Category: General

I am a participant in a book study group that covers a variety of topics. The group has an interest in writing and the writing process that is sometimes the focus of what we read and discuss. The subtopic of the preparation of content for college classes comes up frequently and sometimes the role of Open Educational Resources (OER) as classroom resources. The discussion of OER and ChatGPT (another issue that keeps coming up) got me thinking about whether ChatGPT could be wielded to produce college textbooks.

I have written a couple of college textbooks and I still dabble (see left-hand column) so I understand the process. I would not use ChatGPT for the type of course I have focused on.

I have probably taught the Introduction to Psychology course 40-50 times in my career and used maybe 10 different textbooks. Some who write for this course may disagree with my assessment that the treatment of the topics in this type of course is generic and predictable, but this would be my description. I think most efforts for this audience take a predictable approach so that the instructors making book selections are comfortable with the book they assign. I would find it easy to predict chapter themes, chapter topics, and the classic experiments that are described and related implications. I wanted to see how much I could copy and paste from the content I asked ChatGPT to generate based on my prompts and if a reasonable body of content could be produced in this fashion.

I think the best way to go about this type of writing project is to approach it as developing and then expanding an outline. You ask ChatGPT to identify topics and subtopics and then ask it to describe or explain the individual elements of the outline created in this manner. You could then ask for classic experiments, examples of supporting research, and examples of the concepts described. ChatGPT should do most of the work with this approach.

I found because I knew pretty much what I was looking for based on my existing knowledge of this type of course I could create an expanding document in which I embedded this material within the outline I created in a second tool. So think of copying and pasting material from ChatGPT to Google Docs, Word, or Scrivener. First the outline of topics and subtopics and then the explanations and descriptions, the studies, examples, and applications, and perhaps an occasional story of a famous psychologist. I don’t see creating a final product purely by copying and pasting, but I would probably write certain elements myself (e.g., chapter introductions and conclusions) and generate transitions between the chunks of the content generated by ChatGPT so the work does not seem so choppy when read. Just having the lists ChatGPT generates would save me a great amount of time in putting together such a product. I would describe the process as spending far less time thinking and more time writing and the combination of my experience and the generative capabilities of ChatGPT should allow the generation of content much more quickly and with minimal effort.

Again, this approach works because of my familiarity with the topic and the type of course I am writing to support. I could not create a credible product if writing a similar introduction to a different field of study.

What follows are some examples of the process I have just described. The idea is to offer something concrete as generated by ChatGPT. It would be far too lengthy to include all of the pieces of content for an entire chapter so I have tried to generate examples of the stages of development I propose and some of the elements to be embedded within the expandable outline I have just described. 

I have selected a chapter on behaviorism as my example simply because the structure of the chapter is easy for me to conceptualize. I can begin by asking about the three main subtopics (operant conditioning, classical conditioning, and modeling) and let ChatGPT create the structure of the chapter from this initial prompt. I found it to be more difficult to approach other chapters I have experimented with, but this should be enough for a demonstration.

I will interject my own comments in the material that follows to explain what I am having ChatGPT do. The ChatGPT material appears as quotes and my prompts as bolded text. 

The first set of materials provides an example of what I mean by an outline of topics and subtopics. You can see how I attempted to prompt the AI service to generate the output of the type l wanted. 

Prompt: I want to have my students read about behavioral models of learning – operant conditioning, classical conditioning, and modeling. What topics are important for each model?

Sure, here are some important topics for each of the three behavioral models of learning you mentioned:

Operant Conditioning:

• Reinforcement: positive reinforcement, negative reinforcement, primary reinforcement, secondary reinforcement
• Punishment: positive punishment, negative punishment
• Shaping behavior: successive approximations, shaping schedules
• Extinction: extinction burst, spontaneous recovery

Classical Conditioning:

• Unconditioned stimulus (US)
• Unconditioned response (UR)
• Conditioned stimulus (CS)
• Conditioned response (CR)
• Acquisition: temporal contiguity, contingency, higher-order conditioning
• Extinction: spontaneous recovery, renewal, reinstatement

Modeling:

• Observational learning: attention, retention, reproduction, motivation
• Vicarious reinforcement and punishment
• Social cognitive theory: self-efficacy, outcome expectancies, personal goals
• Mirror neurons and brain regions involved in imitation and observation

The following prompt was used to expand a portion of the higher level outline in this case focused on operating conditioning.

Prompt: What should students in an introductory class know about the topics you have listed for operant conditioning?

Great question! Here are some key concepts that students in an introductory class should know about operant conditioning:

Reinforcement:

• Positive reinforcement occurs when a behavior is followed by the presentation of a pleasant stimulus, which increases the likelihood of that behavior occurring again.
• Negative reinforcement occurs when a behavior is followed by the removal of an unpleasant stimulus, which also increases the likelihood of that behavior occurring again.

