How Computer Games Help Children Learn – Chapter 5

Shaffer, D. (2006). How Computer Games Help Children Learn. New York: Palgrave Macmillan.

This chapter starts to get into learning theory.  The two major schools are succinctly defined:

  1. symbolic – knowledge developed in solving one problem can be used to solve other analogous problems
  2. schematic – facts (declarative) and problem-solving rules/strategies (procedural) knowledge are combined to solve problems

These views are contrasted with situated cognition: a view that all activity (including thinking) is part of a community of practice where newcomers learn through legitimate peripheral perception. However, I suspect that the views can be merged: declarative facts and procedural strategies can be developed through legitimate peripheral perception and refined through symbolic problem-solving pattern-matching within a community of practice that provides feedback.

Game details amplified the epistemological concept:

  • a profession is learned (and practiced) within  a specific place and time (environmental component)
  • the public reflection-on-action process created the personal process of reflection-in-action
  • each person worked on a small part (which made a complex task explicit) but saw the whole process; this instructor-crafted delineation linked the social space and the problem space

By the end of the game, users were able to:

  • offer suggestions (not simply respond)
  • think of audience (not simply the task)
  • justify choices (not simply choose)
  • see the larger impact (not simply the immediate solution)

In short, players felt like journalists “even though they had come to understand how complex and difficult” being a journalist is. The goal was not necessarily to train players to be a specific professional but to be the kind of people who can think like professionals.

The discussion of the relationship between real identity and virtual identity enacted through projective identity was somewhat confusing; how does projective identity differ from virtual? However, it led to the valuable conclusion that games give players a realistic image of a possible self. By showing that epistemic games transferred not just identity but “the collection of professional skills, knowledge, identity, values” (the epistemic frame), Shaffer extends the value of games beyond the game itself.

Shaffer defines a frame as the organizational rules and premises which exist partly in the mind of the players and partly in the structure of the game; the frame is like a pair of glasses that allows participants to filter solutions as irrelevant and leads to an increasingly accurate reflection-in-action. The epistemic frame is the “grammar” of the local culture of a community of practice, and is “what we get when we internalize the community and carry it with us”

Shaffer claims that epistemic frames are a level of description between and across the schema-based and community-based views; while this claim is not expanded, he does expand on the relationship between epistemic frames and community by noting the common qualities:

  • interpretive
  • stable
  • transient
  • generative
  • ubiquitous
  • epistemological

The distinction between simulations and games was instructive:

  • simulations do not have epistemic frames
  • games create a virtual world using a simulation
  • the epistemic frame “is a property of the communities we inhabit in and around that virtual world”
  • epistemic games create the epistemic frame of a community by recreating the process by which individuals develop that community

Instance-based and rule-based learning

Citation

Taatgen, Niels A. and Wallach, Dieter. “Whether Skill Acquisition is Rule or Instance Based is determined by the Structure of the Task.” Cognitive Science Quarterly: 2.2 (2002). 163 -204.

Summary

The traditional view of skill acquisition as “a gradual transition from behavior based on declarative rules in the form of examples and instruction towards general knowledge represented by procedural rules” is challenged by instance theory. While the latter seems to explain the inability of experts to verbalize rules, research on the directional asymmetry of rules seems to support the traditional importance of rules.

ACT-R’s instance-based architecture is based on 2 key arguments:

  • the strategy to use or the memory to retrieve is based on which has the highest expected gain (optimization)
  • declarative memory is activated (and filtered) by environmental demands and past experience (which has been encoded as production rules by procedural memory)

At the symbolic level of ACT-R, procedural rules are applied to declarative chunks which store information in a proposition; chunks are either new (perceptions) or created internally by prior knowledge/experience. Each rule contains a condition- and an action-part, and declarative items are pattern-matched to the condition and applied in the action. The subsymbolic level of ACT-R deals with the choice of which rule to apply according to Bayes’ Theorem (increases base-level activation each time it is retrieved and decays over time).

