Category: General

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The Concept of Disruption Applied to Education

I think of Clayton Christensen as a theorist and writer mainly focused on business. He seems to focus on the challenges of change. Two core ideas have stuck with me. First is the concept of the innovator’s dilemma, which describes the difficulty of moving on from a successful approach when faced with new circumstances. Why does success at one point limit future success. The second core topic considers the opportunities and risks that arise when a field encounters great disruption. These two big ideas are interrelated, as great disruptions may create circumstances challenging an existing successful approach.

Big ideas are not necessarily specific to a given field (e.g., business) and Christensen has made the effort to apply his theories to education. This effort resulted in his 2008 book Disrupting Class, written with Michael Horn and Curtis Johnson. For those interested in K12 education, it should not be difficult to anticipate how Christensen’s core ideas might apply. The methods of K12 teaching are often criticized as unchanging often noting that someone from a century ago would not be lost if suddenly dropped into a classroom of today. The long-standing model involves age-based classes of students reporting to a teacher standing at the front of a room, engaging the class with presentations and discussions covering isolated subjects of math, science, history, and language arts with performance evaluated using tests at regular intervals. This model has existed in pretty much the same approach for a long period of time and seems very resistant to change.

While stable, the K12 model faces significant challenges. Students are often unmotivated with high absentee rates. Despite significant resources invested in the U.S., performance measured by standardized tests lags behind that observed in other countries. The COVID crisis appears to have created problems that remain when students could not meet in the traditional face-to-face classroom situation. Despite the mantras of “all students succeed” and “leave no child behind”, student performance has grown increasingly more variable as students move through the grade levels, bringing many questions of meeting the needs of a diverse population of learners into focus.

These issues exist within an environment of educator complaints of low pay and a lack of public support, with increasing pressure to address an ever-increasing number of goals. Why aren’t students more capable of understanding the basics of money management? Why are kids spending so much time on their phones and reading so little? Why are kids questioning their sexual orientation? Why can’t K12 schools do a better job of preparing those students who are interested in a job after high school rather than pursuing the experiences of higher education? Why do kids have so many psychological problems?

Rereading Disrupting Class

After the delay of a decade or so, I just reread Christensen’s Disrupting Class, which turned out to be an interesting experience in a couple of ways. First, I don’t reread many books because there always seems to be something new I want to read, but the investment of time may be beneficial with a certain type of book. Rereading books that gave you important insights into an emerging or changing situation can be quite informative. The delay between reads allows the accumulation of relevant experiences that allow further reflection on your original conclusions and insights. These intervening experiences can also guide rereading to discover nuances that did not initially stand out. There is more. Some writers who explore an emerging trend are willing to predict specific accomplishments and related time frames. What actually happened? Were the predictions realized, and if not, what factors turned out to prevent the predicted outcomes?

I became interested in Christensen’s analysis of the challenges of K12 education because I thought the challenges I have already outlined could be addressed through the use of technology to individualize learning opportunities in ways that addressed differences in aptitude, existing knowledge, and preferences for different approaches to learning (Christensen used Howard Gardner’s theory of multiple intelligences as a justification for alternate learning experiences.). When translating these suggestions to my own way of thinking about instructional alternatives, I equated Christensen’s suggestions with an updated way of implementing mastery learning, taking advantage of online technology. This was not the term used in his book, but Christensen did mention k12.org as one of his examples and I would define this as an example of online mastery instruction.

Christensen observed that disruptive practices tend to emerge in the margins with mainstream practices being very resistant to disruption. Established mainstream practices fall prey to the innovator’s dilemma, with efforts limited to doing a better job of what is already being done. He predicted that home schooling, credit recovery experiences (a way to address failed or incomplete courses), and unique courses that cannot draw enough students in an individual school would provide the earliest exposure for online, individualized learning experiences.

While online, digital approaches have succeeded on the fringe, these successes are intended to encourage more mainstream adoption. Now this is what I mean by having the opportunity to test predictions. Implementation has not progressed as anticipated. For example, Christensen predicted that by 2019, about 50% of high school courses would be delivered online. This prediction proved to be overly optimistic, and I would guess that any movement in that direction was reversed by negative experiences associated with the total forced online efforts of the COVID years. There are other failed predictions. One concerns the alternative educational materials that would be available to satisfy the multiple intelligences Christensen identified. Rather than a single, common textbook, he predicted that technology would mature to the point that educators and student peers could create learning materials. Course management systems (CMSs) were emerging and these systems would serve to store, sequence, and provide options generated by commercial providers as well as educators themselves. Perhaps some attempts have been made in this direction. I would point to concepts such as Google app-based Hyperdocs and teacher-based content production processes such as Teachers Pay Teachers (TPT) as consistent with this prediction, but the scale of adoption has been small.

I spent some of my time teaching in a graduate program focused on Instructional Design and I did find the concept of teachers as instructional designers of interest. To this point, educators are not prepared as designers and instructional design is different from lesson planning. Teachers would certainly have the background to become instructional designers, but like so many things educators could do, there is always the issue of how their time should be allocated.

Is technology a tool for disruption or will it be limited to isolated and focused improvements?

Perhaps technology is not a tool that will result in educational disruption, but will play a more focused role in improving some specific areas of what we already do. The alternative could be that technology provides the only reasonable means to break free from an approach that seems to be floundering in complexity, inefficiency, and frustration. Perhaps Christensen succumbed to Amara’s law. You may have heard of this but not know to whom the argument has been attributed. When associated with progress involving technology, Amara proposed that we tend to overestimate the impact of technology in the short term and underestimate the impact in the long term. Perhaps we are too impatient.

I understand that one criticism of those who advocate alternatives to the status quo is that when an alternative does not seem to generate much change, the reaction is to claim “you didn’t do it right”. There is certainly some legitimacy in the claim that people are reluctant to abandon good ideas when the ideas do not result in practical successes. Keeping this in mind in combination with the limited success of current approaches, I still believe there are some areas of potential.

Here are some examples:

Teachers can create instructional materials efficiently with the use of AI. Specialized AI tools (e.g., eduaide.ai, Diffit) assist educators in creating instructional materials customized to suit students who prefer different types of learning experiences.

AI offers the opportunity for personalized tutoring. Christensen refers frequently to the impact of tutoring, but he uses tutoring to refer to instructional tutorials. It is true that there are certain ways in which tutorials provide individualized learning experiences. When I think of tutoring, and this has been a topic I have always found interesting, I tend to think of a one-to-one interaction with another individual. Again, AI can approximate this experience and is more accessible and economical than traditional tutors. I have written several posts focused on AI tutors and I encourage educators to explore this possibility. The common complaint that technology is isolating is worth considering. As an adult independent learner, I would counter that all of us end up learning in isolation. Having suggested developing the skills of independent learning to be essential, I do note Christensen lists two requirements that must be met to motivate K12 learners?—?a way to feel competent as a learner and social connections. I agree and see peer engagement within an AI tutoring environment as a very reasonable approach. This strategy offers a combination of AI and peer tutoring.

Individualized learning opportunities?—?I am not ready to give up on online, individualized learning experiences. These experiences might best be used as a supplement to other classroom experiences (i.e., as an alternative to a textbook) or assigned for enhancement or remedial purposes. Khan Academy offers a flexible environment and set of tools that should be available for assignment by educators or access by individual learners.

I am not ready to dismiss the role technology can play in K12 education. I am waiting for those who point to limitations to offer reasonable options.

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I first learned that the association between online activity and health among older individuals may actually be positive from a television news program. Since I spend a lot of time reviewing research related to the use of digital tools, I decided to follow up. Disclosure – I have no expertise in health issues, but I do read a lot of research focused on the cognitive benefits or detriments of technology. I also have considerable personal experience using technology as an older adult, and I thought others may be interested.

There are multiple concerns that technology may have damaging effects that we users may conveniently ignore.

• Technology appears to discourage physical activity, which may result in weight problems and poor physical conditioning.
• Technology may reduce face-to-face social interaction and the benefits (emotional and physical) associated with interactivity. The hostility so common on social media sites when it comes to certain issues, such as political discussions, may have resulted in significant divisions within society. 
• Certain technology tools (AI) may be used in place of the struggle involved with important cognitive tasks (e.g., writing, mathematical problem solving), limiting the learning of important skills.

It is easy to generalize concerns, and it makes some sense that older people are less tech savvy and would be less concerned about the dangers of spending a lot of time online. However, it is always worth collecting the data, and when Baylor and University of Texas at Austin researchers began to look at the published studies,s they concluded that social media activity was actually helpful in multiple areas.

The study that caught my attention was published in the journal Nature Human Behaviour, which reviewed 57 studies involving more than 411,000 adults across the globe, with an average participant age of nearly 69. The researchers Jared Benge and Michael Scullin used a statistical procedure called meta-analysis which is used to identify a general trend across the work of many other researchers. Most uses of this approach also identify variables potentially differentiating the studies and then examine outcomes found in subgroups associated with these differences to possibly identify important factors that might explain how any relationship between variables identified by the larger study might be explained. Sometimes, such an approach can identify a general explanation based on differences in what the smaller studies show. (Summary of study for public distribution).

This pattern of cognitive protection persisted when the researchers controlled for socioeconomic status, education, age, gender, baseline cognitive ability, social support, overall health, and engagement with mental activities like reading that might have explained the findings.

So, the general conclusion was that more use of social media was related to better physical and mental health. Specific causes cannot be identified in what are correlational studies (more on that at a later point) but the authors speculated there were several possible benefits:

• Social connectivity – social engagement is known to facilitate mental and physical health so it seems possible to be able to connect with others even with physical limitations or the inability to drive a car. The access to visual connections (e.g, Apple’s FaceTime) offers a more social interaction.
• Performance enhancement – opportunities such as online banking and shopping encourage independence and keep older individuals more active and involved. Even services that provide assistance with directions keep individuals more active. 

The issue with correlational research

You may be familiar with the phrase correlation is not causation. This means that finding a relationship between one variable (tech use) and another (health variables) does not mean that greater use of your cell phone is responsible for improved health outcomes. You might have immediately made the same observation – what if those with illness or in cognitive decline don’t use their smartphones as much? Researchers can try to statistically control for other variables, but the certainty of the direction of a relationship cannot be guaranteed. The reason more powerful research designs are not applied is easy enough to understand when you think about the topic of this research and many other issues that involve avoiding a negative situation. You cannot ethically create a situation hypothesized to be damaging to see if it really is? 

I decided I should take a look at a couple of the individual studies to see if the design was a simple are “A & B” type of design, and this was clearly the case in some cases.

So, despite the frustration the phrase creates among those seeking a high degree of certainty – “more research is needed”.

Sources:

Benge, J. F., & Scullin, M. K. (2025). A meta-analysis of technology use and cognitive aging. Nature Human Behaviour, 1-15.

