How to Use Web 2.0 Technologies for Education:
Seven Elements of Learning Activity Design
This report makes an effective case that users of Web 2.0 Technologies in a learning context need some kind of training to make them work for education.
Produced as a visiting scholar at Macquarie University under the supervision of Dr. Matt Bower.
Because users don’t know what to do with Web 2.0 technologies, these have been applied in higher education with limited success. Research indicates that successful engagement with learning in the Web 2.0 environment requires an activity framework to create a bridge between learning environments and technology affordances. The context of Web 2.0 and the affordance frameworks for analysing what technology can do are highlighted as the primary approaches to developing a successful integration of Web 2.0 technology in higher education. However, in the process of synthesizing this research, several elements emerged that were commonly employed by researchers to suggest the design of effective learning activities with Web 2.0 technologies. These seven elements identify what to do with Web 2.0 technologies while an analysis of seven pedagogical paradigms suggest how these activities should take place. Considered together, the elements of activity design and the pedagogical frameworks create 49 unique pedagogy/activity intersections that inform the selection and integration of technology from the perspective of student needs. A short description of each intersection and relevant technologies are provided and a proposal is made for their use in developing future technology affordances and pedagogical practices.
Keywords: affordance, pedagogy, technology, Web 2.0, learning activity, behaviorism, cognitivism, social cognitivism, humanism, self-directed, constructivism, connectivism, plan, find, curate, interact, create, publish, assess, teaching and learning, learning environment, technology design, learning design, human-centered learning, activity framework
Download Link – how-to-use-web-2-0-technologies-for-education-kevin-jenson
The Educator’s Challenge
Creating the link between the available tools of learning and the needs of the student is “the art of the educator,” said Montessori (2004, p. 11). However, in the Web 2.0 environment where a single tool like the blog offers affordances for over 50 different kinds of activities (Collis and Moonen, 2008), the art has begun to look more and more like a challenge. When failure to succeed at this challenge can lead to a negative impact on student performance (Lei, 2010), this challenge has become a strategic problem (Bryson, 1995).
The personal computer, the internet, and now Web 2.0 technologies have all been greeted with waves of publicity and high hopes for a new ‘revolutionary’ model of education (Collis & Moonen, 2008; Sternberg, 2012; Suthers, 2012). However, behind the scenes, educators have been reluctant to embrace the technologies (Way & Webb, 2007), institutions do not have the infrastructure in place to support their use (Johnson, et al., 2013), and students themselves do not have the level of expertise that they think they do (Bennett, Bishop, Dalgarno, Waycott, & Kennedy, 2012; Diaz, 2010; Gosper, Malfroy, & McKenzie, 2013). All this has resulted in a misapplication of Web 2.0 technology in the university setting that has not enhanced the learning experience (Gosper, et al.; Sternberg, 2012). In fact, a misunderstanding of how students use Web 2.0 tools for learning (Gosper, et al.) may even be causing students to become “disaffected” with the idea of using them for learning at all (Collis & Moonen, 2008).
In search of a solution to the challenge of using Web 2.0 technologies for education, research has focused on two primary angles. The first explores the influence of educational environments on the use of technology (Brown, Dehoney, & Millichamp, 2015; Fareed, 2010; Mishra and Koehler, 2006; Shahsavar, 2013). The second angle explores the affordances of technology for substitution, augmentation, modification, or redefinition (Puentedura, n.d.) of the learning experience (Bower, 2015; Drexler, Baralt, & Dawson, 2008; Kuswara & Richards, 2011; Looi, et al., 2009; Sun & Chen, 2014). In the past, researchers have suggested activity frameworks as a link between the learning environment and the technology (Conole & Fill, 2005; Levin & Bertram, 1997), but these have not been updated to include Web 2.0 capabilities. Nevertheless, more recent research continues to emphasize responsibility of the educator to bridge this application gap through the design of learning activities (Bower, 2008; Collis & Moonen, 2008). Furthermore, a synthesis of multiple studies (Barnes & Tynan, 2007; Collis & Moonen, 2008; Conole, 2010; Diaz, 2010; Oxnevad, 2013; & Bower, 2015) suggests a previously unrecognized emergence of several fundamental elements of learning activity design, which enable effective selection and use of Web 2.0 technologies for education.
The Context of Web 2.0
Education technology does not exist in isolation, but is part of “an environment or ecosystem – a dynamic interconnected, ever-evolving community of learners, instructors, tools and content” (Brown, Dehoney, & Millichamp, 2015). Learning Management Systems have largely focused on “the dissemination of content and information” (Herrington & Kervin, 2007), but they have potential to incorporate Web 2.0 processes that enable a learning context in which the user assumes the dual role of receiving and creating content (Brown, et al.) Such interactive use of technology exemplifies the read/write web that Tim Berners Lee envisioned in the early days of developing the internet (Richardson, 2010). It is a context in which the technology provides a framework for an ongoing, evolving exchange whose data sources grow richer with time (O’Reilly, 2007).
