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Information literacy in secondary schools in England : challenges to implementing a teaching tool
(2017)
The skills required to become an information literate person should be taught and learned in schools from an early age on. The focus in schools lies in raising student attainment and being information literate has a positive impact on the attainment. However, the teaching of IL is often overlooked in English schools. This thesis introduces the English educational system as well as the National Curriculum and analyses the situation in English secondary schools with focus on the challenges that school librarians face when trying to provide learning opportunities to gain IL skills. Several different IL frameworks, models and standards are evaluated for their suitability as a practical teaching tool for school librarians and teachers. It is concluded that there is a need for an easy-to-use scheme that can be adopted in any secondary school in England and other countries with a similar school system. Building on the analysis of the literature a new Scheme for Teaching IL in English Secondary Schools (STILESS) as well as an exemplary lesson plan will be developed. STILESS and the lesson plan are not only a practical teaching tool but also a useful instrument to promote IL in general across schools with leadership, teachers and students.
Analysing the systematics of search engine autocompletion functions by means of data mining methods
(2017)
In the internet era, the information that can be found about politicians online can influence
events such as the results of elections. Research has shown that biased search rankings can
shift the voting preferences of undecided voters. This shows the importance of studying
online search behaviour, especially in the pre-elections phase, when search results can
have a particular influence on the future political scene of a country.
This master thesis aimed to study the behaviour of online search engines in a period before
the German federal election in 2017. The aim was to ascertain if there is any pattern to be
found in the auto-suggestions for searches related to politicians.
In order to gather data for this experiment, a crawler browsed search engine web pages,
input a name and a surname of a politician, and saved that together with all autosuggestions
from the search engine. The autosuggestions were prepared for the analysis and
divided into semantic groups with the help of clustering algorithms.
Different statistical methods, such as correlation analysis, regression analysis, and clustering
were used to identify patterns in the data. The research showed that there are
no particularly strong patterns in the autosuggestions for searches related to politician’s
names. Only moderate dependence was found between gender and personal topics, and
showed that a higher amount of personal information autosuggestions correspond more
to female politicians.