Chapter 4

Designs of PLE, ONLE, and Linkage Model

by Zac Griffin, Heidi Jacobson, April McNair, Erin Norman, Scott Stephens

Editor: Benning Tieke

Overview

Throughout this chapter, readers will gain a better understanding of what a Personal Learning Environment (PLE), Open Network Learning Environment (ONLE) and Linkage Design Model are as well as the differences between them. Furthermore, readers will gain a better understanding of RSS feeds, adaptive technologies, and personalized learning.

This Wordle depicts many of the key topics and concepts addressed in this chapter. The Personalized Learning Environment (PLE) features prominently in the Wordle as it does in this chapter. We first look to define PLE and its counterpart, ONLE, and then further discuss how the PLE concept relates to Personalized Learning and Adaptive Technology. Based on analysis of the Wordle, it should be clear that this chapter focuses on concepts that are strongly associated with learner-centered applications.

The PLE concept is supported by technologies and web 2.0 tools that can be used to create and customize the PLE experience for a learner. Mobile technologies and tools fully integrate with web 2.0 tools like Symbaloo, Google Chrome, and Netvibes to ensure that learners can take advantage of the PLE and ONLE concepts. Learners should seek to align these concepts and tools in a way that fits their individual needs and educational goals.

Chapter Learning Objectives:

• Learners will be able to define and distinguish between a personal learning environment, open network learning environment, and the linkage design model (Harati, and Liu 2016).
• Learners will discover how to create their own personal learning environment using either Netvibes, Symbaloo, or Google Chrome.
• Learners will create a Coggle.
• Learners will demonstrate their knowledge of creating an infographic
• Learners will demonstrate their understanding of personalized learning strategies

Personal Learning Environment (PLE)

A personal learning environment (PLE) is a digital toolbox that is personalized by the creator. Most commonly organized with a technology interface platform such as Symbaloo, Google Chrome, and Netvibes..

PLE requires the use of Web 2.0 tools and applications to create a personal and virtual learning space that is up-to-date.. A PLE is dynamic; the learner is the active participant and is involved in key functions such as: 1. Collect and curate relevant content, resources into a meaningful collection in a virtual space. 2. Construct and create to develop new knowledge and understanding such as through Wikis or blogs. 3. Collaboration- working with peers to create new knowledge through digital communication (Morrison).

The PLE supports a constructivist learning style where learners create knowledge as they attempt to understand their experiences. Constructvism is built around the theory that learners are not empty vessels to be filled with knowledge, rather learners are actively creating meaning on their own and through interacting with others. In the PLE model learners often select and pursue their own learning. This “banking” concept of learning was originally pur forth by Paolo Freire. In his book Pedagogy of the Oppressed, he argues to treat the learner as a co-creator of knowledge, with the student ultimately in charge of her own learning (Freire,1970).

When creating a PLE it would be helpful to include a diagram of the PLE. The purpose of the diagram is to provide a framework for learning goals, whether the goals are formal or informal, the learner can identify the tools needed for the goals and collect resources to accomplish such goals. Although implementing a PLE diagram isn’t required it might be a tool that is helpful for you to see your PLE at a glance, below is an example of what a PLE diagram created in Coggle would look like.

Figure 2: Example of a PLE spoke diagram that shows one central theme and related subtopics.

Open Network Learning Environment (ONLE)

Figure 3: Visual representation of an ONLE (obtained from Louder, 2016).

Open Network Learning Environments (ONLE) are digital environment where learners are connected to a community sharing ideas, thoughts, information, and knowledge amongst each other. ONLE helps bridge the gap between the PLE and LMS. The LMS is “teacher-centric”. Teachers create courses, upload content, initiate threaded discussions, and form groups. Opportunities for student-initiated learning activities in the traditional LMS are severely limited. (Mott,2010) In comparison, the PLE is “student-centric” and allows the learner to create their own personal learning networks (PLNs) to manage information, create content, and connect with other, while the learners use their own knowledge, experience, and application to create shared learning environment.

