Teologian opiskelijoiden henkilökohtaiset projektit ja opintomenestys

Motivation and personal goals play an important role in the ways in which people direct their behavior. Personal goals are closely connected with well-being but they also relate to how people perform in different achievement domains. Many studies show that evaluating study-related goals as important, easy to attain and non stressful, predict better academic achievements than evaluating them as non attainable and stressful (Salmela-Aro & Nurmi, 1997b). The aim of this study was to describe motivational factors among theology students. They form an interesting group in terms of exploring connections between motivation, spiritual goals and academic achievements. The average duration of graduation at the Faculty of Theology is among the highest at the University of Helsinki. On the other hand, it may be assumed that many theology students have spiritual goals which affect their studies. A special focus was paid on the different evaluations of study-related personal projects and how they are related to academic achievement. A methodology of personal projects (Little, 1983) was used to study what kind of personal goals theology students are engaged in during their studies. In the first part of the questionnaire the subjects (N=133) were asked to describe important personal projects. They were given four numbered lines for their written responses. In the second part the subjects were asked to rate projects concerning their studies according to 13 dimensions using a 7-point Likert-scale. Three subgroups were formed on a K-Means Cluster Analysis on the basis of evaluations of the study-related projects. The groups were named committed, self-fulfillers and non-committed according to their evaluations of their study related projects. Academic achievements among the different groups varied substantially. After two years of studying the students who were in the committed group had completed on an average twenty study credits more than those who were in the non-committed group. Self-fulfillers placed in the middle of the three groups. Committed and self-fulfiller students also reported higher levels of intrinsic reasons for striving towards study-related goals. The results indicate that goals reported at the beginning of studies predicted academic achievement later on. The results also showed that different evaluations of goals have long lasting connections to progress in studying. Implications for student well-being and how these results can be utilized for student counseling are discussed.

University Students' Approaches to Learning, Self-Regulation, and Cognitive and Attributional Strategies : Connections with Well-Being and Academic Success

This dissertation empirically explores the relations among three theoretical perspectives: university students approaches to learning, self-regulated learning, as well as cognitive and attributional strategies. The relations were quantitatively studied from both variable- and person-centered perspectives. In addition, the meaning that students gave to their disciplinary choices was examined. The general research questions of the study were: 1) What kinds of relationships exist among approaches to learning, regulation of learning, and cognitive and attributional strategies? What kinds of cognitive-motivational profiles can be identified among university students, and how are such profiles related to study success and well-being? 3) How do university students explain their disciplinary choices? Four empirical studies addressed these questions. Studies I, II, and III were quantitative, applying self-report questionnaires, and Study IV was qualitative in nature. Study I explored relations among cognitive strategies, approaches to learning, regulation of learning, and study success by using correlations and a K-means cluster analysis. The participants were 366 students from various faculties at different phases of their studies. The results showed that all the measured constructs were logically related to each other in both variable- and person-centered approaches. Study II further examined what kinds of cognitive-motivational profiles could be identified among first-year university students (n=436) in arts, law, and agriculture and forestry. Differences in terms of study success, exhaustion, and stress among students with differing profiles were also looked at. By using a latent class cluster analysis (LCCA), three groups of students were identified: non-academic (34%), self-directed (35%), and helpless students (31%). Helpless students reported the highest levels of stress and exhaustion. Self-directed students received the highest grades. In Study III, cognitive-motivational profiles were identified among novice teacher students (n=213) using LCCA. Well-being, epistemological beliefs, and study success were looked at in relation to the profiles. Three groups of students were found: non-regulating (50%), self-directed (35%), and non-reflective (22%). Self-directed students again received the best grades. Non-regulating students reported the highest levels of stress and exhaustion, the lowest level of interest, and showed the strongest preference for certain and practical knowledge. Study IV, which was qualitative in nature, explored how first-year students (n = 536 ) in three fields of studies, arts, law, and veterinary medicine explained their disciplinary choices. Content analyses showed that interest appeared to be a common concept in students description of their choices across the three faculties. However, the objects of interest of the freshmen appeared rather unspecified. Veterinary medicine and law students most often referred to future work or a profession, whereas only one-fifth of the arts students did so. The dissertation showed that combining different theoretical perspectives and methodologies enabled us to build a rich picture of university students cognitive and motivational predispositions towards studying and learning. Further, cognitive-emotional aspects played a significant role in studying, not only in relation to study success, but also in terms of well-being. Keywords: approaches to learning, self-regulation, cognitive and attributional strategies, university students

Kiinnostusta vai ahdistusta? : Opettajaksi opiskelevien akateemiset tunteet, motivaatio ja opiskelun ongelmat suhteessa hyvinvointiin ja menestykseen aktivoivalla luentokurssilla

