The Science of Reading: A Review of Three Recent Books

I read a lot, but aside from posting short reactions to Goodreads I seldom write longer reviews. I was able to get the book club I attend that typically focuses on history, economics, and writing to read my suggestion – Adrian Jones’ The Science of Reading. Despite the departure from our standard fare, the breadth of the issues covered using reading and learning to read as a base resulted in many engaging discussions. The present political interest of some states in mandating how children should be taught to read, the history of the “reading wars”, the big money up for grabs in school purchases of instructional materials (books), how digital technology changed reading, and why this particular skill is so important have encouraged broader interest in how best to prepare readers and in how we all engage with text. There is something for nearly everyone here.

The three books I will comment on cover these topics with different emphases. The Science of Reading (Jones) offers the strongest historical perspective. Reader, Come Home (Maryann Wolf) emphasizes a brain-based perspective. Willingham’s The Reading Mind approaches the set of topics from a cognitive perspective. All are approachable and as an educational psychologist with a cognitive background, I see value in how different perspectives support and supplement each other. 

Adrian Johns’s “The Science of Reading: Information, Media & Mind in Modern America” 

Johns covers the scientific study of reading from the 1880s to the present and argues that understanding the history of the science of reading is essential to understanding broader historical changes in knowledge, information, and technology. He achieves this by tracing the evolution of how reading has been perceived and taught, particularly in the United States, where reading has been closely linked to concerns about effective participation in a democracy. The assumed connection between reading and the economy and reading and informed citizenship are reflected in the long-term interest of politicians in this subject area. 

The book begins by examining the origins of the science of reading, which can be traced back to the late 19th century, and the interest of scientists in the movement of the eyes (saccades) while reading. Early researchers like James McKeen Cattell (a name many might recognize from an Introduction to Psychology class) focused on the psychophysical aspects of reading, conducting experiments to measure reaction times and investigate how the human eye processes visual information. Cattell’s research led him to advocate for teaching reading through whole-word identification, a method that gained traction in American schools. For those involved in or recognizing the battle over how best to teach reading, the alternative extremes are to emphasize word recognition or sounding out words by what most would describe as phonics. 

However, as Johns illustrates, the science of reading was not solely driven by laboratory findings. An emphasis on mass literacy further fueled the development and adoption of different reading methods, reflecting the intertwined nature of scientific inquiry and societal needs.

Edmund Burke Huey’s influential 1908 book, “The Psychology and Pedagogy of Reading”, marked a crucial shift in the field, arguing that reading was not merely a mechanical skill but a complex social activity shaped by readers’ experiences. Huey emphasized the importance of “apperceptive filling in,” where readers constantly make inferences based on clues from the text, highlighting the active and constructive nature of reading comprehension. Huey remained influential and my own introduction to reading as a fascinating area of study included my reading of an updated edition of Huey’s book in the 1960s. The word recognition position became integrated with the use of context to form one of the two alternative positions proposing how kids should be taught to read. 

Johns dedicates considerable attention to the “reading wars,” the ongoing debates between proponents of phonics-based instruction and those favoring whole-language approaches. He argues that these debates, often framed as a simplistic this or that options, fail to capture the complexity of reading acquisition and the nuances of effective teaching methods. However, as one might recognize from recent media accounts of mandated methods in different states, the methods to be used and the commercial materials to be purchased with state money are often described to parents and the general public in similar simplistic ways. 

The book examines key figures and events in the reading wars, including Rudolf Flesch’s scathing critique of whole-language instruction in his 1955 bestseller, “Why Johnny Can’t Read”. Flesch’s book ignited a public debate, pushing back against the prevailing emphasis on whole-word recognition and advocating for a return to phonics-based instruction.

Johns also discusses the work of Jeanne Chall, whose research, culminating in her 1967 book “Learning to Read: The Great Debate,” offered a more complex perspective on the reading wars. Chall argued that a balanced approach incorporating both phonics and whole-language strategies was crucial, particularly in the early stages of reading development. I was pleased to see that my friend Dick Anderson who once headed the Center for the Study of Reading at the University of Illinois also received some space in Jones’ book as a supporter of a similar balanced approach. 

