Learning Aloud

A model of models

Attempting to explain how learning and thinking work is not easy. After doing this for a living, I am still uncertain what is the best approach. We all know we know things and we all know we learn, but just how we did and do this is often not obvious. To help understand, I would suggest we make use of abstractions that simplify, but still have utility. To have value, a model or abstraction must be consistent with data (observations of how a process works) and be useful in suggesting testable practices.

I could use the jargon of my profession and theoretical perspective on this profession, but I propose that the concepts of thinking and model both meet the conditions I have identified. Thinking implies learning requires mental work (at least most kinds of academic learning). Easy to understand attributes of thinking are helpful. The learner is the one who thinks (or not) so learning ultimately depends on the activity of the learner no matter the circumstances external to the learner. However, others people and experiences outside of ourselves can encourage us to think. We think about things with which we are already more familiar more easily hence what we already know is important in how easily and how successfully we can think. The thinking we do that uses existing knowledge and skills can result in the modification and improvement of what we already know and this is what most mean by learning. These are the basics.

What we accomplish by learning means several different things. We know things after learning that we did not know before. We can do things after learning that we did not do before. We acquire understanding and capabilities. We also acquire information which may or may not be part of understanding.

The idea of a model is helpful in thinking about both understanding and capabilities. The construction of models of understanding and models as actions is a natural process. By natural I mean we as individuals construct models continuously whether to deal with mysteries of daily life or because of formal education. To help students understand and think about the commitment we continuously make to models, I like to ask students what they mean by the concept of a theory. A theory is one type of model. Students often use the word theory in a condescending way perhaps dismissing the information (models) acquired in a course as theoretical implying that theories are not useful. To the contrary, I claim, theories are how we think and if we do not acquire theories we apply from formal instruction, we make them up based on personal experience. A theory, personal or formal, is the abstraction we use to understand, to predict, and decide on action when we encounter a unique life experience – pretty much any new input from the world. What is kind of cool (interesting) is that individuals are quite capable of storing contradictory or at least inconsistent personal and formal theories about the same phenomena. In other words, we may develop one interpretation of a certain kind of situation based on our life experiences that is different than an interpretation we are taught. How can this happen and what can be done about it? This can happen because an external experience activates one internal model or the other. This is one of the frustrations of education. We can help learners acquire a model of the way something works in the world. They can understand this model and use it effectively within the classroom setting, but they can still revert to their own primitive model of how something works when encountering circumstances in their daily life to which the more formal theory should have been applied.

This was a very long introduction to get to the core issue. What are the models educators use to guide their work in helping students learn? This could be a question of whether or not educators activate formal models or personal models to guide their practice. Given what I have said about formal models and naive models (this is the term applied and the intent is to describe models built from field experience without the use of formal guidance), this could be a great topic to consider. I will have to save a discussion of this distinction for another time. Here, I want to share my personal bias when it comes to the utility of several formal theories.

Models of learning

Somewhere in the preparation of educators, most are exposed to multiple models of learning. At the least, they have probably been told about behaviorism, cognition, and constructivism. Recently, some preparation experiences may include some biology – brain structure and function. Certainly, biology and biochemistry have the potential to describe learning most accurately. I think an important issue is whether a more accurate description advances education or not. My personal opinion at this point in time is that our understanding of how the brain functions in learning is rather crude and I am aware of very little that improves on what other models describe and explain. I have an undergraduate major in biology but that was a long time ago and what I know now I describe as the content included about brain biology in your average Introduction to Psychology textbook. The one exception I can think of to my claim that there is nothing unique about a biological perspective is neural plasticity – the finding that long term experiences of a type can restructure the brain to predispose individuals to different patterns of mental behavior. I believe this idea is helpful. However, the interpretation of this phenomenon within education has also been generalized in ways that I think are inaccurate and certainly not a basis for significant changes in practice.

Here is my short list of models (actually categories of models) of learning and a very brief comment on what I see as the core mechanism of each.

