August 2023 – Learning Aloud

Note-taking as a generative activity

When explaining it helps to have examples both for personal understanding and for communication. My more recent interest in long-term notes has provided a useful example that relates well to my long/term interest in generative activities. This specific collection of note collection activities is convenient because the activities are similar yet illustrate important differences. Notetaking is also an activity most have applied and comments on variations in how the activity can be applied are relatable contributing to my efforts to communicate. My more general goal is to help educators understand the purpose behind the assignments or study suggestions they make.

Generative activities are external tasks learners engage in that encourage productive cognitive behaviors. External tasks to influence thinking activities. Several researchers have identified hierarchies that attempt to explain the benefits of the external tasks and differentiate the less and more powerful activities.

Two examples of hierarchies include the proposals of Chi (2009) and Fiorella and Mayer (2016)

Chi (2009) proposed the SOI framework – selective, organizing, and interactive.

Fiorella and Mayer (2016) proposed a similar ICAP framework (reversed here to show the parallels with Chi) – passive, active, constructive, interactive.

Some further clarification may be necessary. Selective seems self-explanatory. When reading selective is the active process of identifying important material. Constructive, when applied to taking notes, has a specific meaning. It implies the integration of new information with what one already knows. For example, thinking of an example (something you already know) associated with a concept or principle just learned creates a new representation. The learner is putting things together or finding applications. Interactive as defined here is a social process. It could relate to processes such as might be involved in cooperative learning. Both parties or even a larger group combine their individual understandings to create a superior composite.

I am relating these hierarchies to note-taking activities as might be explained by Aherns (2022). This author described notetaking in a little different way than might be assumed to apply in a school or college setting. I like to think of it as taking notes for the long term. This might describe the purpose I have for taking notes. I am not taking notes for an exam in a couple of weeks or at the end of the semester. I am not taking notes to write a paper for my instructor. I am engaging in reading for purposes that might be realized in a few years. I want my notes to be useful when in the future I have a need for the information I understood when the note was created, but may not be remembered when that information would be valuable.

I am extending Aherns a bit here, but a sequence based on his writing might include the following:

Reading
Highlighting/ fleeting notes
Smart note
Collaborative note

Here are some clarifications of these terms. Reading (or listening) is the lowest stage and involves the exposure to information. Fleeting notes involve the recording of information with little elaboration. Students tend to take this type of notes while listening to a lecture possibly because they must get the information down while the lecturer continues to speak. Highlighting is similar in that it involves selection with little additional processing. Smart notes is Ahern’s term for notes that I remember him describing as providing sufficient context that a note would make sense to me in the future. In other words, this type of note must stand alone as a useful resource. Such notes would also be understandable by others with reasonable background knowledge.

It is important to recognize that learner engagement in generative activities involves potential rather than guaranteed benefits. Roscoe and Chi offer an interesting way of describing potential. They were writing about peer teaching as a generative activity, but the distinction they identify makes sense when applied to other activities. Their distinction is between knowledge telling and knowledge building. If learners are asked to explain a concept to a peer or summarize a concept as a note, they can repeat what they heard or read or they can interpret what they have heard or read in generating an output. Similarly, learners can merge their notes with a peer or they can compare and contrast their notes resulting in deeper processing of the content.

Because most generative activities involve the production of a product, educators can review these products from time to time to evaluate how active learners are being in thinking about what they are learning.

Insights

A few additional comments to consider as a summary. These ideas are interesting and quite concrete. In addition, the analyses are realistic in recognizing that positive results are not automatic.

First, what may seem to be a similar activity may have different consequences as a function of the kind of thinking applied

Second, it is what the student does in completing a generative task that results in learning not just the task assigned.

References

Ahrens, S. (2022). How to take smart notes: One simple technique to boost writing, learning and thinking.

Chi, M. T. (2009). Active?constructive?interactive: A conceptual framework for differentiating learning activities. Topics in cognitive science, 1(1), 73-105.