The latter part of the book explores the impact of technology on reading, examining the development of teaching machines, programmed learning, and the rise of computers. Johns discusses the work of visionaries like Alan Kay, whose Dynabook project, though never fully realized, envisioned a portable computer designed to replace school textbooks and transform the learning process. Today’s iPad could represent the type of device Kay could only imagine. 

Johns concludes by reflecting on the enduring challenges of reading in the digital age, posing questions about the differences between reading on paper and screens, and the implications of new technologies for literacy and learning.

While providing a thorough historical overview, Johns occasionally delves into intricate details of specific experiments or research methodologies, which may not be of interest to all readers. I find the description of actual studies of great interest and the answer to the question of how researchers study something as invisible as the processes of a mental skill such as reading and come up with explanations of what exactly is going on. 

Reader, come home: The reading brain in a digital world

Maryanne Wolf’s *Reader, Come Home* presents a critical examination of how the digital age is reshaping the human brain’s ability to read deeply. She reviews the brain science of her previous book (Proust and the Squid), but then spends time on whether new technologies presenting text and multimedia alter how the brain adapts to the processing of text. There is more brain science in her first book for those wanting that focus and more focus on screen time, changes in attention span, and reading from a screen versus from paper in the book I am reviewing here.

The central argument of *Reader, Come Home* is that the human brain was not initially designed for reading; instead, reading rewired our brains in ways that changed our thinking. Wolf is deeply concerned that excessive exposure to screen-based media is now rewiring our brains in a different, less beneficial way. The skimming style of reading encouraged by digital platforms, where information is processed quickly and often superficially, is contrasted with the deeper, more reflective reading associated with print books. Wolf stresses that while the brain is highly plastic and can adapt, the quality of that adaptation depends on the stimuli it is exposed to regularly.

Wolf does not argue for a wholesale rejection of digital reading. Instead, she advocates for a balance, calling for the development of what she terms “biliterate” brains. These are brains that are adept at both skimming digital media and engaging in deep, reflective reading. Her emphasis is particularly on children, who, she argues, are especially vulnerable to the negative impacts of digital reading due to the greater plasticity of their brains. If children are exposed only to skimming on digital platforms, they may miss out on the profound cognitive and linguistic benefits that come from deep reading.

The book also provides practical advice for parents and educators. One of Wolf’s key suggestions is to encourage parents to read to their children from printed books rather than relying on digital devices that often come with built-in distractions. The concern is not just about reading comprehension, but also about how children learn to engage with and reflect on complex information, skills that are crucial for higher-order thinking.

A recurring theme in Reader, Come Home is that we are becoming passive consumers of information rather than active, critical thinkers. Wolf’s fear is that if we continue down this path, future generations will lose the ability to think deeply and critically. She draws on cognitive science to show how the brain’s attentional systems are being rewired in ways that diminish our capacity for sustained attention, a vital component of deep reading.

For those who enjoy scientific debates, contrast Wolf and Willingham’s perspective on this issue. Willingham explains the resistance to reading long-form content more as a decreased tolerance for boredom rather than the brain being shaped biologically. 

The Reading Mind: A Cognitive Approach to Understanding How the Mind Reads 

The Reading Mind is a comprehensive exploration of the mechanics of reading, offering both a cognitive and a practical perspective on how our brains process text. Willingham draws from a wealth of psychological research to explain the nuances of reading, from identifying phonemes to building meaning from full texts. 

At its core, The Reading Mind breaks down reading into stages, beginning with letter and sound recognition and culminating in full comprehension of texts. Willingham explains that the ability to match letters to phonemes (basic units of sound) is crucial to reading development, particularly for children. As readers become more proficient, their ability to decode words becomes more automatic, freeing cognitive resources for understanding the meaning of sentences and paragraphs. In other works, the sound of words is initially quite important, but gives way to more automatic recognition of words. This cognitive model serves as a foundation throughout the book as Willingham discusses the importance of both bottom-up processes, like letter recognition, and top-down processes, such as using background knowledge to comprehend texts. I read somewhere that this simultaneous bottom-up and top-down processing can be described as interactive, compensatory processing. This means that multiple processes are going on simultaneously and they work both to support each other. Letter recognition is information by word recognition (the word provides a context that speeds up letter recognition). Words are more quickly recognized and their meaning is more effectively retrieved within meaningful sentences (sentences provide a context that informs word recognition and understanding). Understanding what we read is assisted by what we already know in general and what we know more specifically about the topic we are reading about (again a context effect improving more basic processes). 

