H.E.A.R.T.

Healthy parenting and family resilience in early childhood has been shown to be an important factor in building emotional resilience for the children: it illustrates that when parents have higher emotional resilience, their children tend to have higher emotional resilience as well. However, the tools that available in the market right now only teach people what emotional resilience rather than how to practice it in daily life. This report describes our project to create a virtual reality tool that can not only teach the importance of emotional resilience, but also help the parents develop personal resilience. The system is based on the VR Empathy Training Tool created by a former senior design project in which the user can interact with a crying child and learn how to handle stress under certain circumstances. The new system will add new features so that it can inform users about their stress level and allow the users to track their progress

Nucleation in sheared granular matter

We present an experiment on crystallization of packings of macroscopic granular spheres. This system is often considered to be a model for thermally driven atomic or colloidal systems. Cyclically shearing a packing of frictional spheres, we observe a first order phase transition from a disordered to an ordered state. The ordered state consists of crystallites of mixed FCC and HCP symmetry that coexist with the amorphous bulk. The transition, initiated by homogeneous nucleation, overcomes a barrier at 64.5% volume fraction. Nucleation consists predominantly of the dissolving of small nuclei and the growth of nuclei that have reached a critical size of about ten spheres

Designing a conversational requirements elicitation system for end-users

[Context] Digital transformation impacts an ever-increasing degree of everyone’s business and private life. It is imperative to incorporate a wide audience of user requirements in the development process to design successful information systems (IS). Hence, requirements elicitation (RE) is increasingly performed by end-users that are novices at contributing requirements to IS development projects. [Objective] We need to develop RE systems that are capable of assisting a wide audience of end-users in communicating their needs and requirements. Prominent methods, such as elicitation interviews, are challenging to apply in such a context, as time and location constraints limit potential audiences. [Research Method] The presented dissertation project utilizes design science research to develop a requirements self-elicitation system, LadderBot. A conversational agent (CA) enables end-users to articulate needs and requirements on the grounds of the laddering method. The CA mimics a human interviewer’s capability to rephrase questions and provide assistance in the process and allows users to converse in their natural language. Furthermore, the tool will assist requirements analysts with the subsequent aggregation and analysis of collected data. [Contribution] The dissertation project makes a practical contribution in the form of a ready-to-use system for wide audience end-user RE and subsequent analysis utilizing laddering as cognitive elicitation technique. A theoretical contribution is provided by developing a design theory for the application of conversational agents for RE, including the laboratory and field evaluation of design principles

Designing AI-Based Systems for Qualitative Data Collection and Analysis

With the continuously increasing impact of information systems (IS) on private and professional life, it has become crucial to integrate users in the IS development process. One of the critical reasons for failed IS projects is the inability to accurately meet user requirements, resulting from an incomplete or inaccurate collection of requirements during the requirements elicitation (RE) phase. While interviews are the most effective RE technique, they face several challenges that make them a questionable fit for the numerous, heterogeneous, and geographically distributed users of contemporary IS. Three significant challenges limit the involvement of a large number of users in IS development processes today. Firstly, there is a lack of tool support to conduct interviews with a wide audience. While initial studies show promising results in utilizing text-based conversational agents (chatbots) as interviewer substitutes, we lack design knowledge for designing AI-based chatbots that leverage established interviewing techniques in the context of RE. By successfully applying chatbot-based interviewing, vast amounts of qualitative data can be collected. Secondly, there is a need to provide tool support enabling the analysis of large amounts of qualitative interview data. Once again, while modern technologies, such as machine learning (ML), promise remedy, concrete implementations of automated analysis for unstructured qualitative data lag behind the promise. There is a need to design interactive ML (IML) systems for supporting the coding process of qualitative data, which centers around simple interaction formats to teach the ML system, and transparent and understandable suggestions to support data analysis. Thirdly, while organizations rely on online feedback to inform requirements without explicitly conducting RE interviews (e.g., from app stores), we know little about the demographics of who is giving feedback and what motivates them to do so. Using online feedback as requirement source risks including solely the concerns and desires of vocal user groups. With this thesis, I tackle these three challenges in two parts. In part I, I address the first and the second challenge by presenting and evaluating two innovative AI-based systems, a chatbot for requirements elicitation and an IML system to semi-automate qualitative coding. In part II, I address the third challenge by presenting results from a large-scale study on IS feedback engagement. With both parts, I contribute with prescriptive knowledge for designing AI-based qualitative data collection and analysis systems and help to establish a deeper understanding of the coverage of existing data collected from online sources. Besides providing concrete artifacts, architectures, and evaluations, I demonstrate the application of a chatbot interviewer to understand user values in smartphones and provide guidance for extending feedback coverage from underrepresented IS user groups

Effective Charges Near 56Ni and Production of Anti-Nuclei Studied with Heavy-Ion Reactions

This thesis presents the research performed within two different research groups using heavy-ion induced nuclear reactions. They offer the opportunity to investigate different properties of nuclear matter. The results are based on measurements using a variety of different experimental techniques. The PHENIX experiment measured the production of deuteron and anti-deuterons at mid-rapidity in gold-gold collisions at the Relativistic Heavy-Ion Collider, RHIC. The invariant yields and transverse momentum spectra are presented. The results are not in agreement with a simple coalescence model with a constant coalescence paramete. Excited states of atomic nuclei were populated using fusion-evaporation reactions. The emitted gamma rays were detected in large multi-detector arrays. One experiment was in conjunction with a plunger device. Lifetimes of analogue states in the A=51 mirror nuclei 51Fe and 51Mn were measured using the recoil distance Doppler shift (RDDS) technique. The deduced B(E2) values make possible an investigation of isoscalar and isovector polarization charges. A comparison between the experimental results and large-scale shell-model calculations yields a quantitative estimate of the effective nucleon charges in the fp-shell

Überidentifikation von Lernstörungen bei Kindern mit Deutsch als Zweitsprache. Implikationen für die Normierung von standardisierten Schulleistungstests

This German prevalence study examined disproportionate representation of language- minority students among children identified with learning disorder (LD) according to ICD-10 (WHO, 1992). Most German school achievement tests used in LD diagnostics do not provide separate norms for language-minority students, and thus do not take these children’s second language status into account when evaluating their academic performance. Although this is likely to result in an LD over identification of language-minority students, little is known about the magnitude of this effect. Therefore, we compared the estimation of LD prevalence between native German speaking students (n = 566) and language-minority students (n = 478) when pooled versus group-specific achievement norms were used for LD classification. Three important findings emerged from our study: Firstly, and as expected, significant disproportionality effects occurred under pooled norms. Specifically, the likelihood of being diagnosed with LD amounted to 14–18 % among native German speakers and nearly doubled to 25–30 % among language-minority students. Secondly, disproportionality varied as a function of LD subtype: Whereas no disproportionate representation was revealed for arithmetic LD (F81.2), overidentification of language-minority students was found for verbal LD subtypes (namely, reading disorder [F81.0], spelling disorder [F81.1], and mixed disorder of scholastic skills [F81.3]). Thirdly, disproportionality effects were absent when group-specific norms were used for LD classification that controlled for second-language issues. Challenges that have to be met when testing language-minority students for LD are discussed. (DIPF/Orig.)Die Prävalenzstudie untersucht bei Kindern, die Deutsch als Muttersprache (DaM) bzw. als Zweitsprache (DaZ) sprechen, die Häufigkeit von Lernstörungen nach ICD-10 (WHO, 1992). Die meisten deutschen Schulleistungstests, die zur Lernstörungsdiagnose herangezogen werden, stellen keine gesonderten Normen für Kinder mit DaZ bereit. Es ist anzunehmen, dass dies zu einer Überidentifikation von Lernstörungen bei Kindern mit DaZ führt, da die besondere Spracherwerbssituation dieser Kinder nicht berücksichtigt wird. Dennoch ist bislang wenig über das Ausmaß dieses Effektes bekannt. Die vorliegende Studie vergleicht daher die Lernstörungsprävalenz zwischen Drittklässlern mit DaM (n = 566) bzw. mit DaZ (n = 478) wenn gemeinsame