Behaviorism – emphasis on external events and consequences that increase and decrease the frequency of behaviors.
Cognitive – constructivist (macro) and information processing (micro) – mental activity under the control of the learner. Thinking develops internal models.
Biological – chemical and biological action and storage (internal). Learning results in changes in the brain (vague) and must be accomplished by a combination of chemical and electrochemical actions taking place in physical structure some of which are specialized to accomplish certain things.

I find the concept of fidelity useful in understanding learning. Fidelity could be defined as the exactness of fit between a model and the actual thing/process. One might think that the more exact the fit, the better. We have learned from research on the use of simulations in learning that this is not the case. With simulations, in the early stages of learning, too much realism (match) can overload, confuse, and in some cases produce unproductive emotions. For example, the training of pilots does not begin by putting a novice in the pilots seat and letting him/her explore. The experience would be overwhelming and certainly terrifying. Typically, training makes use of experiences in simulators that simplify the experience to a limited number of actions and possible reactions. Using various techniques and equipment, more and more realism and experiences are added until the more experienced individual can deal with the emotions and complexity of full application.

I see a similarity in the usefulness of models of learning. Behaviorism offers little insight into mental behavior (I think supporters leave that to the biological researchers) and is really more a model of instruction (manipulation of external events). I regard behavioral models as useful for understanding and investigating incentives. I see biological models as eventually having the potential for high fidelity, but I see these models at present as mostly descriptive. At best the future might provide a level of understanding that encourages practices through a process of find out how to produce a given combination of chemical and anatomical conditions. I see the cognitive models as most useful, but differing in level of fidelity with information processing models offering a more detailed level of process clarity. Constructivism offers a broad perspective, but may or may not be sufficient to propose useful interventions. Especially when what seems like a productive process is not, analysis based on information processing models is often usefuL

Models of learning, models of instruction

Comparisons of approaches generated from models of how learning happens are important. Approaches may differ in the external events created, but any event allows “thinking” by learners. The relative effectiveness of different approaches is important. Putting down books, lectures, worksheets, life experiences, or task of one format or another all offer some type of input that learners will attempt to process. The capacity to point to idiosyncratic cases of students who learned from this or that experience is not really justification for much of anything. It is the relative productivity of one approach in contrast to another within defined requirements for a common set of learning circumstances (group size, time allowed, variations in past experiences, etc.) that provide the basis for application.

Arguing that one model of instruction based on this model of learning is superior seems pointless without data allowing those who must evaluate these claims. Models offer different ways to think about learning. These can be helpful in the design of learning experiences, but ultimately, it is the response of learners exposed to these experiences that matter.

[Image included purchased through the Noun Project]

Thinking about computational thinking and professional roles

I have been reading a lot lately about computational thinking and trying to decide what I think about this concept and trying to decide exactly what I think it is. To be clear, computational thinking as an educational goal and learning to program as an educational goal should be differentiated. “Should” is my opinion, but I think it is fair to suggest that as goals these concepts are about accomplishing different things.

My thinking on “computational thinking” goes back a long way. I read all of the Seymour Papert books and included a chapter on programming in the book my wife and I have written for many years. I understood that Papert had a broad vision of what programming could accomplish, but I also read the research challenging the position that programming experiences provided as an educational activity accomplished what I now see described as “computational thinking”. It is not just me that is making this connection (see this recent description of Papert’s work) and it is not just me that thinks the “coding to develop skills other than coding skills” is not really that well supported.

EdTech evangelism related to Papert’s work

Papert’s contribution

In thinking about the recent resurgence of the notion that learning to code offers broad benefits, I have identified what I see as important and interrelated sub-issues. The first is whether a notion such as computational thinking adds anything to the existing prioritization of “higher order” thinking skills. I guess when you get right down to it, both computational thinking and higher order thinking are abstract and likely are interpreted in different ways by different people. The issue is that it seems to me administrators and practitioners buy into a simplistic version of “computational thinking” not necessarily and hopefully not intended by the theorists/researchers and maybe even the evangelists promoting this cause. This simplification may encourage an unsuccessful implementation because the intended approach has been simplified in a way that the intended skills are not developed and not even taught.

