Statistische Modelle und Methoden in den Ingenieurwissenschaften und eine Blended-Learning Einführung
- Statistical models and methods in engineering sciences and a blended-learning approach
Weingartz, Marina; Kamps, Udo (Thesis advisor); Cramer, Erhard (Thesis advisor); Kateri, Maria (Thesis advisor)
Aachen (2016, 2017)
Dissertation / PhD Thesis
Dissertation, RWTH Aachen University, 2016
This PhD thesis deals with a new concept for a statistic course in engineering sciences resulting in a blended-learning structure. The manner of presenting the content intends to promote a strong practical reference basing on DIN Standards and VDI Guidelines. Out of the large number of partly non-networked Standards and Guidelines, in which furthermore other terms are used as in other aereas of statistic contents, resulted a systematic and practice-oriented elaboration of the contents belonging to inferential statistical analysis devided in the areas of point estimation, interval estimation and hypothesis testing. Real life examples form a very important component that should achieve situational learning with a constructivist approach. The aspired blended-learning concept is didacitcallyconsolidated. This fact underlining, several instruments are presented and embedded in the statistical context, such as the Myers Briggs type indicator, Kolb’s learning cycle or the teaching and learning methods by Felder/Silverman.In addition to the modified way of presentation of the contents, a change in the methodology is going to happen. Therefore the existing face-to-face-teaching course proceeds to a blended-learning course. Blended-learning describes a combination of face-to-face and online-/self-learning phases. It presents a chance to help students with different prior knowledge as well as to support individual learning preferences. Studying self-controlled through new designed online-courses by the teaching and learning environment EMILeAstat 2.0 offers in many ways greater flexibilty and above all the possibility of interaction, such as through integrated multiple-choice questions and especially developed applets. Within this thesis both technical as well as content details of the teaching and learningenvironment EMILeA-stat 2.0 are explicitly illustrated. A special focus lies on the developement of several applets by GeoGebra as an alternative to Java applets for the inferential statistical analysis. These should lead to an interactive and competence-oriented learning of the statistical contents. Additionally these applets should motivate an activeand intensive work. Finally this thesis includes two online learning units as components of a future blended-learning course.