The thesis proposes a method for automated model selection using a logistic regression model in the context of prospectivity modeling, i.e. the exploration of minearlisations. This kind of data is characterized by a rare positive event and a large dataset. We adapted and combined the two statistical measures Wald statistic and Bayes' information criterion making it suitable for the processing of large data and a high number of variables that emerge in the nonlinear setting of logistic regression. The obtained models of our suggested method are parsimonious allowing for an interpretation and information gain. The advantages of our method are shown by comparing it to another model selection method and to arti cial neural networks on several datasets. Furthermore we introduced a possibility to induce spatial dependencies which are important in such geological settings.

The purpose of this investigation (a quasi-experimental design called a non-equivalent design group (NEDG)) was to determine if allowing students in a science majors general Chemistry I course the choice in establishing the due dates that their homework was due to the instructor would improve course averages. This study covered two semesters with a total of 288 students participating with n = 158 in the fall and n = 130 in the spring. The students self-selected the homework group, VOICES, that best fit his/her needs which included (1) the instructor's homework schedule, (2) a student-customized schedule or a schedule that followed the exam schedule, or (3) all homework due by the last class day prior to the final exam. Online homework was assigned and graded with individual assignment and homework average grades collected and analyzed. No statistically significant differences were found among the VOICES groups with respect to final course average. Other results of this study replicated findings in the literature; namely, that there is a higher correlation between mathematics skills and course success. Course averages of students who had completed Calculus I or higher were statistically significantly higher than students with less completed mathematics coursework in all VOICES groups. Also, the percentage of successful students in the on-sequence semester (fall) was higher than the percentage of students in the off-sequence semester (spring). No differences were seen in any VOICES group's student demographics or high school chemistry preparation.

... The learning model developed in this study is the ISLANDS learning model. ISLANDS stands for Identification, Setting, List, Application, Note, Duplication, and Scrutinize.This learning model provides space for students to learn independently, it is by searching and finding information about tourism objects and Phrases according to the material, working in groups to practice collaboration skills, sharing information and knowledge, and sharing tasks and responsibilities, leadership, solving problems, actively speaking in German with the help of teachers and native speakers and only focusing on local wisdom which is of course very well known to them, making it easier for them to absorb the material provided by the teacher. In addition, students are also given the opportunity to evaluate the process and their learning outcomes. Thus, it is hoped that this interactive, communicative, active, independent and local wisdom-oriented learning model can increase students’ learning motivation and their mastery of the German language, especially guiding abilities. [Aus: Introduction]

Syftet med denna licentiatuppsats är att pröva en specifik strukturell modell som stöd för elevers utforskande av relationer mellan tal i additiva strukturer även när negativa tal är inkluderade. En intention är att resultatet ska kvalificera undervisningen och utgöra ett stöd vid planering och genomförande av en undervisning avseende ekvationer med additiv struktur utifrån algebraisk undervisning som alternativ till att i första hand finna en lösning med stöd av regler och procedurer. Under studien kartlades, kategoriserades och beskrevs elevernas erfarande av fenomenet relationer mellan kvantiteter. I studien prövades även om, och i sådant fall, på vilka sätt en specifik strukturell modell användes av elever under arbetet med att utforska ekvationers struktur. Elever från årskurserna 3, 8 och 9 deltog i semistrukturerade intervjuer samt i forskningslektioner. Tre grupper av lärare samt två forskare planerade, genomförde och reviderade forskningslektionerna baserat på ansatsen learning study. Fenomenografi var den teoretiska ansatsen för de inledande semistrukturerade intervjuerna. Variationsteori och lärandeverksamhet var de teoretiska ramverken för forskningslektionerna där Davydovs program var en inspirationskälla. För att uppnå syftet