Designing a “Hidden Curriculum Studio” for Graduate Students

I kept thinking about how networked learning design could support graduate students, especially those still learning how higher education works. Graduate school has many forms of hidden knowledge. Students are expected to know how to email faculty, apply for funding, prepare for conferences, find mentors, read academic norms, manage writing, and build a professional identity. However, these skills are not always taught directly. Many students learn them through informal networks, observation, trial and error, or advice from peers.

So I started imagining a learning activity called a “Hidden Curriculum Studio.” It would be a closed or semi-private online space where graduate students collaboratively build a guide to the unwritten rules of graduate education. The goal would not be simply to collect information. The goal would be to help students participate in networked knowledge activities by asking questions, sharing resources, revising ideas, and learning from each other’s experiences.

The activity could have several sections. One section could be called “Things I Wish Someone Told Me,” where students contribute short reflections about academic norms, funding, conferences, or advisor communication. Another section could be “Ask Anonymously,” where students submit questions they are nervous to ask in public. A third section could be “Resource Remix,” where students share useful links, templates, or examples and explain how they might be used. Over time, the class could revise and organize these contributions into a living guide for future students.

This design connects to the idea of produsage because students would not only consume advice from the instructor. They would also produce, revise, and extend shared knowledge for the group. According to Dennen’s study, instructional design for social media lessons requires attention to the learning goal, the activity, the tool, and the expected type of interaction . In this case, the goal would be to make hidden knowledge visible and to help students build confidence as members of an academic community.

For this activity, the choice of tools also needs to take into account whether students are willing to be exposed online or if they prefer to remain "lurkers." This matters because the hidden curriculum can involve sensitive questions about belonging, uncertainty, power, advising, and identity. Pischetola et al. (2022) remind us that networked learning spaces are designed and materialized through relationships, tools, and practices. For this kind of activity, the learning space needs to feel safe enough for students to ask honest questions.

In conclusion, designing networked learning activities is also a way of designing access. When instructors carefully choose tools, structure interactions, and facilitate participation, they can help students not only learn content but also fully enter a community.

Reference

Dennen, V. P. (excerpt of manuscript in progress). Instructional design and development for social media lessons.

Pischetola, M., Wichmand, M., Hall, R., & Dirckinck-Holmfeld, L. (2022). Designing for the materialization of networked learning spacesLinks to an external site.. In Proceedings for the Thirteenth International Conference on Networked Learning 2022.

Reflection on Designing Networked Learning

This week’s module helped me think more carefully about what it means to design networked learning activities. Last week, I started thinking about social media tools in terms of activities rather than platforms. This week pushed this idea further by asking how instructors can intentionally design those activities through learner analysis, tool selection, interaction requirements, learning materials, and facilitation.

One idea that stood out to me is that networked learning activities do not happen just because a tool is available. A class discussion board, shared document, or annotation tool can support learning, but only if the activity is designed with a clear purpose. This connects to Zgheib and Dabbagh’s (2020) discussion of social media learning activities. They emphasize that social media can support various learning activities, but that instructional design decisions matter. Instructors need to consider what learners are expected to do, how they will interact, and the learning outcomes the activity supports.

Learner analysis feels especially important for networked learning. Before asking students to participate online, instructors need to understand students’ access to technology, comfort with online sharing, privacy concerns, communication skills, and prior experience with the tools. Even if students use social media in their personal lives, that does not mean they automatically know how to use digital tools for academic learning. Gülbahar et al. (2017) also show that social media use in higher education requires thoughtful planning and support, not just adding tools into a course because they are popular.

Overall, this week’s readings helped me see that designing networked learning is more complicated than choosing a platform. The instructor has to make decisions about learners, tools, openness, interaction, facilitation, and assessment. A good networked learning activity should help students participate meaningfully, not just post something online. For me, this is an important reminder that technology integration is not about making learning look modern. It is about designing better opportunities for students to connect, create, and learn with others.

References

Gülbahar, Y., Rapp, C., Kilis, S., & Sitnikova, A. (2017). Enriching higher education with social media: Development and evaluation of a social media toolkitLinks to an external site.. The International Review of Research in Open and Distributed Learning, 18(1). https://doi.org/10.19173/irrodl.v18i1.2656Links to an external site.

Zgheib, G. E., & Dabbagh, N. (2020). Social media learning activities (SMLA): Implications for designLinks to an external site.. Online Learning, 24(1), 50-66.

 

What If Graduate School Had a Better “Networked Knowledge Map”?

This week, I started imagining what graduate school would look like if we designed it as a networked knowledge space rather than just a sequence of courses, emails, meetings, and assignments. Graduate students learn a lot from formal coursework, but we also learn from side conversations, shared Google Docs, conference posts, group chats, advisor recommendations, and watching how more experienced scholars participate in academic spaces. Much of this learning is informal, but it is still important.