Several models have been proffered for analysis and design of this dynamic Web 2.0 learning environment using factors like technology affordance, pedagogy, content, and social affordance (Fareed, 2010; Mishra and Koehler, 2006; Shahsavar, 2013). For purposes of this study, the final two factors of content and social affordance are viewed as a lens (like subject of study or age of students) through which users engage with the Web 2.0 environment, rather than part of the environment itself (Bates, 2005; Cram & Richards, 2008; Fowley, 2008, 2011; Suthers, 2012). It is the combination of technology and pedagogy that determines how the users will interact with content and other users. Further exploration of research on these two factors reveals that neither technology affordances nor pedagogical requirements on their own are sufficient for designing an effective Web 2.0 learning experience.
Implications of Technology Affordances
To make sense of the dizzying spectrum of available technologies, many researchers have made use of the affordance framework developed by Gibson in 1977 to describe the “available functionality of the environment whether or not it was used” (Fowley, 2011). In addition to the 15 conceptualisations identified by Fowley, affordances have taken many shapes in the literature: functional affordances (Bower, 2008), mobile affordances (Looi, et al., 2009), social affordances (Kuswara & Richards, 2011), and a multi-dimensional approach to affordances (Sun & Chen, 2014). The usefulness of such frameworks comes from their ability to identify and differentiate the broad range of potential uses for technology in a given setting. Gibson (1979) concedes that “even for the most ‘basic’ affordances…perception might need to develop in some way” (as cited in Oliver, 2005). Without such “perception,” the user will be unable to take full advantage of what the technology can do (Norman, 1999).
The sheer number and variety of available technologies makes it difficult to maintain an awareness of technological affordances. Bower’s (2015) taxonomy of Web 2.0 tools involved looking at over 2000 different links to find 212 technologies useful for teaching and learning. Researchers developing an online database have analysed over 800 potential tools for learning and teaching (Drexler, Baralt, & Dawson, 2008), but the number of affordances for existing tools continue to grow (Snowden & Boon, 2007). This rapidly changing software landscape means that “teachers can no longer expect one or two technologists at a school to keep up with all new developments” (Drexler, et al., 2008, p. 282).
Even if it were possible to perceive all affordances, this is no guarantee that they would be useful. Despite their ability to explain what technology can do, affordance frameworks are limited in their ability to explain what technology should do. For this reason, “technology-led innovations do not in themselves lead to improved educational practices” (Kirkwood & Price, 2013, p. 333), but there is a “dearth” of research on the application of Web 2.0 tools to learning design (Bower, Hedberg, & Kuswara, 2010). In an attempt to make use of these tools, some researchers have suggested new pedagogical paradigms like ‘online collaborative learning’ (Harasim, 2012) or the ‘flipped classroom’ (Bergmann, 2014). The supposed need for such proposals highlights the idea that in an educational context the technology is less important than the “educational purpose and activity” that it supports or enables (Kirkwood & Price, 2012).
Implications of Pedagogy
Although it is easy to blame the technology for difficulties with designing a Web 2.0 learning experience, many researchers suggest that the real problem is actually the pedagogy or the way in which the technology is applied (Conole, 2010; Gosper, Malfroy, & McKenzie, 2013; Herrington & Kervin, 2007). Where technology was linked to an improvement in student performance, researchers found that it was not the presence of the technology, but the way in which it was used that determined the results (Hew & Cheung, 2013). They suggested, “certain pedagogy and instructional strategy should be developed and practiced, along with the use of Web 2.0 technologies, in order to achieve increased students’ performance” (p.58). A short overview of seven pedagogical paradigms explores some of the ways in which pedagogy may have an impact on the way that technology is used.
Learning, in behaviourism, takes place as a learner response to an environment shaped by the teacher or the technology (McLeod, 2013). Behaviourist technology provides this environment in which a stimulus is provided and the response of the student is measured (Merriam, Caffarella & Baumgartner, 2007; Pavlov, 1960). With time, students begin to associate certain responses to given stimuli through the use of reinforcement like the addition of positive or removal of negative stimuli (Skinner, 1953). The technology is optimized with the ability “to predict, given the stimulus, what reaction will take place” (Watson, 1930) and should reward good effort (Marchionini, 2006).
Cognitivist technology continues to depend on the environment to stimulate a learning response, but enables students to reach higher levels of cognitive processing, like those identified by Bloom (1956), through the support of mental and relational support structures (McLeod, 2008). These levels, as defined by Bloom’s Revised Taxonomy progress from remember, understand, and apply to analyse, evaluate, and create resulting in higher levels of recall (Sousa, 2011). The technological environment should reduce cognitive load allowing the student to focus on essential information (Reeves, 1999; Sweller, Ayres & Kalyuga, 2011), but should also use didactic relationships or tools to stimulate the student toward greater cognitive complexity (Jenson, 2015).
Social cognitive software emphasises the influence of the learning community (Wenger, 1990) as a support for the “psychological mechanisms of the self system” that lead to measurable outputs (Bandura, 2001). This implies that the software should enable the development of self-efficacy, explored by Zimmerman (2002), and some way to model examples of student behaviour, explored by Dewey (1938). Depending on the subject and skill level of the learners, different kinds of supports will be needed for different stages of the learning process (Knowles, 1986; Piaget, 1964).
The individual rather than the information takes precedence in the humanist learning environment (Montessori, 2004). Humanist technology does not define outcomes, but an experience or encounter with information that students can then distil into outcomes defined by their needs and strengths as learners (Gardner, 1983), or personal or social interests (Freire, 2010). The facilitator should consider Maslow’s (1943) Hierarchy of Needs in establishing physical comfort for interaction, safety and security on and offline, a sense of belonging and community, and ways to showcase accomplishments in pursuit of a self-actualizing learning experience.
