Lifetime Learning is not about Knowledge Acquisition

We are moving deeper into the age of conception. Daniel Pink described this age as “an era in which mastery of abilities that we’ve often overlooked and undervalued marks the fault line between who gets ahead and who falls behind” (p. 6). From Pink’s book, A Whole New Mind, one can surmise that students in the conceptual age must be able to:

  • create artistic and emotional beauty
  • detect patterns and opportunities
  • craft a satisfying narrative
  • combine seemingly unrelated ideas into a novel invention
  • empathize
  • understand the subtleties of human interaction
  • find joy in one’s self and elicit it in others” (p. 51).

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Many of the traditional practices being used to teach our students in the conceptual age are not engaging learners in authentic ways, thus confining student identity, student agency, and student power. These traditional practices focus solely on the acquisition of topical knowledge and facts.

Schank reminds us that “we need a different approach to knowledge than we currently have” (p. 22). In other words, “we need to teach students to attack the facts and not to replace them with other facts” (Schank, p.22). Moreover, Schank submits that, “students are not taught to use the information they have, to question other information” (p. 23). Continuing down this traditional path will not prompt personalized learning for our students. Therefore, being predisposed to Schank’s advice, I believe that it would behoove educators to move from a knowledge-based education model (which is curriculum design) to a process-based education model (which is instructional design). Schank calls this process-based education model, story-centered curricula.  I’d like to tweak what he calls it, to story-centered design.

“Real knowledge is acquired as a natural part of an employed cognitive process in service of a goal” (Schank, p. 79). Below is a list of Schank’s twelve cognitive processes that underlie learning:

  • Conceptual
    • Prediction
    • Modeling
    • Experimentation
    • Evaluation
  • Analytic
    • Diagnosis
    • Planning
    • Causation
    • Judgement
  • Social
    • Influence
    • Teamwork
    • Negotiation
    • Describing

As students engage in authentic learning experiences, “knowledge acquisition is a natural result of engaging in cognitive processes that are being employed to satisfy a truly held goal” (Schank, p.79). Hence, it is the design of the learning experience that should be the focus. “A good [learning experience] relies on the creation of stories that a student can participate in and feel deeply about” (Schank, p. 90). Perhaps, using stories which are goal-based and involve role play, can be an approach used by teachers as an instructional design model in a web-enhanced classroom.

In sum, lifetime learning is not about knowledge acquisition. It’s about continuous development of the twelve cognitive processes, student identity, student agency, and productive student power.

Reference:

Pink, D. H. (2006). A whole new mind: Why right-brainers will rule the future. New York: Riverhead Books.

Schank, R. C. (2011). Teaching minds: How cognitive science can save our schools. New York: Teachers College Press.

Lesson Enrichment and Lesson Extensions Open the door to Student Voice and Choice for Personalized Learning

Why is it that Lesson Enrichments and Lesson Extensions are typically reserved for the Gifted and Talented student? Perhaps I am wrong, but based on my own experience as a Classroom Teacher and as a Reading Specialist, I haven’t seen many school-aged children offered an opportunity to pick an extension or enrichment activity of their choice to work on. Doesn’t lesson enrichment and lesson extensions increase student voice and choice within their own learning? Below is the Carolyn Coil Model. This model illustrates horizontal and vertical differentiation for students. It also illustrates how lesson extensions and enrichments can enhance personalized learning for students.

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Lesson extension exercises allow students to explore topics that are in the curriculum. This is one method of providing students with a voice and a choice  in their learning. Lesson enrichment exercises, on the other hand, allow students to explore topics that are not in the curriculum. This is another method for giving students choice and voice in what they learn. Lesson enrichments tend to happen during genius hour. While lesson extensions occur within the unit of instruction.

As teachers design instruction that is personalized, they should consider including either an enrichment exercise or an extension exercise within the unit. Both are initiating methods for personalizing learning for students.

Using Computer Science Principles for Decision Making with Design-Oriented Theory

I came across Brian Christian’s and Tom Griffiths’ book entitled, Algorithms to Live By this past summer. I typically don’t read books about math and computer science, but I decided to stretch myself and read books that are outside of my comfort zone while relaxing by the pool.

Brian Christian and Tom Griffiths submit that “algorithms are a finite sequence of steps used to solve a problem” (p. 7). Christian and Griffiths definition got me to thinking, what algorithm can teachers use to solve the problem of personalizing learning for each student within a web-enhanced classroom?

