Hybrid designs

Doering, A. & Veletsianos, G. (2008). Hybrid Online Education: Identifying Integration Models Using Adventure Learning. Journal of Research on Technology in Education. 41 (1). pp. 23-41.

The importance of this article is succinctly presented in a chart defining four models for integrating technology-based instruction. The applicability of the article is that the authors examined how teachers incorporated a computer-based, community-oriented PBL in actual classrooms. Rather than examining teachers’ technical literacy as previous studies have done, the authors ask “how technology is used” and provide real answers.

Previous research suggests three methods that teachers use to incorporate technology:

  1. for efficiency (replacing less efficient methods)
  2. for enhancement (transforming methods)
  3. for entertainment–relaxation and reward (amplifying existing methods)

Doering and Veletsianos define four methods from observing actual use:

 

Focus Community Activities Online
Curriculum Student-student, student-expert Student collaboration Medium (to high)
Activity Student-student Student collaboration and construction High
Standards Student-student, student-teacher Teams, student construction High
Media Student-teacher Passive student consumption Medium

A larger study may provide a full gradient of methods with a near-infinite number of defined paths–or it may provide validation of this four-method topology. Regardless of the methodological count, the article points the way forward in urging us to consider how technology is used in real classrooms. In addition, the article underscores the importance of teacher-teacher collaboration.

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Let’s go on an Adventure

Doering, A. (2006). Adventure Learning: Transformative hybrid online education. Distance Education 27 (2). pp. 197-215.

Despite the unnecessary introduction of a new term (“adventure learning”), this article provides a concise and clear vision of an instructional model with solid grounding in contemporary learning theory and immediate practical application in the classroom. Doering positions adventure learning as an online course taken in the classroom while “teachers are facilitators” (differentiated from the other hybrid model where students take a face to face class augmented with online instruction outside the classroom). Combining collaboration and reflection to transform students into the authentic practitioners of Shaffer’s epistemic games, adventure learning relies on real-time community and fantastic (unknown) environments to provide student motivation.

The seven elements of adventure learning provide the practical application:

  1. begin with a researched curriculum grounded in problem-solving and based on learning outcomes
  2. provide collaboration opportunities among students, peers, experts, and content
  3. utilize the Internet for delivery
  4. provide authenticity with media and text from the field (emphasis is mine)
  5. provide synchronous opportunities
  6. offer pedagogical guidelines (for the teacher)
  7. captivate students through adventure

An interesting variable which is mentioned but insufficiently explored in the research is the importance of teacher-teacher interaction.

Learning networks

Christensen’s 5th chapter proposes a valuable (but ultimately incorrect) three-part business model lens through which he proposes we consider education: consulting (services); value-chain (manufacturing); and user networks (black market). The parenthetical examples are mine: Christensen claims that telecommunications is a user network when in fact it’s a service (access to “wires” owned by a telco) as well as a value-chain (resale of bandwidth); consulting could also be viewed as experts providing a service within a user network rather than a distinct type. However, the metaphor of current public schools as a value-chain model is accurate, as is the view of special education as consultative and unscaleable one-to-one education.

The dismal evaluation of and outlook for textbooks is well-supported (although his terms are inaccurate: commercial systems are actually delivery mechanisms; “high fixed costs” are actually “sunk costs” because a business can have continuing high fixed costs whereas sunk costs such as the investment to create a book are one-time). His argument falls apart, though, in the claim tha,t “people will assemble them [learning kernels] together into entire courses.” If this were possible, libraries would have precluded the need for schools. Learners don’t know how to structure the learning they need because they don’t know the end goal. Learning opportunities or situations or problems must be constructed by experts, although not necessarily subject experts who often make unexplainable leaps in problem-solving.

The attempt to equate Web 2.0 technologies with the need for educational reform also falls short. QuickBase is not a replacement for SAPs’s ERP software; it’s an online service from a software company seeking to change its value-chain distribution model. Second Life is not a 3D world “‘created entirely by its residents;'” it’s a hosted software application whose creators charge real dollars for the service afforded by a virtual space. And finally, the idea that learners can self-educate smacks of self-medication and the potential for uninformed abuse. At the same time, the vision of public school education replaced by user networks guided by experts is enticing.

PBL – pure and simple

While PBL is the topic, the article actually focuses on PBL as an exemplar implementation of constructivism which proposes:

  • Understanding occurs in our interaction with the environment (distributed cognition).
  • Cognitive conflict (again, the Wiggins’ idea of misperception) is the learning stimulus and determines the organization of what is learned (this latter concept is never explained); ideas are tested against alternative views and contexts in a collaborative community of practice.
  • Knowledge evolves through social negotiation which evaluates the viability of individual understandings (the community determines if a particular answer is viable).

Constructivist strategies include collaboration, personal autonomy, generation , and reflection, all of which are embedded in the original PBL model created by Barrows.

