4.+Paradigms

Paradigms
Who is our audience for this bucket?

The tools and technology that comprise your personal learning environment can be organized into their underlying patterns or paradigms of composition and usage. Why would we want to do this? It is a natural instinct of the analytical mindset to organize thought into shared structures to aid in understanding and communication. Hence taxonomies of nearly everything abound: scientific, commercial, literary, artistic, philosophical, and even educational taxonomies (see [|Bloom’s taxonomy]) are everywhere.

By classifying the paradigms of personal learning in a contemporary technological context we speak and understand a common vocabulary of forms; we make connections between tools and practices that might have appeared unrelated; and we advance the dialog and idea space wherein new tools and practices are born like nascent stars forming in molecular clouds. Molecular cloud [|Cepheus] B Paradigms of personal learning environments form bridges to the theoretical foundations of teaching and learning – we will see this as we enumerate our taxonomy. They also help to organize the tools and technologies themselves into useful buckets (from an educational perspective) that don’t necessarily align with conventional practice. Consider this: a search engine has many uses: from finding a plumber to finding a graduate school, it is a general purpose tool par excellence for locating all manner of things on the web. Search engines have a built-in bias in how they display results: most commercial engines use roughly equivalent algorithms for ranking their results based on a kind of glorified popularity contest among websites – the more sites that link to a site (and the more sites that link to //those// sites), the higher its ranking and the more likely it is to appear near the top of search results for a given keyword or phrase. Going from the general to the particular, notably to an educational perspective, is this necessarily the best way to find content for a given research subject? Perhaps in some cases, but certainly not in all. There is most certainly a wealth of hidden informational gems on the internet that don’t rank highly in general purpose search engine results. From a PLE perspective we can expand our paradigm set to include three classifications that are a bit more specialized: discovery engines, answer engines and semantic search. Discovery engines can offer random results or can suggest destinations based on rankings culled from the discovery engine’s community using a technique known as collaborative filtering (another paradigm of PLEs) which may or may not produce the desired results. Answer engines, such as Wolfram|Alpha, turn search on its head. Whereas conventional search typically returns a myriad of results, an answer engine returns only one result: the answer to the user’s question. Answer engines, again unlike conventional search, rely on tightly controlled, structured data and employ a natural language interface to translate the user’s questions into exact parameters and also to translate machine answers into human-readable text. Semantic search analyzes the context of terms for their meaning within a website to attempt to better rank results based on keywords or phrases. While current offerings may fall short of producing the kinds of results that would place them in every teacher and learner’s toolkit, they will take on much greater importance with the advent of the semantic web. Many of our paradigms are interrelated – they partake of aspects of each other’s patterns. They form a heterogeneous field: some are structural (nodes & connectors); some are dynamic (zooming, accreting); some could readily describe modes of being (virtuality). The common threads (there are many) that weave through and between our paradigms form a skein of pathways established by pioneers and early adapters that are becoming increasingly well traveled. Indeed many, if not most, have been developed for a spectrum of purposes that frequently related peripherally at best to the educational milieu. And yet they interlock with uncanny acuity, like pieces in a large, unbounded puzzle. As we define and collate these paradigms there are doubtless newer, edgier versions waiting in the wings for their entrance.

StumbleUpon is the most prominent example of this paradigm. It uses Collaborative Filtering (an automated process combining human opinions with [|machine learning] of personal preference) to create [|virtual communities] of like-minded Web surfers. – [Wikipedia]
 * Discovery Engines**

Answer engines, such as Wolfram|Alpha, turn search on its head. Whereas conventional search typically returns a myriad of results, an answer engine returns only one result: the answer to the user’s question. Answer engines, unlike conventional search, rely on tightly controlled structured data and employ a natural language interface to translate the user’s questions into exact parameters and also to translate machine answers into human-readable text.
 * Answer Engines**

Lessig in Remix, posits 2 cultures: Read-Only: analog technologies, promotes 1 way flow from producers to consumers. Hallmark of monolithic content production (Hollywood Studios, Newspapers, Publishers, etc) which are being disrupted by: Read-Write: Reciprocal relationship between producer and consumer; digital technologies enable RW culture and democratic production.
 * Read / Write**