The article seems to imply that if instance-based learning fails (because the problem is too time-consuming–no discernible pattern), learners will attempt to derive some sort of rule. The authors argue that instance-based learning works best when the relationships between variables is very difficult; rule-based learning (simplified cases) is more successful with a large number of cases with obvious relationships.  The authors then test this hypothesis with two detailed experiments.

The results:

  • Previous research showed no learning through observation without direct rules (explicit relationships)
  • Subsequent research, indicated that exploratory participants did better than observers, but that observers could better verbalize and construct a causal model.
  • This research shows evidence of learning by observing even without rules; and participants seem better able to answer questions about old systems than new. Both results support instance-based learning.

Response

ACT-R seems to offer tremendous explanatory power. However, instance theory seems related to the concept of expert (tacit?) knowledge:

  • know when to apply
  • gets better over time (more instances)
  • gets better with use
  • additive for community

Because production rules that propose new declarative rules are not accounted for in the ACT-R architecture, this “missing link” may be the elusive transfer element; the authors propose partial-matching in ACT-R retrieval as a solution.

In instance theory, encoding and retrieval seem more closely linked to temporal/spatial reality than to attention as the authors claim; however, the view of memory as evolving from algorithmic processing to memory-based processing succinctly describes expert knowledge.

The recommendations for design strategies seem profound:

  • Instance-based learning takes over from rule-based over time
  • Creating declarative rules is the most important (first) step
  • Analogy works best at the start but declines quickly
  • Declarative rule works well at start and persists
  • Instance continues to improve over time to become best

Conditions-based Theory

Ragan, Tillman J. and Smith, Patricia L. (2004) “Conditions Theory and Models for Designing Instruction.” In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology. Mahwah, NJ: Lawrence Erlbaum Associates. 623-649.

The incredible value of this article is in so clearly summing up Gagnés work and relating it to other theories: learning outcomes vary across contents, contexts, and learners; distinctive cognitive processing demands can be supported by different methods, strategies, and conditions. An early discussion of the background for Gagné noted that instructional design effectiveness varied among rote learning, skill learning and problem solving; however, the authors express doubt in a hierarchy of skills (and report that aside from the intellectual category, Gagné later moved away from his original taxonomic chain).

Rule-using (intellectual) skills are stored in hierarchical structures: verbal learning stored as propositional networks or schemata; rules stored as “if…then” productions; problem-solving are interconnections of schemata and productions. Gagné differentiated problem-solving (and concept formation) from other types in that PBL does not include any portion of the solution in the problem itself. One of Gagné’s contributions was to tie external events or instruction to internal events of learning; the latter he suggested were most impacted by prior knowledge; manner of long-term encoding; and requirement for retrieval and transfer to new problems.

The article’s coverage of multiple additions to Gagne’s work was equally clear. Merrill’s Component Display Theory categorizes learning objectives as a performance level (remember, use, or find) and a content type (fact, concept, principle, or procedure). Five operations (based 4 memory structures: associative, episodic, image, and algorithmic) can be conducted on subject matter:

  1. identity (facts)
  2. inclusion (concepts)
  3. intersection (concepts)
  4. order (procedures)
  5. cause (principles)

Reigeluth’s Elaboration Theory proposes three structures: conceptual (parts, kinds, matrices); procedural (order, decision); and theoretical (descriptive, prescriptive). Smith and Ragan argue that a middle ground exists between instruction-supplied and learner-initiated events. Tennyson describes three storage processes (declarative, procedural, and conceptual) and three retrieval processes (differentiating, integrating, and creating). Declarative knowledge is stored as associative networks or schemata; procedural knowledge is related to intellectual skills; and contextual (is this the same as conceptual?) knowledge is related to problem-solving. Finally, Ellen Gagné contributes the idea that declarative knowledge can be represented by propositions, images, linear orderings, or schemata (composed of the first three, while procedural knowledge is represented as a production system.

