In regards to the six categories of Social Media Classifications as presented: blogs, social networking sites, virtual social worlds, collaborative projects, content communities, and virtual game worlds; adult learning has made most use of content communities and blogs/vlogs. Both content communities (YouTube, etc) and blogs/vlogs (Khan Academy, etc) allow for the delivery of rich subject-oriented content from more knowledgeable peers and experts. In both cases, multiple creators exploring subjects from unique frameworks allow students to choose a framework closest to their own worldview.
As with industry, some educators struggle with the loss of control over information in their subject silos (Kaplan, 2010). Other enterprising educators adapt their offerings to blend with and support existing learning mediums, such as YouTube. This reflects changes in the format of the World Wide Web; where it was once content creators delivering material to content consumers, now that line has become blurred. An internet user can be both content creator and consumer, as is commonly the case with Wikipedia, delivering and editing content on a known subject while reading and learning on others.
However, I believe that collaborative projects and social networking sites both have potential as delivery vehicles for adult learning, apprenticeship, and mentorship. Collaborative projects (Wikipedia) allow learners to choose multiple frameworks and base subject matter in learning as well as the ability to edit and expand upon those thoughts themselves. Social networking sites (Facebook, Discord, Linked In) rely on reputation and self-disclosure to create sub-communities of like-minded individuals who can and do brainstorm, crowdsource, and debate the edges of understanding in their content areas.
Traditional use of mobile devices has followed the “sage on a stage” principle of education - putting content on blast from professional media-makers for distribution to learners who will consume the content without adding to it (Mobile, n.d.). Only recently have educators branched out into interactive capabilities, using apps such as Kahoot to collect, aggregate, and visualize classroom data.
While the author seems to feel that mobile devices could be of benefit in tracking student engagement, satisfaction, and understanding, the post-secondary education market is already filled with software packages centered around emporium learning. These packages provide a wealth of data about student interaction with instructional content including both individual tracking and class trends. Some of these packages, including the popular Hawkes software, already run on mobile devices. It might be interesting to add the human sciences to the equation, tracking student confidence in the subject. Studies have already shown that positive math self-concepts translate to better scores on math tests (Chen, 2018); the ability to measure and track positive self-assessment may help students to realize better results.
Among the most interesting concepts is that of context-sensitive learning. We’ve already seen that situated learning creates deeper and more lasting understanding (Brill, n.d.). Students who feel they cannot “do” algebra might be interested to try it in the grocery store, at the bowling alley, or in a restaurant as a real-life application of otherwise abstract knowledge.
Prensky’s premise is that today’s students have fundamentally changed due to the arrival and rapid dissemination of digital technology in the last decades. He believes that students’ brains have physically changed and are different from older generations based on growing up with digital technology (Prensky, n.d.). On a professional anecdotal level, I have to disagree. Every day, I see digital “natives” who have no idea how to use email, or a CMT such as Blackboard, or the college software that is required for successful completion of their course. On the other hand, digital “immigrants” have mastered all of the above.
Rather than their date of birth, the largest factor in knowledge and willingness to learn and master a new digital landscape is largely defined by curiosity and openness to new technology - factors that sometimes show little correlation with age. In every course, students both with and without fundamental foundational skills in digital technology present uneven starting lines; these fundamental skills can be easily taught to students who are flexible, motivated, and see task value. As to rethinking educational content and presentation in a game format, this has always been enormously popular; games such as chess encompassed notions of military logistics, defensive positions, and attack vectors into a fun format as early as the eighth century. Gamifying subjects for a new digital era is not so much a rethinking of educational pedagogy, but a focus on motivational principles that have always existed for quality educational content.
In Part II, Prensky goes on to state that “the brain is, to an extent not at all understood or believed to be when Baby Boomers were growing up, massively plastic ... the old idea that we have a fixed number of brain cells ... has been replaced by research showing our supply of brain cells is replenished constantly.” (Prensky, n.d.) Beyond Prensky’s own examples, many educators are aware of the differences in the brains of children whose parents read to them versus those who do not. Brain reorganization takes place when the subject pays attention to the sensory input and the task, leading to the conclusion that digital literacy happens for those individuals who are innately curious or who explicitly see task value.
The “digital native” versus “digital immigrant” philosophy of today reminds me of the labels “high functioning” and “low functioning” which we once pasted on neurodivergent individuals with autism. German researchers created these distinctions based on which individuals were trainable for employment versus those who were not. However, today we recognize autism as a broad and three-dimensional spectrum of different abilities. Savants in some areas display shortcomings in others, and very few spectrum profiles even come close to matching each other. In much the same way, the digital landscape is wide and varied. Users may be fluent in gaming but ill-equipped to send professional email; may be fluent in social media but ill-equipped to use word processing software. The number of high school students at my college who have missed instructional content simply because it is located further down on the page is enormous; I have helped far too many of them “find” their course lectures, discovered right under the syllabus that they failed to scroll past. These “digital natives” are well aware of techniques in which they perceive task value and woefully unprepared for techniques in which they do not.
