I haven’t posted in a while and it will be a while until I post again. I did not want people to think I had abandoned my blog. We are on an expidition cruise ship exploring Patagonia.
I have a separate blog for our travels and if you are interested I would invite you to take a look. My wife and I are heavy tech users no matter the activities we are engaged in and you may find things you will enjoy.
The following is the Pios XI glacier in Chile and the video shows the glacier calving.
We know that tutoring is one of the most successful educational interventions with meta-analyses demonstrating the advantage to be between .3 and 2.3 standard deviations. In some ways, the explanation of this advantage seems obvious as it provides personal attention that cannot be matched in a classroom. The challenges in applying tutoring more generally are the cost and availability of personnel. One of my immediate interests in the AI tools that are now available is in exploring how students might make use of these tools as a tutor. This is different from the long-term interest of others in intelligent tutoring systems designed to personalize learning. The advantage of the new AI tools is that these tools are not designed to support specific lessons and can be applied as needed. I assume AI large language chatbots and intelligent tutoring will eventually merge, but I am interested in what students and educators can explore now?
My initial proposal for the new AI tools was to take what I knew about effective study behavior and know about the capabilities of AI chatbots and suggest some specific things a student might do with AI tools to make studying more productive and efficient. Some of my ideas were demonstrated in an earlier post. I would still suggest interested students try some of these suggestions. However, I wondered if an effort to understand what good tutors do could offer some additional suggestions to improve efficiency and move beyond what I had suggested based on what is known about effective study strategies. Tutors seem to function differently from study buddies. I assumed there must be research literature based on studies of effective tutors and what it is that these individuals do that less effective tutors do not. Perhaps I could identify some specifics a learner could coax from an AI chatbot.
My exploration turned out to be another example of finding that what seems likely is not always the case. There have been many studies of tutor competence (see Chi et al, 2001) and these studies have not revealed simple recommendations for success. Factors such as tutor training or age differences between tutor and learner do not seem to offer much as whatever is offered as advice to tutors and what might be assumed to be gained from experience do not seem to matter a great deal.
Chi and colleagues proposed that efforts to examine what might constitute skilled tutoring begin with a model of tutoring interactions they call a tutoring frame. The steps in a tutoring session were intended to isolate different actions that might make a difference depending on the proficiency with which the actions are implemented.
Steps in the tutoring frame:
(1) Tutor asks an initiating question
(2) Learner provides a preliminary answer
(3) Tutor gives confirmatory or negative feedback on whether the answer is correct or not
(4) Tutor scaffolds to improve or elaborate the learner’s answer in a successive series of exchanges (taking 5–10 turns)
(5) Tutor gauges the learner’s understanding of the answer
One way to look at this frame is to compare what is different that a tutor provides from what happens in a regular classroom. While steps 1-3 occur in regular classrooms, tutors would typically apply these steps with much greater frequency. There are approaches classroom teachers could apply to provide these experiences more frequently and effectively (e.g., ask questions and pause before calling on a student, make use of student response systems allowing all students to respond), but whether or not classroom teachers bother is a different issue from whether effective tutors differ from less effective tutors in making use of questions. The greatest interest for researchers seems to be in step 4. What variability exists during this step and are there significant differences in the impact identifiable categories of such actions have that impact learning?
Step 4 involves a back-and-forth between the learner and tutor that goes beyond the tutor declaring the initial response from the learner as correct or incorrect. Both teacher and learner might take the lead during this step. When the tutor controls what unfolds, the sequence that occurs might be described as scaffolded or guided. The tutor might break the task into smaller parts, complete some of the parts for the student (demonstrate), direct the student to attempt a related task, remind the student of something they might not have considered, etc. After any of these actions, the student could respond in some way.
A common research approach might evaluate student understanding before tutoring, identify strategy frequencies and sequence patterns during a tutoring session, evaluate student understanding after tutoring, and see if relationships can be identified between the strategy variables and the amount learned.
