Decision frames and schemata [...]

"Decision frames"

Definition: A frame is the context used to describe an idea, question, or decision. Frames heavily influence our interpretations and conclusions by emphasizing (or ignoring) certain aspects of a situation....Psychologists Daniel Kahneman and Amos Tversky explored the effect of decision frames and found that the exact same information can lead to opposite conclusions, depending on the frame used to present the decision

Links to the parents/poultry farmer exercise and how it could be evolved. Raising the question of the relationship between frames and schema.


Seel uses (Rumelhart et al. 1986) to offer the following summary of schema

  1. Human knowledge about the world is represented in memory as the sum of structured units called schemas.
  2. Most schemas are acquired through learning; however, some primitive schemas are assumed to be innate.

    I wonder about the idea of "innate" schemas

  3. There are schemas of objects (e.g., a CHAIR schema), of persons (e.g., a TEACHER schema), of a state of affairs (e.g., a PEACE schema), of abstract concepts (e.g., a COMMUNISM schema), of relationships between objects (e.g., a RELATION: CHAIR-TABLE schema), etc. Schemas of actions and events are often labeled as scripts, a metaphorical use of the corresponding cinematic term. Furthermore, a distinction has been made between situational schemas (= “frames”), action schemas (= “plans”), and “grammars,” which represent the regular structure of narratives and stories (Mandler 1984).
  4. Schemas are not direct, image-like copies of phenomena in the external world but the result of cognitive processing of information. The mind neither copies the world, passively accepting it as a readymade given, nor does it ignore the world.
  5. Schemas have different levels of complexity and abstraction. One can have an abstract schema CHAIR as well as a concrete schema of one specific chair, for example, in a particular dining room.
  6. Schemas have variables which can take on various values. The elements that make up a schema can be called slots. Any important element or schema within a schema may be thought of as a slot that can accept any of the range of values that are compatible with its associated schemata. For example, a very general schema for BUY may have four fairly abstract slots: a seller, a buyer, an object, and money. On different occasions these slots in the BUY schema will be filled differently. In one case the buyer will be a woman, in another a man, the object bought can be either a Hamburger or a book, etc., that is, the concrete slot fillers vary in different environments and situations but fit into the same schema BUY.
  7. A schema can integrate new data into its organizational structure. It can put new information into the existing human database.
  8. Schemas can embed with each other (e.g., CHAIR as a subschema of FURNITURE). This characteristic has to do with the abstract intensification of attributes and the hierarchical organization of schemas of various generalization and abstraction resulting from this process


Decision frames: How cognitive biases affect UX practitioners - references Tversky, A., & Kahneman, D. (1981). The Framing of Decisions and the Psychology of Choice. Science, 211(4481), 453–458. Retrieved from

Seel, Norbert. Schema(s)


Marshall, Sandra (1995?) Schemas in Problem Solving

Walsh, James. (1995) Managerial and organisational cognition

Bednarek, Monika (2005). Frames revisited—the coherence-inducing function of frames

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2 Sigma Problem [...]

Experiments using three types of learning methods

  1. conventional classroom learning;
  2. mastery learning;

    Apparently 1st defined by Bloom. Class sizes of 20 to 30 students, which as a group must get at least 90% on a knowledge test before the class moved on. Formative tests are given to inform corrective procedures and others to test understanding

  3. 1-to-1 tutoring;

    Individual teaching using formative tests and feedback similar to the mastery learning tests.


  1. Two-sigma - 1-to-1

    Average student with 1-to-1 tutoring with mastery performed higher than 98% of students using classroom approach - two standard deviations higher.

  2. 1-sigma - mastery

    average student in this method achieved 84% above conventional classroom

  3. "90% of tutored students and 70% of the mastery learning students attained the level of summative achievement reached by only the highest 20% of the students under conventional instructional conditions" (Bloom, 1984, p. 4)


  1. How do the "smart" or the less motivated students deal with mastery learning's requirement to keep going over a topic until all achieve 90%?
  2. How would a teacher handle this in a traditional setting?


  1. "solving" mentions Khan Academy

    Reliant on student motivation to engage, could be supported.

  2. "solving" mentions Udemy having "massive office hours"

    Lots of tutors available to handle questions. Leading to ideas of call-centres to ensure tutors are available when required. Raising the "AIC" problem where the tutor may not get the content/teaching approach being used in the course.

  3. "solving" talks about various forms of "feedback-corrective" tools - leading toward the personalisation approach and to differentiated instruction and adaptive learning systems (which is what Khan has become somewhat)
  4. "solving" does touch on the idea of Fitbit/IoT type devices providing specific feedback.
  5. "back" talks about CBE and analytics as providing data that can provide teachers with the insight required to support mastery approaches.

Bloom's (1984) suggestions

  1. improving instructional materials
  2. enhancing peer interactions
  3. considering student differences
  4. engaging higher mental processes

Implementation requirements

  1. Identify all that is required to be learned and the order in which it is to be learned.

    Not always possible or desirable in all courses, touches on arguments against learning objectives etc.

    Comment on "back" makes this point and suggests "look to the future and use dynamic learning more aligned with today’s technologies and the capacity to measure learning post hoc".

  2. Develop appropriate instructional methods and formative/summative tests to identify mastery.
  3. Develop/implement corrective measures based on results from tests.

    In a class setting or by single tutor.

