Tag: Metacognition

Using metacognition to move from talking the equity talk, to walking the equity walk

Conversations around equity, diversity, and inclusion are gaining traction on college campuses in the United States. In many cases, these conversations are overdue, so a willingness to even have the talk represents progress. But how can campuses move from talking equity talk to walking the equity walk? How can the buzz be transformed into a breakthrough? This post argues that taking a metacognitive approach is essential to taking steps in more equitable directions.

Becoming more equitable is a process. As with any process, metacognition encourages us to consider what’s working, what’s not, and how we might make adjustments to improve how we are living that process. If college campuses genuinely want to travel down more equitable roads, then they need to articulate their equity goals, map their route, and remove obstacles preventing them from reaching that destination. And if along the way, campuses find that their plans aren’t working, then metacognition can point the way towards a course correction.

A guide and the need for collective metacognition

Equity Talk to Equity Walk; book

In From Equity Talk to Equity Walk: Expanding Practitioner Knowledge for Racial Justice in Higher Education, Tia Brown McNair, Estela Mara Bensimon, and Lindsey Malcolm-Piqueux (2020) offer guidance to campuses wanting to do more than just talk. They argue, for example, that campuses need a shared understanding of equity and diversity. College mission statements are a start, but their lofty words define aspirations but not a path. Big words will never amount to more than talk unless a campus can figure out how to live into those big ideas. For example, it is one thing to pepper conversation with words, like ‘diversity,’ ‘equity,’ and ‘inclusion.’ It’s another thing altogether to develop a shared campus-wide understanding of these ideas and how those ideas need to practiced in the day-to-day life on campus. If institutional change requires shared understanding, then I argue that college campuses need collective metacognitive moments.

Metacognition urges us to establish goals and continually check-in on our progress towards them. Taking a metacognitive approach to institutional change will require that campuses articulate their equity goals with shared understanding of the underlying terms, map a plan to work towards those aspirations, monitor their progress, and make adjustments when appropriate.

  • What are the shared goals around equity? What might it mean to live into these goals in concrete terms?
  • Are these goals widely shared? If not, why not?
  • How can members of the campus community contribute and see themselves in their contribution?

Taking a metacognitive approach can also help locate the “pain points.”

  • Is the lack of progress owing to a lack of shared understanding, a lack of planning, or well-intentioned individuals working at cross-purposes?
  • What can be done to get efforts back on track?

As with any process, metacognitive check-ins around what’s working and what’s not working can point to areas for improvement. Metacognition, therefore, can keep a college campus heading down the equity path.

Progress requires being aware of barriers and working to remove them

Being concrete about the move from equity talk to walking the equity walk requires removing barriers to progress. According to McNair, Besimon, and Malcolm-Piqueux, barriers include individuals claiming not to see race or substituting class issues for race. Taking a metacognitive approach could encourage individuals to get curious about why they claim not to see race or feel more comfortable talking about economic issues. Why might someone be reluctant to consider the extent of their white privilege? Why might a campus be reluctant to acknowledge the reality of institutional racism and its implications?

Taking a metacognitive approach to such questions can honor the fact that talking about inequity can be awkward and uncomfortable. Yet, metacognition also encourages us to ask whether things are working and whether we might need to make adjustments. Walking the equity walk requires asking how white privilege and institutional racism might be inadvertently influencing campus policies and the delivery of instruction. Taking a metacognitive approach encourages campuses to look for ways to make adjustments. Awareness and adjustments are precisely what is needed in the move from equity talk to the equity walk.

By way of illustration,  McNair, Besimon, and Malcolm-Piqueux call on campuses to stop employing euphemisms, such as ‘underrepresented minorities.’ In their view, campus administration, individual departments, and instructors should disaggregate data instead. The thought is that equity issues can be addressed only if they are named. If, for example, the graduation rate of African-American males is lower than other groups, then walking the equity walk requires understanding why and looking for ways to help. If first-generation students are stopping out after their second semester (or their fourth), then campuses that are aware of this reality are positioned to make the necessary adjustments.