Self-directed learning depends on the individual user rather than the environment to drive the learning process. Technology should be flexible enough to support the cognitive, motivational, and behavioural needs of diverse individuals in their process of developing self-mastery (Zimmerman, 2011). This could mean making space for developing strategies, taking inventory of their abilities, assessing their performance, and reflecting on how to improve. Additional supportive processes may be applied to technology enabling “self-evaluation, organization and transformation, goal setting and planning, information seeking, record-keeping, self-monitoring, environmental structuring, rehearsing and memorizing, seeking social assistance, etc.” (Zimmerman, 1990, p. 7).
Knowledge, in constructivism, is a result of interaction (Piaget, 1964) that intentionally builds upon itself in an individual or social context (Dewey, 1938). It is not a finished product, but is dependent upon the individual for interpretation (Kolb, 1984). For this the technology should enable concrete experiences, reflective observation, abstract conceptualization, and active experimentation. Reflection is particularly important to the transformation of the individual (Mezirow, 1991). Both scaffolding and ZPD (zone of proximal development) should be provided by the technology to keep the learner in a state of engagement just beyond what they could reach on their own (Vygotsky, 1978).
Similar to constructivism, connectivism recognizes the rapidly evolving nature of knowledge and emphasizes the development of a process through which individuals can manage access to that knowledge (Siemens, 2004). Rather than transferring information to the individual, the technology should recognize that information exists in digital environments and enable the user to create connections to these environments. The role of the learner is to manage these external knowledge resources effectively rather than internalizing a knowledge base that will soon be outdated.
Web 2.0 Activity Framework
Within the research focus on the Web 2.0 learning environment or technology affordances, a third element emerged as an intermediary link connecting these two factors and enabling users to do something with the Web 2.0 learning environment (Bower, 2008; Collis & Moonen, 2008; Conole & Fill, 2005; Levin & Bertram, 1997). The value of recognizing the implications of the pedagogy or the affordances of technology emerges in the way that these are applied in each particular setting (Gosper, Malfroy, & McKenzie, 2013). After elaborating on the functional affordances of Web 2.0 technologies, Bower (2008) proposed a multi-step process by which educators could build a bridge between the technologies and their educational goals by identifying affordances available from technology and affordances required by selected educational tasks. This task framework was previously employed by Conole and Fill (2005) who used learning activities as the link between the educational context and potential ICT tools. Their approach involved assessing the context, choosing a pedagogical model, and then using a range of tasks (enabled by technology) from which to design an effective learning experience.
The relevance of this task-based approach was recently reinforced by a description of the teacher’s role as providing students with the “resources” and “activities” that empower them to use the internet for learning (Harasim, 2012). Making this connection between technology and activities simpler, Hew and Cheung (2013) presented a classification of technology on the basis of its primary use or functionality. A similar classification of technology by four factors of use had previously been published in 1997 (Levin, 2014). The activity based framework provides an effective link between pedagogy and technology that clarifies what users can do to learn in a digital environment.
Building on the activity-based link between pedagogy and technology, this article updates the task framework to match the affordances of Web 2.0 technologies. From the various sources analysed, seven categories of tasks emerged that are hereafter referred to as the elements of learning activities. These elements are not exhaustive or exclusive but were commonly used by researchers as a means of designing effective Web 2.0 learning experiences. Partially derived from typologies and frameworks published by Barnes and Tynan (2007), Collis and Moonen (2008), Conole (2010), Diaz (2010), Oxnevad (2013), and Bower (2015) these seven elements are summarized by the Responsive Open Learning Environment Project by 16 research groups from the EU and China (ROLE, n.d.).
Although the terms and groupings vary depending on the source, the seven elements commonly used to design learning activities include: plan (plan and explore), find (search and get recommendation), curate (organize and evaluate), interact (view, train, manipulate, communicate), create (create), publish (collaborate and communicate), and assess (reflect, test, evaluate). The close relationship between assessment and its potential impact upon the planning process provided the rationale behind considering these elements as a cycle [Figure 1]. Though they may not always appear in order or require all the others to exist in a particular learning activity, the elements often appear subsequent to one another (create-publish, find-curate, assess-plan).
Each of the elements is described in further detail below with suggestions on implementation, best practices, important considerations, and relevant technologies. The attempt here is not to provide a concrete definition for each element, but to provide a starting point for further research on each one as a means of designing learning activities for the Web 2.0 environment.
Without the task, the technology, the teacher, or some internal schema to guide their approach to technology use, students will be overwhelmed with the complexity of the environment and respond with shallow learning or poor performance (Woo & Reeves, 2007). For this reason, planning is often a role assigned to teachers who write syllabi, make announcements on the LMS, or somehow direct student use of the technology for learning. In the Web 2.0 environment, however, research has found that students are becoming increasingly responsible for the design of their learning experience (Collis & Moonen, 2008). At the same time, learning analytics have developed to the point where assessment of student performance can assist in the design of customized learning pathways (Buckingham-Shum, 2014).