Blogs are multi-paradigm: Producer: everyone is a producer (dissemination) Comments: comments foster a dialog and a community, and the content is owned by nobody, that is, owned by everybody. (accretion) Tags: Tags allow consumers to filter content according to their interests and goals. [ look at tag clouds, underlying tech, where they fit into paradigms ]
 * Blogs**

Pathways – like ants leaving pheromone trails we leave social bookmarking and tagging trails, emailing links to one another, posting them on our blogs, wikis and other artifacts of the read/write web.
 * Tagging/Bookmarking**

Any time your point of view moves from a global, high-level perspective to a more detailed, granular perspective, you are zooming in. Reverse the movement and you are zooming out. Simple. Everyone knows this from photography, film, software and many other domains. This dynamic paradigm has been codified by the interface community in a powerful new classification: the ZUI, or Zooming User Interface. ZUIs feel very natural in the emerging touch interfaces that are increasingly popular and may someday replace the legacy mouse/keyboard/screen GUI paradigm. __the infinite desktop__ Zooming user interfaces employ a metaphor of the infinite desktop. Users can pan across an unbounded 2-dimensional plane, zooming in on objects of interest that they encounter. When we think of zooming we usually think of a visual perspective. But zooming has applications in other perceptual and cognitive areas. Any time you ‘drill down’ within a given subject matter you are zooming. This leads to the paradigm of Semantic Zooming.
 * Zooming/ZUIs**

[] A category that seems to defy definition, semantic zooming has a distinct place among our paradigms. It connects the visual connotation of zooming with semantic modes of information access and processing, forming a unique and highly compelling hybrid. As we zoom in our out on a subject, the context changes. As the context changes, the semantics of the subject in relation to its context may change. As we traverse these different levels of zoom and context, our semantic zooming tool alters the environment of the context, providing appropriate links, references and capabilities. A wide view of the galaxy shows many different kinds of stars. We may select and zoom on a particular star of a particular class. Whereas in the wider view the context is the galaxy of stars and perhaps all stars of the class of the selected, but when we zoom in the context becomes the local neighborhood of the star. Aspects of the star’s neighborhood, gas clouds, black holes and all sorts of other objects and forces on a local level become the refined context and links. References and even capabilities of the application on this level are adjusted semantically.
 * Semantic Zooming**

“A semantic network or net is a graphic notation for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics.” (//Encyclopedia of Artificial Intelligence//, edited by Stuart C. Shapiro, Wiley, 1987, second edition, 1992). Semantic networks represent semantic relationships among concepts, usually with a graphic front-end that resembles a mind-map. The oldest known semantic network was created by Greek philosopher Porphyry to illustrate Aristotle’s method of defining categories by specifying the //genus// or general type and the //differentiae// that distinguish different subtypes of the same supertype. The Tree of Porphyry (pictured below) represents the common scheme of all modern ontological hierarchies that are used for defining concept types.
 * Semantic Networks**
 * Tree of Porphyry – actual depictions appearing in various translations of Porphyry **


 * Tree of Porphyry – schematic **

__WordNet__: Wordnet is a lexical database that groups english words into synonym sets called synsets that can be searched and semantic relationships among words and concepts analyzed. Wordnet can be used interactively online, or employed as a component in artificial intelligence applications.

The Semantic Web, brain child of Tim Berners-Lee (who, as you know, invented the World Wide Web) is a bundle of technologies under development that will add meaning to resources on the web in a standardized way, allowing for people or applications to search for and connect content in a much deeper, more accurate way than with legacy search engines such as Google. Semantic markup and metadata is being added to pages and sites to facilitate this. Since the web is vast it will take time for this relatively new paradigm to reach the widespread application that will make it truly useful, but there are numerous nascent semantic search engines and applications currently available. The Bing search engine, by Microsoft, has a semantic feature in its ‘Related Searches’ capability – search results are accompanied by a list of similar searches selected and grouped by their semantic relationship to the original search term. There are also ‘pure’ semantic search engines reaching the market, such as Hakia, SenseBot and Cognition.
 * Semantic Search**