One of the more powerful demonstrations I know of considers the relative contribution of reading proficiency and subject knowledge to comprehension. Willingham provides an example based on soccer knowledge. The research study I was familiar with used baseball knowledge. Anyway, young readers were classified as more and less proficient readers and more and less knowledgeable of baseball. This gives you four groups – high skill, high knowledge; high skill, low knowledge; low skill, high knowledge; and low skill, low knowledge. All readers were asked to read a description of half an inning of a baseball game and later write what they remembered. This method allows the impact of reading skill to be teased apart from the impact of subject knowledge. The findings demonstrate that existing knowledge had a larger impact on recall than reading skill. 

One of the book’s strengths is its emphasis on the role of motivation in reading. Willingham makes the case that motivation is as critical as cognitive skills when it comes to becoming a proficient reader. Readers who enjoy reading tend to read more, which in turn improves their reading abilities, creating a virtuous cycle. This insight is particularly valuable for educators and parents trying to encourage reluctant readers. Willingham argues that ease of access makes a big difference

A notable section of The Reading Mind addresses the potential impact of technology on reading habits. Willingham acknowledges the concern that digital distractions, such as social media and video games, might reduce the amount of time young people spend reading. However, he points out that these activities have not necessarily displaced reading time for most youth. Instead, he suggests that the issue lies in a reduced tolerance for boredom, which could make sustained reading more difficult for some. While Willingham does recommend limiting screen time, he is skeptical of alarmist claims that technology is fundamentally altering how children’s brains process information. His balanced view on this topic is refreshing in an age of widespread concern about the effects of digital media on cognitive development.

Conclusion:

My efforts here were to give enough of the flavor of each of these books to perhaps convince you to take a look. As I tried to suggest in the beginning of this post, all books are very approachable and take you in multiple directions addressing several topics that seem of current interest. 

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ChatLLM from Abacus AI

I have this issue with AI services. I have identified several services that provide me useful services (image generation for posts, edits for what I write, identification of journal articles relevant to my writing, and chats with my large collection of notes and highlights) The issue is no individual tool does a great job of all of these things and I don’t use any of the several individual tools enough to justify the $20 a month that seems to be the going rate for any given tool. I could keep telling myself that this retirement writing gig should be treated as a hobby that I can afford, but there is a principle involved here. I think of many of my posts as exploring tools for teachers and $75-100 a month in their situation is likely a bit much. 

I have found a general-purpose AI service, the ChatLLM tool from Abacus AI, that I have for me what is unlimited use for $10 a month. For a month or so now, I have been duplicating my use of ChatLLM and ChatGPT and can find very little difference in what the tools generate. Abacus.AI has an introductory offer that is a little confusing. The first month is free, but you must purchase one month and then you can cancel after the first two months. 

The interface is very similar to what you experience with other AI services. There is a prompt field and responses appear above. ChatLLM allows the individual series of chat interactions with a given purpose allowing you to return if you have a reason to pick up again on an earlier exploration.

ChatLLM appears to function as an API allowing you to select from different AI services. This option seems useful for those of us who like to explore the possible differences among the AI services that are available.

AbacusAI offers many features I have not bothered to explain here in detail. I have learned what I thought necessary to meet my personal needs and it should be very easy for anyone with any experience with other platforms to get started. Here is a more complete review by another writer. 

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How many AI tools?

It’s not that I don’t find AI to be useful. I generate a half dozen images a month to embellish my writing. I search for journal articles I then read to examine an educational issue I want to write about. I examine what I have written to identify errors in grammar or syntax or even identify my use of passive voice which I still can’t figure out. My issue is the monthly subscription fees for the multiple tools that best suit these and other uses. It is simply difficult to justify the $20 a month fee which seems to be the going rate for each of the services and the level of use I make of each service

I regard my use of AI as both a benefit to personal productivity, but also a subject matter I explore and write about. Writing about the intersection of technology and education is a retirement hobby and I don’t need to do what I do on a budget. However, I don’t think this is true for everyone and I can set as a personal goal an exploration of the financial issues others might need to consider. AI tools differ in how flexible they are. Often, the less flexible tools are optimized to accomplish a specific set of tasks and this narrow range increases ease of use. Users can find themselves evaluating the cost-effectiveness of options based on ease of use versus total cost.

