Month: April 2018

How to Get the Most Out of Studying

Dr. Stephen Chew has put together a highly lauded series of short videos that share with students some powerful principles of effective learning, including metacognition. His goal was to create a resource that students can view whenever and as often as they want.

They include

  • Video 1: Beliefs That Make You Fail…Or Succeed
  • Video 2: What Students Should Understand About How People Learn
  • Video 3: Cognitive Principles for Optimizing Learning
  • Video 4: Putting the Principles for Optimizing Learning into Practice
  • Video 5: I Blew the Exam, Now What?

Links to the videos can be found here:

https://www.samford.edu/departments/academic-success-center/how-to-study

Dr. Chew also provides an overview handout that summarizes the purposes of the videos, gives guidance on how to use them, and outlines the main points within the videos:

https://www.samford.edu/departments/files/Academic_Success_Center/How-to-Study-Teaching_Resources.pdf


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


Where Should I Start With Metacognition?

by Patrick Cunningham, Rose-Hulman Institute of Technology

Have you ever had a student say something like this to you? “I know the material, I just couldn’t show you on the exam.” How do you respond?

I have heard such comments from students and I think it exemplifies two significant deficiencies.

First, students are over-reliant on rehearsal learning strategies. Rehearsal is drill-and-practice or repetitive practice aimed at memorization and pattern matching. Such practices lead to surface learning and shallow processing. Students know facts and can reproduce solutions to familiar problems, but struggle when the problem looks different. Further, when faced with real-world situations they are often not even able to identify the need for the material let alone apply it. Only knowing material by rote is insufficient for fluency with it. For example, I can memorize German vocabulary and grammar rules, but engaging someone from Germany in a real conversation requires much more than just knowing words and grammar.

Second, students are inaccurate in their self-assessments of their learning, which can lead to false confidence and poor learning choices (Ehrlinger & Shain 2014). Related to this, I have developed a response to our hypothetical student. I ask, “How do you know you know the material?” In reply, students commonly point to looking over notes, looking over homework, reworking examples or homework problems, or working old exams – rehearsal strategies. I often follow up by asking how they assessed their ability to apply the material in new situations. This often brings a mixture of surprise and confusion. I then try to help them discover that while they are familiar with the concepts, they are not fluent with them. Students commonly confuse familiarity with understanding. Marilla Svinicki (2004) calls this the Illusion of Comprehension, and others have called it the illusion of fluency. Continuing the language example, I could more accurately test my knowledge of German by attempting and practicing conversations in German rather than just doing flashcards on vocabulary and grammar rules. Unless we employ concrete, demonstrable, and objective measures of our understanding, we are prone to inaccurate self-assessment and overconfidence. And, yes, we and our students are susceptible to these maladies. We can learn about and improve ourselves as we help our students.

Addressing these two deficiencies can be a good place to start with metacognition. Metacognition is the knowledge and regulation of our thinking processes. Our knowledge of strategies for building deeper understanding and our awareness of being susceptible to the illusion of comprehension are components of metacognitive knowledge. Our ability to regulate our thinking (learning) and apply appropriate learning strategies is critically dependent on accurate self-assessment of our level of understanding and our learning processes, specifically, in metacognitive monitoring and evaluation. So how can we support our students’ metacognitive development in these areas?

To help our students know about and use a broader range of learning strategies, we can introduce them to new strategies and give them opportunities to practice them. To learn more deeply, we need to help students move beyond rehearsal strategies. Deeper learning requires expanding and connecting the things we know, and is facilitated by elaborative and organizational learning strategies. Elaboration strategies aid the integration of knowledge into our knowledge frameworks by adding detail, summarizing, and creating examples and analogies. Organizational strategies impose structure on material and help us describe relationships among its elements (Dembo & Seli 2013).

We can help our students elaborate their knowledge by asking them to: 1) explain their solutions or mistakes they find in a provided solution; 2) generate and solve “what-if” scenarios based on example problems (such as, “what if it changed from rolling without slipping to rolling with slipping”); and 3) create and solve problems involving specific course concepts. We can help our students discover the structure of material by asking them to: 1) create concept maps or mind maps (though you may first need to help them learn what these are and practice creating them); 2) annotate their notes from a prior day or earlier in the period; and 3) reorganize and summarize their notes. Using these strategies in class builds students’ familiarity with them and improves the likelihood of students employing them on their own. Such strategies help students achieve deeper learning, knowing material better and making it more accessible and useable in different situations (i.e., more transferable). For example, a student who achieved deeper learning in a system dynamics course will be more likely to recognize the applicability of a specific dynamic model to understand and design a viscosity experiment in an experiment design class.

To help our students engage in more accurate self-assessment we can aid their discovery of being susceptible to inaccurate self-perceptions and give them opportunities to practice strategies that provide concrete, demonstrable, and objective measures of learning. We can be creative in helping students recognize their propensity for inaccuracy. I use a story about an awkward conversation I had about the location of a youth hostel while travelling in Germany as an undergraduate student. I spent several minutes with my pocket dictionary figuring out how to ask the question, “Wissen Sie wo die Jugendherberge ist?” When the kind stranger responded, I discovered I was nowhere near fluent in German. It takes more than vocabulary and grammar to be conversant in the German language!

We can help our students practice more accurate self-assessment by asking them to: 1) engage in brief recall and review sessions (checking completeness and correctness of their recalled lists); 2) self-testing without supports (tracking the time elapsed and correctness of solution); 3) explaining solutions (noticing the coherence, correctness, and fluency of their responses); and 4) creating and solving problems based on specific concepts (again, noting correctness of their solution and the time elapsed). Each of these strategies creates observable and objective measures (examples noted in parentheses) capable of indicating level of understanding. When I have students do brief (1-2 minute) recall exercises in class, I have them note omissions and incorrect statements as they review their notes and compare with peers. These indicate concepts they do not know as well.

Our students are over-reliant on rehearsal learning strategies and struggle to accurately assess their learning. We can help our students transform their learning by engaging them with a broader suite of learning strategies and concrete and objective measures of learning. By starting here, we are helping our students develop transferable metacognitive skills and knowledge, capable of improving their learning now, in our class, and throughout their lives.

References

Ehrlinger, J., & Shain, E. A. (2014). How Accuracy in Students’ Self Perceptions Relates to Success in Learning. In V. A. Benassi, C. E. Overson, & C. M. Hakala (Eds.). Applying science of learning in education: Infusing psychological science into the curriculum. Retrieved from the Society for the Teaching of Psychology web site: http://teachpsych.org/ebooks/asle2014/index.php

Svinicki, M. (2004). Learning and motivation in the postsecondary classroom. San Francisco, CA: John Wiley & Sons.

Dembo, M. & Seli, H. (2013). Motivation and learning strategies for college success: A focus on self-regulated learning (4th ed.). New York, NY: Routledge.


Developing Metacognition with Student Learning Portfolios

In this IDEA paper #44, The Learning Portfolio: A Powerful Idea for Significant Learning, Dr. John Zubizarreta shares models and guidance for incorporating learning portfolios. He also makes powerful arguments regarding the ability of portfolios to engage students in meaningful reflection about their learning, which in turn will support a metacognitive development and life-long learning.