Administrators should look at institution-wide patterns to see if institutional protocols are impediments to student success. Individual departments should review student progress across programs and within particular courses to see how they might better support student learning. And individual instructors should take a careful look at when, where, and how students struggle with particular assignments, skills, and content. It may turn out that all students are equally successful across all areas. It might also be the case that patterns emerge which indicate that some groups of students could use more support in certain identifiable areas.

A metacognitive approach to institutional change requires that universities, academic departments, and individual instructors articulate their equity goals, track progress, and make adjustments where appropriate. Disaggregating data at all levels (institution-wide, by department, individual courses) can uncover inequities. Identifying those obstacles can be a step towards making the necessary adjustments. This can, in turn, help campuses walk the equity walk.

Improve with metacognition

Taking a metacognitive approach to process improvements encourages individuals (and institutions) to get curious about what works and where adjustments need to be made. It encourages them to continuously assess and use that assessment to make additional adjustments along the way. Colleges and universities have a long way to go if they are to address the realities of systemic inequities. But learning to walk the equity walk is a process. If we know anything about metacognition, we know that it provides us with the resources to offer process improvements. So, I argue, metacognition is essential to learning to move beyond equity talk and actually walking the equity walk.

 

 

 


Metacognitions About a Robot

by Roman Taraban, Ph.D., Texas Tech University

Imagine a time when intelligent robots begin interacting with humans in sophisticated ways. Is this a bit farfetched? Probably not, as there already exist compelling examples of just that. Sophia, a robot, so impressed her Saudi audience at an investment summit in 2017 that she was granted Saudi citizenship. Nadine, another robot, is an emotionally intelligent companion whose “intelligent behavior is almost indistinguishable from that of a human”. The coming exponential rise of artificial intelligence into all aspects of human behavior requires a consideration of possible consequences. If a machine is a billion times more intelligent than a human, as some predict will happen by 2045, what will cognitive and social interactions with such superhuman machines be like? Chris Frith (2012) argues that a remarkable human capacity is metacognition that concerns others. However, what if the “other” is an intelligent machine, like a robot. Is metacognition about a robot feasible? That is the question posed here. Four aspects of metacognition are considered: the metacognitive experience, theory of mind, teamwork, and trust. Other aspects could be considered, but these four should be sufficient to get a sense of the human-machine metacognitive possibilities.

robot and human hand fist bump

Flavell (1979) defined metacognitive experiences as follows: “Metacognitive experiences are any conscious cognitive or affective experiences that accompany and pertain to any intellectual enterprise. An example would be the sudden feeling that you do not understand something another person just said” (p. 906). Other examples include wondering whether you understand what another person is doing, or believing that you are not adequately communicating how you feel to a friend. We can easily apply these examples to intelligent machines. For instance, I might have a sudden feeling that I did not understand what a robot said, I might wonder if I am understanding what a robot is doing, or I may believe that I am communicating poorly with the robot. So it appears to be safe to conclude that we can have metacognitive experiences involving robots.

Other instances of metacognition involving intelligent machines, like robots, are problematic. Take, for instance, mentalizing or Theory of Mind. In mentalizing, we take account (monitor) of others’ mental states and use that knowledge to predict (control) others’ and our own behavior. In humans, the ability to reason about the mental states of others emerges between the ages of 4 to 6 years and continues to develop across the lifespan. In a typical test of this ability, a child observes a person place an object in drawer A. The person then leaves the room. The child observes another person move the object to drawer B. When the first person returns, the child is asked to predict where the person will look for the object. Predicting drawer A is evidence that the child can think about what the other person believes, and that the child recognized that the other person’s beliefs may not be the same as the child’s own knowledge. Theory of mind metacognition directed towards humans is effective and productive; however, theory of mind metacognition directed to intelligent machines is not likely to work. The primary reason is that theory of mind is predicated on having a model of the other person and being able to simulate the experience of the other person. Because intelligent machines process information using algorithms and representations that differ from those humans use, it is not possible to anticipate the “thinking” of these machines and therefore predict their behavior in a metacognitive manner, i.e., having a theory of the other mind. Presently, for instance, intelligent machines use deep learning networks and naïve Bayes algorithms to “think” about a problem. The computational methods employed by these machines differ from those employed by humans.

