The 10 most recently published documents
Cross Cultural Adaptation of Design Thinking in Entrepreneurship Higher Education in Indonesia
(2024)
Entrepreneurship and entrepreneurship education have expanded together, and their conceptual and methodological challenges do not prevent the implementation of entrepreneurial education in educational contexts. The desire for a global workforce that can handle uncertainties and solve problems that cannot be solved by pure analytical inquiry drives the rapidly expanding number of educational programs and activities that are design-based. A growing number of educational programs for entrepreneurs increasingly incorporate design-based methods. However, design thinking-based theoretical assumptions may also be lacking. Despite growing academic interest in design thinking and entrepreneurship education, little is known about design thinking in higher entrepreneurship education, especially in Eastern nations. A Western teaching method, entrepreneurial design thinking may be adapted to many cultures. In this instance, the West has established entrepreneurship education as a respectable study subject and teaching practice in higher education over the past 40 years. The Eastern nations' occurrence varies, including Indonesia. Indonesia is an intriguing research subject since it has over 50% youth due to its abundant natural resources. However, it needs more opportunity-based entrepreneurs and requires assistance in implementing entrepreneurship education with a more innovative, design-based, and successful method. Entrepreneurial design thinking fulfills this demand. Indonesian students and teachers' norm-based attitudes and cultural mindsets towards a new western creative method may hinder entrepreneurial design thinking's acceptance. The literature review found that Indonesian university students are collaborative, compassionate, and practical, like design thinkers. However, they may also be risk-averse, self-restrained, and dependent on teachers as stereotypical Asians. Classroom space, educators' design thinking competence, and university or institution support are further barriers. Additional study into these challenges is needed to adapt design thinking to Indonesian entrepreneurial higher education culturally.
Based on the above research needs, the purpose of this research endeavor is to look into the cultural nuances of the design thinking technique for entrepreneurial higher education and postulate how it could be adapted to other cultures, especially in Indonesia. This thesis uses deductive and qualitative case-study research methods. In particular, the latter used thematic analysis (Braun & Clarke, 2006; Terry & Hayfield, 2021) as the data analysis technique to provide a means and tools for understanding from interviews, class observation, and literature studies. Since this thesis follows the constructivist-relativist research paradigm, it explores contextual and cultural differences in Indonesian entrepreneurship education and its potential and obstacles to adapt the Western teaching methodology of entrepreneurial design thinking in higher education. In summary, this study searches for elements that might aid or hinder the cross-cultural adaption of entrepreneurial design thinking. This research wants to understand how cross-cultural adaptability fits into entrepreneurial design thinking research, especially for Indonesia. This thesis aims to provide new theoretical insights and practical advice on adapting entrepreneurial design thinking from Western to Eastern cultures.
From the findings, this thesis concluded at least seven educational value differentials before adaptation from the exhaustive literature and case study evaluations. For Indonesian entrepreneurship higher education institutions to use entrepreneurial design thinking, they must consider educational culture, technological infrastructure, language, primary audience, learning and teaching style, reasoning patterns, and social-cultural environment. This study provides four practical adaptation recommendations: socialization, externalization, combination, and internalization. Finally, this research demonstrated that cross-cultural adaption of entrepreneurial design thinking in Indonesia might be difficult but worthwhile. This thesis' case study, "School of Business Management – Bandung Institute of Technology (SBM ITB)", showed that Indonesian entrepreneurial higher education might use design thinking as a teaching approach. All stakeholders must improve internally and publicly. Thus, this study recommended integrating most Indonesian higher education institutions' entrepreneurship teaching approaches with a "student-centered" approach that stresses business mentorship, uses design thinking tools and processes, and links them to students' entrepreneurial initiatives.
To summarize, this research contributes to the field since it draws on and combines the findings of several other fields of study, including entrepreneurial education, design thinking, and cross-cultural adaptation. This study stepped out of the "usual and proper" pedagogical ruts to investigate "non-human" cross-cultural adaptability. It has attempted to apply these ideas to a real-world, unique case study in a developing nation (in this case, Indonesia).
This thesis deals with the conception and implementation of a prototype emulator software that can be used to play a broad range of Game Boy games on a conventional desktop computer. The development of such an application is a technically demanding task defined by various challenges such as the correct interpretation of machine instructions, graphics emulation, as well as playability and correctness. As there is no accessible official documentation of Game Boy hardware, the emulator was developed based on the knowledge amassed by Game Boy reverse engineers. Nevertheless, an emulator was developed that can already run a large selection of games. The correctness of the implemented components was verified using dedicated test programs.
Enterprise Collaboration Systems (ECS) sind essentielle Werkzeuge für die Unterstützung der digitalen Zusammenarbeit und ad hoc Projektarbeit in Unternehmen. Mit der zunehmenden Nutzung von ECS steigen auch die Bedeutung und der Bedarf an Analysen zur Schaffung eines verbesserten Verständnisses von digitaler Arbeit. Da Kollaboration sich regelmäßig über mehr als ein System erstreckt, stellt die Heterogenität der Daten verschiedener Systeme für Analysen eine Herausforderung dar, insbesondere weil plattformübergreifende Nutzungsmuster nicht ohne Weiteres nachverfolgbar und vergleichbar sind. Daher wurde die „Collaborative Actions on Documents Ontology“ (ColActDOnt) entwickelt, um Konzepte aus ECS (z. B. Events und Dokumente) einheitlich zu beschreiben. Basierend auf der ColActDOnt wurde ein Datastore implementiert, in welchem die Daten verschiedener Systeme in der Struktur der Ontologie gespeichert werden. Durch den ontologiebasierten Datastore stehen die Daten für Analysen in einheitlicher Form zur Verfügung. In die Datenebene wurden Daten aus dem ECS HCL Connections (CNX), welches somit das initiale Quellsystem darstellt, importiert. Mittels der ColActDOnt wurde außerdem eine abstrakte Ontologieebene mit den Elementen der Ontologie geschaffen. Die Elemente beider Ebenen sind über Beziehungen innerhalb einer Graphdatenbank miteinander und untereinander verknüpft.
Der ontologiebasierte Datenzugriff ermöglicht es dem Benutzer ohne Kenntnisse über die Datenstrukturen des Quellsystems, lediglich mit Domänenwissen über die ColActDOnt, Inhalte abzufragen.
Der Datastore ist als Graphdatenbank (Neo4j) implementiert und somit können Abfragen nativ als Graphenstrukturen visualisiert werden. Weiterhin kann der Prototyp an Business Intelligence Tools wie Microsoft PowerBI angebunden werden und bietet somit die Möglichkeit für tiefergehende Analysen. Die erste Version des Datastores stellt einen wichtigen Schritt in Richtung der Harmonisierung von Trace Data aus ECS dar. In Zukunft sollen weitere Kollaborationssysteme an den Datastore angeschlossen werden, um systemübergreifende Analysen von komplexen Kollaborationsplattformen zu ermöglichen.
The political targets for CO2 reduