Problem solving

This article or section is a stub. It does not yet contain enough information to be considered a real article. In other words, it is a short or insufficient piece of information and requires additions.

Draft

## Contents

* 1 Introduction
* 2 The anatomy of problem solving
* 3 Jonassen problem types
* 4 Bibliography

## Introduction[edit | edit source]

This article aims to address "problem solving" in education, i.e. how one make learners acquire problem-solving skills.

"Problem solving is generally regarded as the most important cognitive activity in everyday and professional contexts. Most people are required to and rewarded for solving problems. However, learning to solve problems is too seldom required informal educational settings, in part, because our understanding of its processes is limited." (Jonassen, 2000)[1].

Jonassen also states that problem solving is not "sufficiently acknowledged or articulated in the instructional design literature" (p.63). But he also mentioned that problem-solving is at the center of practice in contemporary learning theory: "Contemporary conceptions of student-centered learning environments, such as open-ended learning environments (Hannafin, Hall, Land, & Hill, 1994; Land & Hannafin, 1996) [2], goal-based scenarios (Schank, Fano, Bell, & Jona, 1993/1994) [3], and even problem-based learning (Barrows, 1985; Barrows & Tamblyn, 1980) [4] focus on problem-solving outcomes. They recommend instructional strategies, such as authentic cases, simulations, modeling, coaching, and scaffolding, to support their implicit problem-solving outcomes, but they inadequately analyze or explicate the nature of the problems to be solved" (Jonassen, 2000a).

## The anatomy of problem solving[edit | edit source]

There are many definitions of problem solving both in psychology and related fields and computational sciences.

In a computational perspective, in order to solve a problem, a problem-solver has to perform a certain number of internal and external operations. Each domain of action has in principle a certain number of known operators, i.e. generic actions that can be applied to a certain class of mental or physical objects. An operation is thus defined as concrete realization (application) of an operator. An operator is generally more abstract and more general than one of its possible operation instantiation. Each object that can form a problem is a potential domain of action that can be in different states. Such a state is defined by several sub-states that we can called facts. We can call the set of facts that describe a problem a description. Thus technically speaking, problem solving means transforming states by applying operators to the facts, where a fact can be a mental object or a physical object, and an operation a mental or physical process. This potential domain of action can be call problem-domain.

The selection and use of operators is driven by heuristics at various levels that we will not introduce here for the moment.

## Jonassen problem types[edit | edit source]

In his 2000 article [1] on Toward a Design Theory of Problem Solving, Jonassen identifies a number of problem-solving types: (a) logical,(b) algorithmic, (c) story, (d) rule-using, (e) decision making, (0 troubleshooting, (g) diagnosis-solution, (h) strategic performance, (i) case analysis, (j) design, and (k) dilemma. In a 2011 article [5], Jonassen lists (a) story problems, (b) rule-using/rule induction problems, (c) decision-making problems, (d) troubleshooting problems, (e) strategic performance problems, (f) policy problems, (g) design problems, and (h) dilemmas.

He also summarized these problem typologies as five abstract types as presented in the taxonomy of meaningful learning article. Problems are not the same and each type requires different educational designs.

Each problem solving type was caracterized by a number of attribues:

* learning activity
* inputs
* success criteria
* context
* structuredness
* abstractness

Problem type  | Learning activity | Inputs  | Success criteria  | Context  |

Structuredness

| Abstractness

|
|
|
|
|
|

Logical  | logical

control and

manipulation

of limited

variables;

solve puzzle

|  |  |  |  |
Algorithmic | procedural

sequence of manipulations; algorithmic process applied to similar sets of variables; Calculating or producing correct answer

|  |  |  |  |
Story | disambiguate

variables; select and apply algorithm to produce correct answer using prescribed method

|  |  |  |  |
Rule-using | procedural

process constrained by rules; select and apply rules to produce system- constrained answers or products

|  |  |  |  |
Decision making | identifying

benefits and

limitations;

weighting

options;

selecting

alternative

and justifying

|  |  |  |  |
Trouble shooting | examine

system; run

tests; evaluate

results; hypo-

thesize and

confirm fault

states using

strategies (re-

place, serial

elimination,

space split)

|  |  |  |  |
Diagnosis-

solution

| troubleshoot

system faults;

select and

evaluate

treatment

options and

monitor;

apply

problem

schemas

|  |  |  |  |
Strategic

performance

| applying

tactics

to meet

strategy in

real-time,

complex

performance

maintaining

situational

awareness

|  |  |  |  |
Case analysis  | solution

identification,

alternative

actions,

argue

position

|  |  |  |  |
Design  | acting on

goals to

produce

artifact;

problem

structuring

articulation

|  |  |  |  |
Dilemmas  | reconciling

complex,

non-

predictive,

vexing

decision

with no

solution;

perspectives

irreconcil-

able

|  |  |  |  |

## Bibliography[edit | edit source]

1. ↑ 1.0 1.1 Jonassen, D. H. (2007). A Taxonomy of Meaningful Learning. Educational Technology, 47(5), 30–35. Retrieved from https://www.jstor.org/stable/44429440?seq=1#metadata_info_tab_contents
2. ↑ Hannafin, MJ., Hall, C., Land, S., & Hill, J. (1994). Learning in open-ended learning environments: Assumptions, methods, and implications. Educational Technology, 34(8), 48-55.
3. ↑ Schank, R.C., Fano, A., Bell, B., & Jona, M. (1993/1994). The design of goal-based scenarios. The Journal of the Learning Sciences, 3(4), 305-345.
4. ↑ Barrows, H.S. (1985). How to design a problem-based curriculum for the pre-clinical years. New York: Springer.
5. ↑ Jonassen, D. H. (2007). A Taxonomy of Meaningful Learning. Educational Technology, 47(5), 30–35. Retrieved from https://www.jstor.org/stable/44429440?seq=1#metadata_info_tab_contents