As I looked at the research of this type, I happened across a study that applied new AI not to implement tutoring, but to search for patterns within tutor/learner interaction (Lin et al., 2022). The researchers first trained an AI model by feeding examples of different categories identified within tutoring sessions and then attempted to see what could be discovered about the relationship of categories within new sessions. While potentially a useful methodology, the approach was not adequate to account for differences in student achievement. A one-sentence summary from that study follows;
More importantly, we demonstrated that the actions taken by students and tutors during a tutorial process could not adequately predict student performance and should be considered together with other relevant factors (e.g., the informativeness of the utterances)
Chi and colleagues (2001)
Chi and colleagues offer an interesting observation they sought to investigate. They proposed that researchers might be assuming that the success of tutoring is somehow based on differences in the actions of the tutors and look for explanations in narratives based on this assumption. This would make some sense if the intent was to train or select tutors.
However, they propose that other perspectives should be examined and suggest the effectiveness of tutoring experiences is largely determined by some combination of the following:
In differentiating these categories, the researchers proposed that in the learner-centered and interactive labels, the tutor will have enabled an effective learning environment to the extent that the learner asks questions, summarizes, explains, and answers questions (learner-centered) or interactively as the learner is encouraged to interact by speculating, exploring, continuing to generate ideas (interactive).
These researchers attempted to test this three-component model in two experiments. In the first, the verbalizations of tutoring sessions were coded for these three categories and related to learning gains. In the second experiment, the researchers asked tutors to minimize tutor-centered activities (giving explanations, providing feedback, adding additional information) and instead to invite more dialog – what is going on here, can you explain this in your own words, do you have any other ideas, can you connect this with anything else you read, etc. The idea was to compare learning gains with tutoring sessions from the first study in which the tutor took a more direct role in instruction.
In the first experiment, the researchers found evidence for the impact of all three categories of tutor session benefits, but codes for learner-centered and interactive had benefits for performance outcomes relying on deeper learning. The second experiment found equal or greater benefits for learner-centered and interactive events when tutor-focused events were minimized.
The researchers argued that tutoring research that focuses on what tutors do may have yet to find much regarding what tutors should or not do may be disappointing because the focus should be on what learners do during tutoring sessions. Again, tutoring is portrayed as a follow-up to classroom experiences so the effectiveness of experiences during tutoring sessions should be interpreted given what else is needed in this situation.
A couple of related comments. Other studies have reached similar conclusions. For example, Lepper and Woolverton (2002) concluded that tutors are most successful when they “draw as much as possible from the students” rather than focus on explaining. The advocacy of these researchers for a “Socratic approach” is very similar to what Chi labeled as interactive.
One of my earlier posts on generative learning offered examples of generative activities and proposed a hierarchy of effectiveness among these activities. At the top of this hierarchy were activities involving interaction.
Using an AI chatbot as a tutor:
After my effort to read a small portion of the research on effective tutors, I am more enthusiastic about the application of readily available AI tools to the content to be learned. My post which I presented more as a way to study with such tools, could also be argued as a way for a learner to take greater control of a learner/AItutor session. In the examples I provided, I showed how the AI agent could be asked to summarize, explain at a different level, and quiz the learner over the content a learner was studying. Are such inputs possibly more effective when a learner asks for them? There is a danger that a learner does not recognize what topics require attention, but an AI agent can be asked questions with or without designating a focus. In addition, the learner can explain a concept and ask whether his/her understanding was accurate. AI chats focused on designated content offer students a responsive rather than a controlling tutor. Whether or not AI tutors are a reasonable use of learner time, studies such as Chi, et al. and Lepper et al. suggest that more explanations may not be what students need most. Learners need opportunities that encourage their thinking.
References:
Chi, M. T., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive science, 25(4), 471-533.
Fiorella, L., & Mayer, R. (2016). Eight Ways to Promote Generative Learning. Educational Psychology Review, 28(4), 717-741.