formulerades följande forskningsfrågor: Vilka skilda sätt att erfara fenomenet relationer mellan kvantiteter kan urskiljas i elevintervjuer? På vilka sätt använder elever en specifik strukturell modell för att utforska ekvationer? Den första forskningsfrågan besvaras i Artikel 1 som visar att elever erfar relationer mellan kvantiteter som någonting som ska beräknas alternativt någonting som ska relateras. Den andra forskningsfrågan besvaras i Artikel 2 som visar att elever använde sig av den i studien prövade specifika strukturella modellen såsom ett formulär att fylla i alternativt som en lärandemodell och redskap för att identifiera del-helhetsstrukturen mellan tal i en ekvation samt för att välja lämplig operation att lösa ut det obekanta talet.                           Resultaten visar på möjligheten, men även utmaningen, att introducera en algebraisk undervisning med fokus på analys och teoretiska resonemang även för elever med erfarenheter från en alternativ bakgrund. / The aim of the licentiate thesis is to examined a specific structural model in order to support students' exploration of relationships between numbers in additive structures even when negative numbers is included. One intention is that the finding should qualify the teaching and constitute a support when plan and implement teaching regarding equations with additive structure based on algebraic teaching as an alternative to primarily finding a solution with support of rules and procedures. During the study, the students' experiences of the phenomenon relationships between quantities was examined, categorized and described. In the study it was also examined whether and, if so, in what ways a specific structural model was used by students during the work of exploring the structure of equations. Students from grades 3, 8 and 9 participated in semi-structured interviews and in research lessons. Three groups of teachers and two researchers planned, conducted and revised the research lessons based on the learning study approach. Phenomenography was the theoretical approach for the initial semi-structured interviews. Variation theory and learning activity were the theoretical frameworks for the research lessons where Davydov's curriculum was a source of inspiration. In order to achieve the aim, the following research questions were formulated: 1) What different ways of experiencing the phenomenon of relationships between quantities can be discerned in student interviews? 2) In what ways do students use a specific structural model to explore equations? The first research question is answered in Article 1, which shows that students experience relationships between quantities as something to be calculated or something to be related. The second research question is answered in Article 2, which shows that students used the specific structural model examined in the study as a form to fill in alternatively as a learning model and a tool to identify the part-whole structure between numbers in an equation and to choose appropriate operation to find the unknown number. The findings show the possibility, but also the challenge, of introducing an algebraic teaching with a focus on analysis and theoretical reasoning also for students with experiences from an alternative background.

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Die modellbasierte Kalibrierung dynamischer Fahrmanöver an Prüfständen ermöglicht die systematische Optimierung von Steuergerätedaten über den gesamten Betriebsbereich des Fahrzeugs und begegnet somit der steigenden Komplexität in der Antriebsstrangentwicklung. Dabei werden mehrere empirische Black-Box-Modelle zur Abbildung der Zielgrößen für die nachfolgende Optimierung identifiziert. Der Einsatz der statistischen Versuchsplanung ermöglicht eine systematische Abdeckung des gesamten Eingangsbereiches. In jüngerer Vergangenheit werden in der Automobilindustrie vereinzelt Methoden des maschinellen Lernens eingesetzt, um die Anwendung der modellbasierten Kalibrierung zu vereinfachen und die Effizienz zu erhöhen. Insbesondere der Einsatz des aktiven Lernens führt zu vielversprechenden Ergebnissen. Mit diesen Methoden werden Modelle mit einer geringeren Anzahl an Messpunkten identifiziert, während gleichzeitig die erforderliche Expertise für die Versuchsplanerstellung reduziert wird. Eine Herausforderung stellt die simultane Identifikation mehrerer Regressionsmodelle dar, die für die Anwendung des aktiven Lernens auf die Fahrbarkeitskalibrierung erforderlich ist. Hierfür wird im Rahmen dieser Arbeit die aktive Ausgangsselektion (AOS) eingeführt und eingesetzt. Die AOS-Strategie bestimmt dabei das führende Modell im Lernprozess. Erste Veröffentlichungen zeigen das Potenzial der Verwendung von AOS. Statistisch signifikante Ergebnisse über die