跨学科课程

简介

在教学环境的创设过程中使用最频繁的三大理论是行为主义、认知主义以及建构主义。不过，这些理论创建于学习的技术含量尚不高的时期。在近20年中，技术已经重组了我们的生活、交流与学习方式。描述学习原理与过程的学习需求与学习理论应该能反映基本的社会环境。维尔（Vaill）强调“学习必须是一种存在方式——是由个人或集体所形成的一系列不间断的态度与行动。这些个人或集体努力跟上那些令人惊讶的、新奇的、凌乱的、含混的、一再发生的事件，与之齐头并进……”（1996，P.42）

短短40年前的学习者往往在按要求完成学业后进入持续终身的职业生涯。知识信息发展十分缓慢。他们的知识生命以十年为一个衡量单位。今天，这些基本原则已被改变。知识正在呈指数增长。现在许多领域的知识生命已发展至以月或年为衡量单位了。冈萨雷兹（Gonzalez, 2004）这样描述迅速缩短的知识生命所带来的挑战：

“一个最具说服力的因素是不断缩减的知识半衰期。所谓‘知识半衰期’指的是从知识的习得到知识的废弃所经历的时间段。今天我们所掌握的知识中有一半是我们十年前所不知的。根据美国培训与信息协会（ASTD）的统计，在近十年中全球知识量增长了一倍，并正在以每18个月翻一翻的速度递增。为应对不断缩减的知识半衰期，各组织机构被迫开发新的教学方法。”

一些重要的学习趋势

▲许多学习者在一生中将会进入各式各样的、很可能毫无关联的学习领域学习。

▲非正规学习成为我们学习的重要部分。正规教育不再构成我们学习的主体。现在的学习可以各种方式进行——通过各种实践活动，个人网络，或通过完成与工作有关的种种任务。

▲学习是一个持续的过程，需要持续终身。学习与相关工作活动不再分离，而在许多情况下合二为一。

▲技术正在改变（重组）着我们的头脑。我们使用的工具决定着我们的思维。

▲组织与个人都是学习机构。对知识管理的关注日益增长，使我们重视对这样一种理论的需求，即设法解释个人学习与组织学习之间联系的理论。

▲以从前的学习理论（尤其是在认知信息处理领域）为基础的许多学习过程如今可由技术取代或支持。

▲“怎样学”与“学什么”正在被“从哪里学”（了解从哪里可以找到所需要的知识）所补充。

背景

德里斯科（Driscoll, 2000）将学习定义为“一种在人类行为或行为潜力方面的持续的改变......〖这种改变〗一定是学习者的经历及其与世界相互作用的产物”（P.11）。该定义包含了许多行为主义、认知主义与建构主义的特征——即，学习作为一种持续被改变的状态[情感、精神，生理（如种种技能）]，是人的经历及其与学习内容或他人相互作用的产物。

德里斯科（2000，P.14-17）就对学习下定义的复杂性进行了一些探索。相关争论主要集中于：

▲确凿的知识来源——我们是否通过经历获得知识？知识是与生俱来的吗？我们是否通过思考与推理而获得知识？

▲知识的内容——知识是否真的可知？知识是否通过人的经历直接可知？

▲考虑最终集中在与学习有关的三种传统的认识论：客观主义、实效主义与体现主义。

1. 客观主义（与行为主义相似）主张，现实是外部的、客观的，知识是通过经历获得的。

2. 实效主义（与认知主义相似）主张，现实是经过诠释的，知识是通过经验与思考而获得的。

3. 体现主义（与建构主义相似）主张，现实是内部的，知识是经过建构的。

所有这些学习理论都认为，知识是一种客观事物（或状态），即使不是与生俱来，也可以通过推理或经历获得。行为主义、认知主义与建构主义（构筑在传统的认识论的基础上）都力图阐述人类的学习方法。

行为主义认为学习在很大程度上是不可知的，即我们无法了解一个人的体内正在发生着什么（"黑盒理论"）。格莱德勒（Gredler, 2001）将行为主义表述为由构成关于学习的三种假设的数个理论的合成物：

1. 可观察到的行为比了解（人体）内部的活动更重要。

2. 应重视行为的简单成分：具体的刺激与反应。

3. 学习是有关行为的变化。

认知主义经常采用电脑信息处理模式。学习被认为是一种输入过程，在短时记忆区进行处理，并被编码以备长期记忆。辛迪·布艾尔（Cindy Buell）将该过程详述为“在认知理论中，知识被看作是存在于学习者脑中的思维符号。而学习过程是一种方式，通过这种方式这些符号所代表的东西就转化成了记忆。”

