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An active viewing framework for video-based learning 基于视频学习的主动观看框架
Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231682
Samuel Dodson, Ido Roll, Matthew Fong, Dongwook Yoon, N. M. Harandi, S. Fels
Video-based learning is most effective when students are engaged with video content; however, the literature has yet to identify students' viewing behaviors and ground them in theory. This paper addresses this need by introducing a framework of active viewing, which is situated in an established model of active learning to describe students' behaviors while learning from video. We conducted a field study with 460 undergraduates in an Applied Science course using a video player designed for active viewing to evaluate how students engage in passive and active video-based learning. The concept of active viewing, and the role of interactive, constructive, active, and passive behaviors in video-based learning, can be implemented in the design and evaluation of video players.
当学生沉浸在视频内容中时,基于视频的学习是最有效的;然而,这些文献还没有确定学生的观看行为,并在理论上为其奠定基础。本文通过引入一个主动观看框架来解决这一需求,该框架位于一个已建立的主动学习模型中,以描述学生在视频学习时的行为。我们对应用科学课程的460名本科生进行了实地研究,使用为主动观看而设计的视频播放器来评估学生如何参与被动和主动的视频学习。主动观看的概念,以及互动、建设性、主动和被动行为在视频学习中的作用,可以在视频播放器的设计和评估中实施。
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引用次数: 27
An automatic knowledge graph construction system for K-12 education 面向中小学教育的知识图谱自动构建系统
Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231698
Penghe Chen, Yu Lu, V. Zheng, Xiyang Chen, Xiaoqing Li
Motivated by the pressing need of educational applications with knowledge graph, we develop a system, called K12EduKG, to automatically construct knowledge graphs for K-12 educational subjects. Leveraging on heterogeneous domain-specific educational data, K12EduKG extracts educational concepts and identifies implicit relations with high educational significance. More specifically, it adopts named entity recognition (NER) techniques on educational data like curriculum standards to extract educational concepts, and employs data mining techniques to identify the cognitive prerequisite relations between educational concepts. In this paper, we present details of K12EduKG and demonstrate it with a knowledge graph constructed for the subject of mathematics.
基于知识图谱教育应用的迫切需求,我们开发了K12EduKG系统,用于K-12教育学科知识图谱的自动构建。K12EduKG利用异构领域特定的教育数据,提取教育概念并识别具有高教育意义的隐含关系。具体来说,对课程标准等教育数据采用命名实体识别(NER)技术提取教育概念,并利用数据挖掘技术识别教育概念之间的认知前提关系。在本文中,我们介绍了K12EduKG的细节,并用一个为数学主题构建的知识图来演示它。
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引用次数: 23
Transformative approaches in distance online education: aligning evidence to influence the design of teaching at scale 远程在线教育的变革方法:调整证据以影响大规模教学设计
Pub Date : 2018-06-26 DOI: 10.1145/3231644.3232261
P. McAndrew, S. Anastopoulou, E. Scanlon
In this paper we consider the role of sharing evidence online in work in progress to develop a new teaching framework for distance and part-time students of The Open University. The work reported here looks at the motivation for applying evidence and how it can act to support the development of the framework, rather than the framework itself. The approach described is adapted from previous research projects, and focuses on how evidence from internal and external scholarship is gathered and refined through an Evidence Hub that is shared online and open to all in the University. An aspect of the framework (offering greater continuity of study) is selected to show how the methodology applies in practice. In conclusion we highlight the value in adopting evidence-based approaches to support change processes and how sharing collective knowledge can influence decision-making.
