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Exploring Media Literary Education with Dr. Alice Huguet

 

Today and tomorrow, September 10th and 11th, we are fortunate to be joined by Dr. Alice Huguet, a researcher with the RAND Corporation, and co-author of the paper, Exploring Media Literacy Education as a Tool for Mitigating Truth Decay.  The RAND Corporation is leading an initiative to counter what it calls 'truth decay', with a goal to "restore the role of facts and analysis in public life".  This paper is the one of the latest in a series of resources that RAND has produced on the topic.  I encourage you to watch this three minute video, explaining the four trends and main drivers of truth decay, and the RAND Corporation's approach to addressing them.  

Alice will help us unpack current research about Media Literacy Education (MLE), how it applies to adult learners, and share publicly available resources to address misinformation in your classroom.  We will also take a look at the PIAAC Literacy Framework's use-oriented conception of competency in relation to MLE, and how both the framework and MLE support using materials in authentic contexts, for real-life purposes.  To begin, I want to ask Alice to give us some context for understanding what is meant by the term Media Literacy Education (MLE).

In the preface to the report, you define truth decay as “the diminishing role that facts, data, and analysis play in today’s political and civil discourse”, and MLE as a potential tool to reduce truth decay.  You also note that the term media literacy is a broadly defined field, with related and overlapping subfields including: information literacy, news literacy, digital literacy, science literacy, visual literacy, critical media literacy, to name a few.

In our community discussions about media literacy, there has been some confusion around what we mean when we use this term.  We often talk about digital literacy in adult education, but media literacy is less often discussed. Would you help us begin unpacking how adult educators might think about MLE as existing in relationship to digital literacy?

 
Mike Cruse
Career Pathways Moderator
 
Graphic of a big top tent with two main poles.  One pole reads Protectionism, the other reads Empowerment.

Comments

Alice Huguet's picture
First

First, I want to thank you for inviting me to be a part of this forum. I’m excited to learn from this group over the next couple of days, and hopefully beyond.

The classic definition of media literacy (which I will just refer to as ML) is the ability to “access, analyze, evaluate, and communicate” media messages for specific purposes (typically attributed to Patricia Aufderheide). Beyond this, there are many different ways to think about ML and its relationship to related literacies such as digital literacy, information literacy, news literacy, and others.

For our report, we chose to look at ML as the “big tent” under which related literacies fit; this is an approach we learned about from Renee Hobbs at the University of Rhode Island Media Education Lab. She and Michael RobbGreico have a 2013 paper entitled, “A Field Guide to Media Literacy Education in the United States”, which I recommend to members in the group who are interested in a more in-depth introduction to the topic (the image above is taken from this article). This paper explains that the core competencies that ML focuses on – accessing, analyzing, evaluating, and creating media messages – are the common thread among the various strands of media-related literacies. Each of the subfields chooses to look at these abilities through a particular lens; digital literacy, in response to your question, exclusively addresses ML competencies in the context of the digital ecosystem. 

Today, though, most forms of ML will include a digital component, given the centrality of the internet in most of our lives. It’s particularly relevant given the challenges we face today with the spread of misinformation, disinformation, and bias online – what we at RAND call Truth Decay – it does not surprise me that there is a lot of interest in the topic!

Michael Cruse's picture
One hundred

I've posted Alice's graphic on media literacy under the big tent to my opening comment for this thread.  Alice, would you tell us more about the graphic, how it's used to introduce ML and clarify its role among other types of literacies?

I also want to follow-up on the idea out forward in this report that, “[a]t its core, media literacy is made up of several specific competencies, such as the abilities to access, analyze, evaluate, and communicate media messages in a variety of forms”.  This is important because these are skills that adult educators work with learners to develop across different contexts and curricula.  To me, this suggests the potential to include media literacy education in many different forms of adult education programs. 

Adult educators are already using competencies to drive their instruction.  I'm curious what challenges and opportunities you see around connecting media literacy education’s competencies with ones used in adult education settings?  This also ties into the question of assessment.  Are there areas where assessment of media literacy education is looking at learners as not only students, but as citizens, employees, parents, patients, etc.?

Mike

Alice Huguet's picture
First

You’ve touched on three important topics here – ML competencies, connecting ML to education in other forms, and assessing ML. 

First, ML competencies are actually much more complex than the typical definition (access, analyze, evaluate, and communicate) suggests. As educators know, “access” or “analyze” could mean many different things in different contexts. This has been a challenge for us, actually, in that these broad abilities are not standards – they are not precise enough to teach or test (and study) on their own. We suggest in our report that, across subfields of ML, experts convene to work toward agreement around one common set of specific competencies. For instance, instead of saying “evaluate”, we might say: “Evaluate characteristics of information products that indicate the underlying creation processes” (which I pulled from the Association of College Research Libraries’ Information Literacy Competency Standards).

This brings us to the next topic, connecting ML competencies to ones used in adult education settings. I’d like to broaden that – connecting ML competencies to any other educational setting. One wonderful characteristic of ML competencies - as you suggested, Mike - is that they are in many ways content agnostic. You could teach a lesson guided by that standard, “Evaluate characteristics of information products that indicate the underlying creation processes,” in a science class, looking at research evidence. You could apply it in journalism, when discussing reporting processes. You could also think about it in the context of history and how particular versions of historical stories take root. The point is that these are competencies that help guide howwe think, and not whatwe think. As such, I think they are perfectly compatible with adult educators’ everyday practice. While we don’t have one agreed-upon, overarching set of ML standards to pull from, there are resources (each with their particular lens) to use in the meantime. For instance, the Association of College Research Libraries’ standards I drew from in the paragraph above (and many more).

Finally, this brings us to assessment. It is not a common practice in the US – that I am aware of – to assess the ML competencies of adults in their roles as citizens, employees, parents, etc. But it is happening in some places! For instance, Washington State conducted a survey of digital citizenship, media literacy, and internet safety. And outside the US, the European Commission and UNESCO have both made efforts to measure  ML competencies across member nations. 

Of course, it’s also difficult to design high-quality assessments of complex competencies like those in ML when we do not have agreed upon standards to start from… which brings us back to the beginning of this response.

Michael Cruse's picture
One hundred

Thanks, Alice, for your comments. You point out that while ML lacks an established set of standards, it aligns with subject area competencies such as science, journalism, and history, as examples.    The point you note is to guide how we think, and not what we think.  Thinking about thinking, or metacognition, is key to both Andragogy and ML.  

In the report, you note that the National Research Council states that adult literacy instruction “is most likely to lead to durable, transferable learning if it incorporates real-world activities, tasks, and tools” (p.6).  The PIAAC conceptual framework for literacy assessment is based in theory and research that supports using contexts, texts, and cognitive strategies to accomplish “real-world” reading activities.  ML teaches us to examine how messages are constructed from different perspectives, and how different kinds of media and technologies affect our reading of varied, relevant texts.  One concern you note is that the public is becoming increasingly distrustful of media in general.

Based on your interviews for this report, you talk about the need for balancing critical thinking and trust.  You point out that the analytic questioning at the core of ML could be taught in a way that increases levels of cynicism, and even damages trust in credible sources. The experts believed this problem can be avoided. Would you tell us more about how these experts suggest avoiding cynicism, while teaching learners to think critically about the media, and trust sources they find credible?  

Best,

Mike 

Alice Huguet's picture
First

If we think about sources of information like research institutions (universities, think tanks) and journalism outlets, they play an important role in the functioning of our democracy. They offer an additional source of checks and balances on our government (a la the Fourth Estate), and provide information that voters hopefully consider when making decisions in their daily lives and at the ballots. 

The risk that has been pointed out with ML education is that – when taught poorly – it may encourage people to be relentlessly cynical. Cynicism is different than skepticism. If people do not believe any information they receive (even well-researched journalism or rigorous science) then we are no better off than when the public believes everything they read. The experts we spoke with did feel this was avoidable, and it certainly is not a reason to eschew teaching ML. I see it as a reason to be thoughtful about ML and pedagogy. 

One expert we spoke with said that teaching students the processes behind an information product can help them from becoming overly cynical. For instance, I think it would astonish many of us to see the kinds of data and rigorous analyses that contribute to our understanding of climate change. To learn a little bit more about those methods, and gain a respect for their complexity, might inform the degree of credibility one attributes to climate science. Knowing something about this process might help a person decide whether the science is believable when weighed next to an opinion article that refutes it. One expert told us that it might be helpful for news outlets to be more transparent about the processes behind a news story. They could, for example, include a byline with the number of sources they spoke with and their processes for fact checking them, or the amount of time spent researching the story. That could be a way for people to know where to attribute credibility, though I am not sure how realistic it is. 

I hope this is something the participants in this group take into consideration if adopting ML in their practice. There is a difference between “question everything” and “trust nothing”, and the latter is certainly avoidable.

Michael Cruse's picture
One hundred

Your comment that, "teaching students the processes behind an information product can help them from becoming overly cynical" is something that I think applies to so many topics for adults.  The more we understand what's involved in something, the more opportunity we have to ask questions and make decisions for ourselves.  Climate science is a good example.  The same logic also applies to understanding future job market predictions, and exploring how these impact post-secondary training and education opportunities for careers that may not yet exist, or that will  look very different in the future.

I invite members to join in our conversation on ML.  Whether or not you have viewed the short video on truth decay and ML, or read the RAND paper, I hope that you'll share some of your questions and/or observations about teaching ML in adult education classes.  How do you teach learners to question and trust media?  Do you see that as a responsibility of adult educators?

Best,

Mike

Jeri Gue's picture
One hundred

Mike and Alice,

This is all so relevant to how and what we teach our students.  I also believe that “teaching the process behind an information product is so valuable for our students.  It is both easy to believe everything we interact with in media, as well as being suspicious of everything we see!  We need to help our students understand the healthy balance and how to differentiate.  I have used a website, Help to Save the Endangered Pacific Northwest Tree Octopus from Extinction  to demonstrate for students how “real” this creature may seem due to the design of the site. 

Thank you for this great discussion!

Jeri

Deena Welde's picture
First

Thank you for creating this discussion. As a former print and web journalist, I had to find where the line between truth and fiction blurred. Now, as an ESL teacher, I have the honor of helping students do so as they sort through information on their journey to learn English. In regard to digital literacy, I love the depth and range of material and information that digital access offers, and I agree that media literacy is crucial for success. The PIAAC and Rand papers were very helpful in clarifying the significance and nuances of addressing these two areas. My classes have some access to technology, but it is limited. Fortunately, our community based nonprofit has 24 laptops and 2 hot spot tablets for offsite use, but they are shared by more than 300 students in 3-4 sites. I mostly use my cell phone in class with a video adapter to project web images on a whiteboard, and a portable speaker when showing videos. Students use their cell phones to access the internet for reading or research. They also use them to take pictures of projected images to work individually or in groups. It saves money on photocopy expense. In realizing the value of digital literacy, I have to be mindful of the balance between learning a new skill while also using technology. Digital skill levels are not consistent, and students need to be comfortable working at their skill level without getting overwhelmed by also having to master digital skills. Also, not all students have unlimited data plans on their phones, or technology at home, so digital homework assignments are challenging. Despite the challenges, however, most students enjoy working with digital resources, and we have explored many topics. Reading about U.S. holidays led to discussions on culture, traditions, history, government, politics and issues of assimilation. Weather and natural disasters led to discussions on economics and immigration. Food led to studies about recipes, cultural differences, preferences, shopping, containers, marketing, restaurants and budgeting. Our summer was devoted to a vacation theme, and we read about exotic destinations and different cultures, exploring quality of life, and the work/life balance, budgeting, and vocabulary for traveling. All of it involved some form of digital research, sharing and assessing what was read and drawing conclusions. The students loved it. Again, thank you for sharing the discussion and sharing the PIAAC and Rand papers. The PIAAC scenarios were great examples of how to break down information into manageable chunks while also connecting the abstract with the concrete, and the Rand paper illustrated the dangers of fake news. I look forward to reading other comments.

