本文選題：錯誤模型 + 知識庫�。� 參考：《電子科技大學(xué)》2016年碩士論文

【摘要】：信息技術(shù)發(fā)展至今,已經(jīng)涉及到我們工作生活的各方各面,在教育領(lǐng)域,傳統(tǒng)的教育手段和模式很難滿足人們對教育的需求,于是智慧教育、互聯(lián)網(wǎng)教育等概念逐漸被提出。得益于人工智能技術(shù)的發(fā)展,很多專家學(xué)者開始探索將人工智能與教育相結(jié)合,以人工智能技術(shù)與互聯(lián)網(wǎng)的思維相結(jié)合重塑教育模式、內(nèi)容、方法、體系。教育資源的缺乏與分配不均,無疑是教育方式變革面臨的首要問題。本文的主要研究方向是在無教師參與、無參考答案的情況下,由系統(tǒng)自動對學(xué)生的答題內(nèi)容進行評判,包括每個解題步驟的正誤判斷以及在錯誤的情況下評判出具體的錯誤類型。旨在減輕教育人力成本的同時,找出學(xué)生學(xué)習(xí)中的短板,有的放矢,有效促進學(xué)習(xí),為互聯(lián)網(wǎng)教育的一個新探索。本文主要研究內(nèi)容如下:1、初中數(shù)學(xué)常見錯誤類型的研究及認知建模對大量初中數(shù)學(xué)解題中常犯的錯誤進行收集和分類,并對產(chǎn)生這些錯誤的現(xiàn)象及原因等進行深入分析和研究,構(gòu)建一套較為完整的錯誤類型體系,涵蓋初中數(shù)學(xué)幾乎全部概念的錯誤類型。分析和歸納每種錯誤類型的共性與特性,基于人的認知過程,自動構(gòu)建錯誤認知模型。2、錯誤模型知識庫的研究與構(gòu)建運用自然語言處理(NLP,Natural Language Processing)技術(shù),在對題意和學(xué)生答題內(nèi)容進行語義理解和表示的基礎(chǔ)上,以初中數(shù)學(xué)公式、性質(zhì)、定理、推論以及數(shù)學(xué)常識為基準,建立推理的規(guī)則庫。并基于Drools正向規(guī)則引擎與符號計算工具,以題干的已知條件做為推理的起點,構(gòu)建錯誤模型知識庫,該知識庫盡可能多的覆蓋學(xué)生所有正確的答題內(nèi)容以及與知識推理路徑關(guān)聯(lián)的錯誤模型。3、錯誤模型庫在自動判卷中的應(yīng)用基于錯誤模型知識庫,攻克因主觀題一題多解、答題形式多樣化等而造成機器自動閱卷困難這一技術(shù)障礙,構(gòu)建一個智能判卷系統(tǒng)。系統(tǒng)首先利用知識庫結(jié)合符號計算工具對學(xué)生的答題內(nèi)容的每一步驟進行正誤判斷,然后根據(jù)錯誤模型對學(xué)生錯誤的解題步驟進行具體的錯誤類型判斷,并指出學(xué)生錯誤的具體原因,反饋給學(xué)生,讓學(xué)生知其然知其所以然,實現(xiàn)學(xué)生錯誤的精準定位和判卷。
[Abstract]:The development of information technology has been related to all aspects of our work and life. In the field of education, the traditional means and models of education are difficult to meet the needs of people, so the concepts of wisdom education and Internet education have been put forward gradually. Thanks to the development of artificial intelligence, many experts and scholars began to explore the combination of artificial intelligence and education, and reshape the educational model, content, method and system with the combination of artificial intelligence technology and Internet thinking. The lack and uneven distribution of educational resources is undoubtedly the most important problem facing the reform of educational mode. The main research direction of this paper is to judge the students' answer content automatically by the system without the participation of teachers and the reference answers. This includes the correct and wrong judgment of each problem solving step and the judgment of the specific error type in the case of error. In order to reduce the cost of educational manpower, to find out the short board of students' learning, to effectively promote learning, and to provide a new exploration for Internet education. The main contents of this paper are as follows: 1. The research on common error types and cognitive modeling in junior high school mathematics collects and classifies the common mistakes made in a large number of junior high school mathematics problems, and makes a deep analysis and research on the phenomena and causes of these errors. Build a complete system of error types, covering almost all the concepts of junior high school mathematics error types. The commonness and characteristics of each error type are analyzed and summarized. Based on human cognitive process, the error cognitive model. 2. The research and construction of error model knowledge base using NLP Natural language processing (NLP) technology. Based on the semantic understanding and representation of the meaning of the questions and the students' answers, the rule base of reasoning is established on the basis of mathematical formulas, properties, theorems, corollaries and mathematical common sense in junior high school. Based on the drools forward rule engine and symbolic computing tool, the knowledge base of error model is constructed based on the known condition of problem as the starting point of reasoning. The knowledge base covers as much as possible all the correct answers of the students and the error model. 3. The application of the error model base in automatic examination paper identification is based on the error model knowledge base. Because of the technical obstacle of machine automatic marking due to the variety of answer forms, an intelligent marking system is constructed. The system first uses the knowledge base combined with symbolic calculation tools to judge each step of the students' answer questions correctly and wrongly, and then, according to the error model, makes a specific error type judgment on the students' wrong problem-solving steps. It also points out the specific reasons of students' mistakes, gives feedback to students, lets students know what they are doing, and realizes the accurate positioning and marking of students' mistakes.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：G633.6;TP391.1

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1 陳顯玲;初中數(shù)學(xué)錯誤模型研究及其在自動判卷系統(tǒng)中的應(yīng)用[D];電子科技大學(xué);2016年

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