建构主义认为学习者在努力理解他们的经历时，创造了知识。（德里斯科，2000，P.376）。行为主义与认知主义将知识看作是外在于学习者个体的事物，而将学习过程看作一种内化知识的行为。建构主义认为学习者不是等待被充填知识的空瓶子。相反，学习者积极致力于创造学习的意义。学习者常常选择并追求他们自己的学习。建构主义原理承认现实生活中的学习是凌乱而复杂的。仿效这种“模糊”学习的班级将更有效地培养学习者终身学习的能力。

行为主义、认知主义与建构主义的局限性

大多数学习理论的中心法则为：学习发生在学习者个体内部。连认为学习是一种社会制定的过程的社会建构主义观点都认同了个人（及其生理特征，如大脑结构）在学习中的重要性。这些理论都没有提及学习还发生在人体外部（如学习可由技术来储存或操作）。它们同样没能表述学习在各组织内是如何发生的。

学习理论与学习的实际过程有关，但与被学事物的价值无关。在联网的世界上，我们获得信息的方式颇值得探索。评估所学事物价值的需求是一种较高形式的、在学习开始前就应用的技能。当知识量不足时，评估知识价值的过程是学习之本。当知识量充沛时，对知识进行的快速评估就十分重要。在信息的快速增长过程中又产生了其它一些关注点。在今天的环境中，我们需要不通过个人学习的行动——也就是说，我们需要吸收自身知识体系之外的信息来采取行动。因此，对种种联系与模式的合成及识别能力是一种极有价值的技能。

从技术层面来看已建立起来的学习理论，就会发现许多重要的问题。理论家自然会随着条件的变化而继续努力修正与完善那些理论。然而，在某种程度上，基本条件变化太大，不宜再作进一步的修正，而需要一种全新的方法了。

一些探索有关学习理论、技术影响与学习的新科学（混沌与网络）的问题：

▲当知识不再以线性方式获得时，学习理论受到怎样的冲击？

▲当技术取代学习者从事许多认知操作（信息储存与检索）时，学习理论需做出什么调整？

▲我们在快速发展的信息生态中该如何与时俱进？

▲学习理论如何表述缺乏整体理解时所需要操作的情况呢？

▲何为网络的影响？何为关于学习的复杂性理论？

▲混沌作为一种复杂的学习认知过程模式，其影响是什么？

▲随着对不同知识领域间相互联系的认识的增加，如何看待作为学习任务的各系统与生态理论？

一种可供选择的理论

网络技术与连接的建立作为学习活动开始将学习理论引入数字时代。我们不再亲身经历并习得知识，我们的学习能力来自于各种连接的建立。卡伦·斯蒂芬森（Karen Stephenson）认为：

“长期以来，经历被认为是知识的最好的老师。但我们无法经历所有的事，因此他人的经历，乃至其他人，都成为知识的代名词。'我把我的知识储存在朋友处'是一种通过收集人数来收集知识的公理。（出版年代不详）”。

对知识工作者而言，混沌是一种新的现实反映。《科学周刊》（2004）引用了奈吉·科达（Nigel Kalder）的定义，即混沌乃是"秩序的一种隐秘形式"。混沌是可预测性出了故障，并在最初违背秩序的复杂排列中显现了出来。建构主义强调学习者通过制造学习意义的任务努力培养理解力，而混沌强调的是意义一直存在——学习者的挑战在于能够识别出隐藏着的模式。学习意义的制造与专业领域内联系的建立都十分重要。

作为一门科学，混沌能够识别出所有事物相互间的联系。格雷克（Gleik）认为：“举例而言，在天气方面，可通过半开玩笑的'蝴蝶效应'来诠释混沌——一只蝴蝶今天扇动了北京的空气，这将改变下个月发生在纽约的风暴规律（P.8）”。这种比喻强调了一种真正的挑战：“基于原始条件的敏感依赖关系”深刻地影响着我们的所学所为。决策的制定说明了这点。如果用于决策的基本条件改变了，则决策本身也就不再一如既往的正确了。因此，针对模式改变的识别能力与调整能力十分关键。