The ONLE meets the needs of students and teachers by providing opportunity for collaboration. This can be by utilizing social networking and freedom of access to information which allows the student to create a positive learning environment for success. The ONLE “empowers learners to participate in creative endeavors, conduct social networking, organize and reorganize social content, and manage social acts by connecting people, resources, and tools by integrating Web 2.0 tools to design environments that are totally transparent, or open to public view.” (Tu et al.,2012, p 14). In addition the ONLE allows the learner to transition their learning outside of the “rigid structure of academia” and utilize the learning network to create self-directed, created environments that benefit from peer collaboration. “The ONLE model is built around the idea that students are motivated enough, and have the cognitive abilities, to be autonomous learners. The students who lack either motivation or ability, or who are not adequately prepared, may become lost in the online environment. Further, those who are introverted may not thrive as much in the ONLE. Therefore, we as online educators must carefully craft the ONLE so that the learners may roam free, but not stray so far that we lose them” (Tieke, Lesson 3, Key1).

Linkage Design Model

Figure 4: Linkage Design Model Figure retrieved from Tu (2012) Prezi presentation.

The Open Network Linkage Design Model works hand-in-hand with PLE and ONLE. According to Tu (2014), this model, “‘links and connects’ multiple network resources, network learners, and Web 2.0 tools in ONLE to allow learners, instructors, and other ONLE stakeholders to construct and to share their Personal Learning Environments within human network” (p. 5). By using the linkage design model, users would essentially be allowed to access multiple sites from one location without having to remember multiple logins as well.

There are 8 components to the linkage design model: personal portal linkage, widget and gadget linkages, social networking linkages, mobile linkages, InfoViz linkages, and social tagging linkage, RSS linkages and lastly, third party linkages (Tu, 2014).

• Personal Portal Linkage: The personal portal linkage allows users to collect information and Web 2.0 tools in one location, much like a PLE.
• Widget and Gadget Linkage: Widgets and gadgets are tools that can be embedded into other digital items such as webpages and blogs. These can be tools to check the weather, email, RSS feeds, clock, and more. “Allows users to get continuous ongoing information in real time” (Bausman, K., 2017, Nabble post).
• Social Networking Linkages: Social network linkages occur when users connect through social networking sites. Links are created when people like a page, follow a blog, or become followers of other social network users.
• Mobile Linkages: Mobile Linkages refer to the ability to access information and Web 2.0 tools through the use of a mobile device.
• Social Tagging Linkage: Most often seen in Twitter and Facebook posts, users can add tags to their posts and bookmarks in order to link to other related posts, pictures, blogs, saved reference, etc. that have used the same tag. Tagging these resources also allows for greater interaction with the work whether by learner, instructor, or community.
• Third Party Linkage: A third party linkages refers to other resources and tools being linked to your webpage or PLE. Sometimes these sources are running in the background or are linked to IFTTT (if this then that) type of linkages. For example, you can update your blog with a simple email without having to even visit the blog site through a third party linkage (Tu, 2014, p. 9).
• InfoViz Linkages: InfoViz or Information Visuals, most commonly refers to graphics that supply data in an easy to grasp visual manner.

Figure 6: InfoViz (Alp, 2013)

• RSS Linkages: RSS linkages occur when a user uses RSS feeds to stay up-to-date. RSS feeds are typically seen in Twitter feeds, discussion boards, and even email inboxes. With an RSS feed in place on a PLE or website, a user does not have to actually go to the location of the feed to stay updated.
• RSS Feed: RSS Feed stands for Really Simple Syndication or Rich Site Summary (RSS, n. d.). It allows users to access a wealth of information based on topics or websites of interest in one place. The RSS feed is updated as new information is uploaded to websites, news feeds, blogs, or social media outlets you have selected to follow. It will notify you of the newest items first. Different RSS feed Web 2.0 tools offer various features. The RSS is configured based on what URLs are added to the aggregator of choice, bringing the most pertinent news feed to the top (Lee, Miller, Newnham, 2008). For more information, you may find this video from Digitalwirl helpful to better understand RSS (2012).

• RSS Feed Aggregator: Feedly is an aggregator that you can customize with your own preferences (Feedly, n. d.). Users can personalize their learning based on their interests and needs. This tutorial shows you how to create an account and manage the websites you want to follow (Blrt, 2016).

Is RSS dying out? Has it been replaced by Twitter?

Twitter can be a great tool for sharing and receiving information, however, it is updated in real time (Elle, 2014). It does not wait for you to read each article, blog, or website you have subscribed to. Users will only see what is currently in the moment on the feed. Sure, you could go to each site to find the information, but that defeats the purpose of an RSS Feed. When you subscribe to RSS feeds you are interested in, you have access to all updates the website has made since you last viewed it with the option to mark what you have read and save what you haven’t read for later (Stephens, 2012). RSS is alive and well for sites like Feedly, who have gone from 5,000 paid subscribers to 50,000 paid subscribers in two years. “With hundreds of millions of users, the potential to recover billions in lost profits, and uses that we haven’t even thought of yet, RSS will reign for many more years.” (McDowell, 2015). Experts predict that RSS will evolve over time to be even more efficient and effective as technology changes with time.

