A major challenge that faces most families is effectively anticipating how ready to
start school a given child is. Traditional tests are not very effective as they depend on
the skills of the expert conducting the test. It is argued that automated tools are more
attractive especially when they are extended with games capabilities that would be
the most attractive for the children to be seriously involved in the test. The first part
of this thesis reviews the school readiness approaches applied in various countries.
This motivated the development of the sophisticated system described in the thesis.
Extensive research was conducted to enrich the system with features that consider
machine learning and social network aspects. A modified genetic algorithm was
integrated into a web-based stealth assessment tool for school readiness. The
research goal is to create a web-based stealth assessment tool that can learn the user's
skills and adjust the assessment tests accordingly. The user plays various sessions
from various games, while the Genetic Algorithm (GA) selects the upcoming session
or group of sessions to be presented to the user according to his/her skills and status.
The modified GA and the learning procedure were described. A penalizing system
and a fitness heuristic for best choice selection were integrated into the GA. Two
methods for learning were presented, namely a memory system and a no-memory
system. Several methods were presented for the improvement of the speed of
learning. In addition, learning mechanisms were introduced in the social network
aspect to address further usage of stealth assessment automation. The effect of the
relatives and friends on the readiness of the child was studied by investigating the
social communities to which the child belongs and how the trend in these
communities will reflect on to the child under investigation.
The plan is to develop this framework further by incorporating more information
related to social network construction and analysis. Also, it is planned to turn the
framework into a self adaptive one by utilizing the feedback from the usage patterns
to learn and adjust the evaluation process accordingly.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/7293 |
| Date | January 2013 |
| Creators | Suleiman, Iyad |
| Contributors | Ridley, Mick J., Alhajj, R. |
| Publisher | University of Bradford, Computer Science, Electrical Engineering and Computer Science |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
Page generated in 0.0012 seconds