Fiorella, L., & Mayer, R. E. (2016). Eight ways to promote generative learning. Educational Psychology Review, 28(4), 717-741.

Roscoe, R. D., & Chi, M. T. H. (2007b). Understanding tutor learning: Knowledge-building and knowledge-telling in peer tutors’ explanations and questions. Review of Educational Research, 77, 534–574.

Digital for serious reading tasks

I keep encountering colleagues who disagree with me on the value of relying on digital content (e.g., Kindle books, pdfs of journal articles) rather than content they collect on paper. I agree that their large home and office libraries are visually attractive and their stuffed chair with reading lamp looks very inviting. They may even have a highlighter and note cards available to identify and collect important ideas they encounter. A cup of coffee, some quiet music in the background, and they seem to think they are set to be productive.

I have only one of my computers, a large monitor when working at my desk, and a cup of coffee. The advantages I want to promote here are related to my processing of content I access in a digital format. What you can’t see looking at my workspace whether it happens to be located in my home or at a coffee shop is the collection of hundreds of digital books and the hundreds of downloaded pdfs I have collected and can access from any locate when I have an Internet connection. I can work with digital content from my home office without a connection, but I prefer to have a connection to optimize the use of the tools that I apply.

For me, the difference between reading for pleasure and reading for productivity is meaningful. I listen to audiobooks for pleasure. I guess that is a digital approach as well and it functions whether in my home, on a walk, or in the car. For productivity, I read to take in information I think useful to understand my world and to inform my writing about topics mostly related to the educational uses of technology. I think of reading as a process that includes activities intended to make available the ideas that I encounter in what I read and in my reactions to this content in the future. Future uses frequently but not exclusively now involve writing something. At one point when I was a full-time educator I engaged in other additional forms of communication, but in retirement, I mostly write.

My area of professional expertise informs how I work. I studied learning and cognition with an emphasis on individual differences in learning and the topics of notetaking and study behavior. One way to explain my present preoccupations might be to suggest I am now interested in studying and notetaking to accomplish self-defined goals to be pursued over an extended period of time. Instead of preparing to demonstrate what I know about topics assigned to me and with priorities established by someone else with the time span of a week or at most a couple of months, I now pursue general interests of my own preparing to take on tasks I can only describe in vague terms now but tasks that may become quite specific in a year or more. How do I accumulate useful information that I can find and interpret when a specific production goal becomes immediate? The commitment I have made to consume and process digital content is based on these goals and insights.

What follows identifies the tools I presently use, the activities involved as I make use of each tool, and the interconnections among these tools and the artifacts I use each tool to produce.

Step 1 – reading.

In my professional work, I made a distinction between reading and studying. This was more for theoretical and explanatory reasons because most learners do not neatly divide the two activities. Some read a little, reread, and take notes continuously. Some read and then read again assuming I guess that a second reading accomplishes the goals of what I think of as studying. Some read and highlight and review their highlights at a later time. There are many other possibilities. I think of reading as the initial exposure to information much like listening to a lecture is an initial exposure to information. Anything that follows the initial exposure, even if interspersed with other periods of initial exposure, is studying.

My tools – Present tools/services related to this stage of processing – Kindle for books and Highlights (Mac app) for PDFs. Other tools are used for content I find online, but most of my actual productive activity focuses on books and journal articles

Step 2 – initial processing (initial studying)

While reading, I use highlighting to identify content I may later find useful and I take notes (annotation as these notes are connected to the book or pdf). Over time, I found it valuable to generate more notes. Unlike the highlights, the notes help me understand why content I found interesting at the time of reading might have future usefulness.