An RSS Feed can only strengthen learning when it is a part of your PLE. It puts the learner in control of content and the pace in which it is viewed .Learners will need to take time to find valid sources with accurate content to support their learning. Without valid sources, the learner’s content knowledge will suffer. “RSS provides an efficient way for students to keep in touch with faculty, stay informed about coursework and other academic activities, and follow developments in their fields of study, which for many will be an important skill in their professional lives” (Educause, 2017).

Personalized Learning

Overview and applications of PL

Reaching students in a meaningful way is a challenge for all educators. In traditional classrooms, online environments, adult learning situations or professional development, instructors and facilitators have to strike the right chord between general and specific. Indeed, educators, facilitators, and designers have increasingly turned towards customization of the learning experience or Personalized Learning (PL). Song, Wong, and Looi (2012) define the goal of PL, “to develop individualized learning programs for each student with the intent to engage him/her in the learning process to optimize each child’s learning potential and success” (p. 680). With that in mind, Song, wong, and Looi place PL under the category of Differentiated Instruction (p.681).

Differentiated Instruction is an instructional strategy or assessment strategy that seeks to offer different options or solutions for individuals or groups of students while still remaining as part of the whole. More broadly, the NMC Horizon Report for K-12 Education (2015) states that, “the goal of personalized learning is to create possibilities for learners to determine the strategy and pace at which they learn” (p. 26). PL can be designed and implemented in a number of ways with varying goals in mind. For the instructor, designer or facilitator, considering the learning styles, aspirations, cultural/linguistic background, and interests of individual students is an appropriate place to start (NMC Horizon Report k-12, p. 26, 2015).

Applications for PL abound in the digital world. Technology has profoundly impacted the capabilities of instructors and designers to customize learning for their students’ individual needs. Various online learning environments and learning managements systems have build-in PL capacities. This is a direct response to the drive to make PL a practical component on the educational experience (NMC Horizon Report k-12, p. 26, 2015).

Implementation of PL often focuses on a linear design. That is, a modular approach can often be effectively applied to PL design. Svenningson and Pear (2011) note that courses designed for PL, particularly in an online or blended learning environment, can take advantage of the modular design in three notable ways (p. 33). The first is pacing. Student-driven pacing through the coursework is a major component and advantage of PL. The second advantage of the modular design (A→ B→ C→ D) is that students receive prompt and valuable feedback on their learning and even the capability of working backwards (D→ C→ B→ A) if necessary to insure understanding and mastery. Built-in formative and summative assessments allow students to manage their own learning experience in this design. Finally, the implementation of PL in a modular/linear format takes prior knowledge into account. If students already demonstrate understanding of A, B, C then they can skip straight to D. Again, the linear/module design format is an effective application of PL design in online and blended learning environments.

PL vs. PLE

Personalized Learning should not be confused with the Personal Learning Environment (PLE) model, although they may seem superficially similar. PL as an instructional strategy is focused on the learner, even before considering technological applications. This, however, can be somewhat counterintuitive because PL design in rooted in instructivist teaching strategy. In the instructivist model, the framework is much more teacher or institutionally centered. So, even though PL design focuses on the learner’s individual needs, it is ultimately an instructivist approach because all of the materials and criteria for success are provided by the instructor or the educational institution (Heick, 2013) . Conversely, the PLE model is based on the constructivist learning theory, where individuals work to create and develop new knowledge and understanding independent of the instructor.

Adaptive Technologies (AT)

Before we begin our discussion on adaptive technologies that support learners in educational settings, we shall briefly define some relevant terms. Generally, adapting means an adjustment from one situation/condition to another such as software programs and/or persons that are capable of adapting to situations). The technology side, therefore, refers to the application of scientific principles and methods; methods or materials, such as electronic or digital products or systems, to achieve a particular objective like that of learning enhancement. Such a system, in this context, refers to a network of related computer software, hardware, and data transmission devices (soft technology, hard technology and adaptive environments).

An adaptive system adjusts itself to suit particular learner characteristics and the needs of any of the aforementioned learners. Furthermore, adaptive technologies can help to achieve this goal and are typically controlled by the computational, digital devices, that are designed for adapting content to different learners’ needs and their preferences. Contained within the Learning Module (LM), lies the information to be maintained, and by which is a representation of the learner, who is managed by an adaptive system. LMs provide the basis for deciding how to provide personalized content to a particular individual and may include cognitive as well as noncognitive information.