Grammarly as an example

As someone who spends a great deal of time writing, it is worth my time to consider how AI tools can be used to improve the productivity of the time I spend writing and the quality of what I write. Grammarly is a tool suited to such goals. I have relied on both the free and pro versions of Grammarly for a quick way to edit my writing. This is a great product I find helpful. Again, the issue is the Pro version adds another $12 to the total of my monthly subscriptions bill. The free version is great for basic errors, but the pro version is necessary for complete sentence rewrites. It is helpful to compare what I said with the suggested rewrite to decide whether I want to accept the alternate version. 

What I have been exploring lately is the use of more general-purpose AI tools (e.g., ChatGPT, Abacas.ai) as an alternative. I understood that many others were using such tools in this matter, but because I had already been using Grammarly I had not invested the time to explore how more flexible tools might be used to achieve similar goals. Here is the approach I have settled on. I use a multi-level prompt to specify what I want to learn from an evaluation of something I have written. I keep this prompt in a note app so that I can copy and paste the prompt at the beginning of the content I want to evaluate. The prompt appears below. The prompt is written to be flexible so that I can adapt it to different circumstances by adding or removing specific guidelines. For example, I realize I often write in passive voice and some tasks should not include text expressed in this way. The guideline “write in active voice” can be included when I think this approach is important. If writing for a specific age group might be a goal, a guideline related to this goal can be inserted. 

To demonstrate what this prompt accomplishes, I will take what I written here to this point and apply the prompt. 

Evaluate the following content using the following guidelines:

  • Correct spelling errors
  • Correct grammatical errors
  • Correct punctuation errors
  • Write in active voice
  • Suggest corrections
  • Only list the lines containing errors and the proposed corrections
  • Add an explanation for the need for a correction

[Insert content to be evaluated here]

Response from Abacas.ai.

1. **Original:** I examine what I have written to identify errors in grammar or syntax or even identify my use of passive voice which I still can’t figure out.  

   **Correction:** I examine what I have written to identify errors in grammar or syntax and even identify my use of passive voice, which I still can’t figure out.  

   **Explanation:** Added a comma before “which” to correctly punctuate the non-restrictive clause.

2. **Original:** It is simply difficult to justify the $20 a month fee which seems to be the going rate for each of the services and the level of use I make of each service.  

   **Correction:** It is simply difficult to justify the $20-a-month fee, which seems to be the going rate for each of the services and the level of use I make of each service.  

   **Explanation:** Added a hyphen in “$20-a-month” to correctly form a compound adjective and a comma before “which” to punctuate the non-restrictive clause.

3. **Original:** I regard my use of AI as both a benefit to personal productivity, but also a subject matter I explore and write about.  

   **Correction:** I regard my use of AI as both a benefit to personal productivity and a subject matter I explore and write about.  

   **Explanation:** Replaced “but also” with “and” to correct the parallel structure.

4. **Original:** Writing about the intersection of technology and education is a retirement hobby and I don’t need to do what I do on a budget.  

   **Correction:** Writing about the intersection of technology and education is a retirement hobby, and I don’t need to do what I do on a budget.  

   **Explanation:** Added a comma before “and” to separate two independent clauses.

5. **Original:** Grammarly as an example  

   **Correction:** Grammarly is an example.  

   **Explanation:** Added “is” to form a complete sentence.

6. **Original:** Again, the issue is the Pro version adds another $12 to the total of my monthly subscriptions bill.  

   **Correction:** Again, the issue is that the Pro version adds another $12 to the total of my monthly subscription bill.  

   **Explanation:** Added “that” for clarity and changed “subscriptions” to “subscription” for correct singular form.

7. **Original:** What I have been exploring lately is the use of more general-purpose AI tools (e.g., ChatGPT, Abacas.ai) as an alternative.  

   **Correction:** What I have been exploring lately is the use of more general-purpose AI tools (e.g., ChatGPT, Abacus.ai) as an alternative.  