One of the most successful plans for the use of technology is designed by the producers of the hit game Candy Crush. Using tasks and technology to addict the user to overcoming a series of mental challenges, the plan is designed with consideration of an the digital environment, emotion, behavioural feedback, rewards, scaffolded challenges, encouragement, social comparison, and many other factors that seem to come straight from educational theory (Varonis & Varonis, 2015). The same process is used by many games to inspire users to direct their objectives and effort toward an improvement of abilities – important to the development of social and mental processes (Amory, Naicker, Vincent, & Adams, 1999). Borrowing from game designers to create an effective map for educational challenges has led to impressive reports on student engagement (Yeh, 2013). However the standards for designing this type of content are difficult to achieve (Bull, et al., 2010), and teachers may wish to focus their efforts on helping students apply their pre-existing models of technology use in an academic context (Sternberg, 2012) rather than creating content.
The challenge of search in the Web 2.0 environment is not about finding the right answer – a simple query on Google will return about 1 million of them. The challenge is asking the right question. In order to learn, students need to find information, resources, people, experiences, and learning opportunities. In a formal academic setting resources for learning are screened and provided by an institution, but this safety net disappears online. Search engines have made searching much easier by eliminating the “grunt work” of walking through stacks of books, thumbing through files, or interviewing experts (Hew & Cheung, 2013). However, Pariser (2011) warned that leaving the process of finding up to the search engines has reduced our exposure to information as algorithms have a difficult time keeping from simply perpetuating popular links. Specialized search engines like Wolfram Alpha, Search Visualizer, or Bing Academic offer a greater level of control and complexity to their users, but even this is not useful without some sense of how to approach the searching process effectively (White, 2008). A 2008 study of academic search engines showed that each one came with its own unique set of strengths and weaknesses indicating that this is one learning element that may warrant further consideration (Falagas, Pitsouni, Malietzis, & Pappas).
Curation software may be the transforming feature of education exclaimed Good (2012). While such a statement may be overly zealous, curation tools hold many exciting possibilities for learning as they seem designed to meet the connectivist demand for creating means of managing and accessing information rather than simply remembering it (Siemens, 2004). The element of curation determines what will be stored where and in what context or format. Dropbox, Evernote, citation software, and cloud storage allow for the collection and organization and evaluation of information across multiple platforms. Address books and social networks curate groups of people. Some curation processes are crowdsourced (Wikipedia), some are produced by algorithms (Pandora) and others by personal effort (Spotify). Bookmarking services (Pocket), tagging services (folksonomies – White, 2008), and even sharing services (Hootsuite) allow for the collection and sorting of information, people, and activities. Pinterest’s blend of social and curation technologies around images that may lead to the development of a social search process (DeAmicis, 2014), but Reddit ‘s features for organizing and ranking information probably make it the most famous web curation service.
Before the read/write web, internet publishers discovered that they could increase the amount of interaction with their content by creating a more engaging user experience. Users didn’t just want to observe content, they wanted to read, click, annotate, manipulate, and move things around in their environment engaging their social, cognitive, and emotional faculties (Lu & 卢洁, 2012). Multiple authors have studied the potential for web technologies to provide more engaging and flexible kinds of interactions that lead to improved student performance (Hill, 2014; Shahsavar, Hoon, Thai, & Samah, 2013). Examples of complex interactions with people, information, or activities may look like proceeding through the challenges offered by a language learning program like Duolingo, or chatting via Skype with a foreign language instructor. It could be investing time in chopping blocks and running around the Minecraft interface collecting items required for building things or watching videos of other players doing the same thing on Vimeo. It may involve testing code in programming sandboxes or proceeding through the activities in a zyBook.
Creation is a well-known aspect of Bloom’s Taxonomy and is a central element in this cycle because it marks a shift from the student’s function as a consumer of information and experiences to becoming a co-creator of the learning conversation. Fowley (2011) explored this collaborative process in depth with her dissertation study of bloggers and determined that blogs are an example of an artefact “defined by the presence of a literary space, a social space, and a technological space.” Web 2.0 technologies have enabled a new type of knowledge creation in which the information is not transferred, but shared and developed in a “trialogue” between the individual, nature, and the community (Ibid). This is not unique the creation stage as publishing services like Academia.edu allow users to select whether or not to participate in collaboratively assessing or refining this shared artefact. Some authors recommend a socially translucent creative process (Erickson & Kellogg, 2000) that enables others to observe the development of the artefact and learn through this passive form of interaction (Demski, 2013).
In Web 2.0 students can ‘all participate as publishers’ (Brown, 2010). Publication is the means by which the created artefact of an individual or community is made available to the broader community in an exchange that maintains the flow of learning (Demski, 2013). Within this cycle the learner has the dual role of “a receiver and a creator of content” (Brown, Dehoney, & Millichap, 2015). This transmission of information, identified by Fowley (2011) as a primary function of the classroom enjoys a greater range of affordances through the internet. Social media platforms like Facebook and Twitter thrive on the publication and republication of content by individual and corporate users that creates a kind of ongoing social conversation. In this Web 2.0 setting, sharing or publication is “non-traditional,” “informal,” and “much shorter” (Brown, 2008 as cited in White, 2008). Much of web 2.0 publication happens asynchronously, but chat applications through mobile and desktop devices (Skype, Adobe Connect, WeChat) enable instantaneous cycles of publication and engagement through synchronous conversation or collaboration that are reminiscent of the classroom model.