Problems with 2-sigma

  1. VanLehn (2011) in a review article found that human tutoring had an effect size of 0.79 (rather than 2), while intelligent tutoring systems had an effect size of 0.76
  2. "the problem" quotes VanLehn (2011) deeper explorations of the experiments Bloom reported upon,
    • Topic was probabilty
    • tutors were education majors (not necessarily experts in probability)
    • Only one of the 6 studies of human tutoring from Bloom involved 1-to-1 (others had tutors working with 3 students)
    • the "mastery mark" for the class-based mastery was 80%, but for tutoring 90%. That tutors were holding students to a higher standard may explain 2.0 effect size achieved while class-based mastery only achieved 1.0


Bloom, B. S. (1984). Sigma of Problem : The Methods Instruction One-to-One Tutoring. Educational Researcher, 13(6), 4–16.

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.

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Requirements for meaning [...]

An O'Reilly article on machine learning for designers quotes a 1970s education related book on what is required for meaning. In this context, the connection is being made to machine learning. To know "all" requires engagement and exposure over time.

Or, as the educational philosopher Patricia Carini said, “To let meaning occur requires time and the possibility for the rich and varied relationships among things to become evident.” (Source)

For me this links to the difference between telological and ateleological design. With telological design always limited in the time it has, but also because its methods don't rely on "rich and varied relationships".

Carini, Patricia F. "On value in education." Workshop Center [City University of New York], 1987.

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What works, for which students, in what circumstances [...]

The "Clayton Christensen Institute for Disruptive Education" arguing that education research needs to move beyond what works - away from gathering evidence for the "average" - towards understanding why it works for different learners.

In a new white paper out this week, [“A blueprint for breakthroughs,”] ( Michael Horn and I argue that simply asking what works stops short of the real question at the heart of a truly personalized system: what works, for which students, in what circumstances? Without this level of specificity and understanding of contextual factors, we’ll be stuck understanding only what works on average despite aspirations to reach each individual student (not to mention mounting evidence that “average” itself is a flawed construct). Moreover, we’ll fail to unearth theories of why certain interventions work in certain circumstances. And without that theoretical underpinning, scaling personalized learning approaches with predictable quality will remain challenging. (Source)

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Three obstacles to getting started with programming [...]

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Tony Hirst describes 3 obstacles people have to overcome when thinking about getting started with programming.

I’ve often thought that there are several obstacles to getting started with programming. Firstly, there’s the rationale or context: why bother/what could I possibly use programming for? Secondly, there are the practical difficulties: to write and execute programmes, you need to get an programming environment set up. Thirdly, there’s the so what: “okay, so I can programme now, but how do I use this in the real world?” (Source)

The 3rd - "how do I use this in the real world?" - connects with the problem of most digital technologies still being Concrete Lounges i.e. they aren't Protean Digital Technologies

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The New System Will Solve Everything Disease [...]

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Eventually when problems with existing organisational information systems became sufficiently troubling for sufficiently important people it is important that it get solved. The SET Mindset underpinning organisational information systems is based on the assumption that smart people can come design the solution, typically another, better system.

The problem is that many organisational problems are so complex and diverse that there is no right answer and no single system can provide that answer.

(A nascent idea)

Enabled by the tendency for people responsible for organisations and systems believe in Engineering the Right

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Engineering the Right [...]

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Why are so many followers of radical strains of Islam engineers? A new work searches for (and finds) some answers. Roughly, the traits of engineers and extremists overlap.

What the book finds is that engineers are also significantly represented among far right groups, while humanities and social sciences graduates dominate the far left; and the authors argue that the ideology of Islamist radicals, stripped of its religious components, overlaps far more with that of extreme right-wingers than with that of radical left-wingers.

They suggest that the traits that make Islamism attractive to some engineers could also be what makes right-wing extremism attractive to other graduates.

“Political psychology research links a number of personality traits to right-wing attitudes: a propensity to be easily disgusted, a desire to draw rigid social boundaries and a preference for order, structure, and certainty known as ‘need for cognitive closure’,” Dr Hertog said.

“We find that, on average, indicators for these traits are stronger among engineers compared with graduates in general, while they are weaker among students of humanities and social science.” (Source)

Maybe engineers just like things tidy? See The Ideology of Disgust

Belief that there is a single answer and that you have the "right answer" seem to contribute to The New System Will Solve Everything Disease

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Reusability Paradox [...]

David Wiley's identification of the paradox identifying that making a learning object more re-usable, means reducing its pedagogical value

t turns out that reusability and pedagogical effectiveness are completely orthogonal to each other. Therefore, pedagogical effectiveness and potential for reuse are completely at odds with one another, unless the end user is permitted to edit the learning object. (Source)

The assumption is that pedagogical value arises from a high level of contextual knowledge.

The more context a learning object has, the more (and the more easily) a learner can learn from it. (Source)

Increasing pedagogical value reduces reusability

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OER as a participatory activity | Saylor Academy [...]

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If OER is participatory, then the environment should support and encourage participation

But Saylor Academy would absolutely benefit from infrastructure that would encourage us, our students, our partners, and members of the wider open community to really create the open content we need from the open content that we have. (Source)

The Moodle open book project is a small step toward that (Site)

Which in turn raises the issue of the Reusability Paradox

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Hello world! [...]

Welcome to WordPress. This is your first post. Edit or delete it, then start blogging!

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