   **Explanation:** Corrected the spelling of “Abacas.ai” to “Abacus.ai.”

8. **Original:** I understood that many others were using such tools in this matter, but because I had already been using Grammarly I had not invested the time to explore how more flexible tools might be used to achieve similar goals.  

   **Correction:** I understood that many others were using such tools in this manner, but because I had already been using Grammarly, I had not invested the time to explore how more flexible tools might be used to achieve similar goals.  

   **Explanation:** Changed “matter” to “manner” for correct word usage and added a comma after “Grammarly” to separate clauses.

The feedback I have received would normally have resulted in my making several adjustments. I have not done so for you to consider both the original and the suggestions. The feedback makes specific suggestions and I either copy and paste the suggestions to the original source material or simply retype what needs to be fixed. This is clumsier than just clicking on the recommendation offered by Grammarly and it often requires that I use the search function in a longer text to locate the offending sentence. When writing on my desktop machine I simply open a second window and move between one window with the AI feedback and the original document to make adjustments. 

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The Medium is the Message

Marshall McLuhan’s famous declaration “The medium is the message” never made sense to me. It sounded cool, but on the surface there was not enough there to offer much of an explanation. It seemed one of those things other people understood and used, but I did not. Perhaps I had missed the class or not read the book in which the famous phrase was explained.

The expression came up again in the book club I joined while we reading a book by Johns (The Science of Reading). A sizeable proportion of one chapter considers McLuhan’s famous proposal and provided a reference to his first use of the phrase. The original mention was a comment he made at a conference and then continued to develop. 

The page is not a conveyor belt for pots of message; it is not a consumer item so much as a producer of unique habits of mind and highly specialized attitudes to person and country, and to the nature of thought itself (…) Let us grant for the moment that the medium is the message. It follows that if we study any medium carefully we shall discover its total dynamics and its unreleased powers.

Print, by permitting people to read at high speed and, above all, to read alone and silently, developed a totally new set of mental operations.

Johns’ book is about the history of the study of reading as a science with more on how reading and the methods by which reading skill is developed became a political issue. My effort to create a personal understanding of what any of this would have to do with McLuhan now is based on my consideration of different media and what McLuhan had to say specifically about reading. I have come to think about reading as a generative activity which is a topic I write about frequently. From this perspective, reading is an external task that gives priority to certain internal behaviors. In contrast to some other media, reading allows personal control of speed. A reader can take in information quickly or pause to reflect. A reader can reread. Text sometimes requires the reader to generate imagery in contrast to having imagery offered to them as would be the case with video. Reading cannot transfer a complete experience from author to reader and much is constructed by the reader based on existing knowledge. Reading has a social component. In most cases reading involves an implied interaction with an author, but also with others who have interpreted the same input and who often interact to share personal interpretations. 

What McLuhan had to say about media now reminds me of the notion of affordances. Affordance refers to the potential actions or uses that an object or environment offers to an individual, based on its design and the individual’s perception of it. The term was originally coined by psychologist James J. Gibson in the context of ecological psychology to describe the possibilities for action that the environment provides. Affordances can be both obvious (like a door handle that affords pulling) or less obvious, depending on how the individual perceives and interacts with the object or environment. It is this less obvious type of affordance that applies based on expectations for texts and for how we anticipate texts to be used. Factors such as the allowances for controlling speed and pausing with a medium that is essentially static when we are not interacting with it to allow reflection are more like the obvious affordances Gibson proposes.

Those who reject a media effect

Having reached what I hope is an appropriate understanding of McLuhan’s famous insight, I realized that I have encountered a contradictory argument commonly taught within one of my fields of practice (educational technology). This controversy concerns what tends to be called the media effect

The “media effect” refers to the idea that the medium or technology used to deliver instruction (such as television, computers, or textbooks) has a significant impact on learning outcomes. This concept suggests that different media can produce different levels of learning or change the way people learn.

This perspective was challenged by Richard Clark in his influential 1983 article, “Reconsidering Research on Learning from Media.” Clark argued that the media itself does not influence learning; rather, it is the instructional methods and content delivered through the media that determine learning outcomes. Clark famously stated, “media are mere vehicles that deliver instruction but do not influence student achievement any more than the truck that delivers our groceries causes changes in our nutrition.”