Instead of asking only whether graduate students should use LinkedIn, Reddit, blogs, Zotero, Google Docs, or annotation tools, we might ask what kinds of learning activities these tools support. Are students finding resources? Sharing opportunities? Asking questions? Collaborating on writing? Giving feedback? Making sense of confusing academic norms? These are networked knowledge activities because they involve knowledge moving through relationships, tools, and communities (Dennen et al., 2020).

This feels especially relevant to higher education because so much of graduate student learning depends on access to hidden knowledge. Students may need to learn how to prepare for conferences, email faculty, understand funding, find publishing opportunities, manage advisor relationships, or interpret academic expectations. These things are not always taught directly in class. Instead, students often learn them through networks. A student with a strong mentoring network may hear about opportunities earlier and understand academic norms more quickly. A student without that network may feel lost, even if they are academically capable.

This is where I imagine a “networked knowledge map” for graduate students. Instead of organizing resources only by topic, it could organize them by activity. One section could be “Ask,” where students post questions they are afraid to ask elsewhere. Another could be “Share,” where students post funding, conferences, and writing resources. Another could be “Reflect,” where students write short posts about what they are learning. Another could be “Connect,” where students find peers or mentors with similar research interests. This would not just be a database. It would be a living, social learning environment.

In summary, Week 6 helped me see that social media in higher education should not be about communication alone. It can also be about making hidden knowledge visible and building stronger learning networks. For graduate students, especially international students, first-generation students, and those still learning how academia works, this could make a meaningful difference.

References

Dennen, V. P.  Networked Knowledge Activities. Excerpt from a manuscript in preparation.

Dennen, V. P., Word, K., Adolfson, D., Arechavaleta, V., He, D., Hsu, C.-W., Hur, J., Jung, D., Kent, H., Russell, A., & Toth, K. (2020). Using the networked knowledge activities framework to examine learning on social networking sitesLinks to an external site.. In P. Kommers & G. C. Peng (Eds.), Proceedings of the International Conference on Web-based Communities and Social Media 2020 (pp. 165-172). IADIS Press. 

Week 6 reflection Blog

This week’s module helped me shift how I think about social media in education. It pushed me to think beyond the question of “What tool should I use?” and made me ask, “What kind of learning activity does this tool support?” This distinction is important because a tool by itself does not automatically create meaningful learning.

The idea of Networked Knowledge Activities helped me organize my thinking. Instead of treating social media as a collection of platforms, this framework encourages us to examine what learners are actually doing in networked spaces. Learners might search for information, share resources, write, comment, organize ideas, collaborate, reflect, or build knowledge with others. Dennen et al. (2020) explain that social networking sites can support different types of networked knowledge activities, but these activities depend on how people use the tools and what they are trying to accomplish. This helped me see that learning is not located in the technology itself. Learning happens through purposeful activity.

This idea aligns closely with Salomon’s (2016) argument that “it is not just the tool” that matters, but the educational rationale for using the tool. I found this point especially helpful because educators sometimes adopt social media or digital tools because they are popular, new, or familiar to students. However, if the tool is not connected to a clear learning purpose, it may become busywork rather than meaningful learning. For example, asking students to post on a blog is not, in itself, reflective learning. It becomes reflective learning when students use the blog to connect readings to experience, respond to peers, and develop ideas over time.

Overall, social media should not be used in education just because it is available. It should be used because it supports a meaningful learning activity. This shift from tools to activities feels important for my own thinking about higher education. If I want to study or design online learning spaces for students, I need to pay attention not only to what platforms students use, but also to what those platforms help students do.

References

Dennen, V. P., Word, K., Adolfson, D., Arechavaleta, V., He, D., Hsu, C.-W., Hur, J., Jung, D., Kent, H., Russell, A., & Toth, K. (2020). Using the networked knowledge activities framework to examine learning on social networking sitesLinks to an external site.. In P. Kommers & G. C. Peng (Eds.), Proceedings of the International Conference on Web-based Communities and Social Media 2020 (pp. 165-172). IADIS Press. 

Salomon, G. (2016). It’s not just the tool but the educational rationale that counts. In E. Elstad (Ed.), Educational Technology and Polycontextual Bridging (pp. 149-161). Rotterdam: SensePublishers.

Protecting Personal Information Online

Higher education students increasingly use online tools for learning, networking, career development, and community building. Online platforms and AI tools can support learning, but they also ask students to share personal information. They can create digital traces and manage public or semi-public identities. This week’s readings helped me think about how students can participate online while also protecting themselves.

