to compare and analyze the advantages and disadvantages between various neural machine reading models; and to summarize the relevant datasets and evaluation methods in the field of machine reading.
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    Research on knowledge point relationship extraction for elementary mathematics
    YANG Dong-ming, YANG Da-wei, GU Hang, HONG Dao-cheng, GAO Ming, WANG Ye
    Journal of East China Normal University(Natural Sc    2019, 2019 (5): 53-65.   DOI: 10.3969/j.issn.1000-5641.2019.05.004
    Abstract457)   HTML202)    PDF (897KB)(339)      
    With the development of Internet technology, online education has changed the learning style of students. However, given the lack of a complete knowledge system, online education has a low degree of intelligence and a/knowledge trek0problem. The relation-extraction concept is one of the key elements of knowledge system construction. Therefore, building knowledge systems has become the core technology of online education platforms. At present, the more efficient relationship extraction algorithms are usually supervised. However, such methods suffer from low text quality, scarcity of corpus, difficulty in labeling data, low efficiency of feature engineering, and difficulty in extracting directional relationships. Therefore, this paper studies the relation-extraction algorithm between concepts based on an encyclopedic corpus and distant supervision methods. An attention mechanism based on relational representation is proposed, which can extract the forward relationship information between knowledge points. Combining the advantages of GCN and LSTM, GCLSTM is proposed, which better extracts multipoint information in sentences. Based on the attention mechanism of Transform architecture and relational representation, a BTRE model suitable for the extraction of directional relationships is proposed, which reduces the complexity of the model. Hence, a knowledge point relationship extraction system is designed and implemented. The performance and efficiency of the model are verified by designing three sets of comparative experiments.
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    Performance prediction based on fuzzy clustering and support vector regression
    SHEN Hang-jie, JU Sheng-gen, SUN Jie-ping
    Journal of East China Normal University(Natural Sc    2019, 2019 (5): 66-73,84.   DOI: 10.3969/j.issn.1000-5641.2019.05.005
    Abstract380)   HTML201)    PDF (603KB)(315)      
    Existing performance prediction models tend to overuse different types of attributes, leading to either overly complex prediction methods or models that require manual participation. To improve the accuracy and interpretation of student performance prediction, a method based on fuzzy clustering and support vector regression is proposed. Firstly, fuzzy logic is introduced to calculate the membership matrix, and students are clustered according to their past performance. Then, we use Support Vector Regression (SVR) theory to fit and model performance trajectory for each cluster. Lastly, the final prediction results are adjusted in combination with the students' learning behavior and other related attributes. Experimental results on several baseline datasets demonstrate the validity of the proposed approach.
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