Abstract

Educational software is an increasingly important part of education with huge amounts of money being input into the system. However, it is arguable whether the methods currently used are successful in helping children learn (as opposed to actually teaching children). Papert said that the programmes which are in use for education aren’t flexible enough to cater for the needs that children have. He stated that there is an important distinction between teaching a subject and children learning the subject. There are two conflicting camps in education, Instructionism and Constructionism.

Instructionism is the idea of improving the instruction which is provided to children and making computers do the instruction.

Constructionism is giving children things to do so they can learn by doing much better than they can ever learn by doing and interaction with evolving environments. The focus is very much on using the computer as a medium for learning instead of having a computer program teach (i.e. the computer is supplementary to the teacher-the child can explore taught ideas using a software program). Constructionism teaches children how to use knowledge as opposed to how to memorise knowledge. Logo is an example of constructionism as it helps children learn the principles of maths by enabling the children to use maths constructively to build models etc.

Empirical modelling has very close links with constructionism and is an ideal way of providing educational platforms for children. It was initiated by Dr Beynon at Warwick University in 1981.

In this paper it is planned to cover breadth first and depth first search algorithms. These were chosen as students frequently have problems understanding them when they read through an algorithm. It is believed that a visualisation of the algorithms will substantially aid learning.

Both depth first and breadth first searches are examples of uninformed searches. Uninformed searches use only information which is available in the definition of the problem (ie the tree to search) and no extra information (known as a heuristic in informed searches)

Breadth first search expands the shallowest unexpanded node in a tree. It works by adding new unexplored nodes onto the end of a First In First Out (FIFO) queue.

The depth first search expands the deepest unexpanded node in the tree. It adds unexplored nodes into a Last In First Out (LIFO) structure (stack).