The present study explored the use of translation technology in second language (L2) writing by English as a Second Language (ESL) students at the University level. The appropriate role of translation, and specifically translation technology, in L2 curricula has been the subject of theoretical and practical debate. In order to address knowledge gaps relevant to this debate, the present study sought to document students’ current use of translation technology, specifically free online translation (FOT) tools, and their opinions about these tools. The study’s mixed-methods design included video observations and questionnaires regarding FOT use completed by 19 university students enrolled in a high intermediate-level ESL course. Semi-structured follow-up interviews were conducted with the six participants who were observed using FOT tools extensively on the video recordings. Results showed that high intermediate-level ESL students have a primarily positive attitude toward FOT tools. In addition, the majority of students reported using such tools regularly, even though only about one third of the students were actually observed using the tools significantly in the video recordings. Results are discussed in the context of the ongoing debate over whether and how translation technology should be used in L2 classrooms.

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El propósito de este proyecto de investigación es explorar la autopercepción respecto al nivel de dominio de las herramientas de traducción asistida por computadora (TAC) de los traductores colegiados en las lenguas inglés y español en Lima, Perú. Por otro lado, se pretende identificar las necesidades y expectativas de un grupo de profesionales del presente estudio con relación al uso de las herramientas TAC. Esta investigación utiliza, como metodología, la técnica cuantitativa. Se encuestó a los miembros del Colegio de Traductores del Perú con la combinación de lenguas español- inglés con el fin de profundizar los resultados encontrados respecto al nivel de dominio de las herramientas TAC de los traductores certificados. / The purpose of this research project is to explore the self-perception of Spanish – English translators certified by the Peruvian Association of Professional Licensed Translators in Lima, Peru regarding their command of computer-aided translation (CAT) tools. On the other hand, we aim to identify the needs and expectations of the group of professionals of this study in regard to the application of CAT tools. This research uses a method with quantitative technique. The members of the Peruvian Association of Professional Licensed Translators with language pair combination Spanish – English were surveyed (representative sample), in order to deepen the results, found with regard to the certified translators' level of command of CAT tools. / Tesis

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This qualitative study aims to explore the ways in which digital dictionaries andmachine translation are being used to help students learning Swedish as a secondlanguage (L2). The study focuses on teacher’s reasoning for the use of these tools andthe problems they face in trying to implement the guidelines specified around the useof such tools in the Swedish curriculum. Through interviews with teachers of Swedishas a second language, and phenomenographic analysis, the study shows that mostteachers use these tools as a lexical aid rather than a steppingstone to implementingstrategies which facilitate the symbiotic uses of both the students first and secondlanguages in their linguistic development. This due to the controversial issue of usingmachine translation as a pedagogical tool in second language acquisition. Many of theteachers mentioned the shortcomings of both the tools and their own ability to usethem as a problem in implementing these tools. The teachers also had difficultiesdeciding which resources would be best in helping their students develop theirlanguage skills in the target language. Possible solutions for this problem would befurther training for teachers in the uses of machine translation and in which strategiesto teach their students to make didactic use of translation tools like Google Translate.

As databases and traffic over the internet is becoming larger by the day, the performance of sending information has become a target of great importance. In past years, other software architectural styles such as REST have been used as it is a reliable framework and works really well when one has a dependable internet connection. In 2015, the querying language GraphQL was released by Facebook to the public as an alternative to REST. GraphQL made improvements in fetching data by for example removing the possibility of under- and overfitting. This means that a client only gets the data which they have requested, nothing more, nothing less. To create a GraphQL schema and server implementation requires time, effort and knowledge. This is however a requirement to run GraphQL over your current legacy database. For this reason multiple server implementation tools have been created by vendors to reduce development time and instead auto-generates a GraphQL schema and server implementation using an already existing database. This bachelor thesis will pick, run and compare the benchmarks of the two different server implementation tools Hasura and PostGraphile. This is done using a benchmark methodology based on technical difficulties (choke points). The result of our benchmark suggests that the throughput is larger for Hasura compared to PostGraphile whilst the query execution time as well as query response time is similar. PostGraphile is better at paging without offset as well as ordering, but on all other cases Hasura outperforms PostGraphile or shows similar results. / Linköping GraphQL Benchmark (LinGBM)

Text in English / Abstracts in English and Venda / This study has investigated the effectiveness of available tools used for translating from English into Tshivenḓa and vice versa with the aim to investigate and determine the effectiveness of these tools. This study dealt with the problem of lack of effective translation tools used to translate between English and Tshivenḓa. Tshivenḓa is one of South Africa’s minority languages. Its (Tshivenḓa) lack of effective translation tools negatively affects language practitioners’ work. This situation is perilous for translation quality assurance. Translation tools, both computer technology and non-computer technology tools abound for developed languages such as English, French and others. Based on the results of this research project, the researcher did make recommendations that could remedy the situation. South Africa is a democratic country that has a number of language-related policies. This then creates a conducive context for stakeholders with language passion to fully develop Tshivenḓa language in all dimensions. The fact is that all languages have evolved and they were all underdeveloped. This vividly shows that Tshivenḓa language development is also possible just like Afrikaans, which never existed on earth before 1652. It (Afrikaans) has evolved and overtaken all indigenous South African languages. This study did review the literature regarding translation and translation tools. The literature was obtained from both published and unpublished sources. The study has used mixed methods research, i.e. quantitative and qualitative research methods. These methods successfully complemented each other throughout the entire research. Data were gathered through questionnaires and interviews wherein both open and closed-ended questions were employed. Both purposive/judgemental and snowball (chain) sampling have been applied in this study. Data analysis was addressed through a combination of methods owing to the nature of mixed methods research. Guided by analytic comparison approach when grouping together related data during data analysis and presentation, both statistical and textual analyses have been vital in this study. Themes were constructed to lucidly present the gathered data. At the last chapters, the researcher discussed the findings and evaluated the entire research before making recommendations and conclusion. / Iyi ṱhoḓisiso yo ita tsedzuluso nga ha kushumele kwa zwishumiswa zwi re hone zwine zwa shumiswa u pindulela u bva kha luambo lwa English u ya kha Tshivenḓa na u bva kha Tshivenḓa u ya kha English ndivho I ya u sedzulusa na u lavhelesa kushumele kwa izwi zwishumiswa uri zwi a thusa naa. Ino ṱhoḓisiso yo shumana na thaidzo ya ṱhahelelo ya zwishumiswa zwa u pindulela zwine zwa shumiswa musi hu tshi pindulelwa vhukati ha English na Tshivenḓa. Tshivenḓa ndi luṅwe lwa nyambo dza Afrika Tshipembe dzine dza ambiwa nga vhathu vha si vhanzhi. U shaea ha zwishumiswa zwa u pindulela zwine zwa shuma nga nḓila I thusaho zwi kwama mushumo wa vhashumi vha zwa nyambo nga nḓila I si yavhuḓi. Iyi nyimele I na mulingo u kwamaho khwaḽithi ya zwo pindulelwaho. Zwishumiswa zwa u pindulela, zwa thekhnoḽodzhi ya khomphiyutha na zwi sa shumisi thekhnoḽodzhi ya khomphiyutha zwo ḓalesa kha nyambo dzo bvelelaho u tou fana na kha English, French na dziṅwe. Zwo sendeka kha mvelelo dza ino thandela ya ṱhoḓisiso, muṱoḓisisi o ita themendelo dzine dza nga fhelisa thaidzo ya nyimele. Afrika Tshipembe ndi shango ḽa demokirasi ḽine ḽa vha na mbekanyamaitele dzo vhalaho nga ha dzinyambo. Izwi zwi ita uri hu vhe na nyimele ine vhafaramikovhe vhane vha funesa nyambo vha kone u bveledza Tshivenḓa kha masia oṱhe. Zwavhukuma ndi zwa uri nyambo dzoṱhe dzi na mathomo nahone dzoṱhe dzo vha dzi songo bvelela. Izwi zwi ita uri zwi vhe khagala uri luambo lwa Tshivenḓa na lwone lu nga bveledzwa u tou fana na luambo lwa Afrikaans lwe lwa vha lu si ho ḽifhasini phanḓa ha ṅwaha wa 1652. Ulu luambo (Afrikaans) lwo vha hone shangoni lwa mbo bveledzwa lwa fhira nyambo dzoṱhe dza fhano hayani Afrika Tshipembe. Kha ino ṱhoḓisiso ho vhaliwa maṅwalwa ane a amba nga ha u pindulela na nga ha zwishumiswa zwa u pindulela. Maṅwalwa e a vhalwa o wanala kha zwiko zwo kanḓiswaho na zwiko zwi songo kanḓiswaho. Ino ṱhoḓisiso yo shumisa ngona dza ṱhoḓisiso dzo ṱanganyiswaho, idzo ngona ndi khwanthithethivi na khwaḽithethivi. Idzi ngona dzo shumisana zwavhuḓisa kha ṱhoḓisiso yoṱhe. Data yo kuvhanganywa hu tshi khou shumiswa dzimbudziso na u tou vhudzisa hune afho ho shumiswa mbudziso dzo vuleaho na dzo valeaho. Ngona dza u nanga sambula muṱoḓisisi o shumisa khaṱulo yawe uri ndi nnyi ane a nga vha a na data yo teaho na u humbela vhavhudziswa uri vha bule vhaṅwe vhathu vha re na data yo teaho ino ṱhoḓisiso. viii Tsenguluso ya data ho ṱanganyiswa ngona dza u sengulusa zwo itiswa ngauri ṱhoḓisiso ino yo ṱanganyisa ngona dza u ita ṱhoḓisiso. Sumbanḓila ho shumiswa tsenguluso ya mbambedzo kha u sengulusa data. Data ine ya fana yo vhewa fhethu huthihi musi hu tshi khou senguluswa na u vhiga. Tsenguluso I shumisaho mbalo/tshivhalo (khwanthithethivi) na I shumisaho maipfi kha ino ngudo dzo shumiswa. Ho vhumbiwa dziṱhoho u itela u ṱana data ye ya kuvhanganywa. Ngei kha ndima dza u fhedza, muṱodisisi o rera nga ha mawanwa, o ṱhaṱhuvha ṱhoḓisiso yoṱhe phanḓa ha u ita themendelo na u vhina. / African Languages / M.A. (African Languages)