Determining Participation

Hargittai, E. & G. Walejko. (2008). The Participation Divide: Content Creation and Sharing in the Digital Age. Information, Communication and Society, 11(2), 239-256. Retrieved September 23, 2009 from

If Web 2.0 signals a move to a participatory Web, determining why some individuals participate and others do not is critical. Hargittai cites previous research that indicates teens from higher socioeconomic backgrounds are more likely to experience educational gains from home computer and Internet use, and that teens with more access points are more engaged in more online activities. However, she goes on to argue that unequal access to technology is not the only answer, but rather that, “exposure to experiences that increase participatory culture and digital literacy are unequally available.”

Her claim that teens from higher socioeconomic backgrounds are more likely to engage in online participatory and creative activities is not reflected in danah boyd’s analysis of social network sites. However, this apparent discrepancy is resolved when Hargittai lists the creative activities she examined: music, poetry/fiction, photography, and video (to the exclusion of the Facebook/MySpace profiles and comments boyd studied).

The data set (more than 1,000 first year students at a diverse urban university) seems to provide a valid microcosm of the (older) teenage college-bound population. Within that data set, the higher male participation in music confirmed the Pew data previously reviewed. However, the higher participation of white teens in photography was surprising given the proliferation of cell phone cameras; similarly, the higher participation of female and African-American teens in poetry and fiction was unexpected.

The data showed a correlation between participation and parental college attendance (although teens whose parents completed college was actually lower than teens whose parents had completed only a portion of college). The dominant creation format for teens was video; Hargittai attributes this to YouTube, but the now common ability of cell phones to create videos, as well as the concept of “first takes” in online videos (which are characterized by and promoted for low production values) may also be factors. Video and music are less likely to be remixed, although this is likely due to the expense of and skill required for multimedia editing software.

The article’s contribution to the participation debate comes in the final section: the primary correlation to participation is Web skills. In fact, controlling for users with Web skills, there are no gender differences. The conclusion is obvious: digital fluency determines Web participation levels, and thus the development of Web skills is a critical subject for our schools.


Creating Content

Lenhart, Mary Madden. (2005.) Teen Content Creators and Consumers. Pew Internet and American Life Project. Retrieved September 23, 2009 from

The PEW report is filled with statistics, many of which are obvious (broadband users are more likely to create and share content; daily users more likely to share, remix, blog, and create sites for others). Here are some of the more interesting numbers for online teens:

  • 57% create content, and 33% share that content
  • 40% are urban, 28% are suburban, and 34% are rural
  • 19% keep a blog (vs. 7% of online adults); 38% read blogs (vs. 27% of online adults)
  • 57% of bloggers update their blogs once a week or more; 29% update at least 3 times a week

These numbers suggest a high level of creative contribution/activity with a surprising lower-level of participation among suburban teens (particularly given the correlation between usage and households with higher incomes and parents with higher levels of education).

Bloggers and blog readers reveal greater levels of participation (in all Internet activities except gaming) and device variety. However, dispelling the general concerns of parents, 62% of blog readers read only people they know. As expected, blog creators are more likely to share content (69% versus 24%) and remix (35% versus 16%); interestingly, blog readers showed similar levels (50% versus 23% for sharing; 26% versus 16% for remixing). Girls, especially older girls, are most likely to blog.

The study also looked at music consumption which showed almost equal percentages of teens obtaining music from P2P (file sharing), paid services (increasing from the 9% reported in the last report), and email/IM (surprisingly high). 52% say that violating copyrighted is wrong, but 47% disagreed (especially older teens); before we accuse teens of not respecting intellectual property, the report notes these figures parallel reports from adults. And while adults downloaded music in almost equal numbers, teens (especially older boys) downloaded more videos.

An interesting cross-comparison shows that bloggers are more likely to download music and videos but also express greater concerns (52% versus 37%) over copyright. At the same time, 75% of online teens say it’s so easy to download content that it’s unrealistic to expect people not to do it.

