Saturday, August 13, 2011

Complexity of learning process

Learning is a process.

1. What is "complexity"?
2. Predictability, Complexity, and Learning
3. Learning environments are increasingly complex ?
4. Adaptive governance, networks and learning
5. Principles of Learning

Image: Animation of Learning Process, SWWRocBot, A Unique Learning Environment
By Nick Clark,

Read more:

Learning from projects and practice

Complexitatea procesului de invatare (in Romanian)

De-a lungul vremii, in toate domeniile stiintifice se schimba teoriile, metodele si tehnicile de investigare, de aceea dinamica cunoasterii umane influenteaza dezvoltarea generala a societatii umane. Pentru a obtine evolutie si eficienta in viata sa, omul trebuie sa se adapteze continuu la aceste schimbari ale cunoasterii. In domeniul educatiei, si in special al invatarii si perfectionarii, aparitia de noi tehnologii ale informatiei si comunicarii (TIC), imbunatatirea teoriilor pedagogice si psihologice, obliga pe elevi/studenti, profesori, parinti si pe specialisti, sa se adapteze la aceste schimbari.

Ce fac elevii si studentii? Ce fac profesorii si parintii? Ce fac specialistii? Ce fac guvernele tarilor?

Models and Solutions: SWWRocBot
An Example
SWWRocBot, A Unique Learning Environment by Nick Clark (Information Science & Learning Technologies, Robotics Coach, School Without Walls); SWWRocBot is a site laying out the Challenges, Solutions and Results that are the foundations of a unique learning environment.
This learning environment is based on the work of Nick Clark, author of "The Geometry of Learning and the Architecture of Knowledge". It will be a multi-disciplinary, interactive and immersive experience (See animation above) with positive outcomes for the following stake holders:
-The School (School Without Walls)
-The City School System
-Community Business Partners
-Community Organization Partners
-Individuals whose talents are required for the success of the program

The Need for Clear Definitions
"In any scientific discipline there are many reasons to use terms that have precise definitions. Understanding the terminology of a discipline is essential to learning a subject and precise terminology enables us to communicate ideas clearly with other people. In computer science the problem is even more acute: we need to construct software and hardware components that must smoothly interoperate across interfaces with clients and other components in distributed systems. The definitions of these interfaces need to be precisely specified for interoperability and good systems performance. Computer science already has a number of useful clearly defined models of computation whose behaviors and capabilities are well understood. We should use such models as part of any definition of the term computation. However, for new domains of investigation where there are no appropriate models it may be necessary to invent new formalisms to represent the systems under study."
Alfred V. Aho (Lawrence Gussman Professor in the Computer Science Department at Columbia University).


1. What is "complexity"?

Melanie Mitchell (Professor of Computer Science at Portland State University): I would call this a "deceptively simple" question—in fact, it is one of the most difficult questions of all! The field of complexity arose out of the strong feeling of some scientists that there are deep similarities among certain highly "complex" systems in nature, society, and technology. Examples of such systems include the brain, the immune system, cells, insect societies, economies, the World Wide Web, and so on. By "similarities," I don't mean that there are necessarily a single set of principles that governs these disparate systems, but rather that all these systems exhibit behavior that has been described as "adaptive," "life-like," "intelligent," and "emergent." None of these terms have precise meanings, yet, which makes a formal definition of "complex system" impossible at this time. A system with large numbers of interacting components, in which the components are relatively simple compared with the system as a whole, in which there is no central control or global communication among the components, and in which the interactions among the components gives rise to complex behavior. Here, "complex behavior" refers to the informal terms (e.g., adaptive, emergent) that I listed above.

Ref.: Ubiquity, Volume 2011, Number April (2011), Pages 1-6,

2. Predictability, Complexity, and Learning

"One of the most important examples of prediction is the phenomenon of generalization in learning. Learning is formalized as finding a model that explains or describes a set of observations, but again this is useful only because we expect this model will continue to be valid. In the language of learning theory (see, for example, Vapnik, 1998), an animal can gain selective advantage not from its performance on the training data but only from its performance at generalization. Generalizing—and not “overfitting” the training data—is precisely the problemof isolating those features of the data that have predictive value (see also Bialek and Tishby). Furthermore, we know that the success of generalization hinges on controlling the complexity of the models that we are willing to consider as possibilities.”

Predictability, Complexity, and Learning, Neural Computation 13, 2409–2463 (2001) (Articles Communicated by Jean-Pierre Nadal) by William Bialek (NEC Research Institute, Princeton, U.S.A.), Ilya Nemenman (NEC Research Institute, Princeton, New Jersey, U.S.A., and Department of Physics, Princeton University, Princeton, U.S.A.), Naftali Tishby (NEC Research Institute, Princeton, U.S.A., and School of Computer Science and Engineering and Center for Neural Computation, Hebrew University, Jerusalem, Israel)

3. Learning environments are increasingly complex?

Book - Handling complexity in learning environments: theory and research
By Jan Elen, Richard Edward Clark, Joost Lowyck, European Association for Research on Learning and Instruction, 2006, Elsevier Ltd.
What do we mean when we say that "learning environments are increasingly complex"? What do we know about the cognitive processing that occurs during complex learning? How can we provide effective instructional support for students who must learn and apply complex knowledge? These questions, and related issues, have fascinated educators and educational researchers for many years and are they are the focus of this book.


4. Adaptive governace, networks and learning
“Learning is therefore essential for stakeholders to develop their ability to deal effectively with new situations and to prepare for change and surprise.” Örjan Bodin ( Adaptive governance, networks and learning)

Halldén, O., Lundholm C. (2009). 25-30 August. Conceptual Change and the Complexity of Learning. Threshold Questions, Meaning making and Contextuality. Paper presented at the 13th conference European Association for Research on Learning and Instruction, Amsterdam, Holland.

5.Principles of Learning
- Organizing for Effort
- Clear Expectations
- Fair and Credible Evaluations
- Recognition of Accomplishment
- Academic Rigor in a Thinking Curriculum
- Accountable Talk®
- Socializing Intelligence
- Self-management of Learning


"Research in education demonstrates that, by working hard, virtually all students are capable of high achievement. These findings have caused educators to recognize the primacy of effort, rather than following ingrained assumptions about innate aptitude. Effort-based education research has started to demonstrate that properly focused student efforts not only yield high achievement for all students, but can actually create ability. People can become smart by working hard at the right kinds of learning tasks." Institute for Learning (IFL), University of Pittsburgh.

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