记忆与认知融合的个性化OJ习题推荐方法

面对程序设计OJ(Online Judge，在线判题)平台上的大量习题，学生选题时往往带有盲目性，不利于学生快速提高程序设计能力.现有的习题推荐方法往往忽视了认知心理学与学生答题行为的联系，没有充分关注导致学生信息认知过程具有差异性的因素对解题的影响.本文基于认知心理学提出一种记忆与认知融合的OJ习题推荐方法，该方法结合认知心理学中关于工作记忆、长时记忆、外部信息以及非连续性认知发展的工作，通过如下方式考虑这4种因素对学生答题行为的影响：首先，以OJ习题中4个会对学生的工作记忆造成挑战的要素作为习题特征的一部分来辅助推荐方法估计习题的难度，分别为习题描述词数、输入格式词数、输出格式词数和是否为英文题干；然后，使用4个要素（相同知识的学习时间间隔、相同知识的学习次数、学习时间间隔和总体学习次数）建模学生长时记忆的遗忘与学习行为，拟合学生因这两种行为导致的知识状态变化，帮助推荐方法更准确地估计学生的知识点和编程语言掌握程度；接着，将OJ习题加入本文所构造的程序设计领域知识图谱，并在习题与其所考查的知识点之间建立关联，利用知识图谱嵌入方法将习题编码为一个融入了外部知识点相关性信息的向量作为习...     

《快乐学单词》课件制作综述

特色与亮点 本课件采用AS2．0编程技术，设计了一个练习模式和两个游戏模式，可以满足学生不同学期的英语单词学习需要，真正实现了让学生在学习中游戏，在游戏中学习的目的。     

当前大学计算机课程游戏化的探索

在如今的大学课程中,计算机课程变得越来越普及,不仅仅局限于计算机专业的学生学习这门课程,对于其他专业的学生计算机课程的相应学习和了解也是必要的。采用游戏化的手段进行计算机课程的开展是符合课程教育发展变化趋势的,而且通过游戏的娱乐性和自主性,很多学生愿意在游戏的过程中学习技能知识,这样的授课方式能够促进学生的学习效率和质量。课程游戏化是一种“在学中玩,在玩中学”的模式,其能最大限度上激发学生的学习积极性,鼓励学生积极参与到计算机课程教育中来,让原本枯燥的计算机教学富有发展生机和学生学习动力。但是不可否认的是,游戏化体系建设的过程中仍然会存在一定的问题,针对存在的问题具体分析,提出切实可行的意见和建议。     

化学教学中多媒体游戏的应用

在化学教学中加入脑筋急转弯之类的游戏很受学生欢迎。若在游戏中附带相关的化学知识解释与拓展,让学生在喜闻乐见的游戏中思考、分析答案,对培养学生学习化学的兴趣,巩固学生所学知识,锻炼学生发现信息、挖     

基于小学信息技术课的游戏教学法实践分析

对小学生来说,在学习过程中运用游戏教学法正好可以贴合学生的心理,促使学生在学习的过程中提高自主性、积极性。本文通过对游戏教学法的全面解析,结合实际情况,对小学信息技术课中游戏教学法的运用实践提出了几点参考意见。     

初中信息技术教学中游戏化教学的应用探究

开展游戏化教学的意义
　　游戏化教学是在对教材内容的把握之上,将教学与游戏有机结合起来的教学方式。游戏化教学注重对学生主体体验的获得与培养,给予学生广阔的发展空间,使其在游戏的合作环节内增强彼此之间的合作能力,强化对思维创造性的培养,进而使学生处于一种张而不弛的学习氛围中,有效缓解了学生的学习压力,教学效果较为理想。     

让纵横信息数字化学习充满趣味的几点做法

让妙趣横生的导入拉开趣味学习的序幕 1.游戏导入 纵横信息数字化学习教学实验总课题组为我们提供了很多有趣、生动的小游戏.上课伊始,教师向学生展示游戏,学生都表现出跃跃欲试的样子,这时教师再适时地向学生提出游戏要求--必须先学会一定的纵横码输入方法才可以游戏.于是,在强烈的兴趣驱动下,学生的学习热情空前高涨,学习的效率和质量也大大提高,而且在游戏的过程中,巩固了学生的学习成果.正所谓寓教于乐,一举两得.     

基于上下文表示的知识追踪方法

知识追踪是教育数据挖掘领域中一个十分重要的问题,旨在利用可观测到的学生历史交互数据和习题包含的知识点相关信息来推断学生对知识点的掌握情况。已有方法虽在不同程度上取得了一些进展,但大多忽略了使用知识点表示习题的重要性,并且对使用诸如学习因素之类的上下文表示知识点的研究也不够充分。针对上述问题,提出基于上下文表示的知识追踪方法KTCR。首先,综合考虑影响学生学习过程的因素,并基于学生响应数据设计了知识点上下文表示方法,从而基于Q矩阵表示知识点上下文;其次,为了实现习题向量的降维,利用融合上下文信息的知识点和学生响应数据对习题向量进行重表示;最后,结合学生历史交互数据,使用长短期记忆网络对学生的知识状态进行估计。在4个真实数据集上的实验表明了本文方法对于习题嵌入表示的合理性,并且能够有效地估计学生的知识状态。     