My tools – Present tools/services related to this stage of processing – Kindle for books and Highlights (Mac app) for PDFs. Yes, these are the same tools I identified in step 1. However, the integration of these dual roles is accurate as both functions are available within the same tools. One additional benefit of reading and annotating using the same tool and applied to the same content is the preservation of context. The digital tools I use can be integrated in ways that allow both forward and backward connections. If at a later stage in the approach I describe I want to reexamine the context in which I identified an idea, I can move between tools in an efficient way.

Step 3 – delayed processing (delayed studying)

Here I list tools I would use for accepting the highlights and notes output from Step 2 as isolated from the original text. I also include tools I would use for reworking notes to make them more interpretable when isolated from context, adding tags to stored material, adding links to establish connections among elements of information, and initial summaries written based on other stored information. I like a term I picked up from my reading of material related to what has become known as personal knowledge management (PKM). A smart note is a note written with enough information that it will be personally meaningful and would be meaningful to another individual with a reasonable background at a later point in time.

My tools – Readwise to isolate and review highlights and notes from Kindle. Obsidian to store highlights and notes, add annotations to notes, create links between notes, and generate some note-related summaries an comments. I use several AI extensions within Obsidian to “interact” with my stored content and draft some content. Presently, I use Smart Connections as my go-to AI tool. I write some more finished pieces in Google Docs.

Step 4 – sharing

As a retired academic, I no longer am involved in publishing to scientific journals or through textbook companies. My primary outlet for what I write are WordPress blogs I post through server space that I rent (LearningAloud). A cross-post a few of my blog entries to Substack and Medium.

My tools – I write in Google docs and then copy and paste to upload content to the outlets I use.

Obsidian as the hub

Here are a couple of images that may help explain the workflow I have described. The images show how Obsidian stores the input from the tools used to isolate highlights and notes from full-text sources.

As notes are added to Obsidian, I organize them into folders. An extension I have added to Obsidian creates an index of the content within each folder, but at some point the volume of content is best explored using search.

Here is what a “smart note” I have created in Obsidian looks like. The idea of a smart note is to capture an idea that would be meaningful at a later date without additional content. Included in this note is a citation for the idea, tags I have established that can be used to find related material and a link.

This is an example of a “note” Obsidian automatically generated based on book notes and highlights sent by Readwise. I can add tags and links to this corpus of material to create connections I think might be useful. The box identifies a link stored with the content that will take me back to Kindle and the location of the note or highlight. These links are useful for recovering the original context in which the note existed.

Here is a video explaining how my process works.

So, the argument I am making here is that digital tools provide significant advantages not considered in single-function comparisons between paper and screen. Digital is simply more efficient and efficacious for projects that develop over a period of time.

What is generative learning?

Many of the recommendations I make for classroom and even nonschool-affiliated learning strategies are based in my understanding of generative learning. I have described a specific activity as generative in previous things I have written, but I don’t think I have ever made the effort to provide what I mean by generative. I decided I would give this background now both to explain what the term implies to me and to have something I can refer to in the future.

My applied work in educational psychology is based in cognitive psychology. Cognition is just a way of understanding thinking. Unless someone is really interested in digging into the field, I think it helps if I make an effort to translate some of the core ideas. There is always a danger making the complex simple is a bad idea and my efforts at simplification are off target, but I do it anyway. Think of thinking in terms of mental actions. Assume that learners have at their disposal mental actions they can use to accomplish the thinking and learning tasks they encounter. Learners may differ in which actions are selected to tackle a given task, how skillfully the tools are applied, and how effectively they evaluate the outcome of tool application to determine whether or not more needs to be done.