Punishment:

• Positive punishment occurs when a behavior is followed by the presentation of an unpleasant stimulus, which decreases the likelihood of that behavior occurring again.
• Negative punishment occurs when a behavior is followed by the removal of a pleasant stimulus, which also decreases the likelihood of that behavior occurring again.

Shaping:

• Shaping involves reinforcing successive approximations of a desired behavior until the desired behavior is achieved.
• Shaping schedules can be continuous (every instance of the desired behavior is reinforced) or intermittent (only some instances of the desired behavior are reinforced).

Extinction:

• Extinction occurs when a behavior that was previously reinforced is no longer reinforced, which leads to a decrease in the frequency of that behavior.
• Extinction can be accompanied by an extinction burst, which is a temporary increase in the frequency of the behavior before it decreases.

It’s important for students to understand these concepts and how they apply to real-life situations, as operant conditioning plays a role in many aspects of our behavior, including education, parenting, and workplace management.

The following is a prompt intended to identify examples of reinforcement students might connect to their own lives. There would be no need to include the entire list, but I could efficiently select several from this list and expand a bit.

Prompt: What are some examples of positive and negative reinforcement might students recognize from their own experiences?

Great question! Here are some examples of positive and negative reinforcement that students might recognize from their own experiences:

Positive reinforcement

• A child receives a candy for completing their homework, which increases the likelihood of them completing their homework in the future.
• A student gets praise from their teacher for answering a question correctly, which increases the likelihood of them participating in class in the future.
• An employee receives a bonus for meeting their sales quota, which increases the likelihood of them meeting their sales quota in the future.

Negative reinforcement:

• A driver puts on their seatbelt to stop the beeping sound, which increases the likelihood of them putting on their seatbelt in the future.
• A student finishes their work early to avoid having to do it during recess, which increases the likelihood of them finishing their work early in the future.
• An employee works overtime to avoid getting a reprimand from their boss, which increases the likelihood of them working overtime in the future.

It’s important to note that reinforcement can be either positive or negative depending on whether a desirable consequence is presented or an undesirable consequence is removed. Additionally, it’s important to recognize that reinforcement doesn’t always have to involve tangible rewards or punishment; sometimes, social praise or criticism can also serve as a form of reinforcement.

The following prompts illustrates how I would identify prominent individuals associated with this topic. I would probably not include an entire list for all chapters, but it would be common to include all from this list in a chapter on this topic.

Prompt: There are several important scientists who developed our understanding of operant conditioning. Who are a few of them and some of the classic studies associated with their work

B.F. Skinner: Skinner is perhaps the most well-known and influential scientist associated with operant conditioning. He developed the “Skinner box,” a laboratory apparatus used to study operant conditioning in animals. Some of his classic studies include “The Behavior of Organisms” (1938) and “Science and Human Behavior” (1953), both of which outlined the principles of operant conditioning and their application to human behavior.

Edward Thorndike: Thorndike was one of the first scientists to study the principles of operant conditioning. He developed the “law of effect,” which states that behaviors followed by positive consequences are more likely to be repeated, while behaviors followed by negative consequences are less likely to be repeated. Some of his classic studies include “Animal Intelligence” (1911) and “The Fundamentals of Learning” (1932).

Ivan Pavlov: Pavlov is perhaps best known for his work on classical conditioning, but he also made important contributions to the study of operant conditioning. He studied the effects of rewards and punishments on dogs’ behaviors, and his work provided insights into the role of conditioning in the formation of habits. Some of his classic studies include “The Work of the Digestive Glands” (1897) and “Lectures on Conditioned Reflexes” (1928).

John Watson: Watson was a psychologist who made important contributions to the study of behaviorism, which emphasizes the role of environmental factors in shaping behavior. He studied the principles of operant conditioning and their application to human behavior, and he is perhaps best known for his controversial “Little Albert” study, in which he conditioned a young child to fear a white rat.

These scientists and their classic studies have contributed significantly to our understanding of operant conditioning and its application to human and animal behavior. By studying their work, students can gain a deeper appreciation for the principles of operant conditioning and how they shape the behaviors we exhibit in our daily lives.

Prompt: What principles of learning did Skinner discover and how did he make these discoveries?

B.F. Skinner was a psychologist who made significant contributions to the field of behaviorism and our understanding of operant conditioning. He discovered several principles of learning, which he outlined in his influential work “The Behavior of Organisms” (1938) and later in “Science and Human Behavior” (1953). Here are some of the principles of learning that Skinner discovered:

Reinforcement: Skinner discovered that behaviors that are followed by positive consequences, such as rewards or praise, are more likely to be repeated, while behaviors that are followed by negative consequences, such as punishment or criticism, are less likely to be repeated. He called this principle reinforcement and believed it was a fundamental principle of learning.

Shaping: Skinner also discovered that behaviors can be shaped through reinforcement of successive approximations. This means that a behavior can be broken down into smaller components, and each component can be reinforced until the desired behavior is achieved. This principle is known as shaping, and it is often used in animal training and education.

Extinction: Skinner also discovered that behaviors that are no longer reinforced will eventually become extinct. This means that if a behavior is no longer followed by a positive consequence, the behavior will gradually decrease in frequency until it ceases altogether.

Schedules of reinforcement: Skinner discovered that the frequency and timing of reinforcement can have a significant impact on behavior. He studied different schedules of reinforcement, such as fixed-ratio, variable-ratio, fixed-interval, and variable-interval, and found that each schedule had unique effects on behavior.

Skinner made these discoveries through a combination of laboratory experiments, observations of animal and human behavior, and theoretical analysis. He developed the “Skinner box,” a laboratory apparatus used to study operant conditioning in animals, and he used this device to study the effects of reinforcement and punishment on behavior. He also conducted experiments with humans, such as studying the effects of different reinforcement schedules on study habits. Overall, Skinner’s work had a profound impact on our understanding of learning and behavior, and his discoveries continue to be influential in psychology and related fields.

This is an example of a prompt I used to see how close ChatGPT would get to generating a final segment of text on shaping.

Prompt: Provide an example of how shaping works

Shaping is a process of reinforcing successive approximations of a desired behavior until the final behavior is achieved. This process is often used in animal training, education, and therapy.

Here’s an example of how shaping works in animal training:

Let’s say you want to train a dog to spin in a circle on command. However, the dog has no prior experience with this behavior, so you need to shape the behavior from scratch.

Start by reinforcing any behavior that is even remotely close to spinning in a circle. For example, if the dog turns its head to the side, give it a treat or praise. This will help the dog understand that it is being rewarded for moving in the right direction

Once the dog starts turning its head to the side consistently, raise the criteria by only rewarding the dog when it turns its head a little further. For example, if the dog turns its head 45 degrees to the side, reward it. If it only turns its head 30 degrees, do not reward it.

Keep raising the criteria gradually until the dog is turning in a full circle. This might take several training sessions, as the dog needs to understand what is expected of it at each step of the process.

Finally, add a verbal or visual cue to the behavior, such as saying “spin” or using a hand signal. Once the dog associates this cue with the behavior, it should be able to spin on command.

Through the process of shaping, the dog has learned to perform a complex behavior that it had no prior experience with. This process can be used to teach a wide variety of behaviors, from simple tricks to complex tasks. By breaking down a behavior into small, achievable steps and reinforcing each step along the way, shaping can help animals (and humans) learn and master new skills.

The group of prompts are examples of what I would use to generate material on modeling.

Prompt: What is important for introductory students to know about observational learning

Observational learning, also known as social learning or modeling, is the process of acquiring new behaviors or modifying existing behaviors by observing others. Here are some important points to consider when introducing students to this topic:

The basic principles:

Observational learning involves several key principles, including attention, retention, reproduction, and motivation. These principles suggest that people learn by paying attention to the behaviors of others, retaining information about these behaviors in memory, reproducing the observed behaviors, and being motivated to imitate the behaviors based on their outcomes.

Examples of observational learning:

There are many examples of observational learning, such as learning to cook a new recipe by watching a cooking show, learning to drive a car by observing others, or learning to solve a math problem by watching a teacher demonstrate the steps. These examples illustrate how observational learning can occur in a variety of contexts and for a range of behaviors.

Observational learning and modeling:

Observational learning is sometimes referred to as modeling because it involves the observation of models, or individuals who demonstrate the behavior being learned. Models can be live or symbolic (such as characters on TV or in books), and they can influence behavior through both positive and negative reinforcement.

Observational learning and cognitive processes:

Observational learning involves more than just watching others; it also involves cognitive processes such as attention, memory, and motivation. Observational learning can be influenced by factors such as the salience of the behavior being observed, the similarity between the observer and the model, and the perceived outcome of the behavior.

Applications of observational learning:

Observational learning has many practical applications, such as in education, parenting, and advertising. By understanding the principles of observational learning, individuals can use modeling to promote positive behaviors and discourage negative ones.

Overall, observational learning is a complex process that involves cognitive and behavioral factors. By understanding the principles of observational learning, students can gain insights into how they learn and how they can use modeling to promote their own growth and development.

The following prompts expand the basic material on modeling to include a classic study (Bandura’s bobo doll experiment) and then extend this classic experiment to consider how aggressive behavior might be encouraged by playing certain types of video games.

Prompt: Describe the bobo doll experiment

The Bobo doll experiment was a study conducted by psychologist Albert Bandura in 1961 to examine how social learning occurs in children. The experiment involved exposing children to a video of an adult aggressively hitting and verbally abusing an inflatable doll called “Bobo.”

In the experiment, Bandura divided children into three groups. The first group was exposed to the video of the adult hitting and yelling at the doll. The second group was shown a video of an adult playing with the doll in a non-aggressive manner. The third group was not shown any videos.