After agreeing with the fundamental premises (of conditions-based theory, the article demonstrate the lack of a proven link between learning categories and external conditions:

  • competition may be superior when a task is simple, but cooperative goal structures are more effective in problem-solving
  • explicit organization affected achievement although sequence modification did not
  • punishment is more effective than reward for discrimination learning
  • introductory students benefited more from direct guidance while advanced students performed better with more opportunities for autonomy
  • novice learners need more explicit learning guidance in employing cognitive strategies

The call for additional research is apropos although the need for additional knowledge of the relationship between internal learner conditions and subsequent learning is complicated by the difficulty in determining those internal (private) conditions.

First Principles

Impact

I measure conceptual density by the number of highlights; I measure impact by the number of marginal annotations. My copy of Merrill’s, First Principles of Instruction is almost illegible on both counts.

Hypothesis

Merrill argues for the existence of basic methods (first principles) that are always true regardless of the variable method used (as a specific instructional activity/practice or a set of activities/program). The argument seems plausible although those principles are open to debate due to the hypothesis that learning is in direct proportion to the implementation of those principles (i.e., if a set of principles is indeed “first,” we’d need to prove the proportion and disprove any other possible principles). Clark’s four instructional architectures (receptive/lecture, directive/tutorial, guided discovery/simulation, and exploratory/collaborative problem-solving) is worth exploring later.

Merrill’s model (shown later) is compared with two similar models from Vanderbilt and from McCarthy. Merrill then proceeds to expand on characteristics of each of his five principles.

Problem

  • Show learners real-world problem rather than abstract learning objectives
  • 4 levels of instruction
    1. problems
    2. tasks to solve
    3. operations that comprise the tasks
    4. actions that comprise the operations
  • Elaboration theory advocates progression of successively more complex problems
  • If a simple version of a complex problem is difficult to locate, the coach must do some of the problem solving for the learner and do less and less with each successive problem (scaffold)

Activation

  • Learners recall prior knowledge
  • Learners are provided foundational knowledge
  • Learners demonstrate previous knowledge (pre-test as activation rather than assessment)

Demonstration

  • Demonstration must be consistent with goal
    • Examples and non-examples for concepts
    • Demonstrations for procedures
    • Visualizations for processes
    • Modeling for behavior
  • Multiple representations for demonstrations (i.e., Gardner)
  • Three classes of problems
    • Categorization
    • Design (plans and procedures)
    • Interpretation
  • Coaching involves information focusing which is gradually faded (scaffolded)
  • Presenting alternative representations is not sufficient; learners must compare

Application

  • Information-about: recall
  • Parts-of: locate/name
  • Kinds-of: new examples
  • How-to: do
  • What-happens: predict

Integration

  • Knowledge must be transferred to life beyond the instruction
    • Publicly demonstrate (could be high score)
    • Reflect, discuss, defend
    • Create new and personal ways to use
  • Multimedia has a temporary (attention-getting) effect on motivation

Comparison

Finally, Merrill compares his principles with components from other learning theorists:

  • Gardner – emphasis on problem and activation (entry points and analogies)
  • Nelson – emphasis on application via collaboration
  • Jonassen
    • Related cases can supplant memory by providing representations of experiences the learner has not had
    • Behavioral modeling demonstrates how to perform activities
    • Cognitive modeling articulates reasoning used while engaged in activities
    • Scaffold by
      • Adjusting difficulty
      • Restructuring task to supplant lack of prior knowledge
      • Providing alternatives
  • van Merrienboer
    • Multiple approaches to analysis
    • Recurrent skills – require consistent performance – supported by just-in-time information
    • Non-recurrent skills – require variable performance – supported by elaboration
    • Progression of demonstrations
      • Worked-out examples
      • Just-in-time information
      • Models of heuristic methods used by skilled performers
    • Demonstrations are subordinate to practice
    • Demonstration and application are integrated (and iterative?)
    • Product-oriented and process-oriented problems
  • Schank
    • Emphasis on application
    • Goal/mission mapped to story/role (environment)
    • Coaches scaffold
    • Experts tell stories