Godie, E. A., Elfiky, E. R., & Ibrahim, E. E. (2022). Smartphone Use and Its Relation to Cognitive Impairment and Depressive Symptoms among Elderly People. Assiut Scientific Nursing Journal, 10(33), 188-196

Ten years ago or so when I was still involved in teaching graduate courses in instructional design, a kludged way of using Twitter popularly referred to as Twitter Chats emerged and became popular within the education community. It is fair to say that I was not a fan, but in keeping with the charge for my course I spent a lot of time in such chats and exposed my students to the experience through class assignments.

I tried without luck over several years to get a student to do a thesis focused on these chats. I proposed creating a system based on research studies from the past analyzing classroom interaction. How much time was devoted to teacher talk and to student talk? Who initiates questions and who responds? Who responds to responses? Does the teacher rephrase requests for participation based on categories the teacher could be asked to provide about learner characteristics – e.g., male/female, advanced/struggling? What proportion of classroom interaction was devoted to maintenance, content, discipline, socialization?

An observation of my own regarding chats was that they were extremely inefficient in comparison to other technology tools – discussion boards, group video interactions. So what was the point? My proposal to students was that a classification system of chat transcripts would be a way to investigate questions related to chat behavior. 

I never did get a grad student interested in my proposal and then Twitter Chats seemed to fade away. Until now that is. I have switched from being a Twitter (X) user to BlueSky and see that many other educators have as well. I just saw that the old Twitter Chat procedures are now being promoted on BlueSky. This encouraged me to search for something I wrote years ago about my suggestions for improving these chats even though I thought other tools offered educators better learning and communication experiences. What follows is that content minimally modified to be more timely. I have left the original use of Twitter as the focus, but replacing Twitter with BlueSky would be legitimate as the chat techniques are identical. 

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Many educators have taken to using Twitter as a tool for “discussions”. Among participants, these discussions are more commonly described as chats and may be used as a way for students to share content, but more commonly seem a way for educators and their colleagues to interact.

Twitter chats, often called edchats when used in education, tend to follow a particular format partly to take advantage of characteristics of Twitter and partly because the approach is an efficient way to impose a synchronous approach on a tool not necessarily designed to be used in the way it has come to be used. Twitter was developed to share comments with followers. An edchat does not require that participants follow each other.

The essential feature of a Twitter chat is a common hashtag. All comments during a chat must contain the same hashtag. A Twitter hashtag is the symbol # followed by some series of letters or numbers; e.g., #grabechat. Participants in a chat actually search for the designated hashtag rather than watch their Twitter feed.

Following a series of tweets containing a common hashtag during a chat works best with a tool that automatically updates itself so the user does not have to repeat the search over and over again. My tool of choice is Tweetdeck (see image that follows). This tool allows an on-going search to be established based on a designated phrase (e.g., #ndedchat) and will keep this search current.

The other “rules” for a Twitter chat are conventions, i.e., made up rules. To have a synchronous chat, participants need to be online at the same time – e.g., Wednesday at 9 P.M. A variant called a slow chat, uses many of the same techniques but relies on an asynchronous approach – participants connect when they can over a greater amount of time.

The most common approach for a Twitter chat is a question and answer format based on a theme. A “moderator” may generate the questions for the week or participants may share responsibility for this task. Posting the questions before the chat allows participants to prepare. Some participants may even generate answers and then paste them into the chat tool when the questions are presented. This slows the discussion process down for these participants and allows them to spend the time thinking about what others have to say. This approach is uncommon, but would seem to lead to greater reflection (see my criticism of the typical chat that follows this description).

Another convention is used to deal with other typical challenges of an online discussion. Because real-time chats involving many participants have the potential to become disjointed, questions and answers are often numbered; e.g., Q1, Q2, … and A1, A2, … . The appropriate label is added to each question or answer. This approach allows individuals to make clear how their responses match with a specific question or earlier replies from other individuals. A typical hour-long chat seems to be based on 8-10 questions. Note that the inclusion of a hashtag and the indicator for a given question reduces the length of any given tweet.

Critical analysis and suggestions

I have participated in and viewed many edchats. These experiences have resulted in criticisms both of the technical tool and the way chats tend to unfold (the tactics).

I have fallen into analyzing educational technology experiences in terms of tools and tactics and this approach may be useful here. The idea is to separate the consideration of the potential and actual perceived value of the tool (the specific service or application) and tactics (the strategies of use). My assumption in the comments that follow is that the general goal for an edchat is professional development – the acquisition by professionals of new knowledge and skills. The existing tool is Twitter and the tactic is participant responses to a series of approximately 10 questions within an hour long block of time.

Assumed advantages of tool (Twitter) – free, easy to learn, large installed base of users

Assumed advantages of tactic – educators are familiar with a question and answer format and can participate with little preparation

Issues

A general issue with social media is that once a platform (tool) has attracted a user base, new and better tools fail to gain participants because individuals are reluctant to migrate for fear their social connections will be lost. I think this is the case with Twitter in the education community. I think Twitter has inherent issues because of the brief comments it allows. This limitation and the time to enter comments from a keyboard or screen, in my opinion, leads to rather shallow interactions. It may be a great way to learn about new things via links, but it is not a tool suited to meaningful, synchronous discussion.

The edchat format (the tactic) has taken hold and it seems popular to have such chats. There is a certain momentum here. There is also the issue of doing it like everyone else does. Conformity seems to limit a consideration of both tool and tactic.

I tend to look at this setting as if it were a class I was facilitating. As educators, does the typical edchat generate the type of interaction you would want to see in your class. What would you change?

How to improve edchats – some ideas:

Prepare beyond the generation of a lengthy series of questions. Either come up with 2-3 questions of greater depth or offer a common preparation task (read this post, read this book, etc.). Perhaps the moderator for the week should either find a resource or write a position statement.

I find the questions and topics to be too general. As an academic, I understand that since we are frequently described as being abstract and not getting the level of actual application this would seem a strange concern, but review chats and see what you think. I try to recognize my own possible biases here by looking at the responses the questions generate. The questions seem to generate few specific suggestions or examples.

I see very little interaction. Put more bluntly – the discussions are seldom discussions. Sometimes a response from another participant is praised, but there are few reactions, counter examples, requests for clarification, etc. If this was a FTF classroom, the typical edchat would be similar to choral responding rather than a discussion. I would propose these limitations are the result of both the tool (lack of room for depth) and the tactic (too many questions and responding without preparation).

Blogging before discussing might be helpful. Taking a position on an issue before interacting can be productive. Give some thought to your position before you are tainted by what others have to say. Offer an example. Process your own experiences and externalize a position for others to consider. Post before you participate. A moderator and other participants might then use these comments to request clarification or note differences of opinion.

Some comments on tools.

I admit at this point that it is difficult to isolate tool and tactics. I think moving beyond Twitter would be helpful.

I think it is time to consider other tools. I have always had access to discussion tools and I see greater opportunity for depth in synchronous commenting and responding in using these tools.

I understand that folks enjoy the social experience of Twitter chats, but I think it important to consider whether group socializing is the primary goal.

I am not familiar with all of the tools available to educators. Does the state or school offer a general set of tools (a discussion option, a blogging option)? What about Zoom or Teams?

Twitter chats may be the “in thing” but it may be time to think through the tool and the tactics and either make adjustments or move on to a better tool and improved tactics.

Summary:

1) Reduce the number of questions and give more thought to the type of questions used

2) Have a pre-session expectation for preparation of some type. I think expecting a product is always helpful related to this preparation is always helpful. Somehow, the popularization of “flipping” various education experiences should apply here. Prepare before you participate should be the expectation.

3) The moderator needs to encourage more give and take rather than limiting “discussion” to call and response. As I have already suggested, existing position statements that can be contrasted would be a great place to start. I understand the concern with how stating a different position will be received, but the generic positive reactions add little.

4) Consider other technology tools.

5) Generate a discussion summary (perhaps the moderator or a designated discussant). Did the summarizer learn anything?

Given these observations, I encourage you to form your own opinions. I wish Twitter chats had been analyzed more empirically, but to my knowledge this has not been the case at the time this content was generated. It is easy enough to explore on your own.

The following video summarizes some of these ideas.

What can be done to reduce the time spent on our devices has become a very public question. The issue has resulted in K12 schools being legislated in many areas to prevent students from bringing their phones to class and concerns are common that the rest of us should also cut back. I have written previously about the issue when present in young phone users and whether heavy use should be characterized as a physical addiction or a bad habit. 

The present post originated from my continued reading on this topic and an article I read related to the bad habit perspective proposing that adolescent screen time could be reduced if adolescents became more aware of their screen time and begin to think of it in terms of time not spent on other activities they value. This article described a program called Project Reboot and the Clearspace app. The app allows a user to set specific limits and informs the user when limits are being exceeded. The notion of improving mindfulness and the exertion of personal control seemed related to the bad habit perspective. Is awareness enough or should use simply be prevented?

Research on apps that intervene

It seemed likely that researchers would have investigated digital strategies to address a problem that originates with digital devices and I located a meta-analysis of such strategies (Tahmillah and colleagues, 2023). I recommend anyone interested in this topic take a look at this article because it does a great job of categorizing apps and the approaches taken to impact screen time. Just the list of apps was informative (Clearspace was not included) if for no other reasons than many may be unaware that such interventions exist and because the costs vary greatly. Identifying the apps proven to have an impact on behavior in combination with the mechanisms employed by such apps was informative. Simply blocking the use of an app or taking phones away limits screen time, but technology in general and most apps specifically have both opportunities and limitations so most of us in the long run would be better off making decisions about when and for how long to use our apps. 

The classification system

Rahmillah and colleagues proposed the following categories:

1. Block
2. Self-tracking
3. Goal advancement
4. Reward/punishment

Categories were further differentiated by underlying processes. For example, goal advancement, the approach that most interested me, included among the processes the opportunity to set a goal and a goal warning that the limit was approaching. 

The idea of the meta-analysis was to identify the apps that had demonstrated success in reducing screen time and then consider the underlying processes used by the more successful apps. In reviewing the studies found to impact screen time, I discovered the built-in screen time capabilities of iOS were listed (example of iOS screentime study available online). Given the costs I had found associated with other apps, this seemed important to me as users might be reluctant to consider costly apps this seemed important. 

Description of iOS Screen Time Controls

iOS Screen Time offers capabilities that seem examples of the goal-setting and goal-warning processes of a Goal Advancement approach. The following describes how to set up an iOS device to provide these functions.

The following image shows important iOS settings for limiting use. These features are available from the System settings. The Screen Time button (left-hand panel) provides access to multiple settings in the right-hand panel. Goals can often be accomplished in multiple ways and I am describing just one sequence here.

From the following image, note the following. The settings you can implement can be protected by a password (red box near bottom of image). If you wanted to set controls on a child’s phone to prevent access or limit the time on an app, you might want to protect the setting with a password. For your own use, you might not want to use a password offering you a way to ignore the notice that you had reached the limit you had established. You can set limits in several ways – individually or by category. If you decide to set limits by category, you might want to exclude some apps from that category. Always allowed provides a way to do this. 