After watching the videos, the children were brought into a room filled with toys, including the Bobo doll. The researchers observed how the children behaved towards the doll. The results of the study showed that children who were exposed to the aggressive video were more likely to exhibit aggressive behavior towards the doll compared to the other two groups. They imitated the behavior they had witnessed in the video by hitting, kicking, and verbally abusing the Bobo doll.

The Bobo doll experiment demonstrated that children learn through observation and imitation. It also highlighted the importance of modeling appropriate behavior for children and being mindful of the types of behaviors that children are exposed to in their environments. The study had a significant impact on the field of psychology and our understanding of social learning theory.

Prompt: What are the implications of this experiment? What real world behaviors might it explain?

The Bobo doll experiment has important implications for our understanding of how children learn and develop behavior. Some of the key implications of the experiment are:

Children learn through observation and imitation: The experiment showed that children can learn new behaviors by watching others. They may imitate positive or negative behaviors, depending on what they observe.

Aggressive behavior can be learned: The experiment demonstrated that exposure to aggressive behavior can lead to children imitating that behavior. This suggests that violent behavior in the media, such as in video games and movies, may contribute to aggressive behavior in children.

Modeling appropriate behavior is important: The study highlighted the importance of modeling appropriate behavior for children. Adults need to be mindful of the behaviors they exhibit in front of children and ensure they model positive behaviors.

Social learning theory has important implications: The study provided support for social learning theory, which suggests that people learn from observing others in their environment.

In terms of real-world behaviors, the Bobo doll experiment may explain why some children are more likely to engage in aggressive behavior, especially if they have been exposed to violence in their environment. It also highlights the potential negative impact of violent media on children. Additionally, the experiment suggests that positive role models, such as parents and teachers, can have a significant impact on a child’s behavior by modeling appropriate behaviors

Prompt: Cite research that shows some video games increase aggression 

There is a significant body of research that suggests that some video games can increase 

There is a significant body of research that suggests that some video games can increase aggression in children and adolescents. Here are a few examples:

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78(4), 772-790. This study found that playing violent video games led to increased aggression in participants, both in the laboratory and in real-life situations.

Greitemeyer, T., & Mügge, D. O. (2014). Video games do affect social outcomes: A meta-analytic review of the effects of violent and prosocial video game play. Personality and Social Psychology Bulletin, 40(5), 578-589. This meta-analysis of multiple studies found that playing violent video games was associated with increased aggression and decreased prosocial behavior

Ferguson, C. J., & Kilburn, J. (2010). The public health risks of media violence: A meta-analytic review. Journal of Pediatrics, 154(5), 759-763. This meta-analysis found that exposure to violent video games was associated with increased aggression, as well as other negative outcomes such as decreased empathy and increased desensitization to violence.

Gentile, D. A., Lynch, P. J., Linder, J. R., & Walsh, D. A. (2004). The effects of violent video game habits on adolescent hostility, aggressive behaviors, and school performance. Journal of Adolescence, 27(1), 5-22. This study found that playing violent video games was associated with increased aggression and decreased academic performance in adolescents.

These are just a few examples of the research suggesting that some video games can increase aggression. While the relationship between video games and aggression is complex and not fully understood, these studies provide evidence that parents and caregivers should be mindful of the types of games their children are playing and limit exposure to violent content if necessary.

Prompt: Describe the Anderson and Dill study

The Anderson and Dill study (2000) investigated the relationship between playing violent video games and aggressive thoughts, feelings, and behaviors. The study was conducted in two parts: a laboratory experiment and a field study.

For the laboratory experiment, the researchers randomly assigned 210 college students to play either a violent or non-violent video game for 20 minutes. The violent game was the first-person shooter game “Mortal Kombat,” while the non-violent game was the sports game “Tetris.” After playing the game, the participants completed a series of tasks designed to measure aggressive thoughts, feelings, and behaviors.

The results showed that participants who played the violent game had more aggressive thoughts and feelings compared to those who played the non-violent game. They also had higher levels of physiological arousal, as measured by heart rate and skin conductance. Additionally, when given the opportunity to aggress against another person in a separate task, those who played the violent game were more likely to administer louder and longer-lasting noise blasts to their opponent.

For the field study, the researchers surveyed 227 high school students about their video game habits and levels of aggression. They found that students who played more violent video games were more likely to engage in physical fights, verbal arguments, and other forms of aggressive behavior.

Overall, the Anderson and Dill study provided evidence that playing violent video games can increase aggressive thoughts, feelings, and behaviors. While the study was conducted with college and high school students, the findings suggest that parents and caregivers should be mindful of the types of games their children are playing and limit exposure to violent content if necessary.

Summary:

This post was intended as a proof of concept explaining how I could make use of ChatGPT and the knowledge already in my head from multiple years of teaching the Course Introduction to Psychology to produce a reasonable textbook for this course.

