Universiẗat, Diss., 2005--Kassel.

Rational unified process är en systemutvecklingsmetod som ofta används som ramverk och stöd vid utveckling. RUP kan även användas i sin helhet, vilket detta arbete handlar om. Metoden består av 5 itererande faser och har detaljerade beskrivningar på hur varje fas ska genomföras vid en utvecklingsprocess. RUP är dock väldigt komplext i sin helhet och innehåller många aktörer samt iterationer som tar tid och kostar pengar. Trots allt så är RUP en metod som blir allt mer vanligare att använda vid utveckling idag. I RUP så försäkrar man användarens behov via s.k. användarfall. För att ta reda på alla användares behov förespråkar Krutchen (2003) att man träffar användarna och gemensamt ritar upp användarfallsmodeller som täcker in behoven. På detta sätt så försäkrar man sedan att behoven är täckta innan man går vidare till nästa fas i utvecklingen. Användarfall är något som även används i andra metoder. Detta arbete beskriver dock enbart hur det används i metoden RUP. Detta arbete ska undersöka användarmedverkan i teori och praktik vid skapandet av användarfall i RUP. Frågeställningen besvarades genom litteraturstudier samt genom intervjuer av utvecklare angående ämnet. Resultatet visade bland annat att det inte alls existerar användarmedverkan i det omfång som det skulle behövas för att försäkra att man får ett användbart system. Det visar även en skillnad mellan teori och praktik vid användandet av RUP.

The purpose of this research was to investigate whether interactivity yields a learning effect when used appropriately in e-Learning Systems, and whether this effect enhances learning. The importance of interactivity for success in learning has always been paramount; however, little scientific evidence can be found to support this importance (Sims, 2003; Leiner & Quiring, 2008). Thus, this research aims to provide evidence of the impact of interactivity on e-Learning Systems considering three main agents: the learner, the teacher and the system (educational triangle). A key element often found to be related to learning and the three previously-mentioned agents is the concept of feedback. The use of interactivity as part of a feedback mechanism for enhancing learning is well documented in this research. Three empirical studies were designed to investigate interactivity within the educational triangle. These three studies, developed to support the research hypotheses, were conducted based on the framework of positivism and action research paradigms. The first study, entitled “Interactive Pedagogical Feedback”, aimed to gather evidence for how highly interactive pedagogically-designed formative feedback enhances students’ memory and understanding. The two student groups to which the interactive conditions were added showed a significant difference in the post test scores. A one-way ANOVA with a Turkey HSD post hoc test for all pair wise comparisons reveals a significant difference between the transfer and no condition scenario. The second study, entitled “Interactive Audio Feedback”, examined whether the speed enhancements of oral feedback improve the conditions for the production of lecture’s feedback and the quality of the feedback delivered to the students. The use of the interactive condition reduces by 40 to 65% the time it usually takes to prepare feedback for final assignments, and an unpaired Student’s t-test shows significant differences in the use of the two conditions. The final study, “Interactive Texting Feedback”, took a pedagogical approach to provide formative feedback to a student audience using mobile text messages. It aimed to determine whether Interactive Texting Feedback enhances the leaning experience within the e-Learning environment. Inferential analysis demonstrated good correlations in the use and benefits obtained by the introduction of the interactive mechanism. The results indicated that interactivity is critical in promoting and enhancing effective learning. Learning theories led by the generative theory of learning (Wittrock, 1974) and the principles of multimedia learning (Mayer, 2001) provide scientific explanation for this findings.

Med en bakgrund som jazzimprovisatör och med utgångspunkten att frångå teoretiska koncept är syftet med denna studie att undersöka möjliga användningsområden av improvisation inom musikproduktion och hur det påverkar musiken. Frågeställningarna i studien har lydigt: ”Hur kan jag använda improvisation som verktyg för att producera musik? Hur påverkar det i sin tur resultatet? Vad händer med musiken om jag frångår teoretiska koncept och genreideal och låter gehöret leda vägen?”. För att svara på dessa frågor har jag utforskat olika tillvägagångssätt för att använda improvisation och intuition inom musikproduktion. Detta har resulterat i tre musikaliska verk och två konkreta metoder: användandet av befintliga pianoimprovisationer som utgångspunkt för nytt musikaliskt material och en iterativ process för vidareutveckling av musikaliskt material som tar avstamp i improvisation och impulsivitet. Vidare, har denna studie resulterat i breddad kunskap angående improvisation som koncept.

