对四节课例内隐条件性课程资源开发的剖析     

马燕 《山东教育学院学报》2011,26(6):50-54

对课例的评价,其中一个视角就是要考察该课例是否充分地挖掘和利用了数学课程资源。其中,内隐条件性资源主要指教师根据对素材性课程资源的理解,结合外显条件性资源去构建一种适宜于学生学习的环境。一个教学设计或一堂课的教学是否充分地挖掘了内隐条件性资源,就是指该设计是否根据教学目标,为学生构建了一个学习的最佳场景。本文试从内隐条件性资源开发的角度从上述几个方面对有关”验证勾股定理”的四节课例作一剖析。  相似文献   

浅谈拔尖创新人才培养   总被引：1，自引：0，他引：1  

段孟常  吴建华  李建平  宋松和  陆彦文  沈志 《高等教育研究学报》2011,34(Z1):72-74

随着中国高等教育的发展,"钱学森之问"受到越来越多高等院校和高等教育者的关注。本文详细研究了北京航空航天大学在拔尖创新人才培养、数理公共课程教学改革、实验教学改革等方面的具体举措,并提出了一些建议。  相似文献   

UMLS及其在智能检索中的应用     

白海燕  王莉  梁冰 《现代图书情报技术》2012,(4):1-9

调研UMLS构成和建设特点,重点研究UMLS在检索方面的应用实例,分析归纳UMLS在语义化、智能化检索方面的功能设计、实现方法与实际效果,以期为基于集成式知识组织系统的智能检索应用的场景功能设计、技术开发和实现,提供借鉴和参考。UMLS在智能检索中的应用主要包括:(1)扩展检索,主要有同义词扩展、等级结构扩展和词组切分扩展等方法;(2)语义检索,基于概念和概念之间的关系进行检索和结果内容表达;(3)问答式检索,包括问题分析、文献检索、语句提取、答案生成和语义聚类。  相似文献   

对一道高考物理试题解答的商榷     

高峰  王志军  许弟余 《四川职业技术学院学报》2012,22(2):124-126

对一道高考力学题的两种解法提出商榷,并给出了图像解法.  相似文献   

校本课程开发面临的问题与对策     

赛依山  宗永平 《伊犁师范学院学报》2004,(4):78-81

校本课程开发是我国基础教育三级课程管理的重要内容，是目前课程改革的焦点。通过对校本课程的历史及现状的回顾分析，指出校本课程开发面临的困难和挑战，提出校本课程开发的对策，对于学生更好地认识学习的价值，满足学生的实际发展需要，塑造健全的人格，学会生存，掌握实用技能，培养兴趣具有重要意义。  相似文献   

农村社会保障体系的建立和健全     

康耀辉  常春 《通化师范学院学报》2008,29(3):49-52

我国的农村社会保障体系是我国社会保障体系中的重要组成部分,建立健全农村社会保障体系已经受到我国政府乃至整个社会的关注。目前．农村社会保障建设滞后问题日益严重,已直接影响到我国农村社会的稳定和经济的发展。在统筹城乡经济和社会发展的新格局下,必须从农村的实际情况出发,着重分析在建立和健全农村社会保障体系中存在的诸多问题,采取切实可行的相应对策,积极推进农村社会保障制度的建立和健全。  相似文献   

Is underestimation less detrimental than overestimation? The impact of experts’ beliefs about a layperson’s knowledge on learning and question asking     

Jörg Wittwer  Matthias Nückles  Alexander Renkl 《Instructional Science》2008,36(1):27-52

Although prior research has shown that experts tend to overestimate or underestimate what laypersons actually know, little is known about the specific consequences of biased estimations for communication. To investigate the impact of biased estimations of a layperson’s knowledge on the effectiveness of experts’ explanations, we conducted a web-based dialog experiment with 45 pairs of experts and laypersons. We manipulated the experts’ mental model of the layperson by presenting them either valid information about the layperson’s knowledge or information that was biased towards overestimation or underestimation. Results showed that the experts adopted the biased estimations and adapted their explanations accordingly. Consequently, the laypersons’ learning from the experts’ explanations was impaired when the experts overestimated or underestimated the layperson’s knowledge. In addition, laypersons whose knowledge was overestimated more often generated questions that reflected comprehension problems. Laypersons whose knowledge was underestimated asked mainly for additional information previously not addressed in the explanations. The results suggest that underestimating a learner during the instructional dialog is as detrimental to learning as is the overestimation of a learner’s knowledge. Thus, the provision of effective explanations presupposes an accurate mental model of the learner’s knowledge prerequisites.  相似文献   

Semantic matching in machine reading comprehension: An empirical study     

《Information processing & management》2023,60(2):103145

Machine reading comprehension (MRC) is a challenging task in the field of artificial intelligence. Most existing MRC works contain a semantic matching module, either explicitly or intrinsically, to determine whether a piece of context answers a question. However, there is scant work which systematically evaluates different paradigms using semantic matching in MRC. In this paper, we conduct a systematic empirical study on semantic matching. We formulate a two-stage framework which consists of a semantic matching model and a reading model, based on pre-trained language models. We compare and analyze the effectiveness and efficiency of using semantic matching modules with different setups on four types of MRC datasets. We verify that using semantic matching before a reading model improves both the effectiveness and efficiency of MRC. Compared with answering questions by extracting information from concise context, we observe that semantic matching yields more improvements for answering questions with noisy and adversarial context. Matching coarse-grained context to questions, e.g., paragraphs, is more effective than matching fine-grained context, e.g., sentences and spans. We also find that semantic matching is helpful for answering who/where/when/what/how/which questions, whereas it decreases the MRC performance on why questions. This may imply that semantic matching helps to answer a question whose necessary information can be retrieved from a single sentence. The above observations demonstrate the advantages and disadvantages of using semantic matching in different scenarios.  相似文献   

BERT-SMAP: Paying attention to Essential Terms in passage ranking beyond BERT     

《Information processing & management》2022,59(2):102788

Passage ranking has attracted considerable attention due to its importance in information retrieval (IR) and question answering (QA). Prior works have shown that pre-trained language models (e.g. BERT) can improve ranking performance. However, these simple BERT-based methods tend to focus on passage terms that exactly match the question, which makes them easily fooled by the overlapping but irrelevant (distracting) passages. To solve this problem, we propose a self-matching attention-pooling mechanism (SMAP) to highlight the Essential Terms in the question-passage pairs. Further, we propose a hybrid passage ranking architecture, called BERT-SMAP, which combines SMAP with BERT to more effectively identify distracting passages and downplay their influence. BERT-SMAP uses the representations obtained through SMAP to enhance BERT’s classification mechanism as an interaction-focused neural ranker, and as the inputs of a matching function. Experimental results on three evaluation datasets show that our model outperforms the previous best BERT${}_{base}$-based approaches, and is comparable to the state-of-the-art method that utilizes a much stronger pre-trained language model.  相似文献