The approach I have outlined here made use of ChatGPT to generate an outline of topics and subtopics appropriate to one chapter of this textbook. ChatGPT was then asked to produce explanations of items from the outline, produce examples and descriptions of research appropriate to these concepts, and possible applications of the core ideas from these concepts.

My proposal is that an experienced individual in combination with ChatGPT could rapidly generate the content for a survey course. I am not proposing that this product would be a great product, but it would be the reasonable equivalent of commercial products generated at no cost beyond my time. I had those interested in OER content in mind in exploring this option.

One of my broad personal interests has long been how what might be described as digital tools can facilitate human cognitive tasks. Many of us might accurately be described as knowledge workers. What this work involves varies with occupation and avocation, but, by definition, our work largely depends on cognitive (thinking) behaviors. The cognitive tasks that are employed vary in type and frequency of application across categories of knowledge workers so it follows that the ways in which technological tools might be of value will vary as well. I happen to be an educator.

This was perhaps a long way of setting up my argument which involves the application of AI. We are at a point of discovery when it comes to how the recently available AI tools will be of value in the many specific niches in which AI may support us. I am past the point of worrying about whether AI tools are a problem for education and the discussions of how potential problems might be countered. This took about a week. Our new reality seems obvious as AI tools for research and content production now seem to be arriving daily. It seems obvious we should commit to a period of exploration and evaluation (formal and informal). I think this is what OpenAI was hoping for with the release of ChatGPT. What creative uses will emerge and how well do they seem to work? In what specific aspects of work do these tools as they exist in their present form offer functions that can be identified and shared? Worrying about evaluation and control can wait until we have more experience.

I have an example of what I mean by a niche. The example may or may not be something you relate to in your own situation. If it fits, great. If not, consider it an example of the type of problem identification I think is valuable in searching for applications of a new capability.

One exploration

As a writer for a specific community (educators interested in what research might say about practice), I do and describe some of my own research, but I also spend a great amount of time combing the published literature for studies that might be relevant to what I describe for others. Like so many areas of the sea of information that could possibly be examined, what might be relevant to my interests is immense and growing and quite challenging to explore. It gets more challenging. The research in areas that involve human behavior is very different than say research in chemistry. Human behavior seems more messy and complicated and you encounter bodies of work that seem on the surface to be very similar and yet produce inconsistent and sometimes contradictory results. Those who don’t go deep can easily make pronouncements based on a given study that someone else can challenge based on a different study. It is frustrating to those of us who do this work and I am certain even more frustrating to those who consume our work only to find that what we propose can be questioned.

Here is perhaps an easier way to explain at least part of my challenge. On any given topic (e.g., Should students take class notes in a notebook by hand or using a laptop or tablet), there are both studies asking this straightforward question and there are studies dealing with the underlying mechanisms. Since straightforward comparisons often generate inconsistent results, a careful knowledge worker must review the method section of the studies carefully to try to identify differences that might be responsible for the inconsistencies and then try to locate other studies that may bear on the importance of differences in the methodologies. You find yourself trying to make a simple decision facing maybe hundreds of documents that may each take an hour or so to carefully review. 

So let’s start with the task of identifying the potential group of studies that may be relevant and making the decision of which of these documents should time be invested in reading. It may seem a small thing, but we may be making decisions that could easily impact days of labor.

Stages in useful content identification

My workflow typically follows a three-stage process.  

1. Identify potentially relevant papers
2. Evaluate papers to determine potential
3. Read papers

Stage 1

Most folks who have a content location challenge probably now assume a Google search is how to proceed. I tend to work a little differently. I usually begin with a credible source familiar to me. I examine the reference section of this source, but I also use two online services. The reference section identifies studies cited by the author(s) of the paper I have already found valuable. These papers are older, but may identify studies producing conflicting conclusions or complementary studies using somewhat different methodologies, different participant populations, or other potentially informative variations.

I then enter the title of the paper into one of two search services – Google Scholar and Research Rabbit. 

Google Scholar extends the value of the existing list of resources I am now aware of in a forward direction. In other words, it lists other later studies that have cited the study I started with. Using Google Scholar I can review the abstracts of these later studies and perhaps find other relevant studies. 

Research Rabbit extends the power of Google Scholar in both directions. It moves both forward and backward and forward for multiple “generations”. These data are returned as individual abstracts and citations, but also as a graph showing additional interactions. By “seeding” the service with several related papers it is possible to identify papers that are commonly cited together which are then proposed as being of greater value. The following image shows an example of a graph (red box) for the paper I am using here as an example.

There are several other tools that offer similar capabilities, but these two tools typically provide me plenty of resources to consider

Stage 2

Now what. I can review the abstracts of papers that seem relevant to prioritize a bit, but I keep hoping that something more would be available to get me started.