I have two issues related to implementations of what Papert decided to call “constructionism”. Note that constructionism as a philosophy/learning theory or whatever it should be called is not the same as constructivism. The constructs can be related, but constructivism is broader and has a different focus. Constructivism argues that individuals create their own understanding. This is mental activity and exactly how this works is vaguely defined beyond suggesting that learning amounts to bringing external experiences in contact with existing knowledge potentially resulting in the modification of existing knowledge. Simply put – bringing together amounts to thinking. No one can think for you hence you ultimately learn only if you make the effort to think. Constructivism avoids the details. I rely on the understanding of thinking as cognition to handle the details. I don’t see cognition and constructivism as necessarily at odds.

Constructionism (Papert’s idea) emphasized external building (coding) to influence internal thinking. What counts as external building is unclear to me. Those promoting the broader concept of making expand the notion. I also think it obvious that external activity does not necessarily equate to productive thinking. You can certainly have thinking without building (I hope you think when reading this post) and you can have building without thinking (my favorite example if the amount of chemistry we learn from baking).

I have two questions when I think about constructionism and computational thinking.

Question 1: Are these skills and the suggested methods for developing them really new? I don’t think I really know, but I have an opinion. The question matters because we should not be starting over if we already know important things about what it takes. I apply this question in considering two concepts – making (Papert’s constructionism) and computational thinking.

The maker movement reminds me of all of the past work on generative learning. Generative learning was a broader concept (in my opinion) suggesting that it may be productive for some learners to be engaged in external activities to encourage productive thinking behaviors. For example, if learning results from the integration of existing knowledge with new information/experiences, we have long asked learners questions to encourage them making the effort to find such a connection. Being prompted with a question may seem very different from making the effort to create a program to make the computer do a specific thing, but both are external tasks hoped to encourage productive mental behaviors. What have we learned from research on many types of tasks about when the tasks are actually generative and when they are not?

Higher order thinking is not new. Problem-solving as an example is not a unitary process. Many efforts have been made to identify the components of problem-solving, to propose how these components skills might be developed, to evaluate whether these efforts to encourage learning tend to be successful, and whether development in one domain transfers to others. Note that the argument for developing computational thinking for all assumes transfer. It is more than developing programming skills. It seems to me that the subskills and dispositions associated with computational thinking have yet to receive nearly as much attention as that devoted to problem-solving, but I see planning, understanding that debugging is not failure, abstraction and instantiation, problem identification and problem-solving frequently mentioned. Are these skills unique to coding and is coding really the most efficient way to develop these skills within the school environment. Do we know anything that is relevant from past work?

Question 2: Why the enthusiasm for unproven ideas (coding to develop computational thinking, computational thinking as unique from other ways to describe higher order thinking) and why the superficial attempts to implement?

Regarding the enthusiasm for these “new” ideas, here are a couple of ideas that may apply to administrators and classroom educators.

I have been reading Dan Lyon’s recent book Lab Rats . The book criticizes the misapplications of processes and ways of thinking that have resulted from the digital economy. One of the issues Lyons focuses on is the willingness of management to move from unproven practice to unproven practice resulting in serious disruption to organizations and to employees. Why? Lyons proposes that these companies exist within a fearful environment. Change and competition produce uncertainty and fear of what the future might bring. There is this sense of urgency that something must happen making decision makers easy prey for evangelists touting this or that new approach. At least trying something new gives the impression of doing something.

My second thought might be described as “who sweats the details”? A related position is that we all sweat different details. Careful reading of Papert or Aspinall (a new computational thinking evangelist) and the existing relevant research should indicate a careful and realistic picture of what classroom application would look like. I have had the time and the inclination to review this content at this level. I can sweat these details as a function of my professional responsibilities. Few classroom teachers can make this commitment because they must sweat other details. The danger that can result from these different emphases is that there is a resistance to considering what these two different professions require (academic and practitioner) and a reluctance to appreciate what each offers. I don’t pretend to be able to tell a fourth-grade teacher working with a group of students I have never met how to do many things teachers must do on a daily basis. However, I feel perfectly justified telling this teacher that a couple of experiences allowing students to explore Scratch or Ozobot programming will result no benefit to higher order thinking. At best, I think I could explain the basics of the experiences that might have a chance of being successful, but even then I am doubtful of transfer to most “problem solving” tasks these students will encounter. The teacher would then have to decide whether he/she feels it practical to make adjustments necessary to meet these basics.