Clark’s challenge to the media effect emphasized that it’s the instructional design, the way content is presented, and the interaction between learners and content that are crucial for learning, not the medium through which the instruction is delivered.

I always struggled when teaching this position. Instructional designers are expected to consider this argument, but my interpretation never allowed me to understand why this would be true. If I wanted to teach someone the cross-over dribble, wouldn’t it make more sense to begin by showing the move rather than describing it with text? I understand that each of us learns through our own cognitive actions, but how we access inputs (external representations) would seem to matter in what our cognitive behaviors have to work with. When you ask advanced students to deal with arguments such as Clark’s that challenge actions they might be prone to take, it is common to match the challenging position with a source that offers a counterargument. I paired Clark’s paper with a paper written by Robert Kozma. If you are inclined to pursue this controversy, I recommend this combination.

Does it matter?

Possibly. I think we are experiencing changes in how we experience information. Most of us experience more and more video both for entertainment and for learning. It is worth considering how we might be influenced by the medium of input. If we are trying to learn more frequently from video, how do we attempt to process the video experience in a way similar to how we can take control and process text? 

References:

Clark, R. E. (1983) Reconsidering research on learning from media. Review of educational research 53 (4), 445-459.

Johns, A. (2023). The science of reading: Information, media, and mind in modern America. University of Chicago Press.

Kozma, R. B. (1994). Will media influence learning? Reframing the debate. Educational technology research and development, 42(2), 7-19.

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Desirable Difficulty

Despite a heavy focus on cognitive psychology in the way I researched and explained classroom study tactics, I had not encountered the phrase desirable difficulty until I became interested in the handwritten vs. keyboard notetaking research. I discovered the idea when reviewing studies by Luo and colleagues and Mueller and Oppenheimer. Several studies have claimed students are better off taking notes by hand in comparison to on a laptop despite being able to record information significantly faster when using a keyboard. 

Since having a more complete set of notes would seem an advantage. The combination of more notes associated with poorer performance is counterintuitive. Researchers speculated that learners who understood they had to make decisions about what they had time to record selected information more carefully and possibly summarized rather than recorded verbatim what they heard. This focus on what could be described as deeper processing seemed like an example of desirable difficulty. The researchers also proposed that the faster keyboard recording involved shallow cognitive processing.  

Note: I am still a fan of more complete notes and the methodology used when demonstrating better performance from recording notes by hand needs to be carefully considered. I will comment on my argument more at the end of this post. 

Desirable difficulty an idea attributed to Robert Bjork has been used to explain a wider variety of retention phenomena. Bjork suggested that retrieval strength and storage strength are distinct phenomena and learners can be misled when an approach to learning is evaluated based on retrieval strength. I find these phrases to a bit confusing as applied, but I understand the logic. Students cramming for an exam make a reasonable example. Cramming results in what may seem to be successful learning (retrieval strength), but results in poorer retention over an extended period of time (storage storage strength). Students may understand and accept the disadvantages of cramming so it is not necessary that the distinction be unrecognized by learners. In a more recent book on learning for the general public, Daniel Willingham suggests that the brain is really designed to avoid rather than embrace thinking because thinking is effortful. The human tendency is to rely on memory rather than thinking. Desirable difficulty may be a way to explain why some situations that require thinking prevent something more rote. 

Increasing difficulty to improve retention

There are multiple tactics for productively increasing difficulty that I tend to group under the heading of generative learning. I describe generative activities as external tasks intended to increase the probability of productive cognitive (mental) behaviors. I suppose desirable difficulty is even more specific differentiating external tasks along a difficulty dimension. So in the following list of tasks, it is useful to imagine more and less difficult tasks. Often the less difficult task is the option learners choose to apply. In connecting these tactics with personal experience, I would recommend you consider the use of flashcards to conceptualize what would be the easier and the more challenging application. Then, move beyond flashcards to other study tactics and consider if you can identify similar contrasts. 

Retrieval Practice: Testing oneself on the material rather than passively reviewing notes is considered retrieval practice. The classic empirical demonstration of the retrieval practice or the testing effect compared reviewing content versus responding to questions. Even when controlling for study time, spending some time on questions was superior. With the flashcard applications I recommended you consider, answering multiple-choice questions would be less challenging than answering short-answer questions (recognition vs recall).