Previous studies indicate that positive learning experiences are related to academic achievement as well as to well-being. On the other hand, emotional and motivational problems in studying may pose a risk for both academic achievement and well-being. Thus, emotions and motivation have an increasing role in explaining university students learning and studying. The relations between emotions, motivation, study success and well-being have been less frequently studied. The aim of this study was to investigate what kind of academic emotions, motivational factors and problems in studying students experienced five days before an exam of an activating lecture course, and the relations among these factors as well as their relation to self-study time and study success. Furthermore, the effect of all these factors on well-being, flow experience and academic achievement was examined. The term academic emotion was defined as emotion experienced in academic settings and related to studying. In the present study the theoretical background to motivational factors was based on thinking strategies and attributions, flow experience and task value. Problems in studying were measured in terms of exhaustion, anxiety, stress, lack of interest, lack of self-regulation and procrastination. The data were collected in December 2009 in an activating educational psychology lecture course by using a questionnaire. The participants (n=107) were class and kindergarten teacher students from the University of Helsinki. Most of them were first year students. The course grades were also gathered. Correlations and stepwise regression analysis were carried out to find out the factors that were related to or explained study success. The clusters that presented students´ problems in studying as well as thinking strategies and attributions, were found through hierarchical cluster analysis. K-means cluster analysis was used to form the final groups. One-way analysis of variance, Kruskal-Wallis test and crosstabs were conducted to see whether the students in different clusters varied in terms of study success, academic emotions, task value, flow, and background variables. The results indicated that academic emotions measured five days before the exam explained about 30 % of the variance of the course grade; exhaustion and interest positively, and anxiety negatively. In addition, interest as well as the self-study time best explained study success on the course. The participants were classified into three clusters according to their problems in studying as well as their thinking strategies and attributions: 1) ill-being, 2) carefree, and 3) committed and optimistic students. Ill-being students reported most negative emotions, achieved the worst grades, experienced anxiety rather than flow and were also the youngest. Carefree students, on the other hand, expressed the least negative emotions and spent the least time on self-studying, and like committed students, experienced flow. In addition, committed students reported positive emotions the most often and achieved the best grades on the course. In the future, more in-depth understanding how and why especially young first year students experience their studying hard is needed, because early state of the studies is shown to predict later study success.

Measurement of the top quark mass and pp̅ →tt̅ cross section in the all-hadronic mode with the CDF II detector

We present a measurement of the top quark mass and of the top-antitop pair production cross section using p-pbar data collected with the CDFII detector at the Tevatron Collider at the Fermi National Accelerator Laboratory and corresponding to an integrated luminosity of 2.9 fb-1. We select events with six or more jets satisfying a number of kinematical requirements imposed by means of a neural network algorithm. At least one of these jets must originate from a b quark, as identified by the reconstruction of a secondary vertex inside the jet. The mass measurement is based on a likelihood fit incorporating reconstructed mass distributions representative of signal and background, where the absolute jet energy scale (JES) is measured simultaneously with the top quark mass. The measurement yields a value of 174.8 +- 2.4(stat+JES) ^{+1.2}_{-1.0}(syst) GeV/c^2, where the uncertainty from the absolute jet energy scale is evaluated together with the statistical uncertainty. The procedure measures also the amount of signal from which we derive a cross section, sigma_{ttbar} = 7.2 +- 0.5(stat) +- 1.0 (syst) +- 0.4 (lum) pb, for the measured values of top quark mass and JES.

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.

Valokuvallistettu luonto : I. K. Inhan tuotanto luonnon merkityksellistäjänä

Photographized nature I. K. Inha s work as a signification of nature The dissertation examines, through the work of the Finnish photographer and writer I. K. Inha (formerly Konrad Into Nyström) (1865 1930), the different ways in which the signification of nature is achieved. The principal material consists of Inha s work from 1890 to 1925, from which a number of photographs and texts are considered and upon which the photographization of nature is contemplated. The dissertation addresses the issue of how nature is conceived and how the act of photographizing it can be defined. The methodical context of the study is composed of three thematic baskets that structure the material and which consist of narrations on Finnish national perception, nature conservation and understanding the world. In the first case nature is seen as the natural environment encompassing lakes, seashores, forests, and hills, which at the time were often perceived from a utilitarian viewpoint. By the photographization they generated a pictorial narrative that could be shared. The natural environment was thus turned into landscape by means of photography, following the global pictorial concepts picturesque and sublime, as well as the national canons that had been developed in literature and the visual arts. In the narrations concerning nature conservation, the photographization did not merely occur within the limits of presuppositions, but rather nature was given the opportunity to unfold itself. While the photograph was being established as a basis for supporting nature conservation or to highlight the destruction of nature at the hand of Man, an attempt was made to represent subjects that were difficult to convey in photographs, such as nature s power or the miracle of growth. The thesis suggests, that in the third case the concept of nature broke away from its strict interconnection with the natural environment and led to a contemplation of nature that is perceivable in a person. In this context the photograph and the photographization are interpreted as an attempt to understand a person and his or her very existence in the world, while this same existential wonder is seen as being embodied in Inha s portrait of a rune singer and in his photographs of forest interior and water. Further, the thesis asks whether photographing nature could be interpreted as an action similar to the idea of the phenomenological reduction as a means of bypassing the photographer s prevailing way of being.

Almost Stable Matchings by Truncating the Gale–Shapley Algorithm

We show that the ratio of matched individuals to blocking pairs grows linearly with the number of propose–accept rounds executed by the Gale–Shapley algorithm for the stable marriage problem. Consequently, the participants can arrive at an almost stable matching even without full information about the problem instance; for each participant, knowing only its local neighbourhood is enough. In distributed-systems parlance, this means that if each person has only a constant number of acceptable partners, an almost stable matching emerges after a constant number of synchronous communication rounds. We apply our results to give a distributed (2 + ε)-approximation algorithm for maximum-weight matching in bicoloured graphs and a centralised randomised constant-time approximation scheme for estimating the size of a stable matching.