Is this egotistical? Some might think so, but I think it reflects an honest description of what different professionals have committed to do. There would certainly be exceptions, but individuals would need to be brutally honest in considering whether they qualify as an exception or not.

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Why computational thinking?

I wish I could convey my opinion on this topic in a way that is both concise and persuasive. I just don’t think I can. Part of the problem is that the way so many educators think about computational thinking, if they think about it at all, is part of a bigger set of topics that includes coding, computer science, STEM, occupational preparation, and equity of who can eventually take advantage of the occupational opportunities of working with technology. I have different opinions about many of these topics in isolation, but too many in my opinion see the topics as tightly integrated. Second, I question what many understand computational thinking to be and exactly why they think it is valuable. How specific of a description could you provide?

I have this mental image of a fourth-grade teacher engaging her class in an hour of code activity suggesting to parents that these students are being prepared for occupations of the future and are simultaneously developing valuable computational thinking skills that are helpful in many aspects of life. It is my opinion that this educator while unable to write code herself or himself is aware that digital technology relies on code that someone has written, that someone must have the skills to write this code, and that there are many many vocational opportunities for those with the skills to write code. More important to this blog post, the notion of computational thinking is not something the educator can really describe, but this type of thinking is evidently potentially of value to the improvement of performance in other academic areas and eventually when developed in many areas of life. Somehow computational thinking, whatever it is, can be developed through learning to code and these brief activities arranging blocks representing commands that control the dancing cat on the screen are making a contribution to this learning.

I, in no way, am demeaning the capabilities of this hypothetical educator. I am attempting to describe what I think is a very common situation. I admit despite purposeful attempts to read the literature on computational thinking going back to Papert’s works and the careful research on just what students learning to program in LOGO learned and despite thousands of hours writing code myself that I find myself in a somewhat similar situation.

I can point interested parties to definitions of computational thinking and to “standards” intended to identify what must be developed to advance this capability. I will provide what I think is a nice source shortly. I think I understand what at least some of the skills and dispositions described as components of computational thinking are and I recognize the presence of examples of these components in various advanced academic domains.

So, for example, computational thinking involves developing and thinking in terms of abstract, multilayered models. These models are descriptive at one layer, but link to actions (code in the case of programming) and data at other levels. By my understanding, you see examples of these multilayered models in:

Macroeconomics

Biochemistry – Krebs cycle –

Psychology – cognitive models such as information processing theory

Education – Flower and Hayes Model of the writing process

You see methods for testing models in statistical procedures such as path analysis and structural equation modeling.

You can work through this type of exercise yourself if you are trying to understand how to see the presence of some of the components of computational thinking in other areas.

It is not that I don’t see the value in learning many of the skills and dispositions frequently described as computational thinking. It is not that I don’t see the skills and dispositions as important to coding. So, what is my hangup?

Let me start this way. I have just read a report from a group that has labeled itself as Digital Promise entitled Computational Thinking for a Computational World. This report is obviously pro computational thinking. The report goes through the traditional arguments for programming – there are lots of jobs and technology is and will increasingly play an important role in all aspects of life. Then, the authors add an argument based on computational thinking. In support of the general utility of computational thinking, the authors differentiate coding (the specific skill of programming), computer science (coding plus other issues such as ethics, social impact), and computational thinking (the thinking skills and dispositions programmers apply in programming). The present these three topics as a Venn diagram both to argue that they are interrelated, but also independent. So, the thinking skills and dispositions used by programmers have broader utility than just to be used in coding (I guess this is the intent of showing that the circles representing computational thinking and coding are not the same circle.) The leap of logic that is encouraged is that coding and computer science encourage the development of these thinking skills and dispositions that have this broader utility. As a psychologist, I am most familiar with labeling this final claim as transfer.

I will return to the question of transfer at a later point.