Lepper, M. R., & Woolverton, M. (2002). The wisdom of practice: Lessons learned from the study of highly effective tutors. In Improving academic achievement (pp. 135-158). Academic Press.
Lin, J., Singh, S., Sha, L., Tan, W., Lang, D., Gaševi?, D., & Chen, G. (2022). Is it a good move? Mining effective tutoring strategies from human–to–human tutorial dialogues. Future Generation Computer Systems, 127, 194-207.
First and second brain are terms used by those proposing strategies for learning, remembering, and applying that take advantage of external storage tools and techniques. In this descriptive system, your first brain consists of the biological organ in your body and the cognitive activities you can apply within this biological system. This combination of organ and cognitive activity accomplishes what we typically describe as remembering, thinking, and creativity. The concept of a second brain is a way of referencing external devices and activities generating some type of external representations that are intended to augment first brain functions. I purposively have made the generation of an external record a component in my description of a second brain recognizing that external activities that many might describe as study techniques exist that do not involve the generation of an external record. For example, responding to questions is proven as a way to improve retrieval and if done verbally does not involve the creation of anything permanent. Advocates of the second brain concept do emphasize the generation of a record of experiences.
I tend to equate references to the second brain with some system for taking notes. This is a simplification, but a way to quickly provide a reference for those not steeped in this topic. As I have tried to argue when referring to first brain topics, it is more than just the record that is important. It is also the variety of tactics in storage and retrieval and deciding when a given tactic should be applied that can be important.
Finally, first and second-brain proposals can and should include consideration of the interaction between these two systems. As potential users of both brains, we have some control of each system and access to a second brain implementation could change the way we make optimal use of our first brain in comparison to what might be optimal use if we had to rely on the first brain system only.
We all or at least most of us took notes in our high school and college classes. Taking this background as a starting point, you should have a context within which to think about this topic. Now add some additional expectations. What if the goal was not to use a second brain application to prepare for next week’s exam or the paper you had to write in a couple of weeks? What if the goal was to augment your first brain function over several years in order to address life tasks you might not be even able to describe at this time? Even this later question might be applied to formal education because very few were thinking in this way when studying for that next exam or preparing for that next paper. Most of us probably cannot even find or did not keep the second brain artifacts we created while engaged in our formal education.
Now this was a long introduction I hope was valuable in and of itself to some. Many of my previous posts concerned second-brain topics such as note-taking and second-brain technology tools. Please take a look if my introductory comments piqued your interest. I spent the time to generate this overview in order to provide a context for the content that now follows.
The application of tags in first and second brains
One of the interesting characteristics of the work of cognitive scientists and second-brain developers is how there seems to be a reciprocal impact of ideas that originate in one field on the other. While I am at it, I can see a similar reciprocity in the ideas of cognitive and AI researchers. To be clear, cognitive researchers rely on hypothetical concepts to represent yet-to-be-discovered biological functions. This is my way of thinking about the challenges of neuroscientists and cognitive researchers. Obviously, mental activity must be a function of biology, but our mastery of this field is far from being useful in addressing most human learning challenges. A hypothetical construct is a proposed mechanism for how something works that has yet to be explainable via a physical equivalent. So cognitive constructs such as short-term memory, metacognition, associative networks, links, etc. seem to be useful in understanding and even proposing effective learning strategies and this is possible without having to reference or consider the underlying biological mechanisms that must be involved. For example, we can measure short-term memory and we can propose ways to improve the effectiveness of short term without reference to actual biological structure or process. My focus in this post is on the role played by tags in both first and second brains.
Shank and his focus on stories
I have been rereading Roger Shank’s Tell Me a Story. I first read the book probably 30 years ago. How I now relate to this book on human cognition and AI has changed a great deal because of my recent exposure to personal knowledge management (PKM). As the full book title indicates, Tell me a story: Narrative and intelligence is about stories serving a far different role than entertainment. Shank presents stories as playing a central role in how we think, learn, and communicate. Shank goes as far as suggesting that telling a useful story at the right time is a great sign of intelligence. He proposes that an expert is an individual who has a great number of stories relevant to a given area and has these stories indexed so that he/she can tell a useful story at the right time. He recommends that we recognize that our conversations with others often focus on stories with one individual telling a story and then the other person telling a related story to indicate he or she understands and to offer some additional element of information.