Here are four actions with a description of the task to which each would typically be applied:

Attend – maintain certain ideas in consciousness (also called working memory)
Find and retrieve – locate what is already stored (also known as long-term memory) and attend to this content
Link – establish connections between information units stored in long-term memory or that content active in working memory
Elaborate – create or discover new knowledge from the logical and purposeful combination of active and stored memory components
Evaluate – determine whether a cognitive task has been completed successfully

We can often take control and apply these activities without assistance, but motivation or lack of awareness of what activities might be useful can result in important activities not happening. Generative activities (Wittrock, 1974, 1990) are external to the internal mental activities of the learner but can make predictable internal activities more likely to occur. Questions about something a student is trying to learn make a good example. A question is external to the thinking of a learner. However, if I ask a question and you cannot answer, attempting to answer this question should have required you to evaluate your understanding. In attempting to answer my question, you have also probably made the effort to find and retrieve information. One related thing to consider – generative activities may encourage activities that are redundant with activities a learner have initiated on her own. This probably does no harm, but it also might be described as busy work. Cognitive activity is always the mental work of the learner with others only able to manipulate such behaviors indirectly and with less precision than a competent and motivated learner could do for themself.

What are some examples of generative activities? Fiorella and Mayer (2016) have identified a list of eight general categories most educators can probably turn into specific tasks. These categories include:

Summarizing
Mapping
Drawing
Imagining
Self-Testing
Self-Explaining
Teaching
Enacting

Summarizing – To summarize, students think about what they have just learned and then rephrase the most important information in their own words.

Mapping – Mapping is the process of converting words into a visual representation. Mind maps, tables, diagrams, and graphs are all common examples.

Drawing – Drawing is a great way to help your students learn more deeply about the material you are teaching. When students draw, they have to think about what information to include, what to leave out, and how to best represent it visually.

Imagining – Forming a mental representation of new information is surprisingly beneficial for learning. An example is tasking your students to imagine the process of digestion by creating mental pictures of each step.

Self-testing – Self-testing is a highly effective learning method. Educators likely recognize that retrieval practice (self-testing) is presently receiving a lot of attention. Some examples of self-testing include using flashcards and quizzes.

Self-explaining – Self-explaining requires students to recall new information and explain it in their own words. This helps students to understand the material better and to avoid simply repeating back what they have read or heard.

Teaching – Peer teaching is another active strategy requiring the recall and translation of what has been learned to present to others. Teaching involves preparation, delivery, and interaction related to the content to be learned. Most educators intuitively appreciate the unique requirements of teaching and recognize that learning for the self and to inform others involve different activities.

Enacting – I think demonstrating is an acceptable way to explain what the researchers meant by enacting.

Generative learning is a powerful approach to education that encourages learners to actively engage with the material, creating new knowledge and connections. This method, grounded in the work of Fiorella and Mayer (2016), and Brod (2021), among others, is centered around the idea that learning is not a passive process, but an active one that involves the learner in the creation of their own understanding.

The strategies I have listed require learners to select and organize relevant information, elaborate on the material based on personal knowledge, and integrate new information with existing knowledge.

Summarization, for instance, involves concisely stating the main ideas from a lesson in one’s own words. This goes beyond copying words or phrases verbatim from the lesson; rather, it involves selecting the most relevant information from the lesson, organizing it into a coherent structure such as an outline, and integrating it with students’ prior knowledge.

Teaching involves selecting the most relevant information to include in one’s explanation, organizing the material into a coherent structure that can be understood by others, and elaborating on the material by incorporating one’s existing knowledge.

Generative learning is not just about the creation of new content. Brod (2021) emphasizes that generative learning requires the production of a meaningful product that goes beyond the information that is an input. This means that activities like highlighting, which do not result in new content, are not considered generative.

Generative learning strategies are not just for students. They can be used by anyone looking to deepen their understanding of a topic. For example, if you’re reading a book or article, try summarizing the main points in your own words, or explaining the concepts to someone else. You might be surprised at how much more you understand the material!

Fiorella and Mayer (2016) offer one additional observation related to these eight types of activity. Four strategies (summarizing, mapping, drawing, and imagining) involve changing the input into a different form of representation.