The approach I am taking here makes use of the See All Apps and Website Activity option (Green box). 

The Apps activity option shows time spent by app or by category. You can toggle between the two presentations by selecting the button shown in the smaller green box (this would take you to the app view from the category view). Toward the right end of a category, you should see the > symbol. Selecting this symbol will reveal the apps associated with that category. 

You can now select the category or individual apps to set limits. I have selected Instagram. 

I can then set the maximum time per day and customize the time during a week should I want to do something more complex such as extend the limit for the weekend. 

What happens when you exceed your goal? You will encounter the following Time Limit Screen. If you have a password set, you (or someone with the password) would have to enter the password to provide you more time. If no password has been set, you see the following options and can make an informed selection to continue.

Summary

Apps are available to help individuals manage their screen time and research indicates some apps do produce improvement. Of course, research studies do not claim every individual will respond in this manner. Some techniques allow individuals to set goals and inform the user when they have spent the maximum time they intended to spend. This approach is based on the assumption that the lack of awareness is a reason many exceed the amount of time individuals intend to spend and behavior will change when a method of improving awareness is provided. iOS has screen time controls built in that allow for goal setting and goal awareness to be provided. Because these functions come with the operating system on Apple devices a user does not have to spend money on additional capabilities. This post explains how to set goals in iOS. 

Reference

Rahmillah, F. I., Tariq, A., King, M., & Oviedo-Trespalacios, O. (2023). Evaluating the effectiveness of apps designed to reduce mobile phone use and prevent maladaptive mobile phone use: multimethod study. Journal of Medical Internet Research, 25, e42541.

I have resisted the complaints that reading from a screen leads to poorer comprehension for some time. Much of my own research and writing have promoted the educational benefits of technology and the multiple studies and meta-analyses reporting that comprehension is adversely impacted when reading on a computer or tablet seemed a challenge to my message. A recent post on Reading Rockets by Timothy Shanahan identified several new studies I had not read and encouraged another look and another post.

When I first wrote about educational applications of technology, my approach was strongly influenced by David Jonassen’s mid-1990s book Mindtools. Rather than focusing on programming, computer literacy, or drill software, Jonassen proposed that educators consider how using productivity tools (e.g., word processing) could change how learners explored ideas. Again using word processing as an example, how writing to learn with a computer might expand the cognitive benefits of the writing experience beyond that allowed by pencil and paper.

A more recent version of the “tool perspective” (e.g., Etchells, 2024) offers a more nuanced view and notes that tools are in many ways neutral and users determine whether tools will be used effectively or detrimentally. The ills associated with technology are challenging because technology offers opportunities and challenges. However, Etchells proposes that the challenges are not due to addictions or biological triggers. The challenges are often best described as bad habits. This creates a difficult situation for some with the tendency to promote but also allows others to blame the tool as bad and ignore the control and responsibility of the user. With a specific focus on reading from digital devices which of these perspectives makes the most sense? If I maintain a position of advocacy, can I focus on the responsibility of the learner in a way that offers potential solutions rather than blaming the victim?

My “out”

First, I have no concerns about my own nearly total reliance on reading from digital devices. Even if there is a minor hit to my comprehension, my digital reading is part of a process of getting from a review of the ideas of others to my own written output. Digital reading allows me to efficiently highlight and annotate and export these personalizations of my understanding into external storage that I have accumulated over the years and use months and sometimes years later as background for what I write. This is one of those situations in which I cannot direct others to research evaluating the relative effectiveness of this approach to knowledge work, but trying to approximate my process on paper seems pointless. Put another way, reading comprehension is just one component in combination with long-term storage, multi-document processing, organization, retrieval, the flexible connection of stored elements, etc. that are involved in the writing and teaching I do.

Shallow Comprehension

I am starting to find the redundancy of the basic research studies and meta-analyses that have filled journal space in recent years to be mildly irritating. Replication is important in science, but at some point, it is time to move on to replication and extension. The studies that are attempting to study the process of comprehension when reading from a screen or paper are an example of what I mean by extension. Because it is not obvious why reading text on paper or on a screen involves the cognitive processes involved in reading, repetition of differences in the product (answers to test questions) has a decreasing value.

It would seem that the existing research demonstrates an issue, but not an explanation. Reading from a screen and paper would seem to involve the same cognitive subtasks, but the results are not equivalent. The explanation that seems to have gained the widest acceptance (my judgment) is commonly called the “shallowing hypothesis. I attribute this claim originally to Nicholas Carr who wrote a popular book titled “The Shallows”. As I understand the argument, the reading we tend to do online mostly involves what we might commonly describe as skimming. This experience leads to a bad habit that is activated when we read material from a screen even with content we might read more carefully under other circumstances. The reading habits of doomscrolling are proposed to generalize to reading books.

Eye movement research offers a way to watch the process of reading. We know that reading on a screen moves faster and readers overestimate their understanding level. Speed to complete the reading of a passage and requests to estimate the level of understanding by asking for prediction of performance areas variables that are easy enough to collect. I studied the phenomenon of failed reader understanding of their level of understanding and recognized the issue as a problem of comprehension monitoring resulting in poorer calibration. Calibration is simply the accuracy with which you can estimate the quality of a future performance. I always explained it as the decision that a student makes the night before an examination. Can they stop studying now and go out with friends or should they keep working to get the exam score they want? I was interested in calibration as a challenge for less capable readers reasoning that simple fix-up problems such as stopping and rereading sections that were poorly understood was the basic adjustment less capable readers were unable to make. In reflecting on the challenge in the context of college student study behavior, I tended to think of the problem as a matter of efficiency. Students used to tell me they had read chapters several times in preparation for an exam. Rereading is a common strategy among the motivated, but less capable learner. It would be far better to recognize the topics you did not understand and focus on these topics. It would be even better to notice this immediately while reading and while being more aware of the context of the material being read. This would be the ideal time to reread a paragraph or two.

