A Model-driven Approach to the development of a PBL Script Editor

Designing a pedagogically sound and technically executable collaboration script is a highly complex, time-consuming and error-prone task. This paper presents a model-driven approach to enable practitioners to design online PBL courses. Adopting this technical approach, we developed a PBL scripting language that provides natural concepts for the teacher to use in PBL practices. Based on the PBL scripting language, we developed a PBL script editor that facilitates teachers to design, communicate, customize, and reuse PBL scripts. In addition, it provides functions to transform a PBL script to a unit of learning (UoL) represented in IMS Learning Design (LD), which can be executed in an IMS LD run-time environment to scaffold PBL processes

Shaping socio-critical thinking of junior students using problem-based learning and inquiry strategy

This research examined a pattern of integration between problem-based learning (PBL) strategy with inquiry in shaping the critical thinking framework and psychosocial of youth-level students. This study used a qualitative descriptive approach with a literature study method. The data sources were derived from documentation, books, and various related articles. Its analytical techniques used deductions, induction, interpretation, and comparison and analysis of multilayered texts. The results of this research showed that the PBL and inquiry strategies had substantial collation and synergy in the process of forming a framework of critical thinking and psychosocial of youth-level students. It was based on the process of implementing both strategies that emphasized the search for solutions to the problems encountered by conducting investigations, hypotheses, testing, data mining supported by teamwork, collaboration, communication, discussion, and coordination. Thus, the use of the PBL strategy and inquiry integratively is highly recommended in the learning process

Etablierung und Evaluation der kieferorthopädischen digitalen Falldokumentation in der Lehre als problembasiertes eLearningsystem (ePBL)

Ziel der Arbeit war es, das an der Poliklinik für Kieferorthopädie, Münster entwickelte, modifizierte POL-Konzept zu beschreiben, seine Eignung als didaktisches Werkzeug zu untersuchen und ein technisches Hilfsmittel in Form eines Softwareprogramms zur Gestaltung von Lehrmaterial zu entwickeln. Das modifizierte POL ist ein Konzept, welches in sich Eigenschaften des klassischen problemorientierten und des fallbasierten Lernens vereint. Es wurde an die curricularen, personellen und infrastrukturellen Gegebenheiten angepasst und bietet gegenüber den etablierten Formen von POL Vorteile. Eine über drei Semester hinweg durchgeführte Evaluierung durch Studierende zeigte eine deutliche Befürwortung des praktizierten Konzeptes, wobei im zweiten Behandlungskurses hinsichtlich einzelner Teilaspekte weniger Zustimmung festzustellen war. Die im Rahmen dieser Arbeit entwickelte Softwareapplikation dient der Gestaltung elektronischen Lehrmaterials für die Durchführung der modifizierten POL-Seminare

Bayesian networks for classification, clustering, and high-dimensional data visualisation

This thesis presents new developments for a particular class of Bayesian networks which are limited in the number of parent nodes that each node in the network can have. This restriction yields structures which have low complexity (number of edges), thus enabling the formulation of optimal learning algorithms for Bayesian networks from data. The new developments are focused on three topics: classification, clustering, and high-dimensional data visualisation (topographic map formation). For classification purposes, a new learning algorithm for Bayesian networks is introduced which generates simple Bayesian network classifiers. This approach creates a completely new class of networks which previously was limited mostly to two well known models, the naive Bayesian (NB) classifier and the Tree Augmented Naive Bayes (TAN) classifier. The proposed learning algorithm enhances the NB model by adding a Bayesian monitoring system. Therefore, the complexity of the resulting network is determined according to the input data yielding structures which model the data distribution in a more realistic way which improves the classification performance. Research on Bayesian networks for clustering has not been as popular as for classification tasks. A new unsupervised learning algorithm for three types of Bayesian network classifiers, which enables them to carry out clustering tasks, is introduced. The resulting models can perform cluster assignments in a probabilistic way using the posterior probability of a data point belonging to one of the clusters. A key characteristic of the proposed clustering models, which traditional clustering techniques do not have, is the ability to show the probabilistic dependencies amongst the variables for each cluster. This feature enables a better understanding of each cluster. The final part of this thesis introduces one of the first developments for Bayesian networks to perform topographic mapping. A new unsupervised learning algorithm for the NB model is presented which enables the projection of high-dimensional data into a two-dimensional space for visualisation purposes. The Bayesian network formalism of the model allows the learning algorithm to generate a density model of the input data and the presence of a cost function to monitor the convergence during the training process. These important features are limitations which other mapping techniques have and which have been overcome in this research