The other four strategies (self-testing, self-explaining, teaching, and answering practice questions) require additional elaboration. This distinction contrasts ”knowledge-building” and ” knowledge-telling” (e.g., Roscoe and Chi, 2007). Knowledge telling is regarded as the weak form involving a restatement of what is known with limited activation of other existing knowledge (e.g., attempts to generate examples from personal experience) and less extensive monitoring of understanding. In knowledge-building, the strong form, the learner adds to core ideas from existing personal knowledge and in doing to reflects on the core ideas in greater depth resulting in more effective comprehension monitoring.

One additional comment about the eight categories is that the categories were explained by the scholars identifying this category system in terms what the learner could do. While learners could certainly decide to do these things without guidance, it is probably more likely that these external tasks are recommended or assigned by an educator.

What I have described to this point is how I would likely cover this topic in an educational setting. This approach would be designed to be true to what I believe to be the origins of the ideas and learners can then apply what they find useful. Given this background, my own research and practice have both focused on a subset of this list of activities and have taken the general idea of using external tasks to encourage desirable mental activities to recommend activities that share characteristics with the tasks mentioned. I have focused on questions, summarization, teaching, and self-explaining and proposed applications that have included peer tutoring and collaborative notetaking, writing across the curriculum, computer-enabled study environments that involve testing associated with accuracy prediction and data collection that feeds the identification of specific areas needed more work back to students, and the technology-based collection and exploration of notes over extended periods of time to improve personal productivity (smart notes and personal knowledge management). Thinking of external activities that efficiently encourage important cognitive activities has proven a productive way to both think about learning and what tasks may be helpful in helping students learn.

References:

Brod, G. (2021). Generative learning: Which strategies for what age? Educational Psychology Review, 33(4), 1295-1318.

Fiorella, L., & Mayer, R. E. (2016). Eight ways to promote generative learning. Educational Psychology Review, 28(4), 717-741.

Roscoe, R. D. & Chi, M.T. (2007). Understanding tutor learning: Knowledge-building and knowledge-telling in peer tutors’ explanations and questions. Review of Educational Research, 77, 534-574.

Wittrock, M.C. (1974). Learning as a generative process. Educational Psychologist, 11, 87-95.

Wittrock, M.C. (1990). Generative processes of comprehension. Educational Psychologist, 24, 345-376.

Modern Languages Association Paper on AI and Writing Instruction

We have passed mid-summer and educators are beginning to think about their fall classes if they have been doing so for some time. AI is one of those innovations that may be dominating the thinking of some. Do I ignore AI or address it directly? What am I going to do about cheating? Are there specific tools or tactics I should be teaching (or avoiding)?

If you think I have the answers, it is time to move on to another writer. I have been using AI a lot myself and I have been doing my best to locate and review articles on AI in classrooms. I have made a few personal decisions and I have written about them in previous posts. I have decided for now my own work will make use of AI tools that allow me to focus AI on content I designate. So, I am using tools that require that I submit a pdf to be the target of my prompts or that work within a note-taking system I use and can be focused on my own notes and highlights. This works for me. I don’t get assigned tasks from someone else that I must submit to be evaluated. My productivity goals and thoughts about why I benefit from writing guide my choices. Educators may have similar goals for their own work, but also are looking to develop the skills of their students and it is this responsibility that makes things much more complex.

Here is a resource that may be useful. It is not heavy on specific recipes for the use of AI in your classroom, but may be useful if you want a good explanation of just what generative large language model AI services are and an analysis of concerns and opportunities for the application of such services by those who teach writing. If I were to quibble with the authors, it would involve failing to pay enough attention to what I would describe as writing to learn and learning to write. I apologize for that turn of a phrase, but it is one in my bag I like to pull out. My work has often involved researching and proposing the classroom use of generative learning activities. I like to describe generative activities as external tasks intended to encourage productive internal (cognitive) behaviors. So, tasks a learner performs that require productive thinking activities a learner may or may not exercise on their own. Writing to learn is an example and it is based on the expectation that writing requires organization and communication of information you have or can acquire as a benefit to understanding, retention, or application in a way that might not occur if you just tried to think about this information. By definition, it is labor (thinking) intensive and may not be the most efficient way to accomplish understanding, retention, and application. It works because the tasks involved in writing to communicate require work focused on the manipulation of the to-be-learned content. Here is my thought related to AI – tools that make the processes of organization and description less labor intensive may eliminate the cognitive work that may be productive in the process of learning. Producing a better written product or writing more efficiently are different goals. Offloading subcomponents of writing may be helpful in writing and the purposeful control of this offloading may help develop the skills of writing. Hopefully, this differentiation makes some sense and is meaningful.