This thesis was motivated to simulate the evacuation traffic of Mississippi Stated University (MSU) main campus using the Path-Following logic of TSIS/CORSIM and to evaluate a set of traffic management plans. Three scenarios of traffic management plans were developed and tested. A NCT of 123 minutes was projected if evacuate without any plan. In comparison, under a pre-planned traffic management plan the NCT would decrease to 39 minutes. Further, if implement contra flow the NCT would reduce to 21 minutes. If even further adjust the signal timing plans at the university exits a NCT of 20 minutes would be achieved. The sensitivity analysis found that the NCT was sensitive to the CORSIM parameters of free flow speed, time to react to sudden deceleration of lead vehicle and the configuration of driver type, while the effects of discharge headway and start up lost time were not found to be significant.

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Iteration, the repetition of computational steps, is a core concept in programming. Students usually learn about iteration in an entry-level Computer Science class. Virginia Tech's Computational Thinking (CT) course is designed to teach non-CS majors computing skills and new ways of thinking. The course covers iteration on Day 8 of the class. We conducted a pretest before, and three post-tests after, Day 8 of the Computational Thinking class in Spring 2018 on 137 students. The pre-test was intended to measure knowledge of iteration before the material was covered. We found from the post-tests that students' knowledge of iteration did not satisfy the course objectives in Spring 2018, because the knowledge gain shown between pre-test and post-tests was not significant. We developed interactive visualizations and exercises for Fall 2018 and Spring 2019. For three semesters we conducted tests and compared the data from Fall 2018 and Spring 2019 (the treatment) against Spring 2018 (the control). We found that Spring 2019 students had greater knowledge gains than Spring 2018 students. Also, we conducted surveys in Fall 2018 and Spring 2019 from students to learn more about their recall, helpfulness, and reuse of the interactive visualizations. Finally, we analyzed data from the interactive exercises and page use to investigate students' usage behavior. / Master of Science / Iteration is a process of repeating a set of instructions or structures. An iterative process repeats until a condition is met or a specified number of repetitions is completed. Students usually learn about iteration in an entry-level Computer Science class. Virginia Tech’s Computational Thinking (CT) course is designed to teach non-CS majors computing skills and new ways of thinking. The course covers iteration on Day 8 of the class. We conducted a pretest before, and three post-tests after, Day 8 of the Computational Thinking class in Spring 2018 on 137 students. The pre-test was intended to measure knowledge of iteration before the material was covered. We found from the post-tests that students’ knowledge of iteration did not satisfy the course objectives in Spring 2018. In particular, the knowledge gain shown between pre-test and post-tests was not significant. We developed interactive visualizations and exercises that were used during Fall 2018 and Spring 2019. We conducted tests and compared the data from Fall 2018 and Spring 2019 (the treatment) against Spring 2018 (the control). To see if there was a statistically significant difference between the absolute score means of three groups, we used independent sample t-tests. Also we used paired sample t-tests to see if there was a greater knowledge gain after using our invention. By analyzing the results of the t-tests, we found that Spring 2019 students had greater knowledge gains than Spring 2018 students. Also, we conducted student surveys in Fall 2018 and Spring 2019 to learn more about their opinions on recall, helpfulness, and reuse of the interactive visualizations. We analyzed data from the interactive exercises and page use to investigate students’ usage behavior.

Title: Stochastic Dynamic Programming Problems: Theory and Applications Author: Gabriel Lendel Department: Department of Probability and Mathematical Statistics Supervisor: Ing. Karel Sladký CSc. Supervisor's e-mail address: sladky@utia.cas.cz Abstract: In the present work we study Markov decision processes which provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. We study iterative procedures for finding policy that is optimal or nearly optimal with respect to the selec- ted criteria. Specifically, we mainly examine the task of finding a policy that is optimal with respect to the total expected discounted reward or the average expected reward for discrete or continuous systems. In the work we study policy iteration algorithms and aproximative value iteration algorithms. We give numerical analysis of specific problems. Keywords: Stochastic dynamic programming, Markov decision process, policy ite- ration, value iteration