Effektivität gibt es bislang jedoch nicht, weswegen die weitere intensive Untersuchung von Strategien erforderlich ist. In der vorliegenden Arbeit werden regel- und informationsbasierte AOS-Strategien vorgestellt. Letztere wählen das führende Modell basierend auf allen während des Versuchs verfügbaren Informationen aus. Hier erfolgt erstmals die detaillierte Beschreibung und Untersuchung einer normierten modellgütebasierten Auswahlstrategie. Als Modellart werden Gauß’sche Prozessmodelle verwendet. Anhand von Versuchen wird überprüft, ob der Einsatz von AOS gegenüber gängiger statistischer Versuchsplanung sinnvoll ist. Darüber hinaus wird untersucht, ob die Berücksichtigung aller zur Versuchslaufzeit bekannten Informationen zu einer Verbesserung des Lernprozesses beiträgt. Die Strategien werden an Simulationsexperimenten getestet. Diese Simulationsexperimente stellen Grenzfälle echter Versuche dar, die für die Strategien besonders herausfordernd sind. Die Erstellung der Experimente wird anhand von Informationen aus realen Prüfstandsversuchen abgeleitet. Die Strategien werden analysiert und miteinander verglichen. Dazu wird eine anspruchsvolle Referenzstrategie verwendet, die auf den Methoden der klassischen Versuchsplanung basiert. Die Versuche zeigen, dass bereits einfache regelbasierte Strategien bessere Ergebnisse hervorbringen als die Referenzstrategie. Durch Berücksichtigung der momentanen Modellgüte und Abschätzung des Prozessrauschens zur Versuchslaufzeit ist eine weitere Reduktion der Messpunkte um mehr als 50% gegenüber der Referenzstrategie möglich. Da die informationsbasierte Strategie rechenintensiver ist, wird auch ein zeitlicher Vergleich mit unterschiedlichen langen Annahmen für die Fahrmanöverdauer am Prüfstand vorgenommen. Bei kurzen Manöverzeiten ist der Vorteil der informationsbasierten Strategie gegenüber der regelbasierten Strategie nur gering ausgeprägt. Mit zunehmender Manöverzeit nähert sich die abgeschätzte zeitliche Ersparnis jedoch der prozentualen Einsparung der Messpunkte an. Die aus den Simulationsexperimenten abgeleiteten Ergebnisse werden anhand eines realen Anwendungsbeispiels validiert. Die Implementierung an einem Antriebsstrangprüfstand wird dazu vorgestellt. Für die Versuche werden insgesamt 1500 Fahrmanöver an diesem Prüfstand durchgeführt. Die Ergebnisse der Versuche bestätigen die aus den Simulationsexperimenten abgeleiteten Ergebnisse. Die regelbasierte AOS-Strategie reduziert die Anzahl der Messpunkte im Durchschnitt um 65% im Vergleich zur verwendeten Referenzstrategie. Die informationsbasierte AOS-Strategie verringert die Anzahl der Punkte weiter auf 70% gegenüber der Referenzstrategie. Die Modelle der informationsbasierten Strategie sind bereits nach 50% der Punkte besser als die besten Modelle der regelbasierten Strategie. Die Ergebnisse dieser Arbeit legen den ständigen Einsatz der vorgestellten informationsbasierten Strategien für die modellbasierte Kalibrierung nahe. / Model-based calibration of dynamic driving maneuvers on test benches enables the systematic optimization of ECU data over the vehicle’s entire operating range and thus faces the increasing complexity in powertrain development. Several empirical black-box models are identified to represent the target variables for the succeeding optimization. The use of statistical experimental design enables systematic coverage of the entire input range. Recently, machine learning methods have been occasionally used in the automotive industry to simplify applying the process and increase its efficiency. In particular, the use of active learning leads to promising results. It leads to a reduction of the number of measurement points necessary for model identification. At the same time, the required expertise for experimental design is reduced. The simultaneous identification of multiple regression models, which is required for a broad application of active learning to drivability calibration, is challenging. In this work, active output selection (AOS) is introduced and applied to face this challenge. An AOS strategy determines the leading model in the learning process. First publications show the potential of using AOS. However, no statistically significant results about the effectiveness are available to date, which is why these strategies need to be studied in more detail. This work presents rule- and information-based AOS strategies. The latter select the leading model based on all current information available during the experiment. For the first time, this publication provides a detailed description and investigation of a normalized model-quality-based selection strategy. Gaussian process models are used as model type. Experiments are conducted to verify whether the use of AOS is reasonable compared to common designs