So, what do those who study the development of writing skills have to say about AI. The source I am recommending here was developed by experts from the task force associated with the Modern Languages Association charged with developing a working paper on AI and writing instruction. As I have explained already this product steers clear of specific classroom recipes, but identifies legitimate concerns, likely benefits, and proposed actions to benefit writing educators. I will summarize these areas, but encourage writing people review this document. It is not unnecessarily lengthy and to my eye represents a balanced analysis.

The advantages of AI for writing are as follows:

Personalized feedback and support for language learners: AI can provide personalized feedback to language learners, helping them to improve their writing skills. This can be especially helpful for students who are struggling with a particular aspect of writing, such as grammar or punctuation.
Ability to analyze large amounts of text for literary scholars: AI can analyze large amounts of text, helping literary scholars to identify patterns and trends. This can be helpful for research and for understanding the development of literature over time.
Ability to assist students with tasks such as generating ideas, organizing their thoughts, and identifying errors in their writing: AI can assist students with tasks such as generating ideas, organizing their thoughts, and identifying errors in their writing. This can help students to improve their writing skills and to produce higher-quality work.
Potential to democratize writing and make it more accessible to a wider range of learners: AI has the potential to democratize writing and make it more accessible to a wider range of learners. This is because AI can provide personalized feedback and support, and can assist students with tasks such as generating ideas and organizing their thoughts.

The disadvantages of AI for writing are as follows:

Risk that students may rely too heavily on AI-generated outputs and miss out on important writing, reading, and thinking practice: There is a risk that students may rely too heavily on AI-generated outputs and miss out on important writing, reading, and thinking practice. This is because AI can generate text that is grammatically correct and that sounds good, but that may not be accurate or well-informed.
Risk that students may submit AI-generated work as their own, which could lead to issues with academic integrity: There is also a risk that students may submit AI-generated work as their own, which could lead to issues with academic integrity. This is because AI can generate text that is indistinguishable from human-written text.
Potential for bias in AI systems: Finally, there is the potential for bias in AI systems. This is because AI systems are trained on data that is collected from the real world, and this data may be biased. This means that AI systems may generate text that is biased, which could have negative consequences for students and for society as a whole.

The MLA also provided the following policy recommendations.

Writing instructors should recognize that there may be equity issues in the use of AI tools and work to provide equal access to all students.
Writing instructors should engage in ongoing professional development to stay up-to-date on the latest developments in AI and writing instruction.
Writing instructors should collaborate with colleagues, students, and other stakeholders to ensure that the use of AI in writing instruction is effective and ethical.
Writing instructors must emphasize the ethical responsibility that comes with the use of AI tools and spend time with students to consider the misrepresentation of authorship.

Source:

MLA-CCCC Joint Task Force on Writing and AI Working Paper: Overview of the Issues, Statement of Principles, and Recommendations. Available online: https://hcommons.org/app/uploads/sites/1003160/2023/07/MLA-CCCC-Joint-Task-Force-on-Writing-and-AI-Working-Paper-1.pdf

Prebunking offers some advantages over debunking

Preparing learners to deal with the faulty information they encounter in their lives has become another task educators are expected to accomplish. This expectation is a reasonable response to the mixed quality of online resources including some attempts to purposefully mislead viewers.