Perhaps I can do better than use descriptors such as thinking skills and dispositions. There are plenty of examples of other writers making the effort to be more precise in identifying these components. One of my “go to” efforts of this type is provided by the paper entitled “Demystifying computational thinking” by Shute & colleagues (2017). The authors do a nice job of describing the historical background of the effort to identify these components, points to a number of efforts to do so, and offer their own list.

The authors do one other thing I believe is helpful. Again using a Venn diagram, they identify the overlap and uniqueness of computational thinking and the thinking required in another discipline (math). It was this Venn diagram that offered a way to think about my core concern with the emphasis being placed on computational thinking. If thinking skills and dispositions transfer (a complex topic itself), what is unique about the skills involved in coding. Why is coding better than any other domain in which abstraction, model building, testing models against data (experience), modification of approaches when this match is not particularly good (debugging), etc.? For example, how is computational thinking different from whatever one would describe as the collection of skills and dispositions involved in writing? Writing has the advantage of being an important life skill and a generative activity fairly easily adapted to the processing of information and the externalization of the cognition associated with procedures involved in most content areas (writing across the curriculum). Admittedly, writing activities may not presently be implemented to these ends, but why retool to a new area of emphasis, coding to develop computational thinking, when improvements in enhanced writing opportunities, say multimedia writing to persuade, to analyze, and to explain, could offer a more efficient way to develop similar thinking skills and dispositions. Wouldn’t the professional development necessary for teachers to make more effective use of writing as writing to learn be easier to implement than the professional development necessary to prepare most teachers to use coding to learn. Maybe writing across the curriculum needs a similar catch phrase that implies similar benefits to computational thinking. Writing across the curriculum may just need a new marketing campaign.

I suppose one could argue that the overlap in skills noted by Shute and colleagues offer a different insight. Wouldn’t it follow that skills developed through coding also apply in math? Perhaps, but since math is already taught through multiple classes why add an independent and additional way to develop these core skills. Second, see the comments on transfer that follow. A preview – skills are not skills. The version of problem-solving developed through programming is not the same as the version of problem-solving developed through learning and applying math.

A few words about transfer. First, the expectation that general, higher level skills can be developed sounds great, but is very challenging to get done. Problem-solving or critical thinking in one content area do not automatically work in another area (Perkins & Salomon, 1989). It is not that skills developed in one area cannot offer an advantage when encountering what could be similar situations in another area, but that certain conditions must be met to make this transition likely. Perkins and Salomon (Salomon & Perkins, 1989; Salomon & Perkins, 1989) describe a couple of ways to accomplish this goal they label high and low road transfer. Low road transfer takes a lot of time and varied experiences likely impractical in K12 settings and high road transfer requires mediated experiences (instruction) most educators would find they were unprepared to provide. Just to be clear, the ideas advanced by Salomon and Perkins were based on their careful analysis of efforts to demonstrate transfer for students exposed to LOGO programming.

So, what is my present thinking.

First, programming is a valuable vocational skills and the opportunities to explore should be made available. There are challenges. Most programming courses are AP courses and this does not seem like it makes sense as a “coding for all” opportunity. Fitting programming courses into the curriculum in many schools is a challenge and many states do not allow a programming course to count toward math or science requirements. Then, there is the challenge of finding educators with the background to teach these courses. Given what is required for far transfer, skilled instruction is necessary. If we are serious about this area, addressing some of these challenges should be a first step.

The question of which skills and predispositions are developed by which tasks is even more complicated. At present, I don’t see a unique benefit for computational thinking as distinct from other generalizable thinking skills and dispositions that have a history of being developed in other more convenient ways. I could be convinced I am wrong, but I don’t see the research to make this case at present.

Resources:

Digital Promise (2017). Computational thinking for a computational world

Click to access dp-comp-thinking-v1r5.pdf

Perkins, D. N., & Salomon, G. (1988). Teaching for transfer. Educational leadership, 46(1), 22-32.

PERKINS, D., & SALOMON, G. (1989). Are Cognitive Skills Context-Bound? Educational Researcher, 18(1), 16–25.

Salomon, G., & Perkins, D. N. (1989). Rocky roads to transfer: Rethinking mechanism of a neglected phenomenon. Educational psychologist, 24(2), 113-142.

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158.

Low use of purchased software?