How Computer Games Help Children Learn – Chapter 2

Shaffer, D. (2006). How Computer Games Help Children Learn. New York: Palgrave Macmillan.

The Digital Zoo game starts to tie together game qualities with authentic professions (in this case, mechanical engineering). The game is a design simulation with no right answer; goals are realistic tasks (such as build a bridge), and levels are introduced through increasingly difficult requests from clients. Accomplishing each task requires designing, building, and testing various solutions–the basic steps in engineering design–with each solution producing an incremental improvement. Concepts such as center of mass and bracing are learned (and integrated into the students’ deep understanding) during the process.

Students took pre- and post-tests and kept a design notebook which allowed the researchers to investigate numerical and attitude changes; the results were impressive:

  • “players used scientific justifications, on average, five times as often”
  • “design plans became, on average, 55 percent more complex”
  • “Players considered 47 percent more features in making a decision”

Players learned specialized language as part of acting like (and thus learning to think like) an engineer. The language provides labels which improves communication and also highlights what’s important and what’s not.

After describing the research with the game, Shaffer argues that the knowledge gained in the game persists because it’s tied to a particular epistemology. He describes knowledge as more than memory but as the use of symbols which represent knowledge. While words are efficient symbols because they can be written down and studied systematically, computers don’t just store symbols, they process them. While words led to a scientific culture based on symbol storage, computers will lead to as virtual culture based on symbol processing.

Shaffer argues that all games are simulations (microworlds), and that as humans and computers interact in these simulations, players display autoexpressivity. They come to a microworld with a set of beliefs, make decisions based on those beliefs, and and receive responses from the simulation which bring to the surface, challenge, and refine those beliefs. This concept is identical to Wiggins’ idea of eliciting misconceptions as the first step toward deep learning.

How Computer Games Help Children Learn – Chapter 1

Shaffer, D. (2006). How Computer Games Help Children Learn. New York: Palgrave Macmillan.

The Debating Game described in Chapter 1 exposed many familiar aspects of games, but also raised issues that require further consideration:

  • Rules define games (and play) and yet “what matters is presenting an interpretation and defending it” (which seems identical with Bloom’s evaluation level). This dichotomy and the need for authenticity suggests that writing the rules is the most difficult aspect of game creation.
  • Some roles make players care about winning; other roles make players care about self-efficacy. Because roles (and thus end-states) differ, rules vary with roles. This role-rule pairing (actually multiple pairings) create the narrative, another difficult task for the game creator.
  • We play out our real life situations in game roles and rules, and yet fantasy seems a key motivational element in most games (and especially the idea that in games, we can do things we can’t do in real life).
  • The definition of epistemic games as requiring “you to think in a particular way about the world” seems at odds with rules, unless the rule is to think like an historian (like an economist, etc.) which makes writing rules even more difficult.

The characterization of school as a game to teach you how to think like a factory worker suddenly made standardized tests make sense.

How Computer Games Help Children Learn – Introduction

Shaffer, D. (2006). How Computer Games Help Children Learn. New York: Palgrave Macmillan.

The Foreword by James Gee sets up the purpose for the book. The world is full of increasingly complex systems, and standardized education, although noble, fails to teach the problem-solving skills students must acquire to navigate these systems. Shaffer’s solution is to motivate students to role-play authentic professions in games that go “back and forth between the virtual world and real world.” At the same time, standardized content becomes the toolkit students need to win the game.

The Introduction further develops the global competitiveness argument by detailing the standardized (even if technical) jobs that can be done anywhere and focuses on how the U.S. is falling behind in STEM curricula. The solution for Shaffer is the computer, not as a tutor but as a problem-solving tool.

Shaffer next describes his experience teaching at a school which was also a working farm and seeing students willing to work hard at chores because they saw the learning as authentic. He then details the learning and motivation that come from playing games and connects these concepts with video games.