When I was actively involved in this research, this challenge was sometimes studied by inserting different types of errors in text and determining if readers could identify the problems. Create circumstances that should result in a failure of comprehension and see if a reader would recognize the problem. For example, a statement that existing knowledge should indicate was inaccurate or a conflict between two sentences in the same sentence. Better readers were better at identifying these inserted errors. The technique was criticized because it placed readers in the role of proofreaders which is not the way we actually read. At the time, I tried to investigate the issue of monitoring skills by using a similar technique without informing the participants in my research and using eye-movement recordings to determine what happened when readers encountered what should have been confusing sentences. At the time the technique was tricky to use and intrusive as readers had to maintain their heads in a fixed position by bracing their chin on a bar. My research traded one form of artificiality for another.

The sophistication of the equipment has drastically improved and eye movement techniques are being used to compare participants reading from printed material and from a screen. For example, a recent study compared 8th graders in Norway (Jensen and colleagues, 2024) reading items from the national reading assessment exam from paper and screen while recording eye movements. As part of the preparation for the actual experiment, the researchers asked about their experience with reading from paper and screen and determined that many students proposed they comprehended better when reading from screens.

The reading examination itself involved passages of text each followed by several questions. Readers could refer to the written material when answering the questions. The eye movements indicated that readers referred back and forth between text and questions more times while reading from the screen to answer the questions. The researcher found that the readers had greater difficulty understanding when reading from a screen. This makes sense, but the readers still performed more poorly on the questions which would seem to indicate they were aware that they did not understand and could not completely remedy the difficulty.

Solutions

I don’t see online reading as a behavior that will disappear or diminish in frequency. I wonder about the hypothesis that poor habits are developed from heavy exposure to the scanning behaviors that many apply in texting and scrolling through social media feeds such as Twitter, Mastodon, and Blue Sky. I admit my perspective differs because I am a reader of books and journal articles on digital devices. I try to remember that my age means this style of reading (long form) predated any exposure to reading on a device. So my habits could be different allowing strategies of reading to differ between long-form and the types of text more suited a superficial approach.

Shanahan proposes that if technology environments encourage maladaptive behaviors why couldn’t they be engineered to encourage more effective behaviors? I think there are already such opportunities with longer-form content, but the opportunities are both ignored by most readers and not taught in educational settings. The exporting of annotations and highlights I rely on is seldom a part of the reading process of most learners. Here is another example of the transfer of a habit. Younger students are not allowed to highlight or write in their print books and the highlighting behavior of college students has frequently been criticized. I have written extensively about a variety of ways in which educators and learners can increase their active engagement with digital content. I call this collection of techniques layering because the strategies are external to the text itself and yet provide opportunities for processing text (and video) in more cognitively active ways.

Maybe improved awareness is also important. If skimming can be understood as a habit sometimes applied inappropriately in some settings, greater metacognitive awareness of goals and content would seem to be important. Concepts such as deep reading versus recreational reading are not new and the concern for shallow reading on devices is just another version of this older way of thinking about what learning from reading requires.

Sources

Carr, N. (2020). The shallows: What the Internet is doing to our brains. WW Norton & Company.

Etchells, P. (2024). Unlocked: the real science of screen time (and how to spend it better). Hachette UK

Jensen, R. E., Roe, A., & Blikstad-Balas, M. (2024). The smell of paper or the shine of a screen? Students’ reading comprehension, text processing, and attitudes when reading on paper and screen. Computers & Education, 219, 105107.

Jonassen, D. H., & Carr, C. S. (2020). Mindtools: Affording multiple knowledge representations for learning. In Computers as cognitive tools (pp. 165–196). Routledge.

Singer, L. M., & Alexander, P. A. (2017). Reading on paper and digitally: What the past decades of empirical research reveal. Review of Educational Research, 87(6), 1007–1041. https://doi.org/10.3102/0034654317722961.

The benefits and harms done by our devices and the ways we use them is one of those topics likely of at least marginal interest to most people. We wonder whether we have become too dependent and whether existing interests and capabilities have deteriorated. If we are a parent or educator we likely have additional unique interests. Have I messed up my kid by letting him or her use a phone without supervision? How do I keep my students on track in the classroom and are they using AI to complete my assignments quickly without any actual educational benefits? 

My personal interest goes a bit further in that I spent my career preparing educators to use technology in their classrooms and I by training feel responsible for understanding the science behind recommendations made to others. So, I review the research on many topics. From this perspective, I would offer the following observation – it is seldom as simple as it seems. I know this to be true in the social sciences which would be the field covering phone use and the impact on development and learning. This is the case for many reasons and far from a topic I can thoroughly consider here. One comment I used to make to my own students may be helpful. For context, I was trained as a biologist who became a psychologist. My comment was – students tend to see a discipline like chemistry as more sophisticated and complicated than psychology. Consider that the chemicals in that beaker don’t decide whether they feel like reacting today. People are different. 