Spacing (Distributed Practice): Instead of cramming, spreading out study sessions over time is more productive. This method helps improve long-term retention and understanding. Spacing allows some retrieval challenges to develop and the learner must work harder to locate the desired information in memory. See my earlier description of Bjork’s distinction between retrieval strength and storage strength. 

Interleaving: Mixing different types of problems or subjects in one study session. For example, alternating between math problems and reading passages rather than focusing on one at a time. A simple flashcard version of this recommendation might be shuffling the deck between cycles through the deck. Breaking up the pattern of the review task increases the difficulty and requires greater cognitive effort. 

Other thoughts

First, the concept of committing to more challenging tasks is broader than the well researched examples I provide here. Writing and teaching could be considered examples in that both tasks require an externalization of knowledge that is both generative and evaluative. It is too easy to fake it and make assumptions when the actual creation of a product is not required.

Second, desirable difficulty seems to me to be a guiding principle that does not explain all of the actual cognitive mechanisms that are involved. The specific mechanisms may vary with activity – some might be motivational, some evaluative (metacomprehension), and some at the level of basic cognitive activities. For example, creating retrieval challenges probably creates an attempt to find alternate or new connections among stored elements of information. For example, in trying to put a name with a face one might attempt to remember the circumstances in which you may have met or worked with this person and this may activate a connection you do not typically use and is not automatic. For example, after being retired for 10 years and trying to remember the names of coworkers, I sometimes remember the arrangement of our offices working my way down the appropriate hallway and this sometimes helps me recall names. 

I did say I was going to return to the use of desirable difficulty as a justification for the advantage of taking notes by hand. If keyboarding allows faster data entry than handwriting, in theory keyboarding would allow more time for thinking, paraphrasing, and whatever advantage one would have when the recording method requires more time. Awareness and commitment would seem to be the issues here. However, I would think complete notes would have greater long-term value than sparse notes. One always has the opportunity to think while studying and a more complete set of notes would seem to provide the opportunity to have more external content to work with. 

References:

Bjork, R.A. (1994). Memory and metamemory considerations in the training of human beings. In J.  Metcalfe & A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185-205). Cambridge,  MA: MIT Press.

Luo, L., Kiewra, K. A., Flanigan, A. E., & Peteranetz, M. S. (2018). Laptop versus longhand note taking: effects on lecture notes and achievement. Instructional Science, 46(6), 947-971.

Mueller, P. A., & Oppenheimer, D. M. (2014). The pen is mightier than the keyboard: Advantages of longhand over laptop note taking. Psychological science, 25(6), 1159-1168.

Willingham, D. T. (2021). Why don’t students like school?: A cognitive scientist answers questions about how the mind works and what it means for the classroom. John Wiley & Sons.

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YouTube Annotation with Glasp

I take a lot of notes and have done so for years. I have tried many different tools over this time period. Social Annotation is a subcategory of these tools that allows users to share their highlights and notes. The idea is that the sharing of notes allows individuals to find resources they have not personally explored and offer their own discoveries to others. Glasp serves these purposes.

I have written about Glasp on several previous occasions. A unique capability allows built-in AI capabilities to “chat” not only with your own notes, but also the annotations stored by others.

Glasp is a combination of a Profile page that is the online location allowing access to the content you have collected (see above) and a browser extension that provides the means to highlight and annotate the content viewed within your browser. Kindle content is imported automatically. Glasp could provide the storage location for all of your notes, but I export notes to Obsidian to take advantage of more advanced features.

I don’t spend a lot of time collecting information from Youtube because most of writing is based on books and journal articles. There are exceptions when I review tutorials for software tools and want to keep track of specific tactics. I understand that others use YouTube extensively and I wanted to explore the capabilities of Glasp with this information source. The following video is my effort to describe how notes and highlights are generated from YouTube content.

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Potential conflicting benefits of your note-taking tool and approach

As I have explored and used several digital note-taking tools and examined the arguments that have been made regarding how such tools result in productivity benefits, I have identified a potential conflict in what produces more positive outcomes. The recognition of this conflict allows more purposeful execution on the part of the tool user and may better align activities with goals.