Finally, Shaffer describes his central premise: we need to develop epistemic games, games that enable students to learn what it means to be an expert (professional) in a particular discipline, instead of merely learning the facts about the discipline. The introduction concludes with a brief overview of the book’s six chapters.

The introduction reinforced my desire to read the rest of the book. However, I’m hoping the emphasis on innovation is applicable to all disciplines, not just STEM subjects. Also, the view of computers as only providing simulations ignores the communication potential to connect with real, not simulated experts. At the same time, Shaffer’s vision of an imaginary world “where we can do things that we otherwise couldn’t do at all” is a hallmark of games and the promise of Shaffer’s role-playing authentic learning.

CSCL Revisited

CSCL combines many of the theoretical elements we studied in instructional design–constructivist learning, social negotiation of knowledge, the importance of communication transactions–with the area I work in: Internet-delivered instruction. While the group I work with has long advocated the use of student groups as a means to address enrollment scalability, CSCL lends research-based credence to that advocacy with more successful learning outcomes.

One area that troubles me a little is the focus of CSCL on small groups (3-5 students); this size seems better described as a team. My observation of game-based learning (not learning in serious games, but learning nonetheless) is that teams are more effective in solving discrete problems, but that larger groups are required to lend reality to a virtual world simulation. Would an island in SL feel “real” if there were only 4 people walking (flying) around? Can the premise of Dunbar’s number be tested in educational learning environments? Is a critical mass (and the resultant diversity) necessary to create a self-sustaining community?

As far as the module, the only problem I encountered was the rapid-fire pace of the assignments. Basically, an assignment was due every other day (and the days in between were required to get up to speed on the forthcoming assignment). This may prove to be a successful (if demanding) instructional design, implemented specifically to keep us on task; the pace provided a great deal of structure which might prove to be an exercise in self-discipline, especially if the end portion of the course involves a longer project.


While both cooperative and collaborative learning are founded in constructivist theory where knowledge is actively constructed by students, the distinction between cooperative learning as, “a division of labor among participants” and collaborative learning as “mutual engagement of participants in a coordinated effort to solve the problem together” (Roschelle & Teasley as cited in Resta & Laferrière) offers practical clarity as a backdrop. Education is a personal transaction among students and between students and teachers; these activities and transactions can take place only in a cooperative (or collaborative) environment.

Cooperative learning is more teacher-centered because the teacher controls the tasks, facilitates the methods, and may define the end products. Collaborative learning emphasizes personal change and transactions over environmental control and transmission. And in a punny way, collaboration may exclusively involve (evolve) elaboration. However, the sophistication of both students and teachers, as well as the subject matter, determines which method is more appropriate.

Many educational settings overly emphasize competition and individual work, although the former can provide motivation and the latter may be necessary to assure individual accountability. Key components in successful cooperative learning environments include positive interdependence, face-to-face promotive interaction, individual and group accountability, interpersonal and small group skills, and group processing. Clear guidelines on roles and expectations can prevent conflict and lay the groundwork for accurate assessment.

Collaborative learning should be viewed as “knowledge building” which is more concrete than “learning” from the perspective of social practice. Collaborative knowledge building is structured by the intertwining of personal perspectives with group understandings. Learners are influenced by socially-situated contexts, and learning occurs through interactional processes.

The construction of knowledge proceeds on the basis of artifacts already at hand and creates new artifacts from group knowledge-building to formulate, embody, preserve, and communicate new knowledge. The meaning of artifacts and our understanding of that meaning are first created in interpersonal contexts and subsequently may be internalized in an individual as a cognitive artifact. The mental representation is a result of collaborative activities within a socio-cultural context, not first as an internal product which is then expressed externally. Naturally occurring and carefully captured examples of collaborative knowledge building can be rigorously analyzed to make visible the knowledge-building activities at work, the intertwining of perspectives, and the mediating role of artifacts.