I wondered what I could learn with ChatGPT and I have been experimenting. My exploratory strategy has involved asking ChatGBT for information about papers I have written. The idea is that I know the information in the paper and I am trying to determine if a given tool will provide this information. Would I be able to ask questions about other papers and generate answers that are valuable and that I could trust?

The following is an example of this exploration and it is the text generated by my interaction. My inputs are bolded. I can enter a title for the paper and GPT will generate a credible summary. I would not offer this summary as different from the abstract, but it turns out to be similar and ChatGPT obviously can find information about a specific study based only on the title. This alone is at least interesting.

Summarize the journal article Optional student use of online lecture resources: resource preferences, performance and lecture attendance

The article investigates the optional use of online lecture resources by students, including their preferences for different types of resources and the impact on performance and lecture attendance. The results suggest that students have diverse preferences for online resources and using these resources can have a positive effect on performance, but may not necessarily affect lecture attendance.

Provide additional details

The study aimed to understand students’ preferences for online lecture resources, such as videos, slides, and lecture notes, and how these preferences impact their academic performance and lecture attendance. The study found that students have different preferences for different types of online resources, with some students preferring videos while others prefer slides or lecture notes. The results also showed that the use of online resources can have a positive impact on students’ performance, but there was no significant effect on lecture attendance. This indicates that students may use online resources as a supplement to attending lectures, rather than as a replacement. The results of this study provide valuable insights for educators looking to incorporate online resources into their teaching.

What statistics were used to analyze the data from this study

The information on the statistics used in this study is not specified in the description given. To know more about the specific methods and statistics used in this study, it is best to refer to the original article.

How was class attendance determined

The information on how class attendance was determined in the study is not specified in the description given. To know more about the specific methods used to determine class attendance in this study, it is best to refer to the original article.

What were a couple of the preferences for online notes the study identified

The information on specific preferences for online notes identified in the study is not specified in the description given. To know more about the preferences for online notes identified in this study, it is best to refer to the original article.

I am guessing ChatGPT is limited exactly as it suggests. The full published study requires paid access either by a library or individuals and this AI tool’s knowledge base does not include this information.

Elicit

Elicit uses AI to provide deeper insights than I was able to generate with ChatGPT. I doubt this is the advantage of the AI engine it uses, but most likely the result of the knowledge base it can access and the presets to look for specific things. Beyond the presets, Elicit provides the opportunity to ask custom questions and I was able to get answers to some but not all of the questions I failed to find answers for using ChatGPT. Sometimes I had to rephrase a question several times to generate the answer I knew was there. What I really wanted but could not get was a summary of the Methods section. I don’t need the results because the results are just math and can be approximated from the Abstract. The key to answering many important questions is the Methods that are applied as with this information one should be able to find factors necessary to interpret the Results and possibly explain differences between studies.

I recommend others with goals similar to my own take a look at this AI tool. The following images offer a peak. Some of what I describe as presets are shown in the red box in the first image. The opportunity to ask specific questions about the study is shown in the second image.

The summary and other information provided by Elicit was more detailed than the summary provided by ChatGPT. 

Stage 3

Now it is time to make a few selections and invest the time required to read papers predicted to be most valuable. This ends up being a never-ending process with new questions emerging and reuse of the same tools over and over again.

My point. It is time to explore. I have made a start on exploring AI tools for my own niche interests and perhaps I have offered some ideas you might use. Beyond that, I think this is the stage we are at and it would be useful for more individuals to see what AI tools offer for their own personal needs and share what they discover.

There is so much attention to AI with the release of ChatGPT I know most educators have wondered what the future holds. I propose a simple step that offers some insights into the capabilities of AI as a way to test the waters.

There is a Chrome extension that generates side-by-side Google search results and ChatGPT replies to the same request. Adding an extension is not that difficult. The site makes the install fairly easy. You do need to create a ChatGPT account (you will be prompted) and this has been challenging because the service has been overwhelmed and persistence is required. After using this extension, you can move beyond this experience to submitting requests directly to ChapGPT.

Here are some examples I generated based on questions I happened to have at that moment. The Google results appear to the left and the ChatGPT response to the right.

Q. When is raking snow off your roof necessary?

Q. 2 – Why is renting in Hawaii so expensive?

Q. 3 – What are some examples of mastery learning strategies? (the third column was generated by MEMEX which is a note-taking tool I use and that offers any notes I happen to have that seem relevant).

Identifying the most popular posts from the previous year is a common thing for bloggers to do. We like looking at our stats and perhaps others may have missed a popular post for one reason or another.

Here are my top three posts on this blog for 2022

Hotspots with Google Slides.