A recent analysis of K12-level software usage entitled “Towards understanding app effectiveness and cost” written by R Baker and S. Gowda offers a dismal picture of the actual commitment to purchased resources. Using data collected by the BrightBytes Learning outcomes module, analysts were able to determine the committed time and frequency of use for free and purchased apps. The researchers were also able to track gains in some areas using standardized tests administered near the beginning and end of the study. The researchers offer a complete project description in addition to the summary provided online. I have requested the complete study, but the holiday season may have meant the researchers have not yet received this request.

Highlights from this study have been circulated. The online highlights that I have read feature some attention-grabbing data. The median activation of licenses sold was 30% and only 3% of apps reached the level of activity defined as extensive use (10 hours or more during the duration of the study). The data were collected from 58 districts comprising 845 different schools so the sample was substantial.

Some of the most frequently used apps are listed below. The statistic used for comparison is the median number of days on which the app was used. I am unable to report on the exact methodology, but I assume these data require that a school make available a specific app in a given classroom. What is not provided in the overview is the number of schools/classrooms out of all possible schools/classrooms that installed a given app so the comparisons across apps are difficult to interpret. Some apps on this list are free and serve a general function (e.g. Google Drive). Some are more targeted to a specific content area and require payment.

Cengage Learning DigitalAce – 31 days
Sherpath – 19
Spanish Lessons – 13
Big Universe – 10
Zern Math – 10
Tenmarks Math – 9
Carnegie Learning – 8
Google Drive – 8

Some apps showed significant correlations between amount of use and standardized test gains. Some did not.

The data that seem to be generating the most reaction is the low level of overall use for these apps.

As I suggested, these data have encouraged a reaction from several bloggers.

Doug Johnson says that the district he represents also uses the BrightBytes tool to track usage within his district. He says that the level of usage within his district would be far higher and the reason that such data were collected in the first place is to determine what software to keep and what to replace from year to year.

Thomas Arnett offered a reaction I have seen most cited. He interpreted the results using his model of teacher “jobs”. He attempts to identify three of these jobs and explains teachers will make meaningful use of software only if it helps the teacher perform what they see as a job. Arnett also speculates that the very low “extensive” use of nearly all apps as teachers making some, but minimal use of apps as a way to meet administrator expectations.

I have my own opinion as what is going on in these data. I have written on several occasions about the pricing models associated with many applications. I wonder if some of what many might see as unexpectedly low activity is related to pricing models. Often apps are offered as a free “crippled” version, a price per class, and a price for a school. Purchasing the school level may seem easiest to implement and cost-effective, but might provide access to many educators not really committed to use. It would be interesting to know what were the expectations of those making the purchases. This situation might create a situation in which educators had access but did not and were not really required/expected to use the app. It also seems that a different usage picture would exist if educators were provided a budget and allowed to use this budget to purchase class-level access to apps.

Resurgence of RSS

I admit that I am old and perhaps subject to becoming set in my ways. I even have a blog entitled Curmudgeon Speaks which possibly explains a lot. I try to keep up on the newest thinking on technology in education and the newest relevant tools, but I still employ a workflow based in RSS and social bookmarking. The young and innovative may not even know what these services are and promote Twitter, Snapchat, etc. to keep themselves informed. Bah……

Or, maybe not. I found hope in a recent post, discovered on my RSS reader, entitled “It’s time for an RSS revival“. I am guessing this writer is half my age, but he thinks like me. Actually, his slant is a little different. In this nice review of what RSS is, he offers RSS as an alternative to social sources and algorithms. He positions RSS and the RSS reader as a way to control what you want to read.

A quick review. RSS (Really Simple Syndication) is a way to track updates to designated sites. In other words, what additions have been created since I last checked? You specify the sources – mostly blog sites for me. Software referred to as an RSS reader periodically checks these sites and identifies when something changes. The URL (web address) for the new content and some content from the change (the amount depends on what the author allows) appears in a list returned when you use the RSS reader. The reader keeps track of what you have looked at and typically removes this content from the list. Depending on the reader, there may be ways to keep the links to content you find useful. I typically keep what I find useful using another service (Evernote for me). So, to summarize – you identify websites you want the RSS reader to follow, the reader software identifies new content as it appears on these sites and creates a list for you to review, and typically when you review this list you decide to keep or ignore items from this list. Each entry on the list can be used to link to the full content from the original source.

