Browsing by Subject "sosiaalinen media"
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(2021)Tavoitteet. Tutkimuksen tavoitteena oli selvittää tekijät, jotka vaikuttavat lääkelaskennan onnistuneeseen opiskeluun ja miten internetin tietolähteitä hyödynnetään siinä. Tutkielman runkona käytettiin teoreettista mallia opetukseen liittyvistä tekijöistä, joiden avulla opiskelua pystyy tehostamaan ja oppimisen laatua parantamaan. Hypoteesina oli, että liuoslaskut ja infuusioliuoslaskut olisivat haastavia aihealueita. Lisäksi tutkielman lähtökohtana käytettiin tietoa, että sosiaalista mediaa ja sen vaikutuksia opetukseen ei oltu selvitetty riittävästi. Menetelmät. Tutkimus oli luonteeltaan monimenetelmällinen tapaustutkimus. Määrälliseen aineistoon kuuluivat opiskelijoiden lääkelaskennan osaamista arvioiva peruslaskutesti ja kyselyn tulokset liittyen opiskelijoiden haastaviin lääkelaskuihin ja heidän käyttämiinsä tietolähteisiin. Laadullinen aineisto koostui opiskelijoiden käyttämien lääkelaskentaan liittyvien tietolähteiden analyysista. Kohderyhmänä toimi erään ammattikorkeakoulun lääkelaskentaa opiskelevat opiskelijat, joista 19 opiskelijaa vastasi kyselyyn. Tutkimukseen vastaajat olivat sairaanhoitaja- ja terveydenhoitaja- opiskelijoita. Lisäksi aineistoon kuului internetin tietolähteistä löytyvien lääkelaskentaan liittyvien ohjeiden ja ratkaisujen analyysi. Aineiston analyysimenetelmänä käytettiin taulukoita ja kuvaajia Google Formsin ja Excelin avulla. Tulokset ja johtopäätökset. Tutkimuksessa tehdyn kyselyn otosryhmä hallitsi lääkelaskennan eri osa-alueet testin perusteella erittäin hyvin. Kyselyyn opiskelijat luettelivat lääkelaskentaan liittyviä muutamia haastavia osa-alueita, kuten esimerkiksi liuoslaskut ja infuusioliuoslaskut. Internetin merkityksen kasvamisen myötä tulisi suunnitella uusia digitaalisia opetusympäristöjä, joissa annettava tieto olisi luotettavaa. Opiskelijoille tulisi tarjota oppimisalusta, jossa osaamista pystyisi kasvattamaan vaikeustasoltaan asteittain kasvavien tehtävien avulla. Opiskelijan tulisi saada tehtäviinsä välitön palaute niin, että ratkaisu näytettäisiin purettuna selkeisiin ja selitettyihin välivaiheisiin. Oppimisympäristön tulisi myös lisätä, mistä opiskelija löytää tarvittaessa lisätietoa tehtävään liittyvästä teoriasta ja ratkaisuesimerkeistä.