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I’m not a mathematician, nor statistician, nonetheless, after reading Algorithms to live by, I started to understand why mathematicians say that math is all around us. Essentially, algorithms help us to solve problems. Hence, how can we use algorithms to help us solve the problem of personalizing learning for all students within a web-enhanced classroom? What would that algorithm even look like? See my shameless attempt below.Slide1

I’m sure that my attempt at writing an algorithm that represents my problem is not the correct way, nonetheless, I still believe that I am on the right track regarding the usage of algorithms in instructional-design theory.  In any case, as I got deeper into Christian’s and Griffiths’ book, I realized that connections could be made between computer science principles and instructional-design theories for web-enhanced classrooms.

Teachers in a web-enhanced classroom using principles of instructional-design theory should consider employing the following principles from computer science for instructional decision making:

  • The optimal stopping problem – knowing when to stop analysis of information.
  • Explore/Exploit Tradeoff – Start with a period of exploration, then exploit the best options for your purpose.
  • Sorting – organization of information; making order.
  • Caching – storage of information; maintaining direct access to the most needed information.
  • Scheduling – knowing how much time should be allocated to a given task, first things first.
  • Bayes’s Rule – updating a belief about a hypothesis in light of new evidence, forecasting.
  • Overfitting – a statistical model that describes random error or noise within the data instead of an underlying relationship.
  • Relaxation – A relaxation is an approximation of a difficult problem by a nearby problem that is easier to solve. A solution of the relaxed problem provides information about the original problem.
  • Randomness – The lack of pattern or predictability in events. A random sequence of events, symbols or steps has no order and does not follow an intelligible pattern or combination.
  • Networking – making connections between sources of information.
  • Game Theory – a branch of mathematics concerned with the analysis of strategies for dealing with competitive situations where the outcome of a user’s choice of action depends critically on the actions of other users.

There is much to ponder here, and I will continue to grapple with this issue as I document this journey of helping teachers personalize learning for students within their web-enhanced classroom.

Instructional Design Models Should Enhance Student Identity, Promote Student Agency, And Provide Student Power

When designing instruction for web-enhanced classrooms, we must consider the role of student identity, student agency, and student power. Student identity is a continuous formation of the student acting as a subject within a community. In other words, student identity is the ability to be able to identify with the particular discourse or language of the community. As students learn more from the learning community, their ability to identify with the subject allows them to act as a key subject within the community.

Student agency is the making and remaking of the students’ self, the students’ identity, and the students’ relationships. When teachers promote student agency, they are allowing students to make and remake learning tools, learning resources, and learning activities. These acts lead to productive power for our learners.

Student power is cultivated on rich relationships and high quality interactions. Hence, in web-enhanced classrooms, what applications will best help to meet the learning goals while supporting the development of student power, student agency, and student identity?

white-male-1871370_1920Student productive power, is not only having skill and will to achieve goals, but also having independent thought and autonomous action towards self-regulated learning and self-directed learning. Hence, how can instructional-design models tap into student power, student agency,  and student identity? Roger Schank’s Teaching Minds: How Cognitive Science can save our schools listed five issues that he claims educators are not effectively addressing. They are ability, possibility, methodology, constraints, and goal alignment.

  • Ability – whether students can learn whatever it is that you want to teach.
  • Possibility – whether what you want to teach can be taught.
  • Methodology – what method of learning actually would teach what we want to teach.
  • Constraints – whether the selected learning methodology actually will work, given the time constraints and abilities of the students, and other constraints that actually exist.
  • Goal alignment – determine a way that will make what you want to teach fit more closely with real-life goals that your students actually may have.

I’ve contoured Schank’s list of issues in order to fit them into the discourse of personalized learning.

  • Ability – what is the students learning profile?
  • Possibility – what is the students learning potential?
  • Methodology – what instructional design model should be employed?
  • Constraints – what are the limitations of the learning environment and what are the constraints for achieving the learning goal?
  • Goal alignment – what are the teacher’s goal for instruction? What are the student’s goals for learning?

Will Richardson reminds us that we should increase student agency over learning. Our current emphasis on improving teaching is not cultivating the student’s agency, the student’s identity, or the student’s productive power. In other words, we should shift from a focus on teaching practices to a focus on student-centered learning practices.

References:

Schank, R. C. (2011). Teaching minds: How cognitive science can save our schools. New York: Teachers College Press.