My previous posts about RSS and RSS readers.

My recommendation – I would try Feedly. The article I link to in the body of this post contains some other recommendations.

One of the challenges in using RSS is finding good content. You select the original sources rather than follow what others recommend. This is a challenge to which each user likely has an ideal personal solution. Perhaps you have little idea what blogs relevant to your interests exist. I can offer my recommendations, but others who follow blogs probably differ in what they would recommend. It is possible to get greedy and identify far more content than you want to review. A RSS reader at least reduces this challenge to scanning post titles and snippets of content.

Here is my suggestion if you have no other idea about how you would get started. I use a personal RSS aggregator on my server. You can scroll through recent entries to see the titles of the blogs I follow and connect to these blogs. You cannot use this aggregator as your own reader because that would defeat the purpose of allowing me to eliminate the content I have viewed. You can generate a list of blogs you might find useful to add to the RSS reader you adopt.

My YouTube description of RSS and Feedly

Some ideas about protecting your privacy.

Intro comment – this is a very long post. If you are not interested in the background I provide about the online collection of personal information, skip about halfway through this post and you will find a list of specific things I describe that allow you some control over the collection of your information.

Information is becoming the currency of the future

I have been reading the book “21 lessons for the 21st century” by Yuval Harari. The book comes highly recommended by many. I find the picture of the future based on the trends of today as fairly disturbing. One of the issues that comes up again and again in this book is the topic of equality and the related concept of opportunity. It is not so much that folks in the future will not have their minimum needs met, but the likelihood that many will find themselves in situations allowing little meaningful contribution to society and in situations with factors over which individuals will have little control. In so many ways, the rich and privileged will get richer and the rest of humanity will be increasingly left behind.

Harari’s book offers a view of the future impacted by many factors, but he consistently singles out two – AI and biotechnology. The equity issue comes into play because all will not be able to use and benefit from these factors equally. And, over the years, those with greater access (families, groups, maybe countries) will separate themselves from everyone else. AI and biotechnology use data as the essential input and Harari’s reasoning leading to Harari’s conclusion that:

“If we want to prevent the concentration of all wealth and power in the hands of a small elite, the key is to regulate the ownership of data.”

I will likely write about Harari’s book again, but this post specifically addresses the topic of personal data and options for personal control of who is allowed access.

It is not so much that each of us should deny access to our personal data. So many innovations now and in the future will be dependent on access. Rather, it is that we understand the importance of the data we make available and understand and control who we allow to have access. Just to be clear, while I am concentrating on the data potentially generated by our online behavior, data are collected in many ways (e.g., credit card activity) and integrated across many sources for many uses.

Data based on your online behavior

You provide online data in many different ways. When you complete a Facebook survey about your personality or your likes and dislikes, you provide data. When you conduct a Google search, what you search for and how you respond to the results generated, you provides data. When you conduct pretty much any online activity that loads pages that involve “cookies”, you provide data. When you conduct any online activity that goes through your local ISP provider, you provide data. What you do and the frequency with which you do it probably is useful to someone who wants to know about you or at least people generically like you.

Let me start with this. I am not against the collection of personal data. The most likely reason for others to collect data based on your online behavior is to tailor the future information you are shown. Google uses data about you to offer you search results you are more likely to want to see. Many companies collect information about you to deliver ads and other information you will be interested in seeing. More generally, the benefit of providing you information likely to influence you also has value to you because it has value to those who value this information. This information has value to those “others” and they end up funding valuable services so you don’t have to. It is important to recognize that you do not pay for Google, Facebook, Twitter, etc. by sending money to the companies providing you access, content, and services. You pay with your attention and your information

Ads, transparency, and control

The online situation is complicated. You want to use these services and you likely find it beneficial that you pay nothing for most. The services need to make money and selling ads is a way to generate income. Note that it is not just the intermediaries (Google, Facebook, Twitter, etc.) who benefit because many content creators also get a cut sometimes when ads appear, but most likely when ads are pursued for additional information. The companies paying to have ads appear and now most importantly paying to have ads appear to those most likely to respond are the final party in this group of players.