of experiments. Furthermore, we analyze whether taking into account all information known at the time of the experiment helps to improve the learning process. The strategies are first tested on computer experiments. These computer experiments represent borderline cases of real experiments, which are particularly challenging for the strategies. The experiments are derived using information from real test bench experiments. The strategies are analyzed and compared with each other. For this purpose, a sophisticated reference strategy is used, which is based on the methods of classical designs of experiments. The experiments show that even simple rule-based strategies lead to better results than the reference strategy. By considering the current model quality and estimating the process noise during experiment runtime, a further reduction of the measurement points by more than 50% compared to the reference strategy is possible. Since the information-based strategy is more computationally expensive, we perform a time comparison with different assumptions for the driving maneuver duration at the test bench. For short maneuver times, the advantage of the information-based strategy in comparison to the rule-based strategy is only small. As the maneuver time increases, the estimated time reduction approaches the percentage savings of the measurement points. The results derived from the computer experiments are validated using a real application example. The implementation on a powertrain test bench is presented for this purpose. For the experiments, a total of 1500 driving maneuvers are performed on this test bench. The results of the experiments confirm the results of the computer experiments. The rule-based AOS strategy reduces the number of measurement points by 65% on average compared to the reference strategy used. The information-based AOS strategy further reduces the number of points to 70% compared to the reference strategy. The results of this work suggest the use of the presented information-based strategies for model-based calibration.

In this study the status of the active learning methods of teaching employed in Teachers‟ Training Colleges in Ethiopia was examined from the perspective of the trainee-teachers and their lecturers. Factors relating to active learning were discussed within the framework of the social-cognitive constructivists‟ theory, as learning (knowledge construction) requires the direct involvement of an individual, as well as interaction with the social environment. Three main groups of learning theories were investigated, namely the behavioural, the cognitive and the constructivist theories. The behavioural theories emphasise the transmission of information from the teacher to the learner, whereas the cognitive and constructivist theories emphasise the learners‟ construction of knowledge from their own experiences and their interaction with one another. The methods and strategies for teaching Geography in Ethiopian Teachers‟ Training Colleges were also discussed. The empirical research investigated the perceptions of trainee-teacher and lecturers regarding active learning methods at their respective colleges, the current teaching-learning methods and strategies that the trainee-teachers employ, how they experience the current teaching methods and approaches and other related matters, such as class size and facilities, the integration of assessment with active learning, the teachers‟ attitudes towards active learning, whether or not the lecturers receive short-and long-term training on active learning, the support obtained from the managing body, and whether or not any guidelines existed. Interviews were conducted as embedded research that was intended to supplement the quantitative findings. The results of the empirical investigation indicated a lack of systematic and appropriately organised active learning at college level. Some of the barriers that profoundly hindered the use of active learning pedagogies are the lecturers‟ tendency to maintain the traditional (lecture) method of teaching, insufficient pre-service and in-service training, the large class sizes, the lack of administrative support, the scarcity of resources/facilities, the lack of integration between assessment and active learning, and finally, the absence of guidelines. Pertinent information was extracted from the relevant literature and the empirical study to develop a model to address the problem of active learning in Teachers‟ Training Colleges. Thus, a Model of Active Learning, which is relevant to the teaching of the learning of Geography at College level in the Ethiopian context, was developed. / Curriculum and Instructional Studies / D. Ed. (Curriculum Studies)

This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.

