Valmiiksi tavoiteajassa? : teknillistieteellisen alan opiskelijoiden opintojen eteneminen ja opiskelukokemukset tekniikan kandidaatin tutkinnossa

Teknillistieteellisen alan opintoprosessien seuraaminen, arviointi ja kehittäminen -hanketta on toteutettu Opetusministeriön rahoituksella kaikissa Suomen teknillistieteellistä koulutusta antavissa yliopistoissa ja tiedekunnissa vuodesta 2005 lähtien. Hankkeen lähtökohtana on vuonna 2005 voimaan tullut tutkinnonuudistus sekä yliopistolaissa säädetty opiskeluaikojen rajaus. Hankkeen tausta-ajatuksena oli, että tukemalla opiskelijan opintopolkua, opiskelua ja oppimista voidaan vähentää opiskelijan kokemaa yksinäisyyttä ja irrallisuutta tiedeyhteisöstä ja siten tehostaa opiskelun edistymistä. Jotta opintopolkuun ja opintojen edistymiseen voidaan vaikuttaa, tarvitaan riittävästi tietoa siitä, miten opintopolku uudessa tutkintojärjestelmässä etenee ja mitkä seikat hidastavat opintojen etenemistä. Käsillä on hankkeen loppuraportti, jossa kootaan yhteen pääosia kaikista hankkeen tiimoilta kerätyistä aineistoista. Tässä raportissa esitellään pääasiassa yliopistojen opintorekistereistä kerättyä aineistoa opintojen etenemisestä 1.-3. opiskeluvuotena. Toisekseen esitellään tuloksia opiskelijoiden opiskelukokemuksia kartoittavista kyselytutkimuksista, jotka lähetettiin opiskelijoille sekä 1. että 3. vuoden opintojen päättyessä. Pääosin kyselyaineistojen tuloksista tarkastellaan opintoja hidastavia tekijöitä sekä opintojaksojen mitoitukseen ja opiskeluaikaan liittyviä tekijöitä. Rekisteriaineiston analyysit on tehty R-ohjelmistolla. Kyselyaineistoja on sen sijaan käsitelty SPSS-tilasto-ohjelmalla. Tulokset osoittavat, että ensimmäisten opiskeluvuosien aikana tekniikan kandidaatin tutkintoa DIA-valinnan kautta suorittamaan tulleiden opiskelijoiden opinnot eivät aina etene yliopistolaissa asetettujen tavoitteiden mukaisesti, vaan vuonna 2005 opintonsa aloittaneista ja kuusi lukukautta läsnä olleista vajaa viidennes näyttäisi valmistuvan diplomi-insinööriksi tai arkkitehdiksi viidessä vuodessa. Lähes kahden kolmesta voidaan kuitenkin ennustaa valmistuvan seitsemässä vuodessa tai nopeammin. Useimmat opiskelijat uskoivat pystyvänsä kokopäiväisesti opiskellen suorittamaan lukuvuodessa 60 opintopistettä, mutta valmistumista viidessä vuodessa ei sen sijana pidetty kovin realistisena. Opiskelijat kokivat, että heidän opintojaan ovat hidastaneet useimmin epämotivoivilta tuntuvat kurssit, huonot opetusjärjestelyt, ajankäytön vaikeudet sekä motivaation puute. Harvimmin opintojen etenemisen hidastumiseen liitettiin perhesyyt, terveydelliset syyt, fysiikan ja matematiikan kurssien vaikeus sekä luottamustoimet. Ensimmäisen vuoden opiskelijat kertoivat käyttävänsä opiskeluun aikaa keskimäärin 34 tuntia viikossa. Kolmantena opiskeluvuotena puolet opiskelijoista koki työmäärän olevan pienempi tai sopiva saatuihin opintopisteisiin nähden

More testing, less antibiotics - future trends in mastitis treatment?