In aggregating we are gathering together elements that may or may not be related. This is in contrast to accreting, where like adheres to like (blog comments accrete; RSS feeds aggregate; more on both of these manifestations of their respective paradigms to come.) Aggregate materials, such as concrete, mix in sand and stones to form a stronger, harder product than cement alone would produce. Not to force the analogy too much, but aggregating of diverse source material can often form a more persuasive argument, if one’s goal is to persuade. Aggregating content also allows for a broader base (foundation) upon which to predicate theses and hypotheses. __Mashups__: Mashups are forced combinations of often wildly different source materials, resulting in (hopefully) compelling products that are greater (or at least funnier) that the sum of their constituent parts. Mashups are unpredictable – you never know what third thing may emerge from the confluence of two unlike precursors. Typically realized in music, film, or video, Mashups can also consist of text and graphics, the latter both still and animated. As creative products are increasingly digitized and cheap or free editing tools proliferate, Mashups arise as a natural outgrowth of access to materials and tools and a natural predilection to tinker. It sometimes seems like cultural gene splicing and can readily produce fascinating mutations. __RSS__: Real Simple Syndication is an ubiquitous manifestation of content aggregation in the digital age. Most news and information sources on the web now produce RSS feeds to which anyone with a web browser can subscribe. RSS aggregators make for centralized display and access to myriad RSS feeds which update continuously, 24 X 7. This is an excellent tool for both producers and consumers of content as most free blogging sites now automatically provide RSS publish capabilities for members.
 * Aggregating**

Our paradigm of accreting is a huge theme that runs through PLEs like a refrain in a symphony. Any time we publish, share, or collaborate we are potentially accreting. Blog posts accrete commentary and tags; wikis accrete collaborative contributions; photo and video sharing sight accrete comments, ratings, and recommendations. We participate in networked dialogs by posting to others blogs, wikis and sharing sites. Much as Talmudic commentaries elucidate and explain difficult passages of sacred writing, our PLEs’ accretions illuminate networks of content, enriching and deepening the web of reference, cross-reference and semantic dimensions far beyond the scope of any unilateral offering. __Crowdsourcing__: Farming a task out to the net is known as crowdsourcing. We see it as a variant of accreting (and perhaps also aggregating) as many different collaborators come together to create a composite whole that is probably beyond the capability of a sole practitioner or small dedicated team. Rewards may be offered or not, depending on the stakes. Often there is a surprising amount of interest and spare time to produce something very cheaply or even for free that would previously have been expensive and much more time consuming in the pre-networked world. __Surveys__: Certainly nothing new, surveys have gained new life on the Internet. Many free tools enable rapid and wide distribution of confidential surveys that can lend instant credence to a research paper or study. Surveys are an important tool for teaching, learning and achieving astonishing insight into a given problem domain.
 * Accreting**

In many ways the obverse of aggregating and accreting, disseminating distributes rather than concentrates; it scatters seeds to the wind (read: the network) where they may accrete in other locales and perhaps take root and grow. Some vehicles of disseminating might be: content sharing sites, where content can be viewed by many and downloaded if appropriate; RSS feeds, disseminating content to subscribers across the net; mailing lists, where interested parties can subscribe to periodic (usually daily) email digests pertaining to specific areas of focus; and more. __Open Courseware__: Universities across the world have joined MIT’s pioneering venture and published their course materials for any and all to access and download free of charge. For teachers this is an excellent way to disseminate key content and to extend their reach far beyond the classroom and the campus.
 * Disseminating**

The paradigm of search has been identified with the web itself to the point where it is difficult to discern where one leaves off and the other begins. Want to find something on the web? Search for it. Not only standalone search engines, but search functionality built into most major web sites today makes search a ubiquitous and essential tool for navigation and research. Search is so woven into the fabric of our online lives that it is taken from granted. But many innovations in search are afoot, perhaps the most intriguing in the area of Semantic Search and image search. Image search returns results based on text inquiries or, more compellingly, based on an image that the engine returns results that are similar. A search engine consists of three major pieces – the crawler, the indexer and the query processor1 (including the interface that we see.). The crawler is a program that jumps from link to link on the web, gathering information about web pages (nodes) and the links that connect them (connectors). It sends this information to the indexer. The indexer saves all pages for every web site and organizes the content (text, links, text associated with links (anchor text), image alt tags, multimedia content etc.) and associates it with the URL of the web site. Then, when a searcher types a key word or phrase into the interface, the query processor can retrieve URLs that best fit the search terms entered. The index also tags pages with metadata (data about data), such as the language that the page is written in, which helps to refine the search results and makes them more relevant and hence more useful to the searcher. Having achieved the ultimate verb status, Google is by far and away the dominant force in search today. Their PageRank algorithm, which analyzes relevance of content to links, and popularity of links into and out of a given web site to determine its placement on search results, set the standard to which all modern search engines hew. The only paradigm that comes close to challenging search’s dominance on the web is social networks. 1The Search – by John Battelle
 * Search**

