Sebastian Stiehm

Exploring Research Networks with Data Science: A Data-Driven Microservice Architecture for Synergy Detection

The determination of synergies in research networks is often approached by citation and co-authorship analysis using scientific publications. Although the latter methods allow a detailed insight into scientific cooperation and communities, the major part of the data, the text body of the publication, is mostly ignored. Hence, this text body contains valuable data, which can be processed in order to describe entities in a research network, detect synergies between those entities and make use of these synergies, e.g. in further cooperation. This paper aims at the description of a prototypic architecture, which uses data science to detect hidden topics from publication data. These topics are used to describe entities (e.g. institutes or projects) in a research network. Topical proximity between those entities is computed by the application of classification. In order to enable actors to explore the results of these processes, the architecture automatically generates graph-based visualizations of the derived synergies. This paper contains a description and definition of the technical elements and the process chain of the architecture to derive these synergies in network data. This is accompanied by a short outline of results with the prototype.

Blended Learning Through Integrating Lego Mindstorms NXT Robots in Engineering Education

The current program for Mechanical Engineering at the RWTH Aachen University in Germany has more than 1500 students enrolled. Lego Mindstorms’ NXT Robots are fully integrated in the current Engineering Education stream to help students practically apply theoretical concepts. The courses Communication and Organizational Development (KOE) and Computer Science in Mechanical Engineering 1 (INFO1), provided by the interdisciplinary institute cluster IMA/ZLW, follow a newly-designed “blended learning” approach. This institute cluster is composed of the Institute of Information Management in Mechanical Engineering (IMA) and the Center for Learning and Knowledge Management (ZLW). These institutes are currently within the Faculty of Mechanical Engineering at RWTH Aachen University.Two years ago, the course KOE was redesigned and redirected towards a “Flipped Classroom” concept by initiating online lectures and a discussion class. Thus, the tutorial class ROBOFLEX as part of the KOE curriculum is introduced. ROBOFLEX is a two-stage business simulation that enables students to experience realistic virtual communication within computer science and engineering disciplines. Students are divided into groups of about thirty people, and become entrepreneurs and founders of start-ups that specialize in the production of innovative robots for the automotive industry. They create these robots using Lego Mindstorms’ NXT.Since its conception, the course INFO1 has been accompanied by a lab component, where students apply the concepts taught in class in a team-focused software design project. In 2011, the lab concept was changed into a two-stage robotics programming project based on Lego Mindstorms’ NXT Robots and the Java programming language. In the first stage, students practice the fundamental programming concepts that are presented in the lecture by completing a series of exercises in a self-paced manner. The second stage focuses on applied problem-solving. In this stage, pairs of students apply the previously-learned programming concepts to program a “pick-and-place” robot that is equipped with various sensors.The integration of Lego Mindstorms’ NXT Robots into these courses also join the concepts of the two described courses. While KOE delivers organizational and communicational skills, INFO1 provides technical and domain-specific skills. Here, the robots represent the connecting element. The problem-based second stage of INFO1 benefits from the skills that are taught in KOE. Because INFO1 is scheduled in the term following the KOE, it offers a direct opportunity for students to transfer the KOE skill set from the lecture where it was taught into a new context that is primarily concerned with a different subject. Both classes have been evaluated and developed independently in the past. Since last year’s introduction of ROBOFLEX in KOE, synergies between both lectures are becoming a main component of their further developments. In this paper the recent developments in both courses will be compared and discussed. Specific measurable effects concerning learning capability, motivation and learning endurance are being portrayed by using blended learning approaches.Copyright

NEXT LEVEL BLENDED LEARNING FOR AN EXCELLENT ENGINEERING EDUCATION

The digitalization of higher education in general and Blended Learning in particular have been focused for a long time. Next to applying new technology, focusing on interdisciplinary competences during studies has always been part of engineering studies. The lecture Communication and Organization Development (KOE) is a constituent part of the Bachelor’s studies of the faculty of mechanical engineering, addressing 1500 freshmen of the RWTH Aachen University. Since decades the lecture KOE is part of frequently used revision along with an agile progression of applied mixed methods and new demands using the previously mentioned teaching development. The number of first semester students is still increasing and freshmen entering the university are partially minors (17 years old) due to the shortened school career. To master these and further challenges successfully, the didactical method mix is based on a cube model developed by Baumgartner and Payr (1996), which combines different levels and perspectives of a student-centred teaching approach. Elements such as the flipped classroom concept (Bretzmann, J. (2013): Flipping 2.0. Practical strategies for flipping your class, Bretzmann Group, LLC) (Bergmann. J., Sams, A. (2012): Flip your classroom. Reach every student in every class every day. International Society for Technology in Education, ASCD) along with the discussion forum, which were introduced during winter term 2013/2014, have become a permanent feature of KOE. Moreover, blended learning elements were increased by attaching a two-stage business simulation called ROBOFLEX to bring the theoretical elements to an immersive, problem-based learning approach. This paper presents the cube model’s current status as part of the method mix of KOE. Additionally, based on last year’s evaluation results, the paper examines student’s reactions towards the tutorial class ROBOFLEX as well as its potential for optimization. Furthermore a general analysis of the evaluation results from winter term 2013/2014 compared with 2014/2015 is performed to identify scope for the further development of KOE.