Back to the issue of our devices and the research on how we are being impacted. I recently read a book by Jonathan Haidt titled The Anxious Generation. Many people must have read this book because it topped the New York Times best-seller list for several weeks which I interpret to mean many parents and educators are concerned and were attracted by the topic. Recommendations made in the book, for example, no phones in schools, have been implemented in many schools. I read Haidt’s book in response to such changes concerned my pro-technology advocacy should be tempered. I admit my initial reaction was that Haidt made a reasoned and evidence-based case. However, I have learned that such secondary sources need to be vetted and I was concerned by some of Haidt’s rationale which I would describe as “what else besides iPhones and Instagram” could explain the mental health issues of adolescent females. The “what else could it be” logic just seems weak. In reaction, I read a related, but less well-known book by Pete Etchells and as I suspected the issue is complicated. 

What follows is my summary of the two books with some related comments. 

Comparing Perspectives on Screen Time and Mental Health

Haidt’s “The Anxious Generation”

In “The Anxious Generation,” Jonathan Haidt really covers two broad problems he contends have changed the mental health development of children and adolescents. Children have been overprotected in their daily lives resulting in a lack of play and autonomy involving risks and consequences. In contrast, the author argues that the rise of smartphones and social media have been largely unmonitored and unrestricted with negative consequences to mental health. I will comment on both claims, but emphasize technology use. 

Haidt highlights the following key points:

Slow-Growth Childhood: Human children have an extended childhood compared to other mammals, providing them with time to learn through play and social interactions. This extended childhood is crucial for emotional and cognitive development.

Importance of Play: Play-based childhoods are essential for healthy development, fostering skills such as emotional regulation, social competence, and creativity.

Safetyism: Haidt argues that the overprotective parenting style prevalent in the 1990s, termed “safetyism,” limited children’s opportunities for risk-taking and independence, further contributing to anxiety. Haidt uses example older folks will find familiar – e.g., playground equipment that is no longer allowed for safety reasons and reduced independence in moving about the neighborhood or in solo trips to the store.

Negative Impacts of Smartphones: These problems include:

Social deprivation: Replacing face-to-face interaction with screen time.

Sleep deprivation: Late-night phone use cutting into sleep at ages when necessary. The early school start for adolescents is an example of when this becomes a problem.

Attention fragmentation: Constant notifications and multitasking impairing focus.

Addiction: The design of social media platforms promoting addictive behaviors. Etchells will reject this notion if understood as a physiological addition as in drug use.

Puberty as a Sensitive Period: Puberty is a critical period for brain development, making adolescents particularly vulnerable to the negative effects of social media. Hence, experiences allowed at other ages may do less damage.

What evidence suggests that puberty is a “sensitive period” for social media use?

Brain Development: Puberty to a greater degree than the earlier period of childhood involves the pruning of neurons and the strengthening of connections, processes influenced by experiences. This heightened neuroplasticity implies that experiences during puberty, including social media use, can have a greater and more enduring impact on brain structure and function.

Orben’s Research: Amy Orben, found that negative correlations between social media use and life satisfaction were more pronounced in the 10-15 age group, encompassing puberty, compared to older individuals (16-21). This finding directly supports the idea of puberty as a sensitive period for social media’s influence on well-being.

Social Learning and Conformity: Adolescents are conformers, a process that social media platforms can strongly influence. Puberty marks a time when individuals are particularly sensitive to social cues and peer influence, seeking to establish their identity and social standing. Social media platforms, with their emphasis on likes, followers, and influencers, can hijack this natural drive for social learning, potentially leading to distorted perceptions and unhealthy comparisons. Giirls are most vulnerable to social media’s negative effects between 11 and 13, while boys experience this heightened vulnerability between 14 and 15. This timeframe aligns with typical puberty onset, further suggesting that the hormonal and social changes during this period make adolescents more susceptible to the pressures and comparisons prevalent on social media.

In conclusion, the sources provide converging evidence pointing to puberty as a “sensitive period” for social media use. This sensitivity stems from the interplay of rapid brain development, heightened social awareness, and the drive for conformity, all characteristic of adolescent development. This information suggests that social media use during puberty requires particular attention and potentially different approaches compared to other age groups.

Haidt makes use of historical data linking changes in mental health data over the years to changes in the technology available to young people.  Much of Haidt’s argument about the damage done by access to the Internet is made by tracking mental health outcomes against dates associated with changes in technology. Gen Z started to reach puberty in 2009. iPhones became available in 2009. Social media capabilities such as retweets and likes became available in 2009. Front facing cameras in 2010. Facebook acquired Instagram in 2012. His challenge to critics is what else could have accounted for the changes in adolescent mental health that correspond to this time frame.

Recommendations: Haidt proposes four solutions to address this issue: no smartphones before high school, no social media before age 16, phone-free schools, and promoting more unsupervised play and childhood independence.

Etchells’ “Unlocked”

Pete Etchells, in his book “Unlocked: The Real Science of Screen Time (and how to spend it better)” offers a more nuanced view of screen time and its relationship to mental health. 

Etchells first notes that the consequences of technology and social media likely include both positive and negative impacts so explanatory models must take into account both possible outcomes. Etchells’ key arguments include:

Screen Time is Complex: Etchells argues simply measuring the total amount of screen time is insufficient to understand its impact on well-being. What matters is how screen time is used, the specific apps and content consumed, and the context of use.

Limited Evidence for Strong Negative Effects: While some studies have shown correlations between screen time and mental health issues, the evidence is mixed, with many studies showing small or inconsistent effects. The existence of correlations even if the correlation reflected a causal relationship with negative changes in mental health as a consequence do not necessarily show large effects. The impact is typically small. 