Since its inception, the Internet has been a platform for social interaction. Message boards, email and chat arose long before the behemoths Facebook and Twitter. Early communities like Geocities facilitated interaction and a personalized ‘space’ for users in the form of individual web pages. Beyond the obvious dominance of Facebook there are other interesting models within the social networking paradigm. Ning is a free service for building social networks and has a lively educational community. Collaborating through small groups is a capability that has helped to make Ning and similar platforms popular in education. Social networks enable communities to arise around shared interests and practices. Less perceptually immersive than their virtual worlds counterparts such as Second Life, they nevertheless provide a powerful connective force as witnessed by their astounding reach (600,000,000 members worldwide on Facebook as of January 2011). As the larger social networks become increasingly commercialized, community and commerce may go their separate ways. The next big thing will surely come along, as it always does, to disrupt the dominance of these services. But the paradigm remains, and mature platforms such as Ning will continue to serve the educational community in evolving collaborative webs of teachers and learners connecting with one another.
 * Social Networks**

Virtuality encompasses all kinds of synthetic environments, including virtual reality, augmented reality, and, most importantly for personal learning environments and networks, virtual worlds, such as Second Life. Not merely a technology, virtuality can be thought of also as a mode of being: “As Lawrence Lessig (Code, 1999) describes it, the unusual thing about cyberspace is that we can be both here and there at the same time, and the place that is “there” can be constructed, essentially, however we might like. Thus all of our interests are the same as they ever were, but the environment in which we pursue them has becomes untethered from the Earth environment with which we have become so familiar.” 1 And there is no more seamless constructed environment that the virtual – it immerses us perceptually, providing familiar cues for our sense of equilibrium such as perspective (things recede in the distance), gravity, the illusion of solid matter (collision algorithms), and more. The virtual is a very real ‘other’ environment, and we very much have the sensation of //being// ‘there’ when we are immersed. Virtual reality immerses the subject’s senses with simulated inputs – typically in the form of a headset with glasses and earphones. Touch-feedback (haptic) gloves can also be employed to rally yet another class of sensory input for reality generation. Primarily the domain of research and military applications (think fighter jet pilot training), virtual reality has never achieved widespread adaptation. Whereas virtual reality immerses the subject in a computer-simulated environment, augmented reality augments and annotates the natural environment with virtual components. Augmented reality brings virtual reality into the real world and in the process enhances what we can do in real-world scenarios. Usually achieved with special glasses that mix computer generated content with the subject’s visual field, or, alternatively with the use of a smart phone and special software that overlays the camera’s view with generated annotation or objects, augmented reality is a more lightweight approach to virtuality than virtual reality, and has achieved much wider popular application (mostly in the smart phone application domain). Virtual worlds are by far the dominant form of virtuality in today’s world. In contrast to virtual and augmented reality technologies, virtual worlds focus on communities, not the individual; focus on software, not hardware; and are driven largely by commercial motives, not research labs. 1 The most widespread implementation of virtual worlds are so-called MMORPGs, or massively multi-player online role playing games. This nomenclature is somewhat dated, and doesn’t truly describe implementations such as Second Life where many if not most participants would question the assumption that they are playing a role. Virtuality, like social networks, is an emerging platform for large scale community building and social experimentation. Freed from constraints such as space, time, the need for food or oxygen, avatars in virtual worlds are ideal test subjects for purely social and educational pursuits. Entertainment is of course a large draw in virtual worlds, particularly in those environments that are tailored to gaming or adventure pursuits. Virtuality is touted as the next big thing, or practically dead as a popular pursuit, depending on who you speak to and who you read. The proliferation of platforms catering to a wide variety of interests, as well as free open source software (FOSS) kits that enable anyone to build and host a virtual world (OpenSim, an open source version of Second Life, is perhaps the most prominent) ensure that virtuality will continue to spread in application and influence, particularly as it evolves more software intelligence and more sophisticated rendering of environment and characters. 1 Synthetic Worlds, Edward Castronova
 * Virtuality**