In this study the status of the active learning methods of teaching employed in Teachers‟ Training Colleges in Ethiopia was examined from the perspective of the trainee-teachers and their lecturers. Factors relating to active learning were discussed within the framework of the social-cognitive constructivists‟ theory, as learning (knowledge construction) requires the direct involvement of an individual, as well as interaction with the social environment. Three main groups of learning theories were investigated, namely the behavioural, the cognitive and the constructivist theories. The behavioural theories emphasise the transmission of information from the teacher to the learner, whereas the cognitive and constructivist theories emphasise the learners‟ construction of knowledge from their own experiences and their interaction with one another. The methods and strategies for teaching Geography in Ethiopian Teachers‟ Training Colleges were also discussed. The empirical research investigated the perceptions of trainee-teacher and lecturers regarding active learning methods at their respective colleges, the current teaching-learning methods and strategies that the trainee-teachers employ, how they experience the current teaching methods and approaches and other related matters, such as class size and facilities, the integration of assessment with active learning, the teachers‟ attitudes towards active learning, whether or not the lecturers receive short-and long-term training on active learning, the support obtained from the managing body, and whether or not any guidelines existed. Interviews were conducted as embedded research that was intended to supplement the quantitative findings. The results of the empirical investigation indicated a lack of systematic and appropriately organised active learning at college level. Some of the barriers that profoundly hindered the use of active learning pedagogies are the lecturers‟ tendency to maintain the traditional (lecture) method of teaching, insufficient pre-service and in-service training, the large class sizes, the lack of administrative support, the scarcity of resources/facilities, the lack of integration between assessment and active learning, and finally, the absence of guidelines. Pertinent information was extracted from the relevant literature and the empirical study to develop a model to address the problem of active learning in Teachers‟ Training Colleges. Thus, a Model of Active Learning, which is relevant to the teaching of the learning of Geography at College level in the Ethiopian context, was developed. / Curriculum and Instructional Studies / D. Ed. (Curriculum Studies)

賽局理論(Game Theory)大多假設理性決策，單一回合賽局通常可由理論證明均衡(Equilibrium)或是最佳決策，然而如果賽局重複進行，不見得只存在單一均衡，光從理論推導可能無法找到所有均衡。以囚犯困境(Prisoner Dilemma)為例，理論均衡為不合作，若重複的賽局中存有互利關係，不合作可能不是最佳選擇。近年來，經濟學家藉由和統計實驗設計類似的賽局實驗(Game Experiment)，探討賽局在理論與實際間的差異，並以學習模型(Learning Model)描述參賽者的決策及行為，但學習模型的優劣大多依賴誤差大小判定，但誤差分析結果可能與資料有關(Data Dependent)。有鑑於學習模型在模型選取上的不足，本文引進統計分析的模型選取及殘差檢定，以實證資料、配合電腦模擬評估學習模型。 本文使用的實證資料，屬於囚犯困境的重複賽局(Repeated Game)，包括四種不同的實驗設定，參加賽局實驗者（或是「玩家」）為政治大學大學部學生；比較學習模型有四種：增強學習模型(Reinforcement Learning model)、延伸的增強學習模型(Extend Reinforcement Learning Model)、信念學習模型(Belief Learning Model)、加權經驗吸引模型(Experience-Weighted Attraction Model)。實證及模擬分析發現，增強學習模型較適合用於描述囚犯困境資料，無論是較小的誤差或是適合度分析，增強學習模型都有較佳的結果；另外，也發現玩家在不同實驗設定中的反應並不一致，將玩家分類後會有較佳的結果。 / In game theory, the optimal strategy (or equilibrium) of one-shot games usually can be solved theoretically. But, the optimal strategies of repeated games are likely not unique and are more difficult to find. For example, the defection is the optimal decision for the one-shot Prisoner Dilemma (PD) game. But for the repeated PD game, if the players can benefit from cooperation between rounds then the defection won’t be the only optimal rule. In recent years, economists design game experiments to explore the behavior in repeated games and use the learning models to evaluate the player’s choices. Most of the evaluation criteria are based on the estimation and prediction errors, but the results are likely to be data dependent. In this study, we adapt the model selection process in regression analysis and apply the idea to evaluate learning models. We use empirical data, together with Monte Carlo simulation, to demonstrate the evaluation process. The empirical data used are repeated PD game, including four different experimental settings, and the players of the game are from National Chengchi University in Taiwan. Also, we consider four learning models: Reinforcement learning (RL) model, Extend Reinforcement learning (ERL) model, Belief Learning (BL) model, and Experience-weighted attraction (EWA) model. We found that the RL model is more appropriate to describe the PD data. In addition, the behaviors of players in a group can be quite different and separating the players into different sets can reduce the estimation errors.