What about teamwork? According to Frith (2012), humans are remarkable in their ability to work together in groups. Teamwork accounts for humans’ incredible achievements. The ability to work together is due, in large part, to metacognition. The specific factor cited by Frith is individuals’ willingness to share and explain the metacognitive considerations that prompted their decision-making behavior. For group work to succeed, participants need to know the goals, values, and intentions of others in the group. As has been pointed out already, machine intelligence is qualitatively different from human knowledge, so that is one barrier to human-machine group work. Further, the benefits of group work depend on a sense of shared responsibility. It is currently unknown whether or how a sense of cooperation and shared responsibility would occur in human-machine decision making and behavior.

There is one more concern related to machine intelligence that is separate from the fact that machines “think” in qualitatively different ways compared to humans. It is an issue of trust. In some cases of social interaction, understanding information that is being presented is not an issue. We may understand the message, but wonder if our assessment of the source of the information is reliable. Flavell (1979) echoed this case when he wrote: “In many real-life situations, the monitoring problem is not to determine how well you understand what a message means but to determine how much you ought to believe it or do what it says to do” (p. 910). When machines get super smart, will we be able to trust them? Benjamin Kuipers suggests the following: “For robots to be acceptable participants in human society, they will need to understand and follow human social norms.  They also must be able to communicate that they are trustworthy in the many large and small collaborations that make up human society” https://vimeo.com/253813907 .

What role will metacognitions about super-intelligent machines have in the future? Here I argue that we will have metacognitive experiences involving these machines. Those experiences will occur when we monitor and regulate our interactions with the machines. However, it is not clear that we will be able to attain deeper aspects of metacognition, like theory of mind. This is because the computations underlying machine intelligence are qualitatively different from human computation. Finally, will we be able to trust robots with our wealth, our children, our societies, our lives? That will depend on how we decide to regulate the construction, training, and deployment of super intelligent machines. Flavell (1979) often brings in affect, emotion, and feelings, into the discussion of metacognitive experiences. Kuipers emphasizes the notion of trust and ethics. These are all factors that computer scientists have not begun to address in their models of intelligent machine metacognition (Anderson & Oates, 2007; Cox, 2005). Hopefully solutions can be found, in order to enable rich and trustworthy relationships with smart machines.

References

Anderson, M. L., & Oates, T. (2007). A review of recent research in metareasoning and metalearning. AI Magazine, 28(1), 12.

Cox, M. T. (2005). Field review: Metacognition in computation: A selected research review. Artificial intelligence, 169(2), 104-141.

Flavell, John H. (1979). Metacognition and cognitive monitoring. American Psychologist, 34(10), 906-911.

Frith, C. D. (2012). The role of metacognition in human social interactions. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1599), 2213-2223.


Practicing Metacognition on a Chatbot

by Roman Taraban, Ph.D., Texas Tech University

Cognition involves many kinds of processes. There is categorization, problem solving, decision making, and comprehension, among others. Metacognition may involve these processes, but is different from them. Metacognitive thinking is thinking about the processes themselves (Draeger, 2015). That is, thinking about the processes involved in categorization, comprehension, and so on, and how these processes relate to one’s information processing capabilities. John Flavell, who coined the term metacognition, suggested that metacognitive processing relates not only to the individual thinkers but to others as well: “Metacognitive knowledge is one’s stored knowledge or beliefs about oneself and others as cognitive agents, about tasks, about actions or strategies, and about how all these interact to affect the outcomes of any sort of intellectual enterprise” (Flavell, 1999, p. 906). Consideration of how thinking in another person informs one’s own metacognitive knowledge is seldom considered in discussions of metacognition. In this post, I relate how reflecting on how others process information, specifically, how machines process information, can inform a person’s understanding of how he or she processes information. The metacognitive processes of interest here are those related to language processing, and the specific machine processing relates to that of machine systems called chatbots.

