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Title: Learning science through work experience : a Ciência Viva science internships programme for senior secondary students
Author: Alves, Carlos Henrique Catalão
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2007
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This enquiry is a case study of a national summer Science Internships Programme (SIP) for secondary school students (K-10 to K-12) in Portugal. It addresses the case phenomena both in their naturally occurring setting (a research laboratory), and within the broader framework in which they unfold: the Portuguese Ciência Viva SIP. This was carried out by linking a nationwide profiling of the programme – grounded both in demographic data and in the students working as apprentices in the research laboratories of the Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup). Firstly, the national scope of SIP, as it has unfolded over the past nine years, was quantitatively analysed in relation to its 1,150 research apprenticeship programmes, 110 research and academic institutions, 705 research instructors and 4,378 students. Secondly, 1,170 students’ applications and final reports from the last three years, together with a Likert- type survey of 481 apprentices, were both quantitatively and qualitatively analysed to uncover the main themes underpinning the students’ perceptions of their subjective experience. Finally, two participant observations, carried out over the summers of 2003 and 2004, provided the data for a further in-depth exploration of these themes as they emerged from the social and discourse interactions between scientists and students. Field work was carried out over a period of three consecutive years, combining case study and survey with a range of interconnected qualitative methods, such as observation, interviewing and document analysis. Within the framework of a mixed-methodology research design, coding procedures provided the analytical techniques with which to address the qualitative data, whereas statistical analysis was used to explore and eventually reinforce generalizability.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available