Merrill concludes by questioning collaboration as a first principle; however, the analysis of solitary activities may answer the question:

  • a learner alone with a book – the author is the collaborator
  • a learner who makes a discovery – perhaps learning does not exist if it can’t be replicated/shared (if a tree falls…)

A slight expansion of Merrill’s model to position the problem in an environment of learners and coaches may accommodate this concern:

Merrill's Problem Model Extended with Environment

Merrill's Problem Model Extended with Environment

Small groups/worlds

I looked at the Pellegrino article again and still find it directly applicable to what I do in higher ed. His triad of curriculum (scope and sequence), instruction (the teaching) and assessment is right on the money (and ties this article closely to the Bates model). I also started to see common themes emerge:

1. students come with existing knowledge structures which are sometimes inaccurate (the Wiggins’ misperception idea);
2. students must have deep factual and procedural knowledge, understand those facts and procedures in the context of a conceptual framework, and then be able to retrieve and apply the facts and procedures from an organization structured within memory. Pellegrino states (note: check this out since as he doesn’t cite any research directly) that the ability to notice patterns (Wiggins) or draw analogies to other problems (Bloom) is “more closely intertwined with factual and procedural knowledge than was once believed.”
3. metacognition–basically an internal dialogue or reflection–teaches students to take control of their own learning by defining goals and monitoring their progress.

Pellegrino’s four goals of instruction resonated as well:

1. Design meaningful problems;
2. Build scaffolds to help students solve those problems;
3. Give students opportunities for practice using feedback, revision, and reflection activities; and
4. “Promote collaboration and distributed expertise, as well as independent learning.”

Pellegrino amplified this last point  by suggesting teachers have learners work in small groups on complex problems. I recently read an article about an experiment Robert Goldstone, a cognitive psychologist at Indiana conducted that suggested small groups with a few weak connections to other groups are ideal for solving complex problems; large groups with a lot of connections (aka Facebook and wikipedia, aka The Wisdom of Crowds) are best for solving simple problems. I wonder if this maps at all to Dunbar’s Number?

Interestingly, Pellegrino identified a key characteristic of technology-based environments as offering learner control, a point our class made several weeks ago during our discussion!

How do I remember? Let me count 3 ways.

I found the Gagné chapter pretty cool. I’d heard of the 9 events but didn’t realize the underpinnings of the 3 types of knowledge (in memory).

  1. Propositions are declarative and form into networks; they are composed of argument (general)-relation (narrower) pairs although it took me a while to “count” them correctly. What was interesting is that we remember them as ideas not as exact sentences–maybe because we take in the sentence and then adapt it to our schema based on our history. They are easier to acquire but slower to retrieve. Propositions are organized in hierarchies and underlie the ability to reproduce information.
  2. Productions are procedural and form into systems; they are composed of if (condition)-then (reaction) pairs and are more reactive with the environment. They are slower to acquire but faster to retrieve (automatic) because they don’t need to be brought back into short-term memory. Productions are organized into if-then pairs and underlie the ability to operate on information.
  3. Images are different from both and pack more information into a smaller space. Images are used to represent spatial information because of ST memory limits. Images are continuous (analog) while propositions (and procedures?) are discrete (digital).

Here are some things I questioned:

  • 45% correct if 2 ideas are in working memory at the same time vs. 35% if the 2 ideas are separated: doesn’t seem like a huge difference but is used to justify bringing recalled knowledge into short-term memory and immediately connecting it to new knowledge.
  • Declarative knowledge is useful for novel situations while procedural knowledge is important for familiar ones; this seems incongruous with Wiggins’ contention that PBL is best for new situations (or perhaps PBL builds declarative knowledge).
  • Images may be represented  in long-term memory as propositions or as images and propositions; they could be represented only as images (is there research on whether image recall is as fast as procedural recall?).