What follows is a lengthy post about approaches that can be applied to deal with exposure to misinformation. The primary focus is on a technique called “debunking” which represents a general approach for helping individuals not be taken in by misinformation. By general, I mean that the techniques do not involve rejecting specific misinformation by the introduction of convincing information after the initial exposure to misinformation. Prebunking involves approaches that prepare individuals to reject misinformation and as a general strategy has certain advantages of not having to be tailored to address false understandings after false beliefs have taken hold.

For those who want a quick alternative to reading my entire post, I will explain that prebunking involves familiarization with common approaches used to encourage the acceptance of false information. The study I will describe created this sensitivity through short videos. These videos are available and educators may find them useful in their classes. The videos can be found here:

https://inoculation.science/inoculation-videos/

This post uses some language that may be new to those who don’t read the scientific research I read. Allow me to first offer definitions for these terms as used in this research. These concepts are interrelated and I have attempted to identify some of these connections.

Debunk – to provide evidence intended to call into question faulty beliefs

Prebunk/Inoculation – to provide explanations of faulty beliefs before they are encountered in an attempt to prevent acceptance of these flawed beliefs.

Conceptual change – the attempt to bring into awareness and then counter faulty information accepted by a learner

Cognitive conflict – the proposal that a learner must be aware of the inconsistency between an existing belief and information relevant to this belief before change can occur (related to conceptual change and inert knowledge)

Naive theory – a personal theory based on an interpretation of life experiences

Inert knowledge – stored knowledge that is called into awareness only when certain contextual conditions are met. Inert knowledge implies that a second stored understanding also exists that is activated under different conditions. This term is often used to explain how naive theories that are flawed can persist despite learning more appropriate things in an educational setting. Hence, one understanding is activated in a school setting and a different understanding in day-to-day situations outside of school.

Motivated cognition – a psychological concept that refers to the tendency of individuals to interpret and process information in ways that align with their preexisting beliefs, values, and desires. This phenomenon can occur across various domains, such as politics, religion, social issues, and personal beliefs.

Confirmation bias – one example of motivated cognition that involves the selection or interpretation of inputs to sustain existing beliefs.

Conceptual change and naive beliefs

I think of misinformation in terms of what I know about conceptual change. This is a way to understand learning and also changes in understanding. I think of the topic of learning in terms of personal knowledge building. Each of us builds personal knowledge as models of how the world works. We use these models to interpret new experiences and when new experiences do not fit our understanding of how something works (a model), we may make adjustments in our model. Piaget called these two complementary processes assimilation and accommodation. We interpret experiences in terms of an existing model (assimilation) and when this will not work, we adjust or update our model (accommodation). The mismatch between experience and model when recognized is described as cognitive conflict and results in a motivation to create an adjustment.

My exposure to conceptual change theory occurred within the context of science education. There are many concepts in the formal study of science that explain phenomena we experience all of the time (e.g., gravity, inertia). Before we are educated in formal explanations we develop our own models of these phenomena. For example, what I sometimes describe as the “roadrunner” model of inertia and gravity imagines a roadrunner speeding off a cliff and speeding through the air. At a point, the roadrunner realizes it is no longer on solid ground and then plunges straight down. This model is an example of a naive theory – it kind of works, but is not how inertia and gravity actually work. Eventually, we learn a more accurate understanding. Assuming heavy and light objects (say a bowling and tennis ball) fall at the same rate often works as another example. It seems logical, but isn’t accurate.

Some naive theories have an interesting characteristic. They may persist even after learners have learned a more accurate account of a phenomenon. A learner may store and retain inconsistent models. One model active in daily life and the other in the school setting. This is the challenge of inert knowledge. It is thought that this is possible because recall is context dependent and there are some interesting demonstrations that the likelihood of formal knowledge can be activated by preceding a question about a phenomenon by suggesting a context. For example, you may remember from school the story of Galileo’s famous Tower of Pisa experiment before asking which of a heavy or light object will fall fastest. Without the prompt and reminder of the school context, it might seem logical that the heavier object will fall faster. The prompt changes the context.