Does Learner Variance warrant a “Learner-Designer” theory?

Learners are as unique as their fingerprints. So why isn’t their instruction just as unique? I believe it is because of curriculum-theory designs.

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Curriculum-theory designs are description oriented in nature, which means that they focus on the results of any given learning event. They are also deterministic, which means that the attainment of the learning goals are assured with operant conditioning.  Thus, behaviorism is at the core of Curriculum-theory designs. In essence, Curriculum-theory designs ask “What to teach“. Furthermore, these designs are founded on standardization and conformity from the key markers of the Industrial Age.

On the other hand, Instructional-design theories are design-oriented in nature because they focus on the means to attain the given learning goals. They are probabilistic, which means that the prescribed method of instruction will increase the chances of attaining the learning goals byway of instructional conditions, desired outcomes, and the instructional components.  Thus, cognitivism combined with constructivism is at the core of  Instructional design-theories.  Fundamentally, Instructional-design theories ask “How to teach“. Moreover, these designs are founded on customization and diversity from the key markers of the Information Age. To make instruction as unique as the learner’s fingerprints, teachers will have to shift from curriculum-theory designs to instructional-design theories.

To customize instruction for our learners, teachers must first understand the role of learning profiles. Learning profiles provide an in-depth look at the learners’ abilities in various domains. Not all learners are average, and Todd Rose, the director of the Mind, Brain, & Education Program at the Harvard Graduate School of Education, highlighted that notion with his TED Talk on  the jagged learning profile. Hence, using the learning profiles of students will help teachers to shape a powerful personalized instructional-design prescription for diverse learners.

As teachers construct the learning profiles of their students, it is important that they include the student in this process. Reigeluth (1999), submitted that an instructional-design theory should allow for much greater use of the notion of “user-designers” (p. 25). User-designers are both the learners and the facilitators of learning. In other words, as students are interacting with their teacher, their peers, and the content, they are alternating between the role of a learner and that of a facilitator of learning for their peers.  As such, learners should play a major role in designing their own instruction, shifting to a “Learner-designer” theory.

Does learner variance warrant a “Learner-Designer” theory? I will let you decide the answer to that question. Nonetheless, to meet the needs of today’s learners in the conceptual age, instructional-design theories must utilize design-oriented methods that reflect the key markers of the Conceptual age, such as fostering self-regulated learning and self-directed learning, allowing shared decision making, focusing on real-world problems (holistic tasks), and building cooperative relationships through learning teams. To effectively design learning environments for the conceptual age, now more than ever, the voice of learners will have to be added to the design process.

Reference:

Reigeluth, C. M. (1999). Instructional-design theories and models: Volume II. Mahwah, N.J: Lawrence Erlbaum Associates.

Y Design-Oriented Models?

What do design-oriented models look like, sound like, and feel like? I’ve been contemplating the answers to those questions for about two weeks now. Richard Schank’s book, Teaching Minds: How Cognitive Science can Save our Schools, as well as Katie Muhtaris and Kristen Ziemky’s book, Amplify: Digital Teaching and Learning in the K-6 Classroom have helped me to generate a few answers to those questions.

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Looks like:

  • models that support digital communities
    • user-designers, (e.g. content users help to design their learning experiences)
    • online learning and social media
    • incorporation of new literacies
    • develop digital citizens
  • models that support the cognitive process
    • making predictions
    • building models of a process
    • experimenting with information based on failure or success
    • evaluating information on many different dimensions
  • models that support the analytic process
    • making a diagnosis of a complex situation
    • constructing explanations
    • learning to plan
    • conducting needs analysis
    • goal setting
    • detecting causes of events
    • making objective judgments
  • models that support the social processes
    • creating influence within a group
    • working as a productive team member
    • handling conflict
    • practicing negotiation
    • describing problems precisely

Sounds like:

prolific language used to

  • compliment
  • question
  • coach

Feels like:

  • an emphasis on student ownership and creativity
  • student empowerment
  • interdisciplinary learning
  • personalized assessment
  • authentic assignments and projects
  • collaboration
  • abundant access to resources
  • continuous reflection
  • divergent and convergent thinking
  • envisioning, understanding, and communicating meaning
  • inquiry and problem-solving
  • content area experts

Schank reminds us that, “lifetime learning does not mean the continual acquisition of knowledge so much as it means the improvement in one’s ability to [employ cognitive] processes by means of the acquisition and analysis of experiences to draw on. Design-oriented models will help teachers to craft those experiences for their learners in a web-enhanced classroom.