在本文中,我们考虑了在线共享证据在为开放大学的远程和兼职学生开发新的教学框架中的作用。这里报告的工作着眼于应用证据的动机,以及它如何支持框架的发展,而不是框架本身。所描述的方法改编自以前的研究项目,重点关注如何通过在线共享并向大学所有人开放的证据中心收集和完善来自内部和外部奖学金的证据。选择框架的一个方面(提供更大的研究连续性)来展示方法如何在实践中应用。最后,我们强调采用循证方法支持变革进程的价值,以及分享集体知识如何影响决策。
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引用次数: 1
Multimedia learning principles at scale predict quiz performance 多媒体学习原理在规模上预测测验表现
Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231694
Anita B. Delahay, M. Lovett
Empirically supported multimedia learning (MML) principles [1] suggest effective ways to design instruction, generally for elements on the order of a graphic or an activity. We examined whether the positive impact of MML could be detected in larger instructional units from a MOOC. We coded instructional design (ID) features corresponding to MML principles, mapped quiz items to these features and their use by MOOC participants, and attempted to predict quiz performance. We found that instructional features related to MML, namely practice problems with high-quality examples and text that is concisely written, were positively predictive. We argue it is possible to predict quiz item performance from features of the instructional materials and suggest ways to extend this method to additional aspects of the ID.
经验支持的多媒体学习(MML)原则[1]提出了设计教学的有效方法,通常是针对图形或活动的顺序元素。我们研究了MML的积极影响是否可以在MOOC的大型教学单元中检测到。我们编码了与MML原则相对应的教学设计(ID)特征,将测试项目映射到这些特征以及MOOC参与者对它们的使用,并试图预测测试表现。我们发现与MML相关的教学特征,即具有高质量示例和简明文本的实践问题,具有积极的预测性。我们认为,从教学材料的特征来预测测验项目的表现是可能的,并建议将这种方法扩展到ID的其他方面。
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引用次数: 1
Who downloads online content and why? 谁下载在线内容,为什么?
Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231699
Katherine A. Brady, G. Narasimham, D. Fisher
Online learners sometimes prefer to download course content rather than view it on a course website. These students often miss out on interactive content. Knowing who downloads course materials, and why, can help course creators design courses that fit the needs of their students. In this paper we explore downloading behavior by looking at lecture videos in three online classes. We found that the number of days since a video was posted had the strongest relationship with downloading, and non-technical considerations, such as typical classroom size in a student's home country, matter more than technical issues, such as internet speed. Our findings suggest that more materials will be downloaded when a course will be available for limited time, students are less familiar with the language of instruction, students are used to classrooms with a high student-teacher ratio, or a student's internet speed is slow. Possible reasons for these relationships are discussed.
在线学习者有时更喜欢下载课程内容,而不是在课程网站上查看。这些学生经常错过互动内容。了解哪些人下载课程资料,以及为什么下载,可以帮助课程创建者设计适合学生需求的课程。在本文中,我们通过查看三个在线课程的讲座视频来探讨下载行为。我们发现,视频发布后的天数与下载的关系最为密切,而非技术因素(如学生所在国家的典型教室大小)比技术问题(如互联网速度)更为重要。我们的研究结果表明,当一门课程在有限的时间内提供时,学生对教学语言不太熟悉,学生习惯于学生与教师比例高的教室,或者学生的互联网速度较慢时,他们会下载更多的材料。讨论了这些关系的可能原因。
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引用次数: 4
Representing and predicting student navigational pathways in online college courses 表示和预测在线大学课程中学生的导航路径
Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231702
Renzhe Yu, Daokun Jiang, M. Warschauer
Representation and prediction of student navigational pathways, typically based on neural network (NN) methods, have seen their potential of improving instruction and learning under insufficient human knowledge about learner behavior. However, they are prominently studied in MOOCs and less probed within more institutionalized higher education scenarios. This work extends such research to the context of online college courses. Comparing student navigational sequences through course pages to documents in natural language processing, we apply a skip-gram model to learn vector embedding of course pages, and visualize the learnt vectors to understand the extent to which students' learning pathways align with pre-designed course structure. We find that students who get different letter grades in the end exhibit different levels of adherence to designed sequence. Next, we fit the embedded sequences into a long short-term memory architecture and test its ability to predict next page that a student visits given her prior sequence. The highest accuracy reaches 50.8% and largely outperforms the frequency-based baseline of 41.3%. These results show that neural network methods have the potential to help instructors understand students' learning behaviors and facilitate automated instructional support.