Inert knowledge is a significant challenge. How does education (one context) prepare learners for functioning in a different context (daily life)? Learning alone is not enough. It is also necessary to activate and modify existing ways of understanding that are incorrect. That two-step process – activate and then experience limitations – is cognitive conflict. Physical demonstrates work great if preceded by outcomes that are unanticipated. Computer simulations can in some cases provide similar experiences. Even mentioning common misconceptions before providing accurate explanations can be successful. Textbook authors can use this strategy. This approach to conceptual change might be described as debunking.

What is frustrating is that in some situations calling out false understandings and then providing information that supports a different understanding seems inadequate. Our present circumstances with political differences of opinion are a good example. We find it completely illogical when we point what seem obvious contradictions to certain arguments and someone is willing to persist in a flawed understanding. We have encountered a challenge of motivated cognition.

Motivated cognition is a psychological concept that refers to the tendency of individuals to interpret and process information in ways that align with their preexisting beliefs, values, and desires. My favorite example when I was teaching was to recognize the predictable reaction of sports fans who witness a close call say charging or pass interference and come to the opposite opinion on what the correct call should be. Same data, different interpretations easily predicted from the team they were rooting for. Such examples involve a cognitive bias where people are more likely to accept, remember, and give greater weight to information that supports their existing views while disregarding or downplaying information that contradicts their beliefs. In essence, motivated cognition can lead to selective perception and interpretation of information to maintain a preferred mindset or belief system.

This phenomenon can occur across various domains, such as politics, religion, social issues, and personal beliefs. Motivated cognition can significantly influence how people form opinions, make decisions, and engage in discussions or debates. It plays a crucial role in the formation and reinforcement of attitudes, as well as in the persistence and spread of misinformation.

What can be done in such situations which have become predictive of how people take positions on such important issues as climate change or the value of inoculations? Prebunking, originally called inoculation in the research literature, proposes an intervention before flawed inputs have been fully processed. It is technically a little different from techniques that attempt to create cognitive conflict by acknowledging flawed beliefs as might be the case in a textbook. but similar. I came across a field research study making use of short videos to point out common misinformation techniques. The idea is that by labeling misinformation as it is encountered the processing of that information will be modified or the information ignored.

The prebunking intervention in this study (reference appears below) consisted of short videos explaining six different manipulative strategies – using strong emotional language, using incoherent arguments, presenting false dichotomies, scapegoating individuals or groups, and ad hominem attacks (attacking the person rather than the argument). Exposure to these videos (experimental vs control) resulted in more accurate detection of misinformation immediately and after a year. The researchers also tested their technique by posting two of their videos on YouTube as ads and then comparing the impact on those who had viewed and not viewed their ads on a dependent variable – reaction to misinformation.

Other writers have recognized the potential of debunking in the context of predicting AI will only increase the amount and personalization of misinformation. https://thedispatch.com/article/fake-news-meets-artificial-intelligence/

While the researchers do demonstrate significant consequences for exposure to the debunking videos, it is important to recognize the practical magnitude of the benefits is not great. Prebunking videos perhaps like other educational efforts to sensitize learners to propaganda techniques does not come close to eliminating the problem. Debunking efforts must continue as well.

Again, I think educators could make use of the videos the researchers have made available. https://inoculation.science/inoculation-videos/

References:

Nickerson, Raymond S. (June 1998), “Confirmation bias: A ubiquitous phenomenon in many guises”, Review of General Psychology, 2 (2): 175–220

Roozenbeek, J., Van Der Linden, S., Goldberg, B., Rathje, S., & Lewandowsky, S. (2022). Psychological inoculation improves resilience against misinformation on social media. Science Advances, 8(34), eabo6254