Poster201

Contingent allocation of the agri-food budget: comparison of farmer and non-farmer preferences

Agricultural production faces diverse and often conflicting expectations, such as considerations related to environmental protection, food security and risk management, as well as strengthening the profitability and competitiveness of domestic production. In this study, we applied the contingent allocation method to a national agricultural budget to analyse the preferences of farmers and other citizens for allocating budget funds. Survey data collected from 2014 citizens and 518 farmers were used in compositional data analysis where it is considered that each spending decision bears an opportunity cost, and the decision maker faces trade-offs across budget priorities. The allocations of farmers and non-farmers were found to differ considerably. Farmers emphasized agricultural income and the economic resilience of farms as well as the self-sufficiency in food production and soil conditions. Non-farmers emphasized environmental public goods. The allocations were used to form compositional respondent clusters. The first cluster emphasised multiple objectives for agricultural policy, whereas the second cluster was clearly production oriented and the third cluster environmentally oriented. The results highlight the differences between farmers and non-farmers challenging the planning of legitimate agricultural policy

Optimising and scaling up hot water extraction of tannins from Norway spruce and Scots pine bark

Tannins from Norway spruce (Picea abies [L.] Karst.) and Scots pine (Pinus sylvestris L.) bark were extracted with water at different temperatures (60–140 °C) in an ASE-350 system in order to optimize yield. In addition, the effect of chemicals such as urea, sodium bisulfite (NaHSO3), sodium carbonate (Na2CO3), and sodium benzoate on the yield was also investigated. Bark from debarking processes at both a sawmill and a pulp mill were included. The highest overall yield expressed as total dissolved solids (TDS) was obtained with hot water extraction of spruce bark at 140 °C. The TDS was 117 mg/g and it contained 47 mg/g tannins. With an increase in extraction temperature over 100 °C, the proportion of tannins decreased, whereas the proportion of carbohydrates increased. The addition of sodium carbonate improved yield within a 60–90 °C temperature range compared with pure water. Other chemicals did not improve the yield. Pine bark showed similar extraction yields to spruce bark but the proportion of tannins was lower in spruce than in pine. Pure water at 110 °C was chosen to be used for piloting in larger scale 300-liter extraction vessel. Based on the results, a machine-learning approach was applied using seemingly unrelated regression models (SUR). The models were able to predict the extracted tannin yields of spruce and pine bark when extractions were scaled up to 2 liters and then to 300 liters

Anything left for animal disease insurance? A choice experiment approach

201

On Sliced Inverse Regression

In statistics, dimension reduction is a method to reduce the number of variables, which will then be considered in the future analysis of the data. Often the new variables are just suitably chosen linear combinations of the original variables X1, ...,Xp. Well known dimension reduction techniques are principal component analysis (PCA), factor analysis (FA) and independent component analysis (ICA), for example. Sliced inverse regression (SIR) is a dimension reduction method proposed by Li (1991). In sliced inverse regression it is assumed that the new variables are used to explain the variation of a response variable Y , and this is taken into account in the dimension reduction process. The inverse regression function is used to find an estimate of the so called central dimension reduction subspace (central DRS). This thesis presents main theoretical results behind SIR and reports the results of an extensive simulation study. In our simulation study, the performance of three dimension reduction methods, sliced inverse regression, sliced average variance estimate (SAVE) and principal hessian directions (PHD), are compared under various experimental settings. We consider four different choices of dimensions of a vector-valued explanatory variable X, four choices of distributions of X, four different choices of sample sizes, seven different models for the dependence, and two different levels of noise. Finally, a real data set from a study on coronary heart disease risk factors is analyzed using the three different dimension reduction techniques. Keywords: inverse regression, dimension reduction, dimension reduction sub-space, conditional independenc

On Sliced Inverse Regression

In statistics, dimension reduction is a method to reduce the number of variables, which will then be considered in the future analysis of the data. Often the new variables are just suitably chosen linear combinations of the original variables X1, ...,Xp. Well known dimension reduction techniques are principal component analysis (PCA), factor analysis (FA) and independent component analysis (ICA), for example. Sliced inverse regression (SIR) is a dimension reduction method proposed by Li (1991). In sliced inverse regression it is assumed that the new variables are used to explain the variation of a response variable Y , and this is taken into account in the dimension reduction process. The inverse regression function is used to find an estimate of the so called central dimension reduction subspace (central DRS). This thesis presents main theoretical results behind SIR and reports the results of an extensive simulation study. In our simulation study, the performance of three dimension reduction methods, sliced inverse regression, sliced average variance estimate (SAVE) and principal hessian directions (PHD), are compared under various experimental settings. We consider four different choices of dimensions of a vector-valued explanatory variable X, four choices of distributions of X, four different choices of sample sizes, seven different models for the dependence, and two different levels of noise. Finally, a real data set from a study on coronary heart disease risk factors is analyzed using the three different dimension reduction techniques. Keywords: inverse regression, dimension reduction, dimension reduction sub-space, conditional independenc