Michael Cruse's picture
One hundred

Deena, thank you for sharing how you're using media with your English Language Learners (ELLs).  As a former journalist, you're in a unique position to teach your students about the process of making media, and building their media literacy.  The points you mention about learners' access to technology are another challenge many adult educators must think about in their classes.  It's great that you're able to use your cell phone and a video adapter to project web images and show videos in class.  You also mention students using their own devices for research and sharing with the class, while also recognizing the differences in learners' digital skills, data plans, and consistency of access.  These are all important factors to consider when teaching ML in any context, and it sounds like you're  accommodating of these differences among your learners.  

Today, we're going to talk more with Alice about different instructional delivery modes for teaching ML, and learn about some publicly available media literacy education resources.   I invite others to share their stories and ask questions as we continue discussing ML practices with learners.

Best,

Mike 

David J. Rosen's picture
One hundred

Deena, thanks for this great description of how you use Internet media with your students. Could you tell us more about the kinds of challenges that, as immigrants learning English in the U.S., they face in sorting out truth from fiction, distortion or perhaps even manipulative lies in what they read, see or hear from the media? What kinds of challenges, for example, are a result of interacting with text in English, a language they are learning; from discovering the cultural practices of their new country; or from stress they may feel as immigrants living in the U.S.? If you can, also tell us how you help them to overcome those challenges. For example, do you ask them what they don't understand and then explain, or have other students explain?  Do you model reading texts out loud and, as you do, add in verbally or in writing on the text itself or on a screen or chalkboard your (typically unspoken) questions, doubts and insights as you are read the text? Do you then provide English terms to describe the observations you have modeled such as  "evidence (proof)"  "examples" "insight" "source validity" or perhaps other critical reading terms more suitable to their English language level? What other teaching practices have you found that help them sort out truth from fiction, distortion and lies? Your experience as both a journalist and as an English language teacher suggests that you may have some particularly important insights to offer in this discussion; I am eager to hear them.

David J. Rosen

Michael Cruse's picture
One hundred

Today, we are continuing our discussion with Alice on her research for Exploring Media Literacy Education as a Tool for Mitigating Truth DecayI'd like to devote some time to the question of instructional delivery for ML.  In the paper, you note two different modes of instruction.  You mention a stand-alone approach and an integrated approach, both with assets and potential drawbacks.  As we've already discussed, research suggests that ML education needs to be responsive to the context of learners’ needs, backgrounds, and experiences to be successfully integrated into a program.     

Alice, would you explain the two modes of media literacy instruction, and how adult educators can decide for themselves and their learners, which mode is the best for their program?  Secondly, adult educators are very used to assessing and building context with their learners.  Are there examples you can share about unique ways that educators have used learner contexts to help think about introducing ML into their programs?

Best,

Mike

Alice Huguet's picture
First

We don’t advocate for one kind of delivery over any other, because we don’t have comparative research that points to a preferred method. And one kind of ML educational delivery, integrated instruction, is rarely studied. Integrated ML instruction takes place when educators of all subjects bring ML competencies into their teaching. As was mentioned in an earlier post, these skills can be appropriate for a wide range of content areas. The benefit here is that students can see how these skills can be applied in varied contexts, and not isolate them as things you only do in English or in Social Studies. If done well, students would also be receiving more exposure to ML concepts, as it would be brought into all curricula. The downside to integrating ML as a school or as a department is that it’s often the case that when no one person or group owns a thing, no one takes responsibility for making it happen. It might also require more sophisticated training to teach ML in this manner, as teachers would likely be developing their own ways for folding ML into existing content.

On the other end of the spectrum, ML could be taught as a stand-alone course. (Of course, there are stops along this spectrum, it doesn’t have to be all one or the other.) In this case, there is a dedicated class or unit for ML, and there may be a pre-packaged curriculum that provides a scope and sequence and materials for the educator. Some might find this preferable because there is less room for error in implementation, and because there are some good resources out there (why reinvent the wheel?). One concern about this approach though, as experts told us, is that students might think of ML as isolated and not applicable in other areas of life. And, the exposure they receive would be limited to the one class/unit. Finally, if using pre-packaged curriculum educators should be diligent that it feels relevant to their students. 