路易·马托埃斯·罗恰（Luis Mateus Rocha）将自我组织定义为“从随意的初始条件中自发形成的、组织良好的结构、模式或行为”（P.3）。作为一种自组过程的学习要求本系统（个人的或有组织的学习系统）“有信息量地开放，即，它能够将自己与环境的相互联系分类，它必须能够改变自身的结构......”（P.4）.威利（Wiley）与爱德华兹（Edwards）承认自我组织作为一种学习过程十分重要：“捷科（Jacobs）认为各学习领域的自我组织方式与社会昆虫相似：成千上万只蚂蚁途经彼此的信息素痕迹后，会相应地改变他们的行为；类似的，成千上万个人在人行道上相互经过，也相应地改变他们的行为”。在个人水平层面的自我组织是一种建立在公共机构环境内部的、更为庞大的自我组织知识体的微观过程。我们这个知识经济时代中的学习，要求具备这样一种能力，即建立各信息源之间的连接，并由此创造出有用的信息模式。

网络、小世界、微弱的纽带

一个网络可被简单定义为各实体之间的连接。电脑网络、高压电力网络以及各种社会网络都是根据简单的原理履行其职能的，即人、团体、系统、节点、实体能够被连接起来而形成一个完整的整体。网络内部的种种变化具有整体的涟漪效应。

阿尔博特（Albert-Laszlo Barabasi）认为“节点一直为连接而竞争，因为各种联系代表着在一个相互关联的世界上的生存能力”（2002，P.106）。这种竞争在个人学习网络中大部分都受到了压抑，但将有价值的东西置于某些节点，而不置于其它节点上，这也是事实。那些成功地获得较多价值的节点将比其它节点更成功地获得额外的联系。从学习的意义上讲，一种学习概念在将来被联系到的可能性取决于它当前的联系状况。专业化的节点或贡献杰出的节点（可以是领域、理念或团体）有更多的机会获得认可，进而导致各学术界的相得益彰的交流。

微弱的纽带指那些允许信息间短途连接的联系或桥梁。我们的网络小世界上挤满了那些兴趣或知识结构与我们相似的人。举例而言，寻找一份新工作常常发生在微弱的纽带上。这条原则在意外发现、革新与创造性方面很有价值。互不相同的理念与领域之间的连接能够产生新的方法事物。

关联主义

关联主义是一种经由混沌、网络、复杂性与自我组织等理论探索的原理的整体。学习是一种过程，这种过程发生在模糊不清的环境中，将核心成分——（部分）置于个人的控制之下。学习（被定义为动态的知识）可存在于我们自身之外（在一种组织或数据库的范围内）。我们可将学习集中在将专业知识系列的连接方面。这种连接能够使我们学到比现有的知识体系更多、更重要的东西。

关联主义建立在这样一种理解上，即知识基础的迅速改变导致决策的改变。新的信息持续被获得。区分重要信息与非重要信息的能力至关重要。当新的信息改变了建立在昨天决策基础上的知识全景时，我们要有识别能力，这点同样非常重要。

关联主义原理

▲学习与知识建立于各种观点之上。

▲学习是一种将不同专业节点或信息源连接起来的过程。

▲学习可能存在于非人的工具设施中。

▲持续学习的能力比当前知识的掌握更重要。

▲为促进持续学习，需要培养与保持各种连接。

▲看出不同领域、理念与概念之间联系的能力至关重要。

▲流通（精确的、最新的知识）是所有关联主义学习活动的目的。

▲决策本身是一种学习过程。选择学习内容，根据不断变化的实际情况理解新信息的意义。随着影响决策的信息的改变，今天正确的答案到了明天就可能是错误的。

关联主义还提出了许多团体在知识管理活动中所面临的挑战。存在于数据库中的知识需要与合适的领域中合适的人连接在一起，从而形成学习分类。行为主义、认知主义与建构主义没有提及关于有组织的知识与知识转移方面的挑战。

一个组织内部的信息流是衡量该组织有效性的重要成分。知识经济中的信息流相当于工业经济中的油管。信息流的创造、储存与利用应该成为一种关键的组织活动。知识流可比作蜿蜒穿越一种组织生态的河流。在某些区域，河水骤涨；而在另一些区域，河流退落。该组织学习生态的健康性取决于信息流的有效培养。

社会网络分析是理解数字时代学习模式的额外要求。阿特·克莱纳（Art Kleiner）探索了凯伦·斯帝芬森（Karen Stephenson）的"信任量子理论"。该理论“不但解释了如何识别一个组织的集体认知能力，并且还解释了如何培养与提高这种识别力”。在社会网络中，各种活动中心就是那些建立起良好连接的人，他们能够培养并保持知识流。他们的独立性导致了有效的知识流，实现了个人对种种活动状态的有组织性的理解。

关联主义的起点是个人。个人的知识组成了一个网络，这种网络被编入各种组织与机构，反过来各组织与机构的知识被又回馈给个人网络，提供个人的继续学习。这种知识发展的循环（个人对网络对组织）使得学习者通过他们所建立的连接在各自的领域保持不落伍。