Chatbots are computer programs that interact with a person auditorily or through text. They are designed to communicate as much as possible like humans, in order to convey a sense of natural language communication. Chatbots are typically developed for commercial purposes, to provide customer service, for instance, or information about products or places. You will find chatbots on websites for companies, organizations, and events.

Recently I taught a graduate seminar on psycholinguistics, which is concerned with language acquisition, production, and comprehension. I assigned students the task of building chatbots for an application that interested them, for instance, a chatbot that could inform a user of the movies currently playing around town and show times. After students had built their chatbots and demonstrated them to the class, I assigned a written take-home metacognitive activity in which students had to discuss some aspect of the nature of chatbot language, for example, ways in which chatbot language might reduce moral relativity, constrain language interactions, or homogenize language. Students essentially had to think about the language processing constraints in chatbots and how that might affect their language interactions.

Students built chatbots to do everything from helping a student choose colleges for graduate work, college courses, movies, and restaurants, to guiding workouts or choosing a football game to watch. In their subsequent metacognitive reflection assignment, students had plenty to say:

  • Chatbots are peculiar devices.
  • Chatbots do not process language as humans would.
  • Chatbots, because of their limited cognitive capabilities, cannot respond to novel stimuli in conversations and therefore cannot problem-solve or be socially engaging.
  • Chatbots have higher potential of providing logical, true and precise answers than humans.
  • The nature of chatbot language has positive characteristics that reinvent the notion of interaction, and negative characteristics that create many confusions and misinterpretations about the use of a language.

Using chatbots as a foil prompted students to consider the nature of their own language processes. As a few examples:

For example, human communication is not a mere string of words put together to make meaning, rather it employs many other resources which feed communication such as the extralinguistic, paralinguistic and metalinguistic cues in order to achieve successful communication. I think that chatbots cannot perform such complex task as efficiently as most people do.

Although chatbots may serve humans as they interact with them, I think they do so with a structured sort of language which is intended to perform very specific tasks. As human language is inherently relative and creative, I think chatbots need much improvement to sound like humans if we need them to interact more “naturally.” In terms of human language, a unique characteristic is the ability to process linguistic and non-linguistic inputs. As humans we can process such inputs with the help of our background knowledge, working memory and other brain functions. Our judgements are further constrained, shaped or developed by moral relativity, i.e. the philosophical standpoints given or attributed by the cultures and societies we belong.

The students’ reflections on chatbot language processing fit Flavell’s (1999) suggestion that metacognition includes beliefs about others as cognitive agents, that is, as intelligent communicative actors. Often, learning about metacognitive strategies may begin by observing others and implicitly mimicking their behaviors. For instance, as children we may notice someone writing down a phone number or looking up a phone number and we recognize and adopt these specific processes to manage information. Knowledge of the strategies becomes more explicit the first time we fail to apply the strategy and cannot remember a phone number. We observe classmates reviewing notes repetitively and self-testing and adopt these methods of regulating and monitoring study behaviors. We rarely, if ever, create objects like chatbots, as in the present case, and use the objects to reflect on others’ and our own metacognitive processes, as a learning process. However, as AI technology and products become more prevalent, there arise many natural opportunities to think about and compare machines’ processes to our own. Of course, to qualify as metacognitive thinking, reflections on man vs machine processing will have to go beyond superficial comments like “My Alexa is not too smart.” To be metacognitive, thinking has to be about the processes themselves, in the machine and in the person.