This brings us to context. In the simplest sense, when we talk about context, we are referring to an educator (or parent, religious leader, librarian, etc.) making sure the content makes sense for the audience. If I was teaching ML to middle school students, I might want to discuss YouTube or Snapchat or modern technologies that they use to communicate. If I was teaching ML to a group of retired adults, the approach and the tools might look different. When I was a teacher, we called this “accessing prior knowledge” – making the content relatable for folks makes it more likely to stick in their memories.

We also heard from experts about political context. It is a heated time in this country, and the last thing an educator wants to do is select materials that immediately isolate a group of people. Some of the curricula developers we spoke with are very intentional about selecting examples for their programs that are apolitical. I don’t know if that is the best approach, or if it makes more sense to address political issues directly by looking at evidence and facts – again, we don’t have a lot of evidence about this topic – but it is another way that context matters for teaching ML. It’s not unique to ML, but it highlights the importance of knowing your students, their needs, and something about their life experiences in order to scaffold concepts in a way that makes sense to them. 

Michael Cruse's picture
One hundred

These are great points about the differences between these two modes of instructional delivery.  I also appreciate your statement that 'the last thing an educator wants to do is select materials that immediately isolate a group of people'.  Would you talk about some of the publicly available ML resources you've learned about from your research that are helping mitigate truth decay?  Are there any specifically that may work well with different adult learner populations?

Best,

Mike Cruse  

 

Alice Huguet's picture
First

Group members might find it useful to visit our report site at rand.org, and click on the link to download support files (upper right hand of the screen). The excel file includes 50 ML resources that are publicly available. I should emphasize that we did not evaluate these resources (though I think that's an excellent future project!), rather, we compiled and analyzed them descriptively to see what is included in ML programs, curricula, videos, and other educational resources.

I think educators are better situated than anyone else to decide what is appropriate for their students, but that is very challenging if you don't have an idea of what the options are. This file identifies the key skills that each resource focuses on; as we talked about earlier in this discussion, there is a wide range of interpretations of ML - we chose to focus in on resources most relevant to news and information literacy, but there is still a lot of variation within that. So an educator might think about which competencies their students could benefit from additional development in, and start there. We also include other practical descriptors, such as the cost of the resource (if any - many are free) and the intended audience. I encourage everyone to check it out and see if any of the resources listed are useful - but I would also encourage educators to think about how they can create their own or adapt existing resources to better fit their students.

One area that we still struggle with is how to reach adults with ML messages. As educators working with adult students, I wonder if anyone in this group has suggestions for us to consider. How do you think ML education could reach an adult audience, especially those who are not enrolled in formal educational experiences?

Michael Cruse's picture
One hundred

Thanks for sharing where to find the RAND Excel file of ML resources.  These provide a lot of options for educators to consider when thinking about the interests and needs of their learners.  I agree that adult educators are best suited to decide what and how to introduce media literacy into their curriculum, which begins with conversations about our media use.  I'd be curious to know if anyone knows of, or has developed their own, short media survey in order to gain a snapshot of how and what types of media their learners access most frequently.

I also really appreciate what you said about educators creating their own ML resources, or adapt existing ones to better fit the needs of their learners.  I want to draw people's attention to the PIAAC Literacy Framework to Guide Instruction as a reference tool in developing your own ML resources.  The Framework offers Guiding Questions for Designing Contextualized Units (p.24, Exhibit 15) and walks readers through a three-phase process for instructional planning, which includes: 1. contextualizing skilled instruction, 2. considering factors affecting task difficulty to increase student literacy proficiency along a continuum, 3. sequencing instruction for a gradual release of responsibility for control from the teacher to the learner.  The Framework also includes case study examples to illustrate how two educators think through the framework's instructional planning process.

I want to thank Alice for joining us and sharing your research into ML with the LINCS Community.  I hope that we continue using this space to reflect on the full report, which we've only scratched the surface of, and think of ways to imbed ML into the spectrum of adult education classes taught by LINCS members.  If you have questions, would like to brainstorm ideas, or are seeking feedback on work you've already started, please feel free to use this space to help us all learn.  ML is a new topic for many adult learners, and I hope this is only the beginning of a larger conversation on how to make it more accessible across adult education programs.