versus getrennte Schulleistungsnormen zur Leistungsbeurteilung herangezogen werden. Die Studie erbrachte drei wesentliche Ergebnisse: (1) Wie erwartet kam es bei Verwendung gemeinsamer Schulleistungsnormen zu einer deutlichen Erhöhung der Lernstörungsprävalenz bei Kindern mit DaZ. Die Wahrscheinlichkeit einer Lernstörungsdiagnose belief sich für diese Teilstichprobe auf 25–30 % und war damit annähernd doppelt so groß wie bei Kindern mit DaM, für die sich eine Gesamtprävalenz von 14–18 % ergab. (2) Die Gruppenunterschiede variierten dabei in Abhängigkeit des Lernstörungstypus: Während keine signifikant unterschiedlichen Prävalenzraten für die isolierte Rechenstörung (F81.2) nachweisbar waren, zeigten sich für die verbalen Lernstörungstypen (d. h. Lese-Rechtschreibstörung [F81.0], isolierte Rechtschreibstörung [F81.1] und kombinierte Störung schulischer Fertigkeiten [F81.3]) signifikant erhöhte Prävalenzraten für Kinder mit DaZ. (3) Werden hingegen getrennte Schulleistungsnormen zur Lernstörungsdiagnose herangezogen um für die besondere Spracherwerbssituation von Kindern mit DaZ zu kontrollieren, nähern sich die Prävalenzraten beider Gruppen wie erwartet auf ein vergleichbares Niveau an. Es wird diskutiert, welche Herausforderungen sich bei der Lernstörungsdiagnostik von Kindern mit DaZ ergeben. (DIPF/Orig.

LadderBot: A requirements self-elicitation system

Digital transformation impacts an ever-increasing amount of everyone’s business and private life. It is imperative to incorporate user requirements in the development process to design successful information systems (IS). Hence, requirements elicitation (RE) is increasingly performed by users that are novices at contributing requirements to IS development projects. [Objective] We need to develop RE systems that are capable of assisting a wide audience of users in communicating their needs and requirements. Prominent methods, such as elicitation interviews, are challenging to apply in such a context, as time and location constraints limit potential audiences. [Research Method] We present the prototypical self-elicitation system “LadderBot”. A conversational agent (CA) enables end-users to articulate needs and requirements on the grounds of the laddering method. The CA mimics a human (expert) interviewer’s capability to rephrase questions and provide assistance in the process. An experimental study is proposed to evaluate LadderBot against an established questionnaire-based laddering approach. [Contribution] This work-in-progress introduces the chatbot LadderBot as a tool to guide novice users during requirements self-elicitation using the laddering technique. Furthermore, we present the design of an experimental study and outline the next steps and a vision for the future

Cody: An AI-Based System to Semi-Automate Coding for Qualitative Research

Qualitative research can produce a rich understanding of a phenomenon but requires an essential and strenuous data annotation process known as coding. Coding can be repetitive and time-consuming, particularly for large datasets. Existing AI-based approaches for partially automating coding, like supervised machine learning (ML) or explicit knowledge represented in code rules, require high technical literacy and lack transparency. Further, little is known about the interaction of researchers with AI-based coding assistance. We introduce Cody, an AI-based system that semi-automates coding through code rules and supervised ML. Cody supports researchers with interactively (re)defining code rules and uses ML to extend coding to unseen data. In two studies with qualitative researchers, we found that (1) code rules provide structure and transparency, (2) explanations are commonly desired but rarely used, (3) suggestions benefit coding quality rather than coding speed, increasing the intercoder reliability, calculated with Krippendorff’s Alpha, from 0.085 (MAXQDA) to 0.33 (Cody)