The theme of this post is to highlight how metacognition is not only about thinking about one’s own thinking, but also thinking about thinking in the entities – humans or machines – with whom we communicate. Building a chatbot gives students direct contact with the processes in the machine and a bridge to reflecting on their own processes by comparison. It forces students to reflect on strengths and limitations of both kinds of language. There are other instances where this type of metacognitive knowledge comes into play naturally. Take child-directed speech (a.k.a. motherese, baby talk), for instance. Caretakers adjust their intonation, vocabulary, and rhythm when speaking to infant siblings. They have a sense that an infant is processing language differently so they adjust their own processing to accommodate. Similarly, in the classroom or at a conference, we become aware (sometimes depressingly) that our message is not connecting and may try to make adjustments in speed, terminology, examples, etc. The difference between those situations and the present one is that there may not be a moment of deliberate metacognitive reflection – how is the other person processing information compared to how I am processing the information. Flavell reminds us that this, too, is metacognitive. Here I am suggesting that we can make those moments more deliberate, indeed, we can turn them into class assignments!

References

Draeger, J. (2015). Two forms of ‘thinking about thinking’: metacognition and critical thinking. Retrieved from https://www.improvewithmetacognition.com/two-forms-of-thinking-about-thinking-metacognition-and-critical-thinking/.

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906-911. doi.org/10.1037/0003-066X.34.10.906


Hate-Inspired Webforums, PTSD, and Metacognition

by Roman Taraban, Texas Tech University

In linguistics, a register is a variety of speech used for distinct purposes in particular social settings. In a manner consistent with that terminology, I am here using the term discourse register to refer to sets of specific terms and meanings, and to specific vocabularies used by groups in order to achieve distinct purposes. Unlike a dictionary, a register is not so much concerned with the meanings of words as it is with their association with cognitions, affects, and behaviors. A discourse register can link together such disparate phenomena as hate speech, PTSD, and metacognition by virtue of the fact that each has a distinct discourse register, that is, each applies a specific vocabulary and manner of speech. The purpose of this blog post is to suggest that these disparate phenomena are similar by virtue of the way that they operate. The second purpose is to suggest a way of increasing our understanding of metacognitive processing by beginning to implement some of the technology that has already been extensively applied to hate-inspired webforums and trauma-related therapies.

Regarding hate speech, the internet has provided radical right groups the means to organize networks that often promote bias, bigotry, and violence. An example is Stormfront (https://www.stormfront.org/forum/), which was established by white supremacist and ex-felon Don Black in 1996. (Figea, 2015). Right-wing extremists use the internet to build identity and unity with “like-minded” individuals. This has prompted researchers and government analysts to analyze extremist communications in order to gain an understanding of these groups. Importantly, key indicators in the communications are sought out that could indicate future events (Figea, 2015; Figea et al., 2016).

What are the key indicators in extremist communications? The answer lies in part in the concept of a discourse register. It consists of the specific vocabulary and ways of communicating that characterize the shared conversations and practices of a group. For example, Figea (2015) applied machine learning to analyze Stormfront forum exchanges in an attempt to assess the level of three affects: aggression towards an outgroup, racism, and worries about the present or future. A sample of forum posts was classified by humans for the affects, then a machine was trained on the human classifications and tested on a new sample of forum posts. Key indicators for the racism affect were black, race, Jew, protest, and Zionist, corresponding to topics in the forums associated with Black inferiority, Jewish conspiracy, and government corruption (Figea, 2015).

The idea of a shared discourse among a group of individuals provides the theoretical glue that allows binding the activities, speech, and shared identity of groups of individuals. In some cases, the analysis of discourse has provided insights into the motivations and behaviors of extremist and terrorist groups, as described by Figea and colleagues (2015; Figea et al., 2016). In other cases, researchers have applied the idea of discourse and discourse analysis to prosocial activities involving counseling and therapy. Pennebaker and King (1999) proposed that “the way people talk about things reveals important information about them” (p. 1297). In order to assist them in their analyses, Pennebaker and colleagues developed and tested the LIWC (Linguistic Inquiry and Word Count) software. This software has been successfully applied to the analysis of texts in a variety of contexts and applied to a wide range of dimensions. These include analyses of emotionality, social status, group processes, close relationships, deception and honesty, thinking styles, and individual differences (Tausczik & Pennebaker, 2010).