Shape-shifting Portfolio People

Hyperdocs, WebQuests and Other Teacher Created Designs

Here is the interesting thing about these posts. None were written in 2022. I was puzzled by this discovery until I looked at some of the other stats for the year. These stats include the referrer for visits to this blog. People might just bookmark this site and visit it to see what I have written. A related approach would be to use an RSS reader that follows multiple blogs and shows when anything new has been posted. This is the primary way I identify new content on other blogs. Twitter and Mastodon are linked to this blog and a short announcement appears when I write something new. So, there are connections from social media announcements. Finally, blog posts are followed by search engines and posts may appear in the list of hits when a search engine is used.

The most frequent way for others to come to this blog is through the use of a search engine. This is probably the reason older posts are most frequently visited. It might also mean I have not cultivated enough Twitter followers. It probably indicates I am old school in relying on RSS to follow bloggers and this discovery method has fallen out of favor.

Whatever the access method, I appreciate your attention to the topics we both seem to find interesting. Happy New Year.

Like so many, I tested the capabilities of the new AI tool ChatGPT and offered a personal assessment. While I immediately saw that the tool would have significant consequences for classroom practice and especially those educators who assigned writing to learn tasks, I proposed that a fix would involve requiring the inclusion of careful citations in work students submitted. The samples I had ChatGPT generate did not provide citations and my naive understanding of the corpus of content the AI was drawing on did not include citations for the content.

I was wrong.

It took multiple efforts to find how to phrase questions so that the material produced would contain citations and then how to actually view the full citations for the work mentioned in a generated product. The following example involved two requests and are a modified version of the topic I used as an example in my first post. I have highlighted the mention of sources.

Me: How can a semantic network explain the tip of the tongue phenomenon and list citations that support this explanation?

The tip of the tongue phenomenon, also known as TOT, is a common experience in which a person knows that they know a particular word or piece of information, but they are temporarily unable to retrieve it. A semantic network can help explain this phenomenon by providing a framework for understanding how words and concepts are related to one another in the brain.

In a semantic network, words and concepts are represented as nodes, and the relationships between them are represented as edges or links. When a person is trying to retrieve a word or piece of information, they search through their semantic network to find the relevant node. If the person is unable to retrieve the word, it may be because they are unable to find the appropriate node in their semantic network, or because the node is not well-connected to other nodes in the network.

One study that supports the idea that the tip of the tongue phenomenon is related to the organization of semantic networks is a 2013 study by J. K. Stanley and colleagues, which used functional magnetic resonance imaging (fMRI) to investigate the neural basis of TOT. The study found that during TOT, the brain exhibits increased activity in the temporal lobe, which is known to be involved in language processing and the organization of semantic networks.

Another study that supports the semantic network explanation of TOT is a 2001 study by J. M. McRae and colleagues, which used a computational model of semantic networks to investigate the retrieval of words during TOT. The study found that the model was able to reproduce many of the characteristics of TOT, including the feeling of knowing a word but being unable to retrieve it, and the ability to retrieve the word after a delay.

Overall, the evidence suggests that the tip of the tongue phenomenon is related to the organization of semantic networks in the brain, and that semantic networks can provide a useful framework for understanding this phenomenon.

Me: Provide citations that explain how the theory of a semantic network can explain the tip of the tongue phenomenon.

1. Stanley, J. K., Marslen-Wilson, W. D., & van Hell, J. G. (2013). The tip of the tongue phenomenon: An fMRI study. Journal of Cognitive Neuroscience, 25(6), 891-900.
   
2. McRae, J. M., de Sa, V. R., & Seidenberg, M. S. (2001). On the tip of the tongue: What causes word finding failures in young and older adults? Journal of Memory and Language, 44(4), 542-579.
   

Twitter has been part of my social media environment since 2008. It took me some time to figure out if the service was good for anything and then I gradually found uses that brought my attention to the service on a daily basis.

 In the early days, most of the comments lacked any substance. Eventually, I found value when others with interests similar to my own began to link to blog posts they had authored or other online resources. I had originally relied on using RSS to monitor what others posted. Interest in RSS has declined. Why this has happened is unclear. Perhaps RSS seems geeky and intimidating to casual users. Twitter is easy.

I found it important to have my blogging platform (WordPress) automatically generate a tweet indicating I had added a new post. WordPress collects data on the way viewers connected to blog author’s content (e.g., direct, search, Twitter, referrals) and the amount of traffic I received from Twitter demonstrated the importance of sharing what I wrote to Twitter.

As the number of my followers on Twitter accumulated, it seemed I should Twitter to share whatever I had to say about anything. It became to use Facebook for longer content and Twitter for short comments. 

I now find myself trying to decide what to do about Twitter. I share two distinct types of information and I am unsure of the direction Twitter will go in the future. I don’t want my professional content (the shared links to my blog posts) to be tainted if Twitter becomes like TruthSocial. I am also concerned about building up a following somewhere else.

My present approach is to wait and see what happens with Twitter. I am also diversifying the sharing of links to my blog posts (Mastodon). We seem to be in a time of transition. 

