This thesis reports on a meta-analysis of the most relevant employabilitycompetencies to foster refugees’ labour integration which may be potentiallyleveraged through a target language for specific purposes (LSP)MOOCsyllabus. Italso suggests to group the thus identified employability competencies into threecategories tofurther supportLSPMOOC syllabus design and implementation.Themethodology of meta-analysis was based on Cooper’s (2017) five-stage model andguided by exploratory data analysis (EDA) of a dedicated research corpus that wasspecifically tailored for this study. Three data mining tools were used to performnatural language pre-processing and pattern extraction, directed by key terms(employability, competency, competencies, skill, ability, abilities, vocational,refugee,andlabour) used in various query combinations and limiters. IterativeEDApost-processing of metadata generated by these tools, based ontheoretical andsemantic sorting and integration, led to 21 re-aggregated clusters of employabilitycompetencies and the suggested categories for grouping them.The present studyshows that the broader capillarity of data and text mining tools, as well as ofEDA,can contribute toa more encompassing view of employability competencies and oftheLSP as a tool-competency, hence to a greater capillarity ofcompetency-basedVET(Vocational Education and Training) syllabus design, particularly the proposedinnovative type ofLSPMOOC syllabus. / <p>Examination Seminar held by Zoom given that it was a Distance Programme</p>
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-106924 |
| Date | January 2021 |
| Creators | Capocchi Ribeiro, Maria Alice de Fatima |
| Publisher | Linnéuniversitetet, Institutionen för kulturvetenskaper (KV) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0117 seconds