This proposal fits with my own way of thinking about human memory. In cognitive psychology, one way to describe the contents of long-term memory is to propose that meaning is retained in units of information connected by links. This web is different in each individual as differences exist in what units are stored and in how these units are linked. Explaining in detail what cognitive researchers mean by units of information can get pretty dense, but for the present purpose perhaps concepts and facts is close enough. This web is called semantic memory. In addition to the elements of meaning are episodic memories. These episodes are often described as the way we remember events and I always thought we could think of these events as stories. What I heard in class today is an episode with a progression of information. It might also be described as a story.
Some key ideas from Shank’s book:
Intelligence is an abstraction; different experts explain it and sometimes propose how it can be assessed differently. Shank argued that an individual’s use of stories could reveal a lot about how intelligent that person is. Two aspects were informative. The first is having stories worth telling and the second is being aware of which story would be effective when conveyed to a specific individual in a specific situation.
Reminding is using an input in a way that involves the prediction and generalization allowing the retrieval of relevant stored stories. Intelligence is reflected in that capacity to translate new experiences, perhaps stories told by someone else, into effective retrieval cues.
In the process of understanding, we compare experiences with what we have already experienced. This process of reminding is the basis for gaining new insights from differences between similar stories.
Thinking involves indexing. Shank proposed that a useful memory combines specific experiences and indices or labels. The more indices the better. Shank spent a great deal of effort identifying what indices people used proposing that locations, attitudes, challenges, decisions, conclusions, and other labels are used as indices.
We are not necessarily aware of the process of labeling. The application of labels can be assumed based on what individuals recall in response to an input (story/experience). A story that is recalled in response to a story told must share at least one common index.
Understanding is equivalent to the extraction of indices from an input that match the indices associated with stored stories. We learn when the identification of a match between new and old allows further analysis of differences in the stories.
Tags, links, and indices
Careful attention to Shank’s explanation of the value and role of stories is recognition that it is not the stories alone that are important, but the combination of indices and stories. The combination is important, but in addition, it is personalized through the imposition of an indexing approach that creates this productive system. Perhaps thinking about experiences searching for understanding translated as indexing.
So Shank’s importance relies on the combination of indices and stories. Cognitive researchers describe long-term memory in terms of units of information (semantic memory) and episodes linked to facilitate retrieval and understanding.
Those developing and implementing second-brain systems offer tools (e.g., Obsidian, Mem.ai, LoqSeq) offer a digital system for storing notes, for attaching tags to notes, and for linking notes to each other. Notes are not stored as extended documents as might be the case for the handwritten notes taken during a lecture, but as individual ideas or concepts and labeled with multiple tags and one or many connections to other notes. Users are encouraged to review their notes and their system of connections periodically and to add more connections that occur to them. The goal is value over the long term.
Idea for practice
Aside from reflecting on the commonalities across these systems and how the functioning of one system might encourage how another system might be understood, here is one observation that occurred to me while completing this analysis. I don’t think the second brain advocates take advantage of the power Shank sees in how our use of the first brain relies on stories. Perhaps there is some attention to identifying and connecting examples, but I see little attention paid to the storage, tagging, and linking of stories. I told stories as examples when I lectured. In the time I have spent developing my second brain, I don’t remember ever adding and linking one of the stories I tell and I have not documented in my notes the stories I have read as examples in the sources I might translate into notes. If Shank’s argument for the value of stories is valid, not including stories in a second brain would be an opportunity missed.
Reference
Shank, R. C. (1990). Tell me a story: Narrative and intelligence. _Evanston, IL: Northwestern University Process_.
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