Week 4 Reflection

This week’s readings helped me think about social media tools from a more structural perspective. In previous weeks, I focused on networked individuals, online communities, and Personal/Professional Learning Networks. This week shifted my attention to the systems that organize, reward, and shape online participation. Tags, hashtags, algorithms, badges, gamification, and crowdsourcing influence what people see, how they participate, what knowledge becomes visible, and whose contributions are recognized.

One idea that stood out to me is that tags and hashtags can function as lightweight learning tools, but tagging also has limitations. Dennen, Bagdy, and Cates (2018) show that effective tagging in online learning environments depends on both approach and accuracy. If tags are too broad, inconsistent, or unclear, they may not help students find or organize information. This connects to my own experience as a student. In our course blog, for example, tags could help me organize my posts around themes. However, if every student creates completely different tags for similar topics, the system may become less useful for the whole class. This helped me realize that tagging works best when there is a balance between structure and flexibility.

Another important theme this week is crowdsourcing. Crowdsourcing can support learning by enabling many people to contribute knowledge, resources, and experiences. Wilson (2018) discusses how the production of teaching materials can become a learning objective, which connects to the idea that students can learn by creating resources for others. This made me think about how instructors could ask students to collaboratively build glossaries, annotated resource lists, study guides, or collections of examples. In this way, students are not only consuming knowledge but also helping produce shared learning materials.

However, crowdsourcing also raises questions about quality, expertise, and equity. When many people contribute information, not all contributions are equally accurate or useful. This connects to this week's discussion topic about assessing expertise. In online spaces, expertise is not always obvious. I often look for indicators such as credentials, evidence, consistency, and community recognition. That's why I think students need support in learning how to evaluate online information critically.

The readings on badges and gamification also made me think about motivation. Badges can make learning visible and encourage students to try new activities, but they can also become superficial if they only reward completion. Dennen, Arslan, and Bong (2024) discuss optional embedded microlearning challenges in a higher education course, which helped me think about how badges and challenges can support self-directed learning when students have meaningful choices. In my view, a badge should represent more than clicking through a module. It should demonstrate learning, reflection, skill development, or contribution.

Overall, Week 4 helped me understand that tools such as tags, algorithms, badges, and crowdsourcing structures shape what students see, how they participate, and how learning is recognized. For my topic, Social Media Tools and Higher Education, this week was very interesting and useful because it showed me that social media platforms are not just communication spaces. They are systems that organize knowledge, distribute visibility, and influence behavior. As educators, we need to help students use these systems critically and intentionally. Digital literacy should include not only how to use tools, but also how to understand the hidden and visible structures that shape online learning.

References

Dennen, V. P., Arslan, Ö., & Bong, J. (2024). Optional embedded microlearning challenges: Promoting self-directed learning and extension in a higher education courseLinks to an external site. Educational Technology & Society, 27(1), 166-182. https://doi.org/10.30191/ETS.202401_27(1).SP04  

Dennen, V. P., Bagdy, L. M., & Cates, M. L. (2018). Effective tagging practices for online learning environments: An exploratory study of approach and accuracy. Online Learning, 22(3), 103-120.  

Wilson, M. C. (2018). Crowdsourcing and self-instruction: Turning the production of teaching materials Into a learning objective. Journal of Political Science Education, 14(3), 400-408. doi:10.1080/15512169.2017.1415813

Tags, Algorithms, and Visibility

For this week, I want to continue exploring my overall topic, Social Media Tools and Higher Education, through the lens of tags, hashtags, algorithms, and visibility. This week’s readings helped me think more specifically about how information is organized and made visible in online spaces. In higher education, this matters because students do not simply use social media tools; they also depend on these tools to find resources, build professional identities, join communities, and access opportunities.

One important concept this week is tagging. Tags and hashtags are lightweight organizing systems that help people connect posts, resources, conversations, and communities. In higher education, tags can support learning in many ways. For example, students in a course could use hashtags such as #InternationalStudents, #HigherEducation, #CareerDevelopment, #DigitalLiteracy, or #StudentBelonging to organize blog posts, discussion board responses, or shared resources. Tags can help students see patterns in their own learning and find peers with similar interests. Raman et al. (2020) discuss hashtags as an easy entry point for enhancing online discussions, suggesting that tagging can lower the barrier to student participation and connect ideas.

At the same time, tags are not automatically effective. Dennen, Bagdy, and Cates (2018) show that tagging practices in online learning environments require attention to both approach and accuracy. If students use tags that are too general, inconsistent, misspelled, or overly personal, the tag system may become difficult to use. For example, one student might use #intlstudent, another might use #internationalstudent, and another might use #studyabroad, even though they are discussing similar experiences. Without some guidance, students may create what feels like a messy collection of labels rather than a useful learning system. This made me think that instructors should not simply tell students to “use tags” in the practice of higher education. They should also model effective tagging and explain how tags can support: 

• Learning
• Searching
• Reflection
• Assessment.

Algorithms add another layer to this issue. Bucher (2017) argues that people develop an “algorithmic imaginary,” meaning that users form ideas about how algorithms work and adjust their behavior accordingly. This is very relevant to social media tools in higher education. On LinkedIn, students and professionals may carefully consider when to post, which hashtags to use, what wording sounds professional, and which types of content will gain visibility. On TikTok, Instagram, or YouTube, educational creators may adjust their content to satisfy platform algorithms. As a result, users may not only communicate with people but also perform for algorithms.

This creates both opportunities and problems for higher education. On the positive side, algorithms can help students discover useful resources, professional communities, and learning opportunities they may not have found on their own. For example, a student interested in international higher education might begin following one professional organization on LinkedIn and then receive recommendations for related scholars, conferences, webinars, and career pathways. However, algorithms can also narrow what students see. They may amplify popular content over accurate content, emotional content over nuanced content, or already-visible voices over marginalized voices.

This week’s readings made me think more critically about the relationship between tags and algorithms. Tags are more visible and user-controlled. Students can intentionally choose tags to organize and connect their learning. Algorithms are more hidden and platform-controlled. They may help students discover information, but students usually do not fully know why certain content appears in their feeds. For higher education, I think the best approach is to teach students to use tags intentionally while also developing critical awareness of algorithms. Students should understand that what they see online is not simply “what exists,” but what platforms choose to make visible.