LMs have been used in many areas, such as adaptive educational and training systems (e.g., intelligent tutoring systems), help systems, and recommender systems. Adaptive systems may consist of hard or soft technologies (e.g., devices vs. algorithms). Hard technologies are devices that may be used in adaptive systems to capture learner information (e.g., eye-tracking devices) and thus can be used to detect and classify learners’ performance data or affective states such as confusion, frustration, excitement, and boredom. Soft technologies represent algorithms, programs, or environments that broaden the types of interaction between students and computers. For instance, an adaptive algorithm may be employed in a program that selects an assessment task or learning object most appropriate for a learner at a particular point in time. The effectiveness of adaptive technologies hinges on accurate and informative student or learner models. (Shute, V. J. & Zapata-Rivera, D. 2008).

The Linear design as it represents the four process adaptive cycle (Capture, Analyze, Select and Present) as seen in the figure below when following the sequential order of 1, 2, 3, 4, etc. The following scenarios explain the different modeling of adaptive learning.

Figure 10: Linear Design

Scenarios

A complete cycle (1, 2, 3, 4, 5, and 6) - All processes of the cycle are exercised: capturing relevant information, analyzing it, updating the variables that are modeled in the LM, selecting appropriate learning resources and strategies that meet the current needs of the learner, and making them available to the student in an appropriate manner. This cycle will continue until the goals of the instructional activity have been met.

Modifying the adaptive cycle (1, 2, 3, 4, 5, 6, and 9) - The learner is allowed to interact with the LM. The nature of this interaction and the effects on the LM can vary (e.g., overwriting the value of a particular variable). Allowing human interaction with the model may help reduce the complexity of the diagnostic and selection processes by decreasing the level of uncertainty inherent in the processes. It also can benefit the learner by increasing learner awareness and supporting self-reflection.

Monitoring path (1, 2, and 3) - The learner is continuously monitored; information gathered is analyzed and used to update learner profiles (e.g., homeland security surveillance system, analyzing profiles of individuals for risk-analysis purposes). This path can be seen as a cycle that spins off to a third party instead of returning to the learner.

Short (or temporary) memory cycle (1, 7, 5, and 6) - The selection of content and educational resources is done by using the most recent information gathered from the learner (e.g., current test results and navigation commands). No permanent LM is maintained. Adaptation is performed using information gathered from the latest interaction between learner and the system.

Short (or temporary) memory, no selection cycle (1, 2, 8, and 6) - A predefined path on the curriculum structure is followed. No LM is maintained. This predefined path dictates which educational resources and testing materials are presented to the learner.

Soft technologies

Soft technologies represent programs or approaches that capture, analyze, select, or present information. Their primary goals are to create LMs (diagnostic function) or to utilize information from LMs (prescriptive function).

- Quantitative modeling - In general, quantitative modeling of learners obtains estimates about the current state of some attributes.

- Qualitative modeling - Qualitative modeling supports learners by constructing conceptual models of systems and their behavior using qualitative formalisms.

- Cognitive modeling - Cognitive models may be quantitative or qualitative. They help predict complex human behavior, including skill learning, problem solving, and other types of cognitive activities. Generally, cognitive models may apply across various domains, serve different functions, and model well- or ill-defined knowledge

- Machine learning - Machine learning methods applicable for LM include rule or tree (analogy) learning methods, probabilistic learning methods, and instance- or case-based learning approaches

- Bayesian networks - Bayesian networks are related to the machine learning methods and are used within learner models to handle uncertainty by using probabilistic inference to update and improve belief values (e.g., regarding learner proficiencies). The inductive and deductive reasoning capabilities of Bayesian nets support “what if” scenarios by activating and observing evidence that describes a particular case or situation and then propagating that information through the network using the internal probability distributions that govern the behavior of the Bayesian net.

- Stereotype methods - A stereotype is a collection of frequently occurring characteristics of users (e.g., physical characteristics, social background, computer experience). Adaptive methods are used to initially assign users to specific classes (stereotypes) so that previously unknown characteristics can be inferred on the basis of the assumption that they will share characteristics with others in the same class

- Overlay methods - An overlay model is a novice–expert difference model representing missing conceptions, often implemented as either an expert model annotated for missing items or an expert model with weights assigned to each element in the expert knowledge base.

