Beyond Surveys: Analyzing Software Development Artifacts to Assess Teaching Efforts

This Innovative Practice Full Paper presents an approach of using software development artifacts to gauge student behavior and the effectiveness of changes to curriculum design. There is an ongoing need to adapt university courses to changing requirements and shifts in industry. As an educator it is therefore vital to have access to methods, with which to ascertain the effects of curriculum design changes. In this paper, we present our approach of analyzing software repositories in order to gauge student behavior during project work. We evaluate this approach in a case study of a university undergraduate software development course teaching agile development methodologies. Surveys revealed positive attitudes towards the course and the change of employed development methodology from Scrum to Kanban. However, surveys were not usable to ascertain the degree to which students had adapted their workflows and whether they had done so in accordance with course goals. Therefore, we analyzed students' software repository data, which represents information that can be collected by educators to reveal insights into learning successes and detailed student behavior. We analyze the software repositories created during the last five courses, and evaluate differences in workflows between Kanban and Scrum usage

Towards Using Data to Inform Decisions in Agile Software Development: Views of Available Data

Software development comprises complex tasks which are performed by humans. It involves problem solving, domain understanding and communication skills as well as knowledge of a broad variety of technologies, architectures, and solution approaches. As such, software development projects include many situations where crucial decisions must be made. Making the appropriate organizational or technical choices for a given software team building a product can make the difference between project success or failure. Software development methods have introduced frameworks and sets of best practices for certain contexts, providing practitioners with established guidelines for these important choices. Current Agile methods employed in modern software development have highlighted the importance of the human factors in software development. These methods rely on short feedback loops and the self-organization of teams to enable collaborative decision making. While Agile methods stress the importance of empirical process control, i.e. relying on data to make decisions, they do not prescribe in detail how this goal should be achieved. In this paper, we describe the types and abstraction levels of data and decisions within modern software development teams and identify the benefits that usage of this data enables. We argue that the principles of data-driven decision making are highly applicable, yet underused, in modern Agile software development

An Additional Set of (Automated) Eyes: Chatbots for Agile Retrospectives

Recent advances in natural-language processing and data analysis allow software bots to become virtual team members, providing an additional set of automated eyes and additional perspectives for informing and supporting teamwork. In this paper, we propose employing chatbots in the domain of software development with a focus on supporting analyses and measurements of teams' project data. The software project artifacts produced by agile teams during regular development activities, e.g. commits in a version control system, represent detailed information on how a team works and collaborates. Analyses of this data are especially relevant for agile retrospective meetings, where adaptations and improvements to the executed development process are discussed. Development teams can use these measurements to track the progress of identified improvement actions over development iterations. Chatbots provide a convenient user interface for interacting with the outcomes of retrospectives and the associated measurements in a chat-based channel that is already being employed by team members.Comment: Accepted at the 1st International Workshop on Bots in Software Engineering (May 28th, 2019, Montreal, Canada), collocated with ICSE 2019 (https://botse.github.io/

Adding Value by Combining Business and Sensor Data: An Industry 4.0 Use Case

Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data's full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs.Comment: Accepted at International Conference on Database Systems for Advanced Applications (DASFAA 2019

Landnutzungssysteme und sozio-oekonomische Entwicklung: zur theoretischen Begruendung komparativer Wirtschafts- und Sozialgeschichte

Summary in EnglishAvailable from Bibliothek des Instituts fuer Weltwirtschaft, ZBW, Duesternbrook Weg 120, D-24105 Kiel C 186991 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Lernen durch Kulturkontakt Operationalisierung und Methodik ; Darstellung an Hand eines Einzelfalls

UuStB Koeln(38)-930107746 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Promoting stigma coping and empowerment in patients with schizophrenia and depression: results of a cluster-RCT

There is a need for interventions supporting patients with mental health conditions in coping with stigma and discrimination. A psycho-educational group therapy module to promote stigma coping and empowerment (STEM) was developed and tested for efficacy in patients with schizophrenia or depression. 30 clinical centers participated in a cluster-randomized clinical trial, representing a broad spectrum of mental health care settings: in-patient (acute treatment, rehabilitation), out-patient, and day-hospitals. As randomized, patients in the intervention group clusters/centers received an illness-specific eight sessions standard psychoeducational group therapy plus three specific sessions on stigma coping and empowerment ('STEM'). In the control group clusters the same standard psychoeducational group therapy was extended to 11 sessions followed by one booster session in both conditions. In total,N = 462 patients were included in the analysis (N = 117 with schizophrenia spectrum disorders, ICD-10 F2x;N = 345 with depression, ICD-10 F31.3-F31.5, F32-F34, and F43.2). Clinical and stigma-related measures were assessed before and directly after treatment, as well as after 6 weeks, 6 months, and 12 months (M12). Primary outcome was improvement in quality of life (QoL) assessed with the WHO-QOL-BREF between pre-assessment and M12 analyzed by mixed models and adjusted for pre-treatment differences. Overall, QoL and secondary outcome measures (symptoms, functioning, compliance, internalized stigma, self-esteem, empowerment) improved significantly, but there was no significant difference between intervention and control group. The short STEM module has proven its practicability as an add-on in different settings in routine mental health care. The overall increase in empowerment in both, schizophrenia and depression, indicates patients' treatment benefit. However, factors contributing to improvement need to be explored. The study has been registered in the following trial registers. ClinicalTrials.gov:Registration number: NCT01655368. DRKS:Registration number: DRKS00004217