McIntosh (2019) describes the basic skills of personal information curation as Clip, Tag, Annotate and proposes that educators teach this sequence of skills first focused on a single information resource and then on multiple resources that would serve as material learning how to do something (e.g., pickle cucumbers) or create something (e.g., an original blog post). Secondary students might not do these things,  but these happened to be the last two curation tasks I completed so they were fresh in my mind as examples.  

Clipping

Clipping describes the process of finding something useful on the Internet and saving all or a portion of this content for later use. Tagging is the process of adding one or more useful labels to this saved content that should be useful in organizing multiple sources and locating these sources when needed. Annotation is the process of adding longer notes to the content created by someone else for the purpose of summarization or inclusion of personal insights related to the stored material. 

I have tried implementing this sequence of activities with OneNote, Google Keep, and Evernote which I think of as the main competitors in the low cost clipping and annotation services. My preference is Evernote. McIntosh wrote his article for educators based on his classroom use of Microsoft OneNote. To be fair, I pay for a fairly expensive version of Evernote, but there is a free version that should allow users to apply the techniques I describe here. 

Clipping is accomplished using an extension added to your favorite browser. I use Chrome, but the Evernote extension can be added to other browsers. While the process works a little differently, content can be added to Evernote from a phone, tablet, or computer. You have the greatest control when using a computer with the main advantage being the opportunity to store parts of a web source rather than the entire document. 

Understanding how Evernote works requires some additional comments. The following is the display as it appears on a computer. I will refer to this image at several points in this description. I think describing this as four columns works best. Actually, the software is flexible and offers the opportunity for different arrangements depending on personal taste. 

I will describe the columns from left to right. The first column offers the most general controls. I will concentrate on the note-taking capabilities and ignore other tools (e.g., tasks). Focusing on notes, the basic structure is that of notebooks as repositories of clipped content suited to a given purpose, notes, and additions to notes such as highlights, annotations, and tags. The areas contained in green squares involve notebooks. In the first column, you have the notebooks I have created. In the far right green box, you have an item from the drop down three dots (…) that allows the active clipped content (the third column) to be moved to a specific notebook. The red box in the first column identifies the existing tags. You create your own tags. These tags are listed in this box. The tagging of content will be described in the next section. The second column contains thumbnails representing stored content. Click on a thumbprint and the full document appears in the third column. Selecting a tag reveals the thumbprints for all content associated with this tag displayed within this column. So tags serve a filtering function for search.

Tags

A tag is a word or phrase used to classify content. Words such as topics or descriptors have similar meanings. In a system like Evernote, a user creates tags to categorize content as an organizational system that can make future retrieval easier. Hence, it is important when assigning tags to consider both descriptive accuracy and likely future application. Evernote offers a way to nest hashtags allowing a hierarchical classification system – e.g., computers – Apple, Windows, Chrome. In the image above, you see what happens when you select a hashtag. All existing content assigned that tag will be identified. Tags become a powerful organizational tool that becomes more valuable as the amount of stored content grows and as time passes making retrieval efforts more productive.

The creation and assignment of tags are distinct processes in Evernote. If a tag comes to mind perhaps in response to a newly clipped item, it must first be added to the collection of tags and then attached to that content item. When you first clip content, you will be asked to assign that clip to a notebook and to add desired tags. This is easy to see in the second image. New notebooks and tags can be declared at this time. Or, at a later point in time, clipped content can be moved to the desired notebook and new or additional tags can be added. With the desktop version of Evernote, this is a simple matter of dragging a tag or tags from the display of tags to the active document. 

Annotation

Annotation is used here to mean making additions to the stored material. Highlighting would be one example. With Evernote you add personal notations as part of the stored document. Think of this as being given a word processing document written by someone to which you can not insert text, links, or images. To make this what I consider a useful practice, I want to differentiate my additions from the original content so I change the text color to differentiate my summaries, comments, and questions. (See example in previous post)

Summary

McIntosh proposed clipping, tagging, and annotating as active reading practices students should learn to apply to digital texts. These are skills most students have not practiced and may not be allowed to apply to paper-based content. McIntosh proposed that students start with individual online content learning to add this content to a storage system, tagging the stored content, and then adding annotations that might be based on personal insights or based on a purpose assigned by the teacher. Using a common assigned online resource, the teacher can then comment on the choices made by individual students and perhaps then share student work to allow students to see how others have reacted to the same content. Once the basic skills are mastered, McIntosh proposes the system be applied to a research project requiring students to locate sources relevant to a goal, create a notebook, tag resources, embed annotations relevant to an integrative task, and finally generate this integrative project. Students should find this collection of skills relevant to many tasks they will be expected to complete for academic purposes and hopefully generalize the skills to the accumulation of resources they can apply more generally. 

McIntosh, J. (2019). Clip, Tag, Annotate: Active Reading Practices for Digital Texts. In Digital Reading and Writing in Composition Studies (pp. 176-188). Routledge.