GBS aka CBR aka PBL

While Schank’s work in scenario-based learning is well-known, this article expands his approach to encompass a broader design theory he calls goal-based scenarios and a learning theory he terms case-based reasoning; both seem derived from problem-based learning. CBR is postulated as the way in which experts solve problems and is essentially learning from prior experience via analogy: a case is a memory, and experts have large libraries upon which they draw. CBR enables reasoning across contexts, and while it seems obvious that experts organize their libraries through indexing (labeling and filing), little practical design advice is offered. The general suggestion to design roles and goals to create motivational and sensible contexts is tied to Schank’s narrative approach. More generally applicable are the ideas that goals produce expectations, and that expectation failures demand explanations; the necessity of failure as a primer for learning (reminiscent of Wiggins’ misperceptions) suggests building learning experiences with high probability of non-optimal solutions. The most useful aspect of the article came in the editorial comment (not from Schank) that teaching is the transition from learning theory to design theory.

The 7 components of GBS include:

  1. Goal – process knowledge or content knowledge learning goal
  2. Mission – performance goal provided for initial motivation
  3. Story – narrative for immersion and context leading to motivation
  4. Role
  5. Operations – activities with decision points leading to consequences
  6. Resources – well-organized stories to compare with cases in memory
  7. Feedback – consequences, coaching, and domain expert stories

Expertise: a long and winding road

The idea that experts tackle problems that increase their expertise seems supportive of the self-efficacy behind ACT-R:

  • Reinvestment – the  motivational aspect
    • conserving resources to have energy to put back into new problems
  • Progressive problem-solving – the cognitive aspect
    • tackling more difficult problems AND tackling more complex representations of recurrent problems
    • represents working at the edge of competence (ZPD)

Pattern learning, which occurs without extensive effort, involves choosing the right patterns and recognizing when no pattern fits. This seems to equate with imagaic memory which is efficient for spatial and temporal data.

Procedural learning starts as step-by-step problem solving which become “chunked” into a single procedure; while this automaticity frees resources, it becomes a handicap in the improvement of performance. However, automaticity does not inevitably lead to inflexibility if automated skills are building blocks to new skills that are not automated.

However, learning is also pattern and procedural learning; what distinguishes expertise is the seeking of complexity. Complexity is described as a matter of the number of constraints. Because most real-world problems are not reducible to a step-by-step economy, we use simplified representations (akin to simulations). The class of problems that are endlessly complex are the constitutive problems of a domain.

Experts are motivated by:

  • flow:  total absorption and feeling of control, loss of normal time; becomes addictive to the point that problems are invented
  • second-order environment: the expert sub-culture where your recognition as an expert matters to you (not useful in fostering early development of expertise)
  • heroism: effort disproportionate to rewards

Competitive environments foster expertise. However, so do expert sub-cultures which may not always involve competition but always involve recognition of success and help/cooperation leading to success. The expert environment constantly changes as the experts become more expert; the reason experts help each other is to help that environment conttinue to get more difficult (i.e., inventing problems). Expert teams are one example: everyone more or less knows how to do everything so the focus is on the goal, not on individual achievement.

Cognition: Revisited (Part I)

Winn provides not only a comprehensive overview of cognitive theory but also a thorough integration of constructivist contributions, producing a revised cognitivism which seems both compelling and common sensical. The only issue I had with the article is the length and semantic density; the flip side is that I think I’ll be referring to this article for years to come.

Winn starts by covering the basic issues in cognitive theory:

  • information is represented by internal symbols which map to the  outside world through translations
  • the internal and external worlds are separated physically as well as phenomenologically
  • the separation applies to timing (toss and catch) as well as location
  • the internal representations are idiosyncratic and thus only partially accurate

Four dissatisfactions with this essentially computational view of learning are:

  1. cognitive activity is prompted by environmental changes that are not represented  internally
  2. cognitive activity is not separate from context and is embedded in an environment
  3. the learner and the environment are coupled not separate, although the learner’s history of environmental adaptation is more important than the environment itself (which suggests quantum entanglement)
  4. knowledge value is personal; while anything a learner constructs is of value personally, the community assigns permanent value

Since all 4 dissatisfactions involve the influence of environment, Winn then outlines 4 new approaches to cognition that incorporate environmental aspects:

  • System theory: interactions between learner and environment are complex, mutual, dynamic, and often nonlinear
  • Biological: learning is an adaptation to environment
  • Neuroscience: cognition changes as a result of interaction with the environment, and learning causes physical changes to the central nervous system (interestingly, this branch suggests that the complex behaviors that led to cognitivism may actually be a chain of behavioral S-R events)
  • Neural networks: networks represent information through the way in which the nodes are connected and changes in these connections are the “processes by which learning takes place.”

After tracing the early evolution of Gestalt to behavioral psychology, Winn succinctly differentiates behavioral and cognitive theories: “cognitive psychology is concerned with meaning, while behavioral psychology is not.” And while I may appreciate the freedom of cognitivisim, the real impetus for my belief in this approach lies in the research showing that the parts of the brain that are active when learners report a mental image are the same parts that are active when the learner views an image. Finally, some science behind the idea of imagaic memory. The section concludes with a discussion of levels of cognitive theory: some mechanisms can be explained in biological terms; those which cannot, can be explained in more abstract metaphors for what is taking place.