Jaeger et al. (2014) examined the associations between trauma-related experiences (e.g., PTSD, depression, anxiety) and the content of the narratives written by trauma patients. The researchers found significant differences between daily vs trauma-related narratives in the use of cognitive-mechanism words (e.g., cause, know, ought) and negative emotion words (e.g., hate, worthless, enemy). There were also strong associations between the words that patients used and the severity of their trauma. The approach and outcomes in Jaeger et al. was similar to that employed by Figea and colleagues.

A perk of the LIWC software is that it allows individuals to develop their own specialized dictionaries and to import those dictionaries into LIWC to analyze language use for evidence of the target constructs. When individuals express sadness, they use words like sad, loss, cry, alone (Pennebaker & King, 1999). Sadness is part of a person’s emotion register. Can we apply this analytic approach to metacognition and ask, What is the discourse of metacognition? As instructors, how do the ways we talk about teaching reflect a metacognitive register – i.e., words that reflect an understanding of cognitive functioning, learning, limitations, self-regulation, monitoring, scaffolding, and so on. How do the ways we talk about students, classrooms, homework, and student collaboration mirror metacognitive understanding and processing? Current technology allows us to begin exploring these questions. Following the model provided in Figea (2015; Figea et al., 2016), one place to start might be this Improve With Metacognition (IWM) forum. The analysis of published scholarship on metacognition would be another source of texts to use to train and analyze a machine to detect key metacognitive indicators in texts. Human coders would code sentences in a sample of the texts as involving or not involving metacognition. These classification would be used to train a machine. After training, the machine would be tested on a new sample of texts.

Development of a metacognitive register is subject to the same constraints as any good scholarship. The developers need to be experts in the area of metacognition, and they need to have a clear grasp of how metacognition works. The linguistic analysis dictionary that they develop needs to be accurate and comprehensive. It needs to be a team effort – one individual cannot do it alone. The dictionary needs to be tested for construct validity, internal consistency, and for reliable test results across a variety of participants and contexts. In spite of the challenges inherent in the task, the prospect of a ready analytic tool for metacognition could help in advancing the application of the powerful cognitive suite of metacognitive processes in classrooms.

 

References

Figea, L. (2016). Machine learning for affect analysis on white supremacy forum. Downloaded from https://uu.diva-portal.org/smash/get/diva2:955841/FULLTEXT01.pdf .

Figea, L., Kaati, L, & Scrivens, R. (2016). Measuring online affects in a white supremacy forum. In IEEE Xplore. DOI: 10.1109/ISI.2016.7745448

Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: Language Use as an individual difference. Journal of Personally and Social Psychology, 77(6), 1296-1312.

Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24-54.


The Great, The Good, The Not-So-Good of Improve with Metacognition: An Exercise in Self-Reflection

By Aaron S. Richmond, Ph. D., Metropolitan State University of Denver

Recently, Lauren, John, and I reflected on and discussed our experiences with Improve with Metacognition (IwM). We laughed and (no crying) found (at least I did) that our experiences were rich and rewarding. As such, we decided that each of us would write a blog on our experience and self-reflection with IwM. Therefore, I’m up. When thinking about IwM, the theme that kept surfacing in my mind is that we are Great, Good, and on a few things—Not-So-Great.

The Great

Oh, how can I count the ways of how IwM is Great. Well, by counting. In my reflection on what we have accomplished, it came apparent that here at IwM, we have been highly productive in our short existence. Specifically, we have published over 200 blogs, resources about metacognition measures, videos, instruction, curated research articles, and teaching metacognition (see our new call for Teaching with Metacognition). We have created a space for collaborators to gather and connect. We have engaged in our own research projects. We have had over 35 contributors from all over North America and a few from beyond, who have ranged from preeminent scholars in the field of metacognition and SoTL to graduate students writing their first blog. Speaking for Lauren and John, I can only hope that the explosion in productivity and high quality research and writing continues with IwM.