One of the biggest risks for higher education students is that learning activities can leave a lasting digital footprint. A student may write a blog post, comment on a public discussion, upload a profile picture, share a résumé, post about research interests, or ask for advice in an online community. These actions may feel small in the moment, but they can remain searchable and visible later. Dennen (2016) reminds us that digital course artifacts can include not only words and images, but also sounds, clicks, and other forms of learner data. For students, one important privacy strategy is to pause before posting and ask: Who can see this? Could this be connected to my real name? Would I be comfortable if an instructor, employer, family member, classmate, or future colleague saw it? This does not mean students should avoid participating online. Instead, students should make intentional choices about what to share in different spaces. For example, Reddit may allow more anonymity, but information quality and moderation can vary. 

This is especially important for international students. International students may have additional concerns about privacy because online information can affect professional opportunities, visa-related anxiety, cultural expectations, and personal safety. They may feel less comfortable sharing political views, immigration concerns, or personal struggles in public spaces. Therefore, educators should not assume that all students experience online participation in the same way. What feels like a low-risk activity for one student may feel much more serious for another.

Kumar and Byrne (2022) argue that privacy literacy should be understood as an educational issue. I agree with this because students are often expected to use digital tools without being taught how to evaluate privacy risks. Higher education institutions should help students understand privacy settings, data collection, audience collapse, platform terms, and long-term digital identity. For example, students can be encouraged to use strong passwords, turn on two-factor authentication, review privacy settings, avoid oversharing sensitive information, and separate personal and professional accounts when appropriate.

We, as educators, also have responsibilities. If an instructor asks students to use social media, the instructor should clearly explain the purpose of the activity, what students are expected to share, who can see their work, and how it will be assessed. Students should have alternatives if they are uncomfortable using a public platform. For example, instead of requiring everyone to post publicly on LinkedIn or Instagram, an instructor could allow students to submit a private reflection, use a pseudonym, post in a closed course space, or share only with classmates.

There is also an intellectual property issue. Students should know whether their posts, images, videos, or projects may be reused by the instructor, the institution, or the platform. If instructors want to use student work as examples in future classes, they should ask permission and remove identifying information when possible. This respects students not only as learners, but also as creators.

Overall, I think higher education students can benefit from social media tools, but they need privacy literacy and institutional support. Online participation can help students build professional networks, find resources, and join learning communities. However, students should not have to trade privacy for participation. A thoughtful approach to social media in higher education should help students ask better questions: What am I sharing? Who benefits from this data? Who might see it later? What choices do I have? Protecting personal information online is not about avoiding digital tools. It is about learning how to use them with awareness, agency, and care.

References

Dennen, V. P. (2015). Technology transience and learner data: Shifting notions of privacy in online learning. Quarterly Review of Distance Education, 16(2), 45–60.

Dennen, V. P. (2016). Ownership of digital course artifacts: Who can access and use your words, images, sounds, and clicks? Quarterly Review of Distance Education, 17(4), 5–19.

Kumar, P. C., & Byrne, V. L. (2022). The 5Ds of privacy literacy: A framework for privacy education. Information and Learning Sciences, 123(7–8), 445–461.

Week 5 Reflection

This week’s readings helped me think more critically about intellectual property, privacy, and ethics in Web 2.0 and social media learning environments. In previous weeks, I focused on how social media tools can support learning, communities, professional learning networks, tags, algorithms, and crowdsourcing. This week pushed me to think about the responsibilities that come with using those tools. Online learning is not only about participation and connection. It also involves questions about who owns digital work, who can access learner data, how long information remains visible, and whether students can participate safely.

One important idea from this week is that student-created digital artifacts are not just assignments. They can include words, images, sounds, clicks, posts, comments, videos, and other traces of learning. Dennen (2016) raises important questions about who can access and use these digital course artifacts. This made me reflect on my own participation in blogs, discussion boards, and online communities. When I write a blog post for class, it is part of my learning and also part of my digital identity. It may remain searchable or visible beyond the class. This means educators should be transparent about how student work will be used, who can see it, and whether students have choices about privacy.

In online learning spaces, students often share more data than they realize. They may share names, photos, locations, identities, opinions, learning behaviors, clicks, and social connections. Dennen (2015) discusses how technology transience changes privacy in online learning. Tools may disappear, change ownership, update policies, or store learner data in ways students did not expect. This is especially important when educators ask students to use external platforms rather than institutionally supported tools.

This week also made me think about privacy literacy. Kumar and Byrne (2022) describe privacy literacy as something that should be taught rather than assumed. I think this is important because many students know how to use social media casually, but that does not mean they understand how to manage privacy, audiences, data collection, or digital footprints. 

This connects strongly to my interest in higher education and international students. International students may face additional privacy concerns when participating online. They may worry about visa status, immigration rules, political expression, cultural expectations, future employment, or being misunderstood in a second language. For these students, being required to participate publicly on social media may not feel like a neutral assignment. Educators should recognize that students have different levels of comfort and risk when making their learning visible online.

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.