学生导航路径的表示和预测,通常基于神经网络(NN)方法,已经看到了它们在人类对学习者行为缺乏了解的情况下改善教学和学习的潜力。然而,它们在mooc中得到了突出的研究,而在更制度化的高等教育场景中却很少被探讨。这项工作将这种研究扩展到在线大学课程的背景下。将学生通过课程页面的导航序列与自然语言处理中的文档进行比较,我们应用skip-gram模型来学习课程页面的向量嵌入,并将学习到的向量可视化,以了解学生的学习路径与预先设计的课程结构的一致程度。我们发现,最终获得不同字母分数的学生对设计顺序的依从程度不同。接下来,我们将嵌入的序列放入一个长短期记忆架构中,并测试其预测学生在给定先前序列的情况下访问下一页的能力。最高准确率达到50.8%,大大优于基于频率的41.3%的基线。这些结果表明,神经网络方法有可能帮助教师了解学生的学习行为,并促进自动化教学支持。
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引用次数: 5
The relationship between scientific explanations and the proficiencies of content, inquiry, and writing 科学解释与内容、探究和写作熟练程度之间的关系
Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231660
Haiying Li, J. Gobert, Rachel Dickler
Examining the interaction between content knowledge, inquiry proficiency, and writing proficiency is central to understanding the relative contribution of each proficiency on students' written communication about their science inquiry. Previous studies, however, have only analyzed one of these primary types of knowledge/proficiencies (i.e. content knowledge, inquiry proficiency, and writing proficiency) at a time. This study investigated the extent to which these proficiencies predicted students' written claims, evidence for their claims, and reasoning linking their claims to the evidence. Results showed that all three types of proficiencies significantly predicted students' claims, but only writing proficiency significantly predicted performance on evidence and reasoning statements. These findings indicate the challenges students face when constructing claim, evidence, and reasoning statements, and can inform scaffolding to support these challenges.
考察内容知识、探究熟练程度和写作熟练程度之间的相互作用,对于理解每种熟练程度对学生关于科学探究的书面交流的相对贡献至关重要。然而,以前的研究一次只分析了这些主要类型的知识/熟练程度中的一种(即内容知识、探究熟练程度和写作熟练程度)。本研究调查了这些熟练程度在多大程度上预测了学生的书面主张、主张的证据以及将主张与证据联系起来的推理。结果表明,所有三种熟练程度都能显著预测学生的要求,但只有写作熟练程度能显著预测学生在证据和推理陈述方面的表现。这些发现表明学生在构建索赔、证据和推理陈述时面临的挑战,并可以告知脚手架来支持这些挑战。
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引用次数: 1
Information overload and online collaborative learning: insights from agent-based modeling 信息过载和在线协作学习:来自基于代理的建模的见解
Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231701
Shimin Zhang
This paper investigates information overload (IO) in large online courses by developing an Agent-based Model (ABM) of student interaction in a computer-supported collaborative learning (CSCL) environment. Student surveys provided ABM model parameters, and experimental results suggest unique visitor count to be a superior metric than user activity level for IO detection. ABM of synchronous/asynchronous platforms demonstrates how additional channels can be introduced to effectively combat IO. As work in progress, we look forward to validating model recommendations with activity data in online classrooms.
本文通过在计算机支持的协同学习(CSCL)环境中开发基于agent的学生交互模型(ABM),研究了大型在线课程中的信息过载(IO)问题。学生调查提供了ABM模型参数,实验结果表明,对于IO检测,唯一访问者计数是比用户活动水平更好的度量。同步/异步平台的ABM演示了如何引入额外的通道来有效地对抗IO。随着工作的进行,我们期待着通过在线课堂的活动数据来验证模型建议。
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引用次数: 3
The effects of adaptive learning in a massive open online course on learners' skill development 大规模在线开放课程中适应性学习对学习者技能发展的影响
Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231651
Y. Rosen, I. Rushkin, Rob Rubin, Liberty Munson, Andrew M. Ang, G. Weber, Glenn Lopez, D. Tingley
We report an experimental implementation of adaptive learning functionality in a self-paced Microsoft MOOC (massive open online course) on edX. In a personalized adaptive system, the learner's progress toward clearly defined goals is continually assessed, the assessment occurs when a student is ready to demonstrate competency, and supporting materials are tailored to the needs of each learner. Despite the promise of adaptive personalized learning, there is a lack of evidence-based instructional design, transparency in many of the models and algorithms used to provide adaptive technology or a framework for rapid experimentation with different models. ALOSI (Adaptive Learning Open Source Initiative) provides open source adaptive learning technology and a common framework to measure learning gains and learner behavior. This study explored the effects of two different strategies for adaptive learning and assessment: Learners were randomly assigned to three groups. In the first adaptive group ALOSI prioritized a strategy of remediation - serving learners items on topics with the least evidence of mastery; in the second adaptive group ALOSI prioritized a strategy of continuity - that is learners would be more likely served items on similar topic in a sequence until mastery is demonstrated. The control group followed the pathways of the course as set out by the instructional designer, with no adaptive algorithms. We found that the implemented adaptivity in assessment, with emphasis on remediation is associated with a substantial increase in learning gains, while producing no big effect on the drop-out. Further research is needed to confirm these findings and explore additional possible effects and implications to course design.