The Good

Ok, it is not just Good—this is just another thing that is great. IwM has produced some amazing blogs. I can’t review them all because, this time I will keep to my word count, but I would like to highlight a few insightful blogs that resonated with me. First, Edn Nuhfer recently wrote a blog titled, Collateral Metacognitive Damage (2017, February). The title is amazing in itself, but Ed extolls the use of self-assessments, why approach and perspective of self-assessment matters most (be the little engine that could vs. little engine who couldn’t), and provides a marvelous self-assessment tool (http://tinyurl.com/metacogselfassess ). I have already shared this with my students and colleagues. Second, one of the topics I would never have thought of, was Stephen Chew’s blog on Metacognition and Scaffolding Learning (2015, July). I have used scaffolding (and still do) throughout all of my courses, however, I never considered that by over-scaffolding, that I could reduce my student’s ability to calibrate (know when you know or don’t know something). That is, by providing too much scaffolding, it may cause students to be highly over confident and overestimate their knowledge and skill. Third, Chris Was wrote about A Mindfulness Perspective on Metacognition (2014, October). I have been begrudgingly and maybe curmudgeonly resistant to mindfulness. As such,  I was skeptical even though I know how great Chris’ research is. Well, Chris convinced me of the value of mindfulness and its connection to metacognition. Chris said it best, “It seems to me that if metacognition is knowledge and control of one’s cognitive processes and training in mindfulness increases one’s ability to focus and control awareness in a moment-by-moment manner, then perhaps we should reconsider, and investigate the relationship between mindfulness and metacognition in education and learning.” There are literally dozens and dozens of other blogs that I have incorporate into both my teaching and research. The work done at IwM is not merely good, it is great!

The Not-So-Good

IwM has been a labor of love. Speaking for myself, the work that has been done is amazing, exhausting, invigorating, productive, and fulfilling.  However, what I believe we have been “Not Great” at is getting the word out. That is, considering that there are over 200 blogs, resources, curated research articles, collaborations, etc. I believe that one of the things we are struggling with is spreading the gospel of metacognition.  Also, despite the fact that Lauren, John, and I have travelled across the globe (literally) promoting IwM at various conferences, so few people know about the good work being done. Moreover, notwithstanding that we have 258 email subscribers, I feel (passionately) that we can do better. I want and desire for other researchers and practitioners to not only benefit from the work we’ve done but to contribute to new IwM blogs, resources, research, and collaboration.

As I do with all my blogs, I will leave you with an open-ended question: What can we do to spread the word of the Great and Good work here at IwM?

Please give me/us some strategies or go out and help spread the word for us.

References

Chew, S. (2015, July). Metacognition and scaffolding student learning. Retrieved from https://www.improvewithmetacognition.com/metacognition-and-scaffolding-student-learning/

Nuhfer, E. (2017, February). Collateral metacognitive damage. Retrieved from https://www.improvewithmetacognition.com/collateral-metacognitive-damage/

Was, C. (2015, October). A mindfulness perspective on metacognition. Retrieved from https://www.improvewithmetacognition.com/a-mindfulness-perspective-on-metacognition/


Exploring the potential impact of reciprocal peer tutoring on higher education students’ metacognitive knowledge and regulation

Backer, Keer and Valcke’s study “explores the potential of reciprocal peer tutoring to promote both university students’ metacognitive knowledge and their metacognitive regulation skills. The study was conducted in a naturalistic higher education setting, involving 67 students tutoring each other during a complete semester.”

Backer, Liesje De. (May 2012) . Exploring the potential impact of reciprocal peer tutoring on higher education students’ metacognitive knowledge and regulation. Instructional Science, Volume 40, issue 3, pp 559-588. http://link.springer.com/article/10.1007/s11251-011-9190-5

Exploring the potential impact of reciprocal peer tutoring on higher education students’ metacognitive knowledge and regulation


5 Things Every Student Should Know Before Starting College

This article is about Geddes’ five tips to students who are entering college. Once you read the subtitles, I’m sure you will be intrigued to read this brief article.