One way to identify note-taking goals is to use a long-standing approach differentiating generative and external storage benefits. This distinction was proposed long before PKM and was applied in the analysis of notes taken in classroom settings. The generative benefit proposes that the process of taking notes or sometimes of taking notes in a particular way engages our cognitive (mental) processes in ways that improve retention and understanding. External storage implies that our memory becomes less effective over time and having access to an external record (the notes) benefits our productivity. In practice (e.g., a student in a classroom) both benefits may apply, but one benefit depends on the other activity. Taking notes may not be beneficial, but to review notes one must have something to review. This is not always true as notes in one form or another can be provided or perhaps generated (for example AI identification of key ideas), but taking your own notes is by far the most common experience. In a PKM way of thinking, these two processes may function in different ways, but the classroom example should be familiar as a way to identify the theoretical benefits of note-taking.

I have written about the generative function of note-taking at length, but it is important to point out some unique specifics that apply to some digital note-taking tools. A source such as Ahrens’ Taking Smart Notes might provide the right mindset. I think of generative activities as external actions intended to produce a beneficial mental (cognitive) outcome. The idea is that external activities can encourage or change the likelihood of beneficial thinking behaviors. One way of operationalizing this perspective is to consider some of the specific activities Ahrens identified as external work resulting in such cognitive benefits. What are some of these activities? Isolating specific ideas and summarizing each as a note. Assigning tags that characterize a note. Making the effort to link notes. Periodically reviewing notes to generate retrieval practice, to reword existing notes, and to add new associations (links).

Retrieval is easier to explain. Note-taking apps with highly effective search capabilities make it easy to search and surface stored information when it might be useful. Links and tags may also be useful in this role, but search alone will often be sufficient.

What about the potential conflict?

The conflict I see proposes that some tools or approaches rely more heavily on search arguing in a way that generative processes are unnecessary.

I starting thinking about this assumption when contrasting the two note-taking systems I rely on – Mem.ai and Obsidian. While Mem.AI and Obsidian could be used in exactly the same way, Mem.ai developers argued that the built-in AI capabilities could eliminate the need to designate connections (with tags and links) because the AI capabilities would identify these connections for you. Thus when retrieving information via search, a user could use AI to also consider the notes with overlapping foci. If a user relied on this capability it would eliminate the work required to generate the connections manually created in Obsidian, but this approach would then also avoid the generative benefits of this work. 

AI capabilities fascinate me so I found a way to add a decent AI capability to Obsidian. Smart Connections is an Obsidian plugin that finds connections among notes and allows a user to chat with their notes. So, I found a way to mimic Mem.ai functionality with Obsidian. 

I find I have found a way to alter my more general PKM approach because of these capabilities. Rather than taking individual notes while reading, I can annotate and highlight pdfs, books, and videos and export the entire collection for each source and then bring this content into both Mem.ai and Obsidian as a very large note. Far easier than taking individual notes, but at what generative cost?

Smart Connections has added a new feature that even facilitates the use of the large note approach. Connections finds connections based on AI embeddings. An embedding is the mathematical representation of content (I would describe as weights based on what I remember of statistics). The more two notes embeddings’ weights are similar the more the notes consider similar ideas. Smart Connections used embeddings to propose related notes. Originally embeddings were generated at the note level and now at the “block” level. What this means (block level) is that Smart Connections can find the segments of a long document that have a similar focus as a selected note. 

Why is this helpful? When I read long documents (pdfs of journal articles or books in Kindle), I can export a long document containing my highlights and notes generated from these documents. With Smart Connections I can then just import this exported material into Obsidian and use Smart Connections to connect a specific note to blocks of all such documents. I can skip breaking up the long document into individual notes and assigning tags and creating links.

Why is this a disadvantage? Taking advantage of this capability can be a powerful disincentive to engaging in the generative activities involved in creating and connecting individual notes the basic version of Obsidian requires. 

Summary

As note-taking tools mature and add AI capabilities, it is important for users to consider how the way they use such tools can impact their learning and understanding. The tools themselves are quite flexible but can be used in ways that avoid generative tasks that impact learning and understanding. If the focus is on the retrieval of content for writing and other tasks, the generative activities may be less important. However, if you start using a tool such as Obsidian because a book such as Smart Notes influenced you, you might want to think about what might be happening if you rely on the type of AI capabilities I have described here. 

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

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