Etchells critiques the reliance on anecdotal evidence and highlights methodological limitations in much of the research. One of the most important methodological limitations appears to be whether self-reports versus verifiable behavioral data are used. Even though digital devices are great at recording data on use (consider your iPhone and your daily report of time spent on different apps) many studies ask users to estimate their time spent and activities experienced. When studies using self-report data are compared with studies using the more accurate data collected by the devices used, significant relationships are typically found only with the self-report data. Etchells speculates that user awareness of screen time issues and potential negative consequences results in participants in these studies interpreting negative personal experiences as an indication of too much screen time. How individuals feel about their screen use and their perceived self-control are more strongly related to well-being than objective measures of screen time.

Importance of Habits over Addiction: Etchells argues that framing excessive screen use as “addiction” is unhelpful, as it focuses on abstinence as the only solution. Instead, he advocates for viewing screen use as a set of habits that can be modified to promote well-being. The author argues that the assumption of a physiological explanation (probably dopamine) within the framework of addictions such as drug abuse is what readers of many of the negative books assume. Addiction tends to ignore agency and motivation. This is where the notion of habits also fits and solutions that are more nuanced with a focus on decision-making and awareness.

Oversimplification of “Screen Time”: Etchells argues that simply measuring the total amount of time spent in front of screens is too crude a metric to be meaningful. He emphasizes that “screen time” encompasses a vast range of activities, from educational apps to social media to video games, each with potentially different effects on well-being. He contends that focusing solely on duration ignores the crucial factors of content, context, and purpose of use.

Reliance on Anecdotal Evidence: Etchells criticizes the prevalent use of anecdotal stories to support claims about the negative impacts of screen time. While these stories can be compelling and relatable, he argues they often lack scientific rigor and can lead to biased conclusions. We like stories because we often can relate to such examples and do not necessarily make the same connections with the data in graphs and statistics. Of course, coming up with examples that fit a perspective does not necessarily fit what is most common in a sample of participants. He points out that reliance on anecdotes is particularly problematic in a relatively new field like digital technology research, where robust, longitudinal data is still limited.

Methodological Issues in Correlational Studies: Much of the research on screen time relies on correlational studies, which can only demonstrate associations, not causal relationships. Etchells highlights the problem of “third variables,” unaccounted-for factors that might influence both screen time and mental health, leading to spurious correlations. For example, pre-existing mental health conditions, family dynamics, or socioeconomic factors could contribute to both increased screen time and negative well-being outcomes, creating the illusion of a direct link where none exists. Correlations showing relationships do not necessarily convey the magnitude of an effect. A significant correlation in a large population may be associated with a very small impact.

Lack of Theoretical Framework: Etchells argues that the field lacks a robust theoretical framework to guide research and interpret findings. He suggests that without clear theoretical models, researchers are left to “grasp at straws,” making tenuous connections between screen time and a wide range of outcomes without a solid foundation for understanding the underlying mechanisms. This lack of theoretical grounding makes it difficult to develop testable hypotheses and draw meaningful conclusions. Haidt’s “what else could it be” argument fits here.

Ambiguous Terminology: Etchells points out the imprecise use of terms like “addiction” and “attention” in screen time research. He criticizes the tendency to label excessive screen use as “addiction” without sufficient evidence of a true physiological dependence. He also argues that “attention” is often used interchangeably with “self-control,” leading to conceptual confusion and misinterpretations of research findings.

Insufficient Attention to Positive Effects: Etchells argues that the focus on potential negative consequences of screen time has overshadowed research on its potential benefits. He acknowledges that excessive or problematic screen use can be detrimental, but he emphasizes the importance of recognizing the many ways in which technology can enhance learning, communication, and social connection. He encourages a more balanced approach that considers both the positive and negative aspects of screen time.

In conclusion, Etchells urges caution against drawing sweeping conclusions about the impact of screen time based on the existing research. He advocates for more nuanced investigations that consider the complexities of technology use, adopt rigorous methodologies, and develop strong theoretical frameworks to guide future research.

Comparing Haidt and Etchells

While both Haidt and Etchells acknowledge the potential downsides of excessive screen time, they differ significantly in their overall perspectives and conclusions:

Emphasis on Negative Effects: Haidt emphasizes the negative impacts of smartphones and social media, attributing a rise in mental health problems directly to these technologies. Etchells, on the other hand, acknowledges the potential for harm but argues that the evidence for strong negative effects is weak and often overstated.

Role of Social Media: Haidt sees social media as a particularly harmful force, promoting conformity, comparison, and addiction. Etchells recognizes these potential issues but maintains that social media can also have positive benefits, depending on how it is used. The existence of both positive and negative outcomes argues against simplistic abstinence/avoidance and urges more careful personal awareness and control. 

Solutions: Haidt proposes strict limitations on smartphone access and social media use, advocating for a return to a more play-based childhood with less adult supervision. Etchells focuses on promoting healthy screen habits, emphasizing individual agency and self-regulation rather than strict restrictions.

Conclusion

Haidt’s “The Anxious Generation” presents a compelling, albeit alarming, argument about the potential downsides of technology for young people. However, Etchells’ “Unlocked” offers a more balanced perspective, urging caution against oversimplifying the complex relationship between screen time and mental health. The key takeaway is that understanding the nuances of screen use and focusing on developing healthy habits is crucial for mitigating potential harms and maximizing the benefits of technology. Both books highlight the importance of critical thinking and evidence-based approaches when evaluating the impact of technology on our lives.

Sources:

Etchells, P. (2024). Unlocked: the real science of screen time (and how to spend it better). Hachette UK

Haidt, J. (2024). The anxious generation: How the great rewiring of childhood is causing an epidemic of mental illness. Random House.