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(2019)The Greater Region of Luxembourg is the largest cross-border labor market in the European Union with the greatest number of cross-border workers in the area. European integration, the Schengen Area, and socio-economical divergences have been the main factors facilitating human cross-border movements in the area and thus the birth and expansion of the borderland community. Despite the freedom of movement, country borders have not been erased and socio-economic divergences have not been levelled. In addition, the spatial extent of the daily movements is not well known. Thus, it is important to study cross-border dynamics and try to separate daily movements from infrequent mobility patterns. Thus far, cross-border mobility studies have mainly leaned on national registers and census data. These datasets have mostly been too scarce in trying to understand the complexities of cross-border mobility. Many studies have only focused on aggregate-level movement patterns, and the viewpoint of individuals has been missing. Hence, there has been a growing need for individual-level data to be applied in cross-border mobility research. In this study, a person-based approach is employed using geotagged Twitter Big Data to study spatio-temporal cross-border mobility patterns in the Greater Region of Luxembourg. The aim is to examine how to implement social media in cross-border research as well as how to separate daily cross-border movers from infrequent border crossers and consequently move beyond aggregate-level inspections. Being one of the first studies of its kind, a heuristic programmatic approach is utilized. To the writer’s knowledge, social media data sources have not been applied previously to distinguish different cross-border mobility types. All developed scripts in this study are openly available on Digital Geography Lab’s GitHub -pages (https://github.com/DigitalGeographyLab/cross-border-mobilitytwitter) to promote open science and to introduce new quantitative method tools for cross-border mobility research. The results show that social media can be implemented in cross-border mobility research, and social media Big Data can provide a relatively good proxy for daily cross-border mobility of people on a regional level. Aggregate-level cross-border mobility patterns and activity location densities correspond closely with previous studies, and outcomes from temporal variation inspections indicate a valid cross-border mover type identification; Twitter users classified as daily cross-border movers seem to be more mobile on weekdays whereas infrequent border crossers on weekends. Daily cross-border mobility patterns also provided new information about the spatial extent of the movements. In addition, heuristic approach resulted in high accuracy in home detection; the “unique weeks” algorithm introduced in this study produced an accuracy of 88.6 % with respect to the ground truth. Although the results are promising on a regional level, they should be considered in relation to population densities and Twitter use activity; attributes that both vary spatio-temporally and thus can cause bias. Further studies and method development are also needed to draw global conclusions about cross-border mobility; other geographical areas and study settings could result in varied outcomes. In addition, some solutions with data and methods should be considered with a critical stance due to scarcity of valid references. Yet, this study has identified that the coverage of geotagged Twitter data is dependent on data acquisition processes and that Twitter can provide valuable information for cross-border mobility research. In future studies, multi-level data acquisition processes are recommended jointly with person-based approach combining spatio-temporal and content analysis methodologies.
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(2023)The direct economic impacts of the global tourism industry account for 4 % of global GDP and 8 % of global greenhouse gas emissions. The industry is in transformation caused by climate change, political instability and rapid technological development. In addition, the relationship between biodiversity conservation and tourism as well as the growing popularity are considered megatrends impacting the sector. Traditional mass tourism destinations, such as the Canary Islands, may start seeing new kinds of visitors, if traveling to exotic destinations becomes difficult as a result of these transformations. Understanding transformations affecting tourism requires information about tourists’ mobilities, interests and preferences. However, traditional data collection methods may not necessarily be suited for studying quickly changing tourism. The need for Information about visitations to natural and protected areas is especially high, as traditional tourism indicators, such as flights and accommodation statistics do not tell where the tourists spend time. Social media data may enable production of new kind of knowledge and studying nature-based tourism in a new way. In this thesis, I intent to assess the role of nature in tourism in the Canary Islands, Spain using data from the photo-sharing platform Flickr. First, I compare the spatiotemporal patterns of Flickr data against official data about tourism flows to confirm the feasibility of Flickr as a data source in the Canary Islands context. I then try to understand the importance of nature visitations and differences in nature visitation patterns between visitors from different countries. Finally, I turn to analyse contents of the images to see what kinds of nature-related topics are important for each group, making use of a deep learning and cluster detection algorithms. I verify the results of my empirical analysis with data collected through interviewing experts familiar with Canary Islands tourism. The results of my research show that Flickr reflects Canary Islands tourism patterns moderately well, and that it can be used to produce information about differences in nature visitation patterns. Protected areas are shown to be important and central for Canary Islands tourism, but differences in interest toward these areas between groups are notable. Results of the content analyses show that while differences between groups exist, both nature-related content and photos of humans are important in content posted from PAs. Verification data collected through expert interviews shows that the observed differences between groups correspond to the experts’ perceptions about differences between different groups. The findings of my thesis demonstrate the importance of nature and protected areas in Canary Islands tourism and confirm earlier knowledge about the use of Flickr in studying nature visitations. The results may inform future research in the Canary Islands. More broadly, they provide information about the feasibility and limitations of the use of social media data for nature-based tourism research.
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