- Pedagogical Agents - Pedagogical means that these programs are designed to teach, and agent suggests that the programs are semiautonomous, possessing their own goals and making decisions on what actions to take to achieve their goals (i.e., a programmer has not predefined every action for them). The current generation of pedagogical agents is interactive and sometimes animated; for example, students can speak to agents that can speak back, often have faces and bodies, use gestures, and can move around a computer screen. (Johnson et al, 2000)

Hard Technologies

- Biologically Based Devices - Some biologically based devices were originally developed to support learners with disabilities (i.e., assistive technologies); however, many are being created or repurposed to support learner models for both cognitive and noncognitive student data.

- Speech-Capture Devices - These devices allow users to interact with the computer via speech, instead of relying on typing their input; consequently, this approach is valuable for individuals with physical disabilities that preclude typing, for young children who cannot yet type, etc.

- Head-Gesture Capture Devices - Many computers currently are equipped with video cameras. Processing the image provides a means to track head position and movement. Software by Visionics Corp., for example, provides this capability.

Assistive Technologies

Disabilities and non-native language status can be major obstacles to learning from a computer. Examining adaptations in light of a validity framework can be valuable if not essential for ensuring effectiveness. See the Special Needs Opportunity Window (SNOW, 2006) web site for information about the different kinds of adaptive technologies for people with disabilities.

- Adaptive Hypermedia Environment - Adaptive hypermedia environments or systems (AHSs) are extended from an intelligent tutoring system foundation and combine adaptive instructional systems and hypermedia-based systems and is distinguished between two different types of AHS: (1) adapting the presentation of content (i.e., different media formats or orderings), and (2) adapting the navigation or learning path, via direct guidance; hiding, reordering, or annotating links; or even disabling or removing links

- Adaptive Educational Hypermedia Environment - A particular type of AHS is an adaptive educational hypermedia system (AEHS). The hyperspace of AEHS is kept relatively small given its focus on a specific topic; consequently, the focus of the LM is entirely on the domain knowledge of the learner (Brusilovsky, 1996).

- Collaborative Learning Environment - An alternative approach to individualized learning is collaborative learning—that is, the notion that students, working together, can learn more than by themselves, especially when they bring complementary, rather than identical, contributions to the joint enterprise (Cumming and Self, 1989).

The Future of Adaptation

Adaptive systems will continue to evolve as new technologies appear in the field and old ones transform and become more established. The future of the field is wide open in that it can evolve in different ways. Such evolution will depend on factors such as the emergence of new technologies, new media, advances in learning, measurement, artificial intelligence, and general policies and standards that take hold (or not) in relation to adaptive instruction and learning. The algorithmic properties of AT is open to the next generation to compete in this field with their own conception and algorithms.

AT vs PL

While adjunct to one another, the differences are quite plain because, as stated earlier that PL is more considered an instructional strategy where the focus is on the learner, whereas AT, is more of a constructivism strategist’s tool that focuses on the external means to deliver the content to the learner. By processes and steps, the method becomes operative to provide the learner the vehicles that adapt to the personal needs and/or preferences to assist in the learning process.

Chapter Reflections

In this chapter, you were asked to define Personal Learning Environments (PLE) and Open Network Learning Environments (ONLE). Furthermore, this chapter helped to clarify the differences between these two concepts and their relationship with the more traditional and “teacher-centric” model of a Learning Managements System (LMS). Readers should be able to create their own PLE and use the ONLE concept to bridge the gap in deficiencies of the LMS design model. This concept of combining PLE and ONLE is known as the Open Network Linkage Design Model with can be used to link multiple learning environments and embedded web 2.0 tools that support these designs.

Finally, this chapter offers discussion on the concepts of Personalized Learning (PL) and Adaptive Technology (AT) and how they play a role in the larger realm of PLE and ONLE. PL can be somewhat associated with the PLE concept, however, as discussed in the chapter the two should certainly not be considered synonymous. Adaptive Technologies (AT) are also discussed in the this chapter. Specifically, how these technologies, in their various forms, can be used to help individuals meet their specific learning needs and goals.

It is worth noting that many of the design ideals of PLE and ONLE are built around a framework of Open Educational Resources (OER). OER are typically digital resources that are available for no cost to the learner. OER originated from various sources, but primarily are associated with higher education institutions and individuals involved in higher education. OER are significant and relevant to the overall discussion of this chapter because OER support the PLE and ONLE concepts.

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