Subjective Stress in Hybrid Collaboration

Hybrid collaboration between human and machine antagonists is currently discussed as the most likely scenario of future manufacturing within the next 10 years because it considers technological developments and preserves human workplaces at the same time. However, not only technical feasibility plays a role in the design of these future collaborations, but also the psychological and social effects must be considered. This paper analyzes the subjective stress level of humans in dependence of the characteristics of robots (2 × 2 design with either an industrial or a humanoid robot that was performing either reliable or faulty). A virtual experiment has been conducted to simulate a collaborative hybrid task, including a pre- and post-survey to test. Results do not show any effect of condition, but significant effects of time. The results suggest that the experiment has generally been perceived as slightly stressful, but the appearance and behavior of the robot has no effect on the subjective stress level.

Augmenting research cooperation in production engineering with data analytics

Understanding how members of a research team cooperate and identifying possible synergies may be crucial for organizational success. Using data-driven approaches, recommender systems may be able to find promising collaborations from publication data. Yet, the outcome of scientific endeavors (i.e. publications) are only produced sparingly in comparison to other forms of data, such as online purchases. In order to facilitate this data in augmenting research cooperation, we suggest to combine data-driven approaches such as text-mining, topic modeling and machine learning with interactive system components in an interactive visual recommendation system. The system leads to an augmented perspective on research cooperation in a network: Interactive visualization analyzes, which cooperation could be intensified due to topical overlap. This allows to reap the benefit of both worlds. First, utilizing the computational power to analyze large bodies of text and, second, utilizing the creative capacity of users to identify suitable collaborations, where machine-learning algorithms may fall short.

Shaping the Future Through Cybernetic Approaches of Social Media Monitoring

Scientific research and development programs (R&D programs) are national instruments to sustainably secure innovative capability and competitiveness. Due to an increasing rate of change in all societal functional areas, these programs have to be continuously advanced, but also new R&D programs have to be tendered. Prospectively more societal impulses have to be taken into account for the advancement of R&D programs and the ex-ante determination of program contents. Here, the methodical basis is characterized by the analysis of social needs. In terms of substance, sources of Social Media (SM) work out perfectly as data or text corpora: Everyday life is becoming increasingly digitally networked and a large part of interpersonal communication is realized via SM. SM represent a pool of qualitative and quantitative data in order to reflect societal moods. It can be regarded as untouched, raw and unevaluated data. Existing methods of Social Media Monitoring (SMMO) use this information as a basis for trend analysis, issue monitoring and the detection of influencers. SMMO is no temporal specific action, but rather an open-ended task. The conventional application fields of SMMO primarily relate to commercial market research, corporate communications and public relations. In this context SMMO is used with the intent of an overall social and political use, interest or benefit. A new approach is currently being developed by considering methods of system theory and cybernetics. Using this theoretical, system-oriented framework, R&D programs can be constructed as socio-technical, complex living systems. Finally, cybernetic SMMO allows for a continuous and active involvement of the society into politics. It supports program management and research promoters of publicly funded R&D projects by taking into account social impulses for the advancement of R&D programs and the ex-ante determination of program contents. Cybernetic SMMO enables an active shaping of the future according to societal developments, trends and needs.

Operationalizing Regional Innovative Capability

The capability of regions to continuously innovate constitutes a key factor for competitiveness. Regional innovative capability is described through a complex interaction of the dimensions human, organization and technology, which needs to be measured in a differentiated manner. This paper aims at the identification and operationalization of regional innovative capability. The objective is to compile an extended set of indicators to provide a basis for a further development towards a measurement and management tool that enables the more precise evaluation of the innovation capability of a region, as well as statements on sensitive control factors of regional development.

Measuring Regional Innovative Capability – Development of an Extended Set of Indicators

Regional innovative capability is described through a complex interaction of the dimensions human, organization and technology, which needs to be measured in a differentiated manner. The objective of this paper is the development and testing of an extended set of indicators as the basis of a measuring instrument for regional innovative capability. Therefore, three existing approaches provide the basis for the compilation of this extended set. Influenced by fundamental and formal requirements, key indicators as well as certain add-on indicators are identified, which are verified on the example of the Aachen region in Germany (This paper represents the working process and the results of an unpublished master’s thesis by the first author. The full validation of the extended set of indicators on the example of the Aachen region can be provided by the author). The Aachen region shows many distinct characteristics of indicators allowing a reflection of regional innovative capability. This developed set of indicators represents the basis for a further development towards a measurement and management tool that enables the more precise evaluation of the innovation capability of a region, as well as statements on sensitive control factors of regional development.