Best,

Mike

 

 

 

 

Michael Cruse's picture
One hundred

Dr. Alice Huguet, a researcher with the RAND Corporation, and co-author of the paper, Exploring Media Literacy Education as a Tool for Mitigating Truth Decay joined us for a discussion around what RAND calls 'truth decay' - or the spread of misinformation, disinformation, and bias online – with a goal to "restore the role of facts and analysis in public life."  We also looked at the PIAAC Literacy Framework in relation to Media Literacy (ML).

Alice shared the classic definition of ML as the ability to “access, analyze, evaluate, and communicate” media messages for specific purposes.  This definition is attributed to Patricia Aufderheide.  She noted that RAND chose to look at ML as the “big tent” under which related literacies fit, an approach from Renee Hobbs at the University of Rhode Island Media Education Lab. Hobbs and Michael Robb-Greico wrote, “A Field Guide to Media Literacy Education in the United States”, which she recommended as an in-depth introduction to the topic.

One expert the RAND team spoke with said that teaching students the processes behind an information product can help them from becoming overly cynical. Alice gave the example of the data and rigorous analyses that contribute to our understanding of climate change. “To learn a little bit more about those methods, and gain a respect for their complexity, might inform the degree of credibility one attributes to climate science. Knowing something about this process might help a person decide whether the science is believable when weighed next to an opinion article that refutes it.”

Competencies

We discussed how ML competencies are more complex than Aufderheide’s definition (access, analyze, evaluate, and communicate) suggests. Alice commented that these four terms can mean many different things in different contexts. She noted that these broad abilities are not standards because they are not precise enough to teach or test their own. The RAND team suggests that ML experts convene to work toward agreement around a single set of specific competencies.  She gave the example that instead of saying “evaluate”, a more specific competency might read: “Evaluate characteristics of information products that indicate the underlying creation processes.”  This definition is drawn from Association of College Research Libraries’ Information Literacy Competency Standards.  She highlighted the point that these are competencies that help guide how we think, and not what we think. Therefore, we need to look at ways to teach the metacognitive practices underlying ML.

Instructional Delivery Models

We considered the two instructional delivery models used in teaching ML.  Integrated ML instruction takes place when educators of all subjects bring ML competencies into their teaching.  The benefit is that students learn how these skills can be applied in different contexts.  Alice observed that the downside to integrating ML is that when no person or group owns responsibility for teaching ML, it may not happen.   ML can also be taught as a stand-alone course, which ensures that it is addressed with learners.  Contributing experts told Alice that in this model, learners may think of ML as isolated and not applicable in other areas of their life.  The PIAAC Literacy Framework also offers Guiding Questions for Designing Contextualized Units (p.24, Exhibit 15) and walks readers through a three-phase process for instructional planning.

Member, Deena Welde, a former journalist turned ESL educator, shared her creative use of technology to help learners develop both digital and media literacy skills.  “I have to be mindful of the balance between learning a new skill while using technology. Digital skill levels are not consistent, and students need to be comfortable working at their skill level without getting overwhelmed…”  Despite their varied digital literacy skill levels, her learners are all involved in digital research, sharing and assessing what they read online, and drawing conclusions.  She highlighted that “the PIAAC scenarios are great examples of how to break down information into manageable chunks while also connecting the abstract with the concrete." 

Assessment

We discussed that while it’s not a common practice to assess the ML competencies in the U.S., Washington State conducted a survey of digital citizenship, media literacy, and internet safety that may serve as a good example for other states to consider.  Outside the US, the European Commission and UNESCO have both made efforts to measure  ML competencies across member nations. 

ML Resources

Alice highlighted a list of 50 ML resources on the RAND report site that would be valuable for those thinking of teaching ML to review.   The Excel file identifies the key skills that each resource focuses on and focuses in on resources most relevant to news and information literacy.  She also encouraged members to think about how they can create their own, or adapt existing resources, to better fit their students.

An Ask of Members

Based on her research for this report, Alice commented that one area she and her team struggled with is how to reach adults with ML messages. She asked the following, “…as educators working with adult students, I wonder if anyone in this group has suggestions for us to consider. How do you think ML education could reach an adult audience, especially those who are not enrolled in formal educational experiences?”

 

 

randomness