Five Tips

  1. Your Professors Hate Your Favorite High School Teachers!
  2. Understand the 80/20 Rule / 20/80 Rule Shift
  3. Read Material Before Class
  4. Know the Difference Between Memorizing and Learning
  5. Be Confident. You are not broken

Geddes, Leonard. (2015) . 5 Things Every Student Should Know Before Starting College. The Learnwell Projects. Retrieved from http://www.thelearnwellprojects.com/thewell/5-things-every-student-should-know-before-starting-college/

5 Things Every Student Should Know Before Starting College

 


Assessing Metacognition and Self-Regulated Learning

This article “provides an overview of the conceptual and methodological issues involved in developing and evaluating measures of metacognition and self-regulated learning.” Sections in this article discuss the components of metacognition and self-regulated learning as well as the assessment of metacognition.

Pintrich, Paul R.; Wolters, Christopher A.; and Baxter, Gail P., “2. Assessing Metacognition and Self-Regulated Learning” (2000). Issues in the Measurement of Metacognition. Paper 3.

Assessing Metacognition and Self-Regulated Learning


A Metacognitive Learning Cycle: A Better Warranty for Student Understanding?

Blank’s study “proposes a revised learning cycle model, termed the Metacognitive Learning Cycle, which emphasizes formal opportunities for teachers and students to talk about their science ideas. Working collaboratively, the researcher and a seventh-grade science teacher developed a 3-month ecology unit based on the revised model.” Results showed that even though students that were in the metacognitive classroom didn’t gain more content knowledge of ecology, they did however have more “permanent restructuring of their ecology. “

Blank, M. Lisa. (2000). A Metacognitive Learning Cycle: A Better Warranty for Student Understanding? Science Education, Volume 84, Issue 4, pages 486-506, July 2000.

A Metacognitive Learning Cycle: A Better Warranty for Student Understanding?

 


Metacognitive Development as a Shift in Approach to Learning: An in-depth study

Case and Gunstone conducted a study on students who were enrolled in an engineering course and after conducting series of interviews, they were able to provide detailed information about students’ metacognitive development or “lack thereof.”

Jennifer Case & Richard Gunstone (2002) Metacognitive Development

as a Shift in Approach to Learning: An in-depth study, Studies in Higher Education, 27:4,

459-470, DOI: 10.1080/0307507022000011561

Metacognitive Development as a Shift in Approach to Learning: An in-depth study

 


Metacognition and Learning: Conceptual and Methodological Considerations

This is the first issue of the new international journal Metacognition and Learning. Journal provides “A kaleidoscopic view on research into metacognition.” It is a great introduction to metacognition and includes ten issues “Which are by no means exhaustive.”

Metacognition and Learning, 2006, Volume 1, Number 1, Page 3. Marcel V. J. Veenman, Bernadette H. A. M. Hout-Wolters, Peter Afflerbach

Metacognition and Learning: Conceptual and Methodological Considerations


The Role of Metacognitive Knowledge in Learning, Teaching, and Assessing

“Metacognitive knowledge is a new category of knowledge in the revised Taxonomy.” According to Pintrich, strategic knowledge, self-knowledge and the knowledge of tasks and their contexts are the three important types of metacognitive knowledge.

Paul R. Pintrich (2002) The Role of Metacognitive Knowledge in Learning, Teaching, and

Assessing, Theory Into Practice, 41:4, 219-225, DOI: 10.1207/s15430421tip4104_3

The Role of Metacognitive Knowledge in Learning, Teaching, and Assessing

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Metacognition as Part of a Broader Perspective on Learning

This article includes six instructional strategies that promote self-regulation and ways that motivational cognitive and metacognitive skills can be enhanced using these strategies.

Research in Science Education, 2006, Volume 36, Number 1-2, Page 111. Gregory Schraw, Kent J. Crippen, Kendall Hartley

 

Promoting Self-Regulation in Science Education: Metacognition as Part of a Broader Perspective on Learning