“The assessment has become a learning opportunity,” said Eric Mazur, a professor at Harvard University (Demski, 2013). The ‘course model’ of pedagogy will be replaced by a fully developed system of analytics that responsively customize the learning plan described as the first element in this cycle (Siemens, 2010/2013). Information and communication technologies in educational platforms enable precise and personalized measurements of the individual learner (Suthers, 2012). This data has the potential to inform multiple stages of the learning process in the context of MOOCs or formal LMS systems like Moodle or Blackboard (Wilson & Mai, 2014). However, Gray et al (2012) described the challenge that academics face in assessing Web 2.0 learning at a deeper level than clicks, views, and posts. Essay analytics move one step beyond this with automatic summaries and assessment of freestyle writing (Van Labeke, Whitelock, Field, Pulman, & Richardson, 2013), but there is still room for development of more effective tools of assessment. For example Stanford is researching the potential for artificial intelligence to assess and measure a variety of inputs from students to “characterize their learning over extended periods of time” (Bumbacher, Schneider, Worsley, & Blikstein, 2013).
Intersection of Activity (What) and Pedagogy (How)
The struggle to improve student performance through the use of technology is not an issue of technology, or even of pedagogy, but of the difficulty in bridging the gap between what students need (pedagogy) and what the technology can do (affordance). Research has suggested the use of an activity framework with seven common elements with which users can identify what to do with Web 2.0 technologies for learning. Carrying this research one step further, the various elements of activity design can be viewed in light of the pedagogical frameworks that influence how technologies are used. The framework of seven activity elements in conjunction with the influence of the pedagogical paradigms enables educators to identify what to do with technology and how this should be done [Figure 2]. For example, within the element of publishing, the humanist model will be concerned with the impact of the process on the identity of the individual (e.g. academia.edu), where the behaviourist model will attempt to create a stimulus to which publication is the measurable response (turnitin.com). The wiki is a technology that blends multiple models together in a single platform. What the student posts is likely a response to some prompt by the professor (behaviourism) that creates a connection with other resources (connectivism), to crowdsource a knowledge base (constructivism) that has an impact on the learning community (social cognitivism).
When the seven elements of the learning cycle above intersect with the seven pedagogical frameworks described in the first section, 49 unique technological environments emerge. Descriptions or examples of technology for each intersection of activity and pedagogy can be found in the appendix or a fully interactive version online. The requirements to support these combination of what and how will determine which technologies have the right affordances to be adopted into a particular learning context (Hew & Cheung, 2013). In response to the chaotic use of constructivism in classrooms, Dewey (1938) observed that experience without a roadmap is not helpful to student learning. Likewise, technology users need a roadmap with which to design an effective learning experience. Without such a selection process, users risk a mismatch between the medium of the technology and the intentions of the educational program (White, 2008).
Just as learning design may be informed by the technology, so technology design might benefit from the pedagogy applied to its use. Successful Web 2.0 applications like Facebook and Candy Crush include all of the seven elements in some form. In order for software to differentiate itself from current offerings, it may be helpful to keep in mind that some of the 49 intersections of the elements and pedagogical paradigms provide a blue ocean of opportunities for development. For example, many education technologies overlook the plan and find stage because of a behavioural context of pedagogy in which these functions are performed by the teacher. However, in the Web 2.0 context, a feature that enables individuals and groups to develop their own schema for the use of information, activities, and human resources in an online environment should be well-received. Similarly, very few technologies exist that offer users access to all seven elements within a single pedagogical framework (e.g. Twitter limits its functionality to a connectivist framework) or to multiple pedagogical frameworks for a single element (e.g. sharing widgets could differentiate themselves by more than the colour range of their buttons if they recognized that publishing has different demands depending on the pedagogical approach in which it is applied).
Limitations and Recommendations
While supported by a wide range of existing research, the elements suggested in this study may be more difficult to use in practice than they were to develop in theory. Additionally, the relationship of these activity elements to the various pedagogical paradigms is somewhat unusual, though very necessary to the selection of which technology affordances will be useful. Furthermore, the ambiguous nature and diverse range of both elements and pedagogical models may limit the ability of practitioners to apply these concepts. The examples of 49 intersections of the elements and pedagogies are just a starting point from which to stretch the limitations of current thinking on the way in which technology is designed and used in education. Time and space limited their full development and exploration in this study, but these and other intersections would be profitable to pursue further in the future.
An additional hurdle to the value of this proposal is the lack of any comprehensive resource for determining which technologies best support certain activities and pedagogical frameworks. Even in this study, the technologies examined were not particularly designed for education (e.g. Google, Skype, and Twitter) and may be more challenging to appropriate for education than bespoke applications. Various review software exists, but none that enables crowdsourcing of information about what elements, pedagogical approaches, age groups, class-sizes, etc. influenced the selection and use of a certain technology. This resource would be a valuable source of ideas for how to use technology, and which technology to use.