Reference:

Muhtaris, Katie, and Kristin Ziemke. 2015. Amplify: digital teaching and learning in the K-6 classroom.

Schank, R. C. (2011). Teaching minds: How cognitive science can save our schools. New York: Teachers College Press.

Four Instructional Architectures

Teachers are instructional architects for learning. Hence, understanding the learning process is crucial to building powerful learning experiences. In a web-enhanced classroom, understanding the learning process enhances the creation of learning environments that support personalized learning. Clark (2008) submitted that there are four architectures that illustrate the different models of learning. As instructional architects, teachers should be familiar with each of them. They are Receptive, Directive, Guided Discovery, and Exploratory.

The Receptive architecture is grounded in the absorption learning model. Through this lens, the learner is passive and his role is to simply receive the information. This particular architecture may be employed when the teacher chooses to use a video in the lesson for information transmission or a webinar.

The Directive architecture is grounded in the behavioral learning model. Through this lens, the learner builds knowledge by providing a response that is deemed correct based on a predetermined answer and frequent feedback. This architecture may be employed in a web-enhanced classroom when the teacher chooses to use a web-based program for instruction that is designed to support the basic acquisition of functional skills.

The Guided Discovery architecture is grounded in the cognitive learning model. Through this lens, the learner constructs his or her knowledge and skills through project based learning (PBL) or authentic learning tasks. This architecture may be employed in a web-enhanced classroom when the teacher chooses to integrate technology with PBL. This particular architecture also works well with the five learning environments identified by the TIM Matrix.

They are:

  • active learning – students are actively engaged in using technology as a tool rather than passively receiving the information from the technology.
  • collaborative learning – students use technology tools to collaborate with others rather than working individually at all times.
  • constructive learning – students use technology tools to connect new information to their prior knowledge rather than to passively receive information.
  • authentic learning – Students use technology tools to link learning activities to the world beyond the instructional setting rather than working on decontextualized assignments.
  • goal-directed learning – Students use technology tools to set goals, plan activities, monitor progress, and evaluate results rather than simply completing assignments without reflection.

The Exploratory architecture is grounded in the experiential learning model.  This type of learning is also known as open-ended learning. This architecture “offers the greatest amount of learner control of all the four architectures” (Clark, 2008, p.10). Through this lens, the learner takes responsibility for his or her learning, giving the learner more control.  This architecture is the ultimate form of personalized learning and it may be employed in a web-enhanced classroom when the teacher chooses to allow the students to choose their own focus for lesson extensions or to choose their own focus for lesson enrichment.

When planning for instruction, the instructional goals must be at the forefront. Depending upon the goals, teachers can choose to employ a mixture of architectures to support learning attainment or one specific architecture. “Each architecture has best applications, depending on the learners and the instructional goal” (Clark, 2008, p. 10). Clark also submits that teachers should keep these two questions in mind when designing lessons:

  • what is the background and prior knowledge of the learners?
  • what is the type of task or concept to be learned?

Architectures are usually defined during the instructional design process.

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As instructional architects, teachers can not build lessons without considering the background knowledge of their students. Directive architectures work well for students with little to no prior knowledge of the content. Guided Discovery architectures work well for students with some content knowledge while Receptive architectures work well for students with adequate prior knowledge of the content. Exploratory architectures work well when students have significant prior knowledge of the content in conjunction with good metacognitive skills (Clark, 2008).

In sum, to design effective lessons for web-enhanced classrooms, teachers must:

  1. consider the standard
  2. convert the standard into workable objectives
  3. consider student prior knowledge in relation to the standard and objectives
  4. consider the concept to be learned within the standard and objectives
  5. design a task that teaches the concept and also utilizes one of the five learning environments from the TIM Matrix
  6. determine how learners will get feedback from the teacher
  7. determine how learners will give feedback to the teacher

Knowing the four architectures of learning will further strengthen lesson design add value to a web-enhanced classroom.

Reference:

Clark, R. C. (2008). Building expertise: Cognitive methods for training and performance improvement.

Florida Center for Instructional Technology. (n.d.). The Technology Integration Matrix. Retrieved from https://fcit.usf.edu/matrix/wp-content/uploads/2016/11/TIM_Summary_Descriptors.pdf