程序设计中的纵横训练方法初探

程序设计教学与传统课程有许多不同,体现在学生对程序设计习题的编程方法等方面很不适应。文章提出利用程序设计习题纵横方向的变化来组织系列习题,使程序设计训练的难度和编程方法都能循序渐进地深入,以达到提高学生学习积极性和培养学生计算思维能力,进而提高教学质量的目标。     

在化学课堂教学中实施探究性教学的实践与体会

在化学教学中,充分利用和发掘教材资源,激发学生的探究意识;在实验中加强探究学习,培养学生的创新意识;在验证性实验中培养学生的探究意识;精心设计探究性习题,提高学生探究思维能力。     

Enhancing 5th graders’ science content knowledge and self-efficacy through game-based learning

Many argue that games can positively impact learning by providing an intrinsically motivating and engaging learning environment for students in ways that traditional school cannot. Recent research demonstrates that games have the potential to impact student learning in STEM content areas and that collaborative gameplay may be of particular importance for learning gains. This study investigated the effects of collaborative and single game player conditions on science content learning and science self-efficacy. Results indicated that there were no differences between the two playing conditions; however, when conditions were collapsed, science content learning and self-efficacy significantly increased. Future research should focus on the composition of collaboration interaction among game players to assess what types of collaborative tasks may yield positive learning gains.     

Online behavior change detection in computer games

Player Modelling has been receiving much attention from the game community in the recent years. The ability to build accurate models of player behavior can be useful in many aspects of a game. One important aspect is the tracking of a player’s behavior along time, informing every time a change is perceived. This way, the game Artificial Intelligence can adapt itself to better respond to this new behavior. In order to build models of player behavior, researchers frequently resort to Machine Learning techniques. Such methods work on previously recorded game metrics representing player’s interactions with the game environment. However, if the player changes styles over time, the constructed models get out of date. In order to address this drawback, this work proposes the use of and incremental learning technique to track a player’s behavior during his/her interaction with the game environment. Our approach attempts to automatically detect the moments in time when the player changes behavior. We apply a change detection technique from the area of Data Stream Mining that is based on incremental clustering and novelty detection. We also propose three modifications to the original technique, in order to formalize change detection, improve detection rate and reduce detection delay. Simulations were performed considering data produced by the Unreal Tournament game, showing the applicability of the method to online tracking of a player’s behavior and informing whenever behavior changes occur.     

一个打字游戏的设计与实现

该游戏能够显示玩家输入汉字的数量,并实时地以动画的形式呈现,能够增强学习汉字输入的乐趣。采用客户端/服务器结构,利用Windows套接字技术实现信息传递。     

Automatic fuzzy decision making system with learning for competing and connected businesses

We study uncertainties surrounding competition on business networks and board games. We investigate these uncertainties using concepts of fuzzy logic and game theory. We investigate how the payoff of the players is affected by a number of factors. These include the level of connectivity or number of links, the number of competitors, possible constraints on the networks and on the boards, as well as choice of strategy adopted by competitors. We introduce one fuzzy player in the game. This player uses fuzzy rules to make strategic decisions. We introduce learning to train and analyze how the fuzzy player adapts over time during the game.     

Machine learning in digital games: a survey

Artificial intelligence for digital games constitutes the implementation of a set of algorithms and techniques from both traditional and modern artificial intelligence in order to provide solutions to a range of game dependent problems. However, the majority of current approaches lead to predefined, static and predictable game agent responses, with no ability to adjust during game-play to the behaviour or playing style of the player. Machine learning techniques provide a way to improve the behavioural dynamics of computer controlled game agents by facilitating the automated generation and selection of behaviours, thus enhancing the capabilities of digital game artificial intelligence and providing the opportunity to create more engaging and entertaining game-play experiences. This paper provides a survey of the current state of academic machine learning research for digital game environments, with respect to the use of techniques from neural networks, evolutionary computation and reinforcement learning for game agent control.     

基于教学事件的应用游戏评价指标研究

应用游戏是指以知识传播、技能训练、情志培养等应用需求为主要目的, 集趣味性、知识性、功能性等特征为一体的游戏类型。在一些国家也称严肃游戏或功能游戏。 本文针对目前存在的应用游戏评价指标缺乏对游戏与教育特性结合程度考察的问题,研究了 游戏与教育过程在严肃游戏中的并发关系,将应用游戏的使用过程理解为通过游戏形式组织 起来的教学过程,以信息加工理论模型延伸出9 个教学事件为参考,设计了相应的应用游戏 评价指标。尝试通过考核应用游戏对9 个教学事件的支持程度,评价其对教育目的的支持程 度,最终衡量其作为应用游戏的实用价值。     

Improving behavior of computer game bots using fictitious play

In modern computer games, "bots" - intelligent realistic agents play a prominent role in the popularity of a game in the market. Typically, bots are modeled using finite-state machine and then programmed via simple conditional statements which are hard-coded in bots logic. Since these bots have become quite predictable to an experienced games player, a player might lose interest in the game. We propose the use of a game theoretic based learning rule called fictitious play for improving behavior of these computer game bots which will make them less predictable and hence, more a enjoyable game.     

将选择题嵌入到飞行棋之中

将选择题嵌入到飞行棋游戏之中,玩家在掷骰子之后必须选择问题的答案才能移动棋子,如果答案选择正确则棋子正向移动,否则,棋子反向移动。因此,正确地选择答案就成了取胜的重要因素,这可以促使玩家主动记忆相关知识,从而提高了学习效率。