Because users don’t know what to do with Web 2.0 technologies, they have been applied in higher education with disputable success. Research indicates that successful engagement with learning in the Web 2.0 environment requires an activity framework to create a bridge between learning environments and technology affordances. Analysis of the research has identified several elements of learning activities that educators can use for learning design with Web 2.0 technologies. These seven elements identify what to do with Web 2.0 technologies while the seven pedagogical paradigms inform how these activities should take place. Considered together, these suggest 49 unique pedagogy/activity intersections that could inform the selection and integration of technology or the development of future technology affordances and pedagogical practices. Future research on these elements should study their impact and identify best practices for their application in multiple pedagogical contexts of teaching and learning in the Web 2.0 environment.
Chart and Taxonomy of web-2-0-learning-tools with application in various learning environments.
Amory, A., Naicker, K., Vincent, J., & Adams, C. (1999). The use of computer games as an educational tool: Identification of appropriate game types and game elements. British Journal of Educational Technology, 30(4), 311-321. doi:10.1111/1467-8535.00121
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1-26.
Barnes, C. & Tynan, B. (2007). The adventures of Miranda in the brave new world: Learning in a Web 2.0 millennium. Research in Learning Technology, 15(3), 189-200.
Bates, A. T. (2005). Technology, e-learning and distance education. London: Routledge.
Bennett, S., Bishop, A., Dalgarno, B., Waycott, J., & Kennedy, G. (2012). Implementing Web 2.0 technologies in higher education: A collective case study. Computers & Education, 59(2), 524-534.
Bloom, B. (1956). Taxonomy of educational objectives; the classification of educational goals. New York, NY: Longmans, Green.
Bergmann, J. (2014). Flipped-learning toolkit: Let’s talk tech [Blog post]. Retrieved from http://www.edutopia.org/blog/flipped-learning-lets-talk-tech-jon-bergmann
Bower, M. (2008). Affordance analysis–matching learning tasks with learning technologies. Educational Media International, 45(1), 3-15.
Bower, M. (2015). Deriving a typology of Web 2.0 learning technologies. British Journal of Educational Technology. doi: 10.1111/bjet.12344
Bower, M., Hedberg, J. G., & Kuswara, A. (2010). A framework for Web 2.0 learning design. Educational Media International, 47(3), 177-198.
Brown, S. (2010). From VLEs to learning webs: the implications of Web 2.0 for learning and teaching. Interactive Learning Environments, 18(1), 1-10.
Brown, M., Dehoney, J., & Millichap, N. (2015). What’s Next for LMS? EDUCAUSE Review 50(4) Retrieved from http://er.educause.edu/articles/2015/6/whats-next-for-the-lms
Bruner, J. (1960) The process of education. Cambridge, MA: Harvard University Press.
Bryson, J. M. (1995). Strategic planning for public and nonprofit organizations : a guide to strengthening and sustaining organizational achievement (Rev. ed.). San Francisco, CA: Jossey-Bass
Buckingham-Shum, S. (2014). How do learning analytics “act” in education? [Slideshow]. Retrieved from http://simon.buckinghamshum.net/2014/05/how-do-learning-analytics-act-in-education/
Bull, G., Thompson, A., Searson, M., Garofalo, J., Park, J., Young, C., & Lee, J. (2008). Connecting informal and formal learning experiences in the age of participatory media. Contemporary Issues in Technology and Teacher Education, 8(2), 100-107. Retrieved from http://www.citejournal.org/vol8/iss2/editorial/article1.cfm
Bumbacher, E., Schneider, B., Worsley, M., & Blikstein, P. (2013). Multimodal learning analytics [Web page]. Retrieved from https://tltl.stanford.edu/project/multimodal-learning-analytics
Collis, B., & Moonen, J. (2008). Web 2.0 tools and processes in higher education: Quality perspectives. Educational Media International, 45(2), 93-106.
Conole, G. (2010). Facilitating new forms of discourse for learning and teaching: Harnessing the power of Web 2.0 practices. Open Learning, 25(2), 141-151.
Conole, G. & Fill, K. (2005). A learning design toolkit to create pedagogically effective learning activities. Journal of Interactive Media in Education, 2005(08).
DeAmicis, C. (2014). If google search is for information, Pinterest wants to be search for inspiration [Blog post]. Panda Media. Retrieved from https://pando.com/2014/04/25/if-google-search-is-for-information-pinterest-wants-to-be-search-for-inspiration/
Demski, J. (2013). 6 Expert Tips for Flipping the Classroom. Retrieved from http://www.cetla.howard.edu/teaching_strategies/docs/expertFlipping.pdf
Dewey, J. (1938). Experience and education. New York, NY: Macmillan.
Diaz, V. (2010). Web 2.0 and emerging technologies in online learning. New Directions for Community Colleges, 2010(150), 57-66.
Drexler, W., Baralt, A., & Dawson, K. (2008). The Teach Web 2.0 Consortium: A tool to promote educational social networking and Web 2.0 use among educators. Educational Media International, 45(4), 271-283.
Erickson, T., & Kellogg, W. A. (2000). Social translucence: An approach to designing systems that support social processes. ACM transactions on computer-human interaction (TOCHI), 7(1), 59-83.
Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses. The FASEB journal, 22(2), 338-342.
Fareed, W. (2010). Affordances analysis of an audioblog and suggestions for its recruitment and use in oral lessons. International Journal of Instructional Technology and Distance Learning, 7, 55-65. Retrieved from http://www.itdl.org/Journal/Aug_10/article04.htm
Fowley, C. (2011). Publishing the Confidential (Doctoral dissertation, Dublin City University). http://core.ac.uk/download/pdf/11310467.pdf
Freire, P. (2010). Pedagogy of the oppressed (30th anniversary ed.). New York, NY: Continuum.
Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York, NY: Basic Books.
Good, R. (2012). Why curation will transform education and learning: 10 key reasons [Blog post]. Retreived from http://www.masternewmedia.org/curation-for-education-and-learning/
Gray, K., Waycott, J., Clerehan, R., Hamilton, M., Richardson, J., Sheard, J., & Thompson, C. (2012). Worth it? Findings from a study of how academics assess students’ Web 2.0 activities. Research in Learning Technology, 20. Retrieved from http://www.researchinlearningtechnology.net/index.php/rlt/article/view/16153
Harasim, L. (2012). Learning theory and online technology. New York, NY: Routledge.
Herrington, J., & Kervin, L. (2007). Authentic learning supported by technology: Ten suggestions and cases of integration in classrooms. Educational Media International, 44(3), 219-236.
Hew, K. F., & Cheung, W. S. (2013). Use of Web 2.0 technologies in K-12 and higher education: The search for evidence-based practice. Educational Research Review, 9, 47-64.
Hill, P. (2014). White House report on big data will impact ed tech [Blog post]. http://mfeldstein.com/white-house-report-big-data-will-impact-ed-tech/
Jenson, K. (2015). Behind the screens: Developing a digital learning literacy. Retrieved from https://www.academia.edu/12279274/Behind_the_Screens_Developing_a_Digital_Learning_Literacy
Johnson, L., Adams Becker, S., Cummins, M., Freeman, A., Ifenthaler, D., & Vardaxis, N. (2013). Technology outlook for Australian tertiary education 2013-2018: An NMC Horizon Project regional analysis. Austin, Texas: The New Media Consortium.
Kirkwood, A., & Price, L. (2013). Missing: Evidence of a scholarly approach to teaching and learning with technology in higher education. Teaching in Higher Education, 18(3), 327-337.
Kolb, D. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall.
Kuswara, A. U., & Richards, D. (2011). Realising the potential of Web 2.0 for collaborative learning using affordances. Journal of Universal Computer Science, 17(2), 311-331. Retrieved from http://jucs.org/jucs_17_2/realising_the_potential_of/jucs_17_02_0311_0331_kuswara.pdf
Lei, J. (2010). Quantity versus quality: A new approach to examine the relationship between technology use and student outcomes. British Journal of Educational Technology, 41(3), 455-472.
Levin, J. A. (2014). Taxonomies of educational technology uses: Dewey, Chip and me. E-Learning and Digital Media 11(5). Retrieved from http://ldm.sagepub.com/content/11/5/439.full.pdf+html
Looi, C. K., Wong, L. H., So, H. J., Seow, P., Toh, Y., Chen, W., … & Soloway, E. (2009). Anatomy of a mobilized lesson: Learning my way. Computers & Education, 53(4), 1120-1132. Retrieved from http://www.sciencedirect.com/science/article/pii/S036013150900133X
Lu, J., & 卢洁. (2012). Using social networking environments to support learning engagement inhigher education (Doctoral dissertation, The University of Hong Kong (Pokfulam, Hong Kong)). Retrieved from http://hub.hku.hk/handle/10722/188742
Marchionini, G. (2006). Toward human-computer information retrieval [Lecture]. The Information Association for the Information Age. Retrieved from http://www.asis.org/Bulletin/Jun-06/marchionini.html
Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370. Retrieved from http://www.researchhistory.org/2012/06/16/maslows-hierarchy-of-needs/
McLeod, S. A. (2008). “Bruner”. Retrieved from http://www.simplypsychology.org/bruner.
McLeod, S. A. (2013). Behaviourist Approach. Retrieved from www.simplypsychology.org/behaviorism.html
Merriam, S. B., Caffarella, R. S., & Baumgarther, L. M. (2007). Learning in adulthood. San Francisco: Jossey-Bass. 11(4), 275-281
Mezirow, J. (1991). Transformative Dimensions of Adult Learning. San Francisco , CA: Jossey-Bass.
Mishra, P., & Koehler, M. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. The Teachers College Record, 108(6), 1017-1054.
Montessori, M. (2004). The Montessori method: The origins of an educational innovation: Including an abridged and annotated edition of Maria Montessori’s The Montessori method (G. Gutek, Ed.). Lanham, MD.: Rowman & Littlefield.
Norman, D.A. (1999). Affordance, conventions, and design. Interactions, 6(3), 38–43.
O’Reilly, T. (2007). What is web 2.0: Design patterns and business models for the next generation of software. Communications & Strategies 65, 17-37. Retrieved from https://mpra.ub.uni-muenchen.de/4578/1/MPRA_paper_4578.pdf
Oliver, M. (2005). The problem with affordance. E-Learning and Digital Media, 2(4), 402-413.
Oxnevad, S. (2013). Digital differentiation [Blog post]. Cool Tools for 21st Century Learners. Retrieved from http://d97cooltools.blogspot.com.au/p/digital-differentiation.html#.Vin6tdIrKUk
Pariser, E. (2011). Beware online filter bubbles [Video file]. TED Conference Presentation. Retrieved from http://www.ted.com/talks/eli_pariser_beware_online_filter_bubbles
Pavlov, I. (1960). Conditioned reflexes: An investigation of the physiological activity of the cerebral cortex. New York, NY: Dover Publications.