我们报告了在edX上自定进度的微软MOOC(大规模开放在线课程)中自适应学习功能的实验实现。在个性化的自适应系统中,学习者向明确定义的目标的进展被不断评估,评估发生在学生准备好展示能力时,支持材料是根据每个学习者的需要量身定制的。尽管有自适应个性化学习的前景,但许多用于提供自适应技术的模型和算法缺乏基于证据的教学设计,缺乏透明度,也缺乏对不同模型进行快速实验的框架。ALOSI(自适应学习开源计划)提供了开源的自适应学习技术和一个通用框架来衡量学习收益和学习者行为。本研究探讨了两种不同策略对适应性学习和评估的影响:学习者被随机分为三组。在第一个自适应组中,ALOSI优先考虑了补习策略——为学习者提供掌握程度最低的主题项目;在第二个适应性组中,ALOSI优先考虑了连续性策略,即学习者更有可能按顺序提供类似主题的项目,直到掌握为止。对照组遵循教学设计师设定的课程路径,没有自适应算法。我们发现,在评估中实施适应性,强调补救,与学习收益的大幅增加有关,而对辍学没有太大影响。需要进一步的研究来证实这些发现,并探索其他可能的影响和对课程设计的影响。
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引用次数: 22
Supporting answerers with feedback in social Q&A 在社交问答中提供反馈以支持答案
Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231653
John Frens, Erin Walker, Gary Hsieh
Prior research has examined the use of Social Question and Answer (Q&A) websites for answer and help seeking. However, the potential for these websites to support domain learning has not yet been realized. Helping users write effective answers can be beneficial for subject area learning for both answerers and the recipients of answers. In this study, we examine the utility of crowdsourced, criteria-based feedback for answerers on a student-centered Q&A website, Brainly.com. In an experiment with 55 users, we compared perceptions of the current rating system against two feedback designs with explicit criteria (Appropriate, Understandable, and Generalizable). Contrary to our hypotheses, answerers disagreed with and rejected the criteria-based feedback. Although the criteria aligned with answerers' goals, and crowdsourced ratings were found to be objectively accurate, the norms and expectations for answers on Brainly conflicted with our design. We conclude with implications for the design of feedback in social Q&A.
先前的研究已经检查了使用社交问答(Q&A)网站来寻求答案和帮助。然而,这些网站支持领域学习的潜力尚未实现。帮助用户写出有效的答案对于答题者和答题者的学科领域学习都是有益的。在这项研究中,我们研究了以学生为中心的问答网站Brainly.com上,众包的、基于标准的回答反馈的效用。在一项有55名用户参与的实验中,我们比较了当前评级系统与两种具有明确标准(适当、可理解和可概括)的反馈设计的看法。与我们的假设相反,回答者不同意并拒绝了基于标准的反馈。虽然标准与回答者的目标一致,而且众包评分被发现在客观上是准确的,但Brainly上对答案的规范和期望与我们的设计相冲突。我们总结了社交问答反馈设计的启示。
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引用次数: 5
期刊
Proceedings of the Fifth Annual ACM Conference on Learning at Scale
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