I am not against ads and I doubt that blocking ads would allow the positive components of this model to continue. No ads, no free services or free content. What I find objectionable with ads is when information is shared between sites. In other words, I assume it is acceptable when the provider of a site collects information directly (this is the compensation for the service and in some cases the content provider), but not when information collected from the use of one service is shared with another service. It is the lack of transparency when information is shared across services that I think violates the assumption a user makes or at least should make when visiting a given site.

Here are all of the strategies I can think of that would allow you a greater degree of control over who sees your data and what is done with it.

Don’t send all of your data to the same providers

Use options

There are multiple search engines. You are not bound to Google. DuckDuckGo works well.

Limit your dependence on a given company for multiple services. If you use Google for search, there are other email services available. For example, those who use Apple hardware can use the Apple email system.

I also think it would be great if groups of individuals who want to communicate would consider using different services. The network effect is the challenge. This label means people use a service because the people they want to interact with use the same service. The network effect limits exploration and even the consideration of better services. Facebook and Twitter represent great examples. There are alternatives to each – e.g.. Mastodon for Twitter and Diaspora for Facebook. You don’t have to use alternatives continuously. However, those wanting to both explore technology and have a specific purpose for interacting could easily use an alternative service for this specific purpose. For example, educators wanting to engage in an EduChat could easily use Mastodon instead of Twitter for this specific activity.

Become more aware of how you are being tracked

If you use the chrome browser, consider adding the Ghostery extension. Ghostery is a powerful privacy extension. It blocks ads and stops trackers. The extension identifies the cookies that are associated with a given site and allow users to decide whether to block or allow in future visits to a given site.

Block ads that share information among providers

Again, if you are a Chrome browser user, consider the extension Disconnect Facebook™ pixel & FB™ tracking. This extension prevents Facebook from following you when you are not on Facebook.

Limit the information your ISP can collect about you (you already pay the ISP)

1.1.1.1 – This service takes a little more effort to install. 1.1.1.1 is an alternate DNS to that provided by your ISP. A DNS translates web addresses you request into the four number identifier used by servers. An ISP can use the information gleaned from DNS traffic to figure out which websites you’ve been visiting, even if you use HTTPS. By replacing the DNS of your provider with the DNS of a service that does not collect your information, you limit the information you share with your IP.

Consider a system that allows you to pay providers directly rather than pay providers with the information you allow to be collected.

Brave is a new browser now based on Chrome (this recent update is important because it allows users to install Chrome extensions). Brave blocks cookies and scripts unless a user acts to override this extension.

Brave allows a user to make a monthly contribution that is used to compensate the authors of visited sites as a way to replace potential ad revenue.

Argument analysis

An essential component of the critical thinking involved in issue-based disputes is the capacity to understand the logic and evidence offered in different sides of an argument/debate. Of the skills now deemed essential to 21st-century functioning, engaging in and understanding arguments may be among the most important. The openness of the online world and cable TV channels with specialized political foci would be examples of why the capacity to analyze positions has increased in importance. So, to compete with those who prioritize coding and STEM initiatives, I have been making the case for this overlooked, but critical skill.

I have tried to offer some suggestions for how argumentation/debate could be taught. One example would be the structured approach provided by Kialo. This is a tool that structures an argument for participants and increases participant awareness of the components of an argument as it is being advanced. This post focuses on a template for MindMup which is intended to be used to analyze an argument already made. The core goal in each approach is to increase the awareness of positions taken and related reasons and evidence for these positions. The capacity to step back and consider pro and con reasons and evidence is what is missing in so many naturally occurring debates.

MindMup (you probably note the similarity to MindMap) is a general purpose tool for organizing ideas. The argumentation analysis approach described in the link I provide above is a specialized template for this online tool. The advantage over a more general purpose “mind mapping” tool is the relabeling of common mind mapping tools (e.g., add reason, add objection). As an example, I have reworked a small section of a debate I hosted in Kialo as a MindMup visualization.