朗道尔（Landauer）与杜迈斯（Dumais）（1997）探索了这样一种现象，即"人们获取的信息比他们接触到的信息多得多"。他们提出"有些知识领域包含了大量微弱的相互联系，一旦适当地加以利用，能够通过一种相互干扰过程极大地扩大学习量"。这里的中心就是关联主义。这种模式识别与连接我们自己的"知识小世界"的价值对我们个人学习影响巨大。

约翰·希里·布朗（John Seely Brown）提出了一种有趣的观点，即因特网在许多人做出的小小努力与少数人做出的大量努力间起到了一个杠杆作用。其中的关键在于由不同寻常的节点所建立起来的连接支持并强化了现存的多数努力活动。布朗给出了一个关于马里科帕县社区大学系统项目的实例。在一个指导项目中，老年人与小学生被联系在一起。孩子们听那些“祖父母”的话，比听自己父母的话更容易，这个指导项目“真正地帮助那些教师......许多人（老年人）的小努力弥补了少数人（老师们）的大努力。"（2002）。这个通过个人网络的延伸而扩大学习、知识与理解力的实例是关联主义的缩影。

意义

关联主义的概念在生活的各方面都有意义。本文将重点放在其对学习的影响上，但下述几方面同样受到了关联主义的影响：

▲管理与领导。管理并统筹资源以期获得想要的结果，这是一种非常有意义的挑战。我们应该意识到完整的知识体系不可能存在于一人的脑中，这需要通过不同的途径来创立一种有关该体系的概述。集结各种观点的不同团体是完整地探索各种理念的重要结构。革新是一种额外的挑战。今天大多数的突破性的想法曾经作为一种次要的或边缘的成分而存在。对知识经济的生存而言，组织、培养并综合各种信息观点的影响是非常重要的。从学习的系统观点来看，"想法付诸实施"的速度也同样得到了促进。

▲媒体、新闻，信息。这种趋势已在进行中了。各种主流媒体正在受到开放、实时、双向的博客信息流的挑战。

▲与有组织的知识管理相关的个人知识管理。

▲学习环境的设计。

结论

管道比管道中的内容物更重要。我们对明天所需知识的学习能力比我们对今天知识的掌握能力更重要。对所有学习理论的真正挑战是在应用知识的同时，促进已知的知识。不过，当知识为人所需，而又不为人知时，寻出出处而满足需要就成了十分关键的技能。由于知识不断增长进化，获得所需知识的途径比学习者当前掌握的知识更重要。

关联主义表述了一种适应当前社会结构变化的学习模式。学习不再是内化的个人活动。当新的学习工具被使用时，人们的学习方式与学习目的也发生了变化。在认识到新的学习工具所带来的影响及学习意义的环境变化方面，教育领域已经落于人后了。关联主义提供了一些学习者在数字时代成功学习所需的学习技能与学习任务的见解。

References

Barabási, A. L., (2002) Linked: The New Science of Networks, Cambridge, MA, Perseus Publishing.

Buell, C. (undated). Cognitivism. Retrieved December 10, 2004 from http://web.cocc.edu/cbuell/theories/cognitivism.htm.

Brown, J. S., (2002). Growing Up Digital: How the Web Changes Work, Education, and the Ways People Learn. United States Distance Learning Association. Retrieved on December 10, 2004, from http://www.usdla.org/html/journal/FEB02_Issue/article01.html

Driscoll, M. (2000). Psychology of Learning for Instruction. Needham Heights, MA, Allyn & Bacon.

Gleick, J., (1987). Chaos: The Making of a New Science. New York, NY, Penguin Books.

Gonzalez, C., (2004). The Role of Blended Learning in the World of Technology. Retrieved December 10, 2004 from http://www.unt.edu/benchmarks/archives/2004/september04/eis.htm.

Gredler, M. E., (2005) Learning and Instruction: Theory into Practice – 5th Edition, Upper Saddle River, NJ, Pearson Education.

Kleiner, A. (2002). Karen Stephenson’s Quantum Theory of Trust. Retrieved December 10, 2004 from http://www.netform.com/html/s+b%20article.pdf.

Landauer, T. K., Dumais, S. T. (1997). A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of Acquisition, Induction and Representation of Knowledge. Retrieved December 10, 2004 from http://lsa.colorado.edu/papers/plato/plato.annote.html.

Rocha, L. M. (1998). Selected Self-Organization and the Semiotics of Evolutionary Systems. Retrieved December 10, 2004 from http://informatics.indiana.edu/rocha/ises.html.

ScienceWeek (2004) Mathematics: Catastrophe Theory, Strange Attractors, Chaos. Retrieved December 10, 2004 from http://scienceweek.com/2003/sc031226-2.htm

Stephenson, K., (Internal Communication, no. 36) What Knowledge Tears Apart, Networks Make Whole. Retrieved December 10, 2004 from http://www.netform.com/html/icf.pdf.

Vaill, P. B., (1996). Learning as a Way of Being. San Francisco, CA, Jossey-Blass Inc.

Wiley, D. A and Edwards, E. K. (2002). Online self-organizing social systems: The decentralized future of online learning. Retrieved December 10, 2004 from http://wiley.ed.usu.edu/docs/ososs.pdf.

【编者按】Editor’s Note: This is a milestone article that deserves careful study. Connectivism should not be con fused with constructivism. George Siemens advances a theory of learning that is consistent with the needs of the twenty first century. His theory takes into account trends in learning, the use of technology and networks, and the diminishing half-life of knowledge. It combines relevant elements of many learning theories, social structures, and technology to create a powerful theoretical construct for learning in the digital age.

Behaviorism, cognitivism, and constructivism are the three broad learning theories most often utilized in the creation of instructional environments. These theories, however, were developed in a time when learning was not impacted through technology. Over the last twenty years, technology has reorganized how we live, how we communicate, and how we learn. Learning needs and theories that describe learning principles and processes, should be reflective of underlying social environments. Vaill emphasizes that “learning must be a way of being – an ongoing set of attitudes and actions by individuals and groups that they employ to try to keep abreast of the surprising, novel, messy, obtrusive, recurring events…” (1996, p.42).

Learners as little as forty years ago would complete the required schooling and enter a career that would often last a lifetime. Information development was slow. The life of knowledge was measured in decades. Today, these foundational principles have been altered. Knowledge is growing exponentially. In many fields the life of knowledge is now measured in months and years. Gonzalez (2004) describes the challenges of rapidly diminishing knowledge life:

“One of the most persuasive factors is the shrinking half-life of knowledge. The “half-life of knowledge” is the time span from when knowledge is gained to when it becomes obsolete. Half of what is known today was not known 10 years ago. The amount of knowledge in the world has doubled in the past 10 years and is doubling every 18 months according to the American Society of Training and Documentation (ASTD). To combat the shrinking half-life of knowledge, organizations have been forced to develop new methods of deploying instruction.”

l Many learners will move into a variety of different, possibly unrelated fields over the course of their lifetime.

l Informal learning is a significant aspect of our learning experience. Formal education no longer comprises the majority of our learning. Learning now occurs in a variety of ways – through communities of practice, personal networks, and through completion of work-related tasks.

l Learning is a continual process, lasting for a lifetime. Learning and work related activities are no longer separate. In many situations, they are the same.

l Technology is altering (rewiring) our brains. The tools we use define and shape our thinking.

l The organization and the individual are both learning organisms. Increased attention to knowledge management highlights the need for a theory that attempts to explain the link between individual and organizational learning.

l Many of the processes previously handled by learning theories (especially in cognitive information processing) can now be off-loaded to, or supported by, technology.

l Know-how and know-what is being supplemented with know-where (the understanding of where to find knowledge needed).

Driscoll (2000) defines learning as “a persisting change in human performance or performance potential…[which] must come about as a result of the learner’s experience and interaction with the world” (p.11). This definition encompasses many of the attributes commonly associated with behaviorism, cognitivism, and constructivism – namely, learning as a lasting changed state (emotional, mental, physiological (i.e. skills)) brought about as a result of experiences and interactions with content or other people.

Driscoll (2000, p14-17) explores some of the complexities of defining learning. Debate centers on:

l Valid sources of knowledge - Do we gain knowledge through experiences? Is it innate (present at birth)? Do we acquire it through thinking and reasoning?

l Content of knowledge – Is knowledge actually knowable? Is it directly knowable through human experience?

l The final consideration focuses on three epistemological traditions in relation to learning: Objectivism, Pragmatism, and Interpretivism

1. Objectivism (similar to behaviorism) states that reality is external and is objective, and knowledge is gained through experiences.

2. Pragmatism (similar to cognitivism) states that reality is interpreted, and knowledge is negotiated through experience and thinking.

3. Interpretivism (similar to constructivism) states that reality is internal, and knowledge is constructed.

All of these learning theories hold the notion that knowledge is an objective (or a state) that is attainable (if not already innate) through either reasoning or experiences. Behaviorism, cognitivism, and constructivism (built on the epistemological traditions) attempt to address how it is that a person learns.

Behaviorism states that learning is largely unknowable, that is, we can’t possibly understand what goes on inside a person (the “black box theory”). Gredler (2001) expresses behaviorism as being comprised of several theories that make three assumptions about learning:

1. Observable behaviour is more important than understanding internal activities

2. Behaviour should be focused on simple elements: specific stimuli and responses

Cognitivism often takes a computer information processing model. Learning is viewed as a process of inputs, managed in short term memory, and coded for long-term recall. Cindy Buell details this process: “In cognitive theories, knowledge is viewed as symbolic mental constructs in the learner's mind, and the learning process is the means by which these symbolic representations are committed to memory.”

Constructivism suggests that learners create knowledge as they attempt to understand their experiences (Driscoll, 2000, p. 376). Behaviorism and cognitivism view knowledge as external to the learner and the learning process as the act of internalizing knowledge. Constructivism assumes that learners are not empty vessels to be filled with knowledge. Instead, learners are actively attempting to create meaning. Learners often select and pursue their own learning. Constructivist principles acknowledge that real-life learning is messy and complex. Classrooms which emulate the “fuzziness” of this learning will be more effective in preparing learners for life-long learning.

A central tenet of most learning theories is that learning occurs inside a person. Even social constructivist views, which hold that learning is a socially enacted process, promotes the principality of the individual (and her/his physical presence – i.e. brain-based) in learning. These theories do not address learning that occurs outside of people (i.e. learning that is stored and manipulated by technology). They also fail to describe how learning happens within organizations

Learning theories are concerned with the actual process of learning, not with the value of what is being learned. In a networked world, the very manner of information that we acquire is worth exploring. The need to evaluate the worthiness of learning something is a meta-skill that is applied before learning itself begins. When knowledge is subject to paucity, the process of assessing worthiness is assumed to be intrinsic to learning. When knowledge is abundant, the rapid evaluation of knowledge is important. Additional concerns arise from the rapid increase in information. In today’s environment, action is often needed without personal learning – that is, we need to act by drawing information outside of our primary knowledge. The ability to synthesize and recognize connections and patterns is a valuable skill.

Many important questions are raised when established learning theories are seen through technology. The natural attempt of theorists is to continue to revise and evolve theories as conditions change. At some point, however, the underlying conditions have altered so significantly, that further modification is no longer sensible. An entirely new approach is needed.

l Some questions to explore in relation to learning theories and the impact of technology and new sciences (chaos and networks) on learning:

l How are learning theories impacted when knowledge is no longer acquired in the linear manner?

l What adjustments need to made with learning theories when technology performs many of the cognitive operations previously performed by learners (information storage and retrieval).

l How can we continue to stay current in a rapidly evolving information ecology?

l How do learning theories address moments where performance is needed in the absence of complete understanding?

l What is the impact of chaos as a complex pattern recognition process on learning?

l With increased recognition of interconnections in differing fields of knowledge, how are systems and ecology theories perceived in light of learning tasks?

Including technology and connection making as learning activities begins to move learning theories into a digital age. We can no longer personally experience and acquire learning that we need to act. We derive our competence from forming connections. Karen Stephenson states:

“Experience has long been considered the best teacher of knowledge. Since we cannot experience everything, other people’s experiences, and hence other people, become the surrogate for knowledge. ‘I store my knowledge in my friends’ is an axiom for collecting knowledge through collecting people (undated).”

Chaos is a new reality for knowledge workers. ScienceWeek (2004) quotes Nigel Calder's definition that chaos is “a cryptic form of order”. Chaos is the breakdown of predictability, evidenced in complicated arrangements that initially defy order. Unlike constructivism, which states that learners attempt to foster understanding by meaning making tasks, chaos states that the meaning exists – the learner's challenge is to recognize the patterns which appear to be hidden. Meaning-making and forming connections between specialized communities are important activities.

Chaos, as a science, recognizes the connection of everything to everything. Gleick (1987) states: “In weather, for example, this translates into what is only half-jokingly known as the Butterfly Effect – the notion that a butterfly stirring the air today in Peking can transform storm systems next month in New York” (p. 8). This analogy highlights a real challenge: “sensitive dependence on initial conditions” profoundly impacts what we learn and how we act based on our learning. Decision making is indicative of this. If the underlying conditions used to make decisions change, the decision itself is no longer as correct as it was at the time it was made. The ability to recognize and adjust to pattern shifts is a key learning task.

Luis Mateus Rocha (1998) defines self-organization as the “spontaneous formation of well organized structures, patterns, or behaviors, from random initial conditions.” (p.3). Learning, as a self-organizing process requires that the system (personal or organizational learning systems) “be informationally open, that is, for it to be able to classify its own interaction with an environment, it must be able to change its structure…” (p.4). Wiley and Edwards acknowledge the importance of self-organization as a learning process: “Jacobs argues that communities self-organize is a manner similar to social insects: instead of thousands of ants crossing each other’s pheromone trails and changing their behavior accordingly, thousands of humans pass each other on the sidewalk and change their behavior accordingly.”. Self-organization on a personal level is a micro-process of the larger self-organizing knowledge constructs created within corporate or institutional environments. The capacity to form connections between sources of information, and thereby create useful information patterns, is required to learn in our knowledge economy.

A network can simply be defined as connections between entities. Computer networks, power grids, and social networks all function on the simple principle that people, groups, systems, nodes, entities can be connected to create an integrated whole. Alterations within the network have ripple effects on the whole.

Albert-László Barabási states that “nodes always compete for connections because links represent survival in an interconnected world” (2002, p.106). This competition is largely dulled within a personal learning network, but the placing of value on certain nodes over others is a reality. Nodes that successfully acquire greater profile will be more successful at acquiring additional connections. In a learning sense, the likelihood that a concept of learning will be linked depends on how well it is currently linked. Nodes (can be fields, ideas, communities) that specialize and gain recognition for their expertise have greater chances of recognition, thus resulting in cross-pollination of learning communities.

Weak ties are links or bridges that allow short connections between information. Our small world networks are generally populated with people whose interests and knowledge are similar to ours. Finding a new job, as an example, often occurs through weak ties. This principle has great merit in the notion of serendipity, innovation, and creativity. Connections between disparate ideas and fields can create new innovations.

Connectivism is the integration of principles explored by chaos, network, and complexity and self-organization theories. Learning is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual. Learning (defined as actionable knowledge) can reside outside of ourselves (within an organization or a database), is focused on connecting specialized information sets, and the connections that enable us to learn more are more important than our current state of knowing.

Connectivism is driven by the understanding that decisions are based on rapidly altering foundations. New information is continually being acquired. The ability to draw distinctions between important and unimportant information is vital. The ability to recognize when new information alters the landscape based on decisions made yesterday is also critical.

l Nurturing and maintaining connections is needed to facilitate continual learning.

l Ability to see connections between fields, ideas, and concepts is a core skill.

l Currency (accurate, up-to-date knowledge) is the intent of all connectivist learning activities.

l Decision-making is itself a learning process. Choosing what to learn and the meaning of incoming information is seen through the lens of a shifting reality. While there is a right answer now, it may be wrong tomorrow due to alterations in the information climate affecting the decision.

Connectivism also addresses the challenges that many corporations face in knowledge management activities. Knowledge that resides in a database needs to be connected with the right people in the right context in order to be classified as learning. Behaviorism, cognitivism, and constructivism do not attempt to address the challenges of organizational knowledge and transference.

Information flow within an organization is an important element in organizational effectiveness. In a knowledge economy, the flow of information is the equivalent of the oil pipe in an industrial economy. Creating, preserving, and utilizing information flow should be a key organizational activity. Knowledge flow can be likened to a river that meanders through the ecology of an organization. In certain areas, the river pools and in other areas it ebbs. The health of the learning ecology of the organization depends on effective nurturing of information flow.

Social network analysis is an additional element in understanding learning models in a digital era. Art Kleiner (2002) explores Karen Stephenson’s “quantum theory of trust” which “explains not just how to recognize the collective cognitive capability of an organization, but how to cultivate and increase it”. Within social networks, hubs are well-connected people who are able to foster and maintain knowledge flow. Their interdependence results in effective knowledge flow, enabling the personal understanding of the state of activities organizationally.

The starting point of connectivism is the individual. Personal knowledge is comprised of a network, which feeds into organizations and institutions, which in turn feed back into the network, and then continue to provide learning to individual. This cycle of knowledge development (personal to network to organization) allows learners to remain current in their field through the connections they have formed.

Landauer and Dumais (1997) explore the phenomenon that “people have much more knowledge than appears to be present in the information to which they have been exposed”. They provide a connectivist focus in stating “the simple notion that some domains of knowledge contain vast numbers of weak interrelations that, if properly exploited, can greatly amplify learning by a process of inference”. The value of pattern recognition and connecting our own “small worlds of knowledge” are apparent in the exponential impact provided to our personal learning.

John Seely Brown presents an interesting notion that the internet leverages the small efforts of many with the large efforts of few. The central premise is that connections created with unusual nodes supports and intensifies existing large effort activities. Brown provides the example of a Maricopa County Community College system project that links senior citizens with elementary school students in a mentor program. The children “listen to these “grandparents” better than they do their own parents, the mentoring really helps the teachers…the small efforts of the many- the seniors – complement the large efforts of the few – the teachers.” (2002). This amplification of learning, knowledge and understanding through the extension of a personal network is the epitome of connectivism.

The notion of connectivism has implications in all aspects of life. This paper largely focuses on its impact on learning, but the following aspects are also impacted:

l Management and leadership. The management and marshalling of resources to achieve desired outcomes is a significant challenge. Realizing that complete knowledge cannot exist in the mind of one person requires a different approach to creating an overview of the situation. Diverse teams of varying viewpoints are a critical structure for completely exploring ideas. Innovation is also an additional challenge. Most of the revolutionary ideas of today at one time existed as a fringe element. An organizations ability to foster, nurture, and synthesize the impacts of varying views of information is critical to knowledge economy survival. Speed of “idea to implementation” is also improved in a systems view of learning.

l Media, news, information. This trend is well under way. Mainstream media organizations are being challenged by the open, real-time, two-way information flow of blogging.

l Personal knowledge management in relation to organizational knowledge management

The pipe is more important than the content within the pipe. Our ability to learn what we need for tomorrow is more important than what we know today. A real challenge for any learning theory is to actuate known knowledge at the point of application. When knowledge, however, is needed, but not known, the ability to plug into sources to meet the requirements becomes a vital skill. As knowledge continues to grow and evolve, access to what is needed is more important than what the learner currently possesses.

Connectivism presents a model of learning that acknowledges the tectonic shifts in society where learning is no longer an internal, individualistic activity. How people work and function is altered when new tools are utilized. The field of education has been slow to recognize both the impact of new learning tools and the environmental changes in what it means to learn. Connectivism provides insight into learning skills and tasks needed for learners to flourish in a digital era.

Barabási, A. L., (2002) Linked: The New Science of Networks, Cambridge, MA, Perseus Publishing.

Buell, C. (undated). Cognitivism. Retrieved December 10, 2004 from http://web.cocc.edu/cbuell/theories/cognitivism.htm.

Driscoll, M. (2000). Psychology of Learning for Instruction. Needham Heights, MA, Allyn & Bacon.

Gleick, J., (1987). Chaos: The Making of a New Science. New York, NY, Penguin Books.

Gonzalez, C., (2004). The Role of Blended Learning in the World of Technology. Retrieved December 10, 2004 from http://www.unt.edu/benchmarks/archives/2004/september04/eis.htm.

Gredler, M. E., (2005) Learning and Instruction: Theory into Practice – 5th Edition, Upper Saddle River, NJ, Pearson Education.

Kleiner, A. (2002). Karen Stephenson’s Quantum Theory of Trust. Retrieved December 10, 2004 from http://www.netform.com/html/s+b%20article.pdf.

Rocha, L. M. (1998). Selected Self-Organization and the Semiotics of Evolutionary Systems. Retrieved December 10, 2004 from http://informatics.indiana.edu/rocha/ises.html.

ScienceWeek (2004) Mathematics: Catastrophe Theory, Strange Attractors, Chaos. Retrieved December 10, 2004 from http://scienceweek.com/2003/sc031226-2.htm.

Stephenson, K., (Internal Communication, no. 36) What Knowledge Tears Apart, Networks Make Whole. Retrieved December 10, 2004 from http://www.netform.com/html/icf.pdf.

Vaill, P. B., (1996). Learning as a Way of Being. San Francisco, CA, Jossey-Blass Inc.

Wiley, D. A and Edwards, E. K. (2002). Online self-organizing social systems: The decentralized future of online learning. Retrieved December 10, 2004 from http://wiley.ed.usu.edu/docs/ososs.pdf.

George Siemens is an instructor at Red River College in Winnipeg, Manitoba, Canada. He is enamored with the potential of technology to transform learning and is convinced that existing educational perspectives need to be revised to meet the needs of "today's students". Contact him at gsiemens@elearnspace.org.

George Seimens is author and editor of the eLearnspace website at www.elearnspace.org. It offers rich resources on elearning.

You are invited to subscribe to elearnspace’s twice-weekly blog summary email with eLearning Resources and News for managers, developers, and facilitators. To subscribe, click here. You can also read it online at http://www.elearnspace.org/blog/.