Piaget, J. (1964). Development and learning. in R.E. Ripple & V.N. Rockcastle (eds.), Piaget Rediscovered (pp.7-20). Retrieved from http://www.psy.cmu.edu/~siegler/35piaget64.pdf
Puentedura, R. (n.d.). SAMR Model [Web page]. Retrieved from https://sites.google.com/a/msad60.org/technology-is-learning/samr-model
Reeves, W. (1999). Learner-centred design: A cognitive view of managing complexity in product, information, and environmental design. Thousand Oaks, CA: Sage Publications.
Richardson, W. (2010). Blogs, wikis, podcasts, and other powerful web tools for classrooms. Thousand Oaks, CA: Corwin Press.
Rogers, C. (1951). Client-centered therapy: Its current practice, implications, and theory. Boston, MA: Houghton Mifflin.
ROLE. (n.d.). About [Web page]. Retrieved from http://role-widgetstore.eu/about
Shahsavar, Z. (2013). Practicing Socratic questioning in a blended learning environment: An innovative strategy to promote critical thinking. International Journal of Social Media and Interactive Learning Environments, 1(2), 184-198.
Shahsavar, Z., Hoon, T. B., Thai, Y. N., & Samah, B. A. (2013). Promoting tertiary level students’ critical thinking through the use of Socratic questioning on the blog. Social Sciences & Humanities, 21, 57-70.
Siemens, G. (2004). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1). Retrieved from: http://www.elearnspace.org/Articles/connectivism.htm
Siemens, G. (2010). What are learning analytics? [Blog post]. Retrieved from http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/
Siemens, G. (2013). Intro to learning analytics [Video file]. Retrieved from https://www.youtube.com/watch?v=KqETXdq68vY
Skinner, B. F. (1953). Science and human behavior. Retrieved from http://www.bfskinner.org/product/science-and-human-behavior-pdf/
Snowden, D. J., & Boone, M. E. (2007). A leader’s framework for decision making. Harvard Business Review, 85(11).
Sousa, D. A. (2011). How the brain learns. Thousand Oaks, CA: Corwin
Sternberg, J. (2012). ‘It’s the end of the university as we know it (and I feel fine)’: The Generation Y student in higher education discourse. Higher Education Research & Development, 31(4), 571-583.
Sun, H., & Chen, L. (2014). A framework for analysing the social affordance of Web 2.0 tools. International Journal of Social Media and Interactive Learning Environments, 2(1), 37-59. doi:10.1504/IJSMILE.2014.059695
Suthers, D. (2012). Connecting levels and methods of analysis in networked learning communities [Blog post]. Retrieved from http://engaged.hnlc.org/story_comments/list/17
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory: Exploration in the learning sciences, instructional systems and performance technology. New York, NY: Springer.
Tondeur, J., Van Braak, J., & Valcke, M. (2007). Towards a typology of computer use in primary education. Journal of Computer Assisted Learning, 23(3), 197-206. doi:10.1111/j.1365-2729.2006.00205.x
Van Labeke, N., Whitelock, D., Field, D., Pulman, S., & Richardson, J. T. E. (2013, April). ‘OpenEssayist: extractive summarisation and formative assessment of free-text essays’, Proceedings of the 1st International Workshop on Discourse-Centric Learning Analytics, Leuven, Belgium, April 2013. Retrieved from http://oro.open.ac.uk/37548/1/LAK%20final.pdf
Varonis, E. M., & Varonis, M. E. (2015). Deconstructing Candy Crush: What instructional design can learn from game design. The International Journal of Information and Learning Technology, 32(3), 150-164. doi:10.1108/IJILT-09-2014-0019
Vygotsky, L. S. (1978) Mind in Society. Cambridge, MA: Harvard
Watson, J. B. (1930). Behaviourism (revised edition). Chicago, IL: University of Chicago Press.
Way, J., & Webb, C. (2007). A framework for analysing ICT adoption in Australian primary schools. Australasian Journal of Educational Technology, 23(4).
Wenger, E. (1990). Toward a theory of cultural transparency. Unpublished doctoral dissertation, University of California, Irvine.
White, G. (2008). ICT trends in education. Digital Learning Research, 2.
Wilson, L., & Mai, P. (2014). Learning analytics and MOOCs: A path to improving educational quality access? Social Media Lab [Blog post]. Retrieved from http://socialmedialab.ca/2014/learning-analytics-and-moocs-a-path-to-improving-education-quality-access/
Woo, Y., & Reeves, T. C. (2007). Meaningful interaction in web-based learning: A social constructivist interpretation. The Internet and Higher Education, 10(1), 15-25.
Yeh, P. C. (2013). PaGamO, the world’s first ever MOOC-based multi-student social game platform [Video file]. Retrieved from https://www.youtube.com/watch?t=428&v=WKAWPqRtIe0
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3-17.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70.
Zimmerman, B. J. (2011). Barry Zimmerman discusses self-regulated learning processes. Thomson Reuters. Retrieved from http://archive.sciencewatch.com/dr/erf/2011/ 11decerf/11decerfZimm/