Intelligent Tutoring Systems (ITS) can be used to provide personalized assistance in Learning Management Systems (LMS). The main drawbacks to this end are that they are usually self-contained or system-dependent, and that integrations often lack in a representation of the considered learning content structure (i.e., are not structure-aware). The former prevents the reuse of devised didactic concept implementations. The latter can cause the user to get lost during the guidance process. Both are challenging to overcome because of the heterogeneous, LMS-specific approaches to structuring learning content. The aim of this thesis is to investigate frame conditions for reusing structure-aware ITS functionalities across LMS and to illustrate the gained insights in an elaborated ITS. Therefore, a system architecture is proposed that can retrieve the required learning process data from an LMS by employing a thin adaptation layer and transform this data into a generic data structure. This structure is used for providing assistance and for generating and integrating the learning content structure representation. The focus of this thesis is on the system-independent implementation of structure-aware intelligent assistance scenarios. The approach will be evaluated in terms of applicability and effectiveness considering different state-of-the-art LMS.

Collaborative learning (CL) is an active learning method that has been shown to improve students' learning. Although the integration of CL in small face-to-face lectures was implemented and evaluated successfully, the implementation within large (e.g., over 200 students) and remote settings is still a subject of research. Challenges such as the goal-oriented group formation of students with spatial or even geographical disparity, or the collaboration itself prevent the actual use. Thus, our goal is to overcome those issues and enable CL in the most diverse classrooms. Therefore, we present a prototype that combines the idea of Audience Response Systems (ARSs), which allow students to participate during the lecture using their mobile devices, with short-term collaborative activities. Based on students' previously given answers, our approach allows the formation of groups using different algorithms and settings. Within these groups, several types of interactions can be defined, e.g., a chat is provided to initiate discussions, or a voting to determine a group answer. The approach is designed to be highly customizable by allowing lecturers to create their individual teaching scenarios based on elements defined in a unified metamodel. All runtime data that is not specified by this model, e.g., the assignment of students to groups, is added using the concept of roles.

Learning in universities can be characterised as self-regulated learning [1]: students have to set learning goals, apply learning strategies, assess their progress, correct their strategies and redefine goals in cyclic phases. IT-based educational tools enhance lectures and provide numerous opportunities to support students in mastering the demands of self-regulated learning (e.g., by providing quizzes). The data basis of such tools can be utilised in order to support students during the phases of (self-)assessment and correction. Additionally, teachers can benefit from fine-grained analysis of individual lectures and assessments of the student group's conduct in order to improve their lectures. Both, teachers and students directly benefit from structured feedback on individual as well as group performance. However, the information required for beneficiary feedback to students and teachers must be presented in an ordered and well-prepared fashion. Evaluation centres can offer an automated extraction of important information based on the underlying data stored by the IT-based educational tools. In this short position paper we present a prototypical evaluation centre and discuss how it can assist teachers in improving their lectures, while it provides students with required information for correct self-assessment at the same time. The proposed evaluation centre was tested in different settings and preliminary results based on subjective user feedback can be extracted.

University courses in STEM fields benefit tremendously from the introduction of IT-based education tools (IET). Learning is not limited to knowledge of, but extended to knowledge acquisition through IET utilization. With the introduction of IETs, the preparation of lectures and courses becomes increasingly challenging for lecturers. Lecturers must know the IET's functional range and the didactical concept underlying each. They must also consider the interaction of IETs among each other and their behavior under different circumstances (time in semester, audience size, etc.). Additionally, didactics preferences of lecturers gravely influence IET selection. Only few solutions exist with an approach utilizing guided selection of IETs; none of which offers comprehensive explanations of didactics concepts and influencing factors. Attempting to address these problems, we added a guided IET selection to our AMCS platform which includes nine IETs not suitable for utilization at the same time. Our guided IET selection addresses didactical design as well as feasible und suitable combinations of the IETs. Any suggestion to lecturers at lecture or course creation time bases on a simple algorithm, which takes all of the before-mentioned aspects into consideration, while at the same time providing useful hints and explanations to the lecturers.

For lecturers at universities timely feedback from their students is very important in order for them to improve their teaching with adaptations targeted at the students’ requirements. Classical evaluation methods address overall evaluations at the end of a semester, commonly with paper-based questionnaires. However, this does not provide direct benefit to the students of that course as adaptations will most likely be carried over into the next iteration of the same course. For this reason, students’ motivation to participate in these surveys decreases over time. Therefore, we propose a tool support for continuous evaluation during the conduct of a course available the whole semester, including direct feedback during the lecture, formative evaluation during the entire course, and a summative evaluation at the end of the course or semester. For that purpose, we expanded the functionality of the interactive Auditorium Mobile Classroom Service (AMCS), which was developed to support students in self-regulated learning (SRL) processes during classical university lectures. In the present article the concepts and features of AMCS for evaluation are described. Furthermore, we report first experiences from a field test in two university lectures.

Teaching in classic courses offers too little interaction between docents and students and should be improved. Addressed approaches include a range from Simple Voting Systems to Clickers and Audience Response Systems, and interaction and Student motivation may be improved therein. However, different university course settings are affected in different ways by these systems. Therefore, this paper presents a comparison of a selected range of these systems (implemented as tool kits) within two course settings, namely lecture readings and exercise tutorials. These tools are Audience Response Systems, Question and Answer Systems (Q&A Systems), Discussion Systems (Panels), as well as Virtual Whiteboard Feedback Systems. A synopsis of feasibility for different settings is provided and concluded with important results on the distinguishability of Q&A Systems and Panels.

Even though classical lectures at universities are criticized for lacking interactivity and treating students like passive receptors of information they are still very popular. Due to the big amount of students, interaction between teacher and students is difficult to realize. Several projects address this problem by offering technical solutions which aim at increasing the interactivity during classes or lectures â?? classic clicker-systems as well as solutions in which students use their own smartphones, netbooks or tablet-PCs. Based on research on self-regulated learning (SRL) processes we developed the already existing tools one step further: instead of only providing questions we designed Auditorium Mobile Classroom Service (AMCS) â?? a program which offers several possibilities to interact during a lecture. AMCS supports students to regulate their own learning process during the lecture. Learning questions are one core element to support them. On the basis of the results of the learning questions specific advices and hints are sent to the studentsâ?? smartphones or notebooks. The features increase the interactivity between the content and students and the interaction in the lecture hall. In the present article the program AMCS is described. Furthermore we report first experiences from a field test in a university lecture.

The idea of the future internet of services is to combine several services of numerous service providers to new value-added services or applications. To sell these services on so-called service marketplaces the providers have to ensure a high quality of service execution. But how could a provider of a composed service ensure the quality of the whole application, if single services of other providers fail or do not reach the required level of quality? This article describes an approach for modeling adaptation in web service compositions to ensure a guaranteed quality of service for the whole composite service. A special adaptation mechanism is the rebinding of single services while the process is executed if the services fail or could not reach the needed QoS level. We will present a solution for modeling these rebinding concepts in BPEL processes and the infrastructure to support this adaptation mechanism at run-time.