Answering questions about virtual identities and VR/AR communities at SXSW, Eva Hoerth of WXR Fund posits that the way we identify one another and establish interpersonal trust may be deprecated by sophisticated digital identities (How VR, 2021). Because our avatars don’t match our “real” appearance, the manner in which humans have primarily identified one another is deprecated - simple visual recognition of our forms. I disagree wholeheartedly with this notion. In Jung’s concepts of the psyche, he talks about the “Persona,” a topic thoroughly explored in “Map of the Soul” (Jung, 2019). Persona, derived from a Latin word meaning mask, refers to the pleasant face we put on as we approach and interact with other people. As part of being a social animal, we reconstruct our outward faces to match the situation. At work, I put on my work persona; with friends, I put on my friendly persona. While based on our true self, all of these personas serve to mask some aspects of our personality while putting others at the forefront. Digital avatars simply take the persona to a new level. VR and AR identities, while masking our physical form, can be a closer abstract or artistic representation of self, allowing a quicker connection on a more intimate intellectual or emotional level. Complex verification strategies of the type to determine who is really who already exists in the human framework. Players of MMORPGs such as World of Warcraft have quickly learned that the appearance of their teammates may bear no actual relation to their real appearance, and have compensated by evaluating other players’ loyalty and ability based on words and actions (Cline, 2012).
As brick-shaped headsets and chunky graphics give way to smaller devices and smoother appearances, VR and AR environments become more realistic and give freedom to educators that exist in no other medium. Students may someday play the role of a 1920s cop to learn about Prohibition or play the role of a blood platelet to learn about the circulatory system. If we remove ourselves completely from our physical forms, the possibilities of interacting with a simulated physical world are endless.
Cognitive apprenticeship practices strive to place learning practices in a rich and varied context that is meaningful and authentic to students. It is called apprenticeship to differentiate it from tutoring, mentoring, coaching, and volunteerism by focusing on interaction in the context of activity with a socially-recognized task value. Problem (Project) Based Learning exemplifies the cognitive apprenticeship model by constructing knowledge based on real-world problems with a strong emphasis on experiencing a variety of situations in which skills are repeatedly applied.
Scaffolding is a metaphor to describe the type of assistance offered by a teacher, tutor, or peer to support learning. Assistance is only offered with respect to those skills that are beyond the learner’s current capability. Errors are expected and self-correction is emphasized as a means to strengthen learning. As the student masters the task, the instructor begins to fade into the background, offering fewer supports until the student stands alone. In Bruner’s The Process of Education, he argued that any subject can be taught to any child at any stage of development if it is presented in the proper manner. All children with natural curiosity and a desire to learn will become competent if the task is neither too easy nor too difficult. Educators must present material at a level that challenges but does not overwhelm; scaffolding helps bring more complex tasks within reach.
Although the study worked with elementary school children, I chose “A Taxonomy of Student Engagement with Educational Software: An Exploration of Literate Thinking with Electronic Text,” in which the authors attempt to create a taxonomy of digital literacy, using a specific classroom software (Bangert, 2001). This study sought to document types of interaction and levels of student engagement with digital learning software in the course of a normal school environment.
The authors identified seven distinct categories of engagement which the authors arranged in order of complexity - not as an ordered structure. Interestingly, I was able to identify each student archetype in my daily work; categorizing students in this manner helps to organize my thinking about their digital abilities, level of interest, and possible changes in assessment or scaffolding which might help them to be more successful with the software. Theoretically, success with the software should translate into success with the instructional content.
At the lower end, the authors identified students who had “disengaged;” many of these students continued to type or interact superficially with the system, but mostly as a cover for mental or emotional withdrawal. These students may see little task value in the course content and even less in learning the new system.
The authors classified a level of “unsystematic engagement” where students sometimes left lessons half-completed and seemed to jump around the system, with learning still very low or nonexistent. These students might successfully complete some tasks but do not link the tasks together for higher-order thinking. They, at best, learn disconnected “facts” about the software and its content. Structure and task value may also be of benefit to these students.
“Frustrated Engagement” is a level with which I am exceedingly familiar. These students understood the learning objectives, possessed clear goals when working with the software, but were unsuccessful in accomplishing their goals. While these students have higher expectations for learning, seeing task value, and having formulated clear learning goals, they cannot accomplish those goals within the software framework. These students frequently experience negative thoughts, confusion, aggression, agitation, and distress. Working with these students, we frequently have them step back and outline a bigger picture - one which includes learning the technology as well as the content. Once students step back and see the necessary steps, they frequently climb those steps on their own.
“Structure Dependent Engagement” represented about 50% of students in the study, which mirrors what we see at the postsecondary level. These students aren’t clear on the structure of the course content or instructional ladder, but they can follow the software menu step by step from beginning through the end of the course. Many of these students never really feel like they have mastered the software but often feel accomplishment at completing the modules in order and watching their bar graph proceed to 100 percent.
“Self-Regulated Interest” students can be difficult to deal with, as they create personal goals within the software to make it relevant to their work or interest. Rather than proceed through the structured learning objectives, they see subject elements which have immediate appeal and begin working through those aspects. These students learn the software in an oblique manner, jumping around topics and taking what they need from the system in order to function. These students typically work well on their own, although they may end up with gaps in their learning objectives.
“Critical Engagement” is a level at which students begin to investigate the operational limitations or boundaries of the software. At this level, students explore freely, trying to trip up the system or determine if there are shortcuts to completion of the course content. These students have a self-initiated knowledge-building strategy that normally covers not only the instructional content but also the functionality of the software.
Finally, the study observed no students who met the criteria for “Literate Thinking,” defined as interpreting software content from multiple and meaningful perspectives. These students manipulate the software to get at meaning from multiple vantages, tying together related ideas in new and often personal ways. These students move beyond the content level to integrate their new knowledge into personal values and experience. At the postsecondary level, we have few of these students but we do recognize them.
Overall, students in the study fit into a left-tailed distribution with the lower orders of engagement having a handful of students and the higher orders having none. It would be most interesting to repeat this study at the postsecondary level with adult students to see what sort of distribution manifests.
Bibliography
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