In a similar way to the read/write paradigm, nodes and connectors are foundational to the experience of personal learning environments and networks. If you think about it, all networks are composed of nodes and connectors. Nodes are devices, such as computers, routers, switches, servers, etc. and connectors are the actual transport material (cables, or in the case of wireless, the electromagnetic spectrum). Likewise, to personalize the metaphor, all human networks are composed of nodes and connectors: the nodes being people (primarily) and the connectors being the various technologies that connect them, such as computer networks, phone networks, tin cans and strings, etc. In the connectivist view, “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.” 1 For connectivists, the connections we make to knowledge and other learners is more important than a classical view of knowledge, since the various ‘bodies of knowledge’ which are increasingly overlapping, multi-dimensional amalgams of individuals, databases and clouds of messages moving between them are so dynamic that we cannot hope to internalize their various permutations in the way that a classical education sought to internalize the accepted canon of cultural artifacts. Our knowledge now resides (partially at least) ‘out there’ in the networks, and our knowledge is more accurately gauged by the totality of our connections than by our individual, internal knowledge ‘store’. Nodes and connectors are also the primary components of the brain and nervous system. Nerve cells function as nodes, and the axons and dendrites that connect them serve as connectors. As a paradigm, this pair runs the gamut from the smallest units of information exchange and storage to the largest. At all scales of learning environments and networks, nodes and connectors are the fundamental constituents for information and knowledge exchange and flow. 1 Connectivism: A Learning Theory for the Digital Age – George Siemens ([])
 * Nodes and Connectors**

Collaborating involves two or more people or groups working together toward a common goal. Many of the tools of the read/write web are collaborative in nature, some allowing real-time co-creation of documents, mind-maps, wikis, and more. The web provides a global, 24 x 7 platform for collaboration par excellence, and is unparalleled in human history for this capability alone. Writers may collaborate; musicians may collaborate; teachers and learners may collaborate on assignments, projects, and all manner of artifacts of knowledge. Collaboration is nothing new – it is as old as civilization and perhaps older, as the hunt was often a collaborative effort to bring food to the tribe. But today’s technological capabilities allow and foster collaboration at a pace and scope that makes it almost a new category of human activity. Cross-domain collaboration may point the way toward solutions to intractable problems that individuals or specialized groups could not solve. The concepts of [|consilience] and [|intersubjectivity] are given instrumentality via the engines of collaboration.
 * Collaborating**

Our paradigm of swarming is perhaps best described as collaboration by flash mob. Swarming is ad hoc collective problem solving, which, like collaborating, has a common goal as the objective, yet which also has aspects of natural phenomena, like swarms of bees, schools of fish, or flocks of birds. “Swarming is a type of collaboration in which large numbers of geographically dispersed people quickly self-organize in a peer-to-peer network to deal with a problem or opportunity. It's a fluid, shifting network with no central control or hub. A swarm can be as complex as a global business network or as simple as a "cell phone posse"”1 Swarms use online tools, such as Google Docs, to attack a problem or assignment in a massively parallel approach: a real-time mob of connected intelligence. Swarms of connected humans can exhibit emergent phenomena and spontaneous order, like their animal or software counterparts, with a potential for much greater insight and creativity, as each element is a high-order intelligence in its own right. 1 Meeting of the Minds: Technology for business "swarming" By Kathleen Melymuka Computerworld.com ([])
 * Swarming**

As our reach extends across the net and across the globe we increasingly need to filter the massive amount of information that is flowing through our personal learning environment and network via all of the various inputs we have established. Spam filters are a widespread manifestation of filtering, and the Bayesian statistical classification algorithms many of them employ have wider applications in the filtering of datasets. NASA employs Bayesian filtering in its AutoClass system for classifying stars according to characteristics to subtle for the human analyst to detect. “AutoClass takes a database of cases described by a combination of real and discrete valued attributes, and automatically finds the natural classes in that data. It does not need to be told how many classes are present or what they look like -- it extracts this information from the data itself.” 1 The advanced search function in Google allows for filtering search results based on simple Boolean rules: ‘ands’, ‘ors’, and ‘nots’ connecting various words and phrases. Google’s advanced scholar search affords even more fine-grained control over search results. As more tools become available for semantic search**, incredibly sophisticated intelligent filtering of content will become routine. 1http://ti.arc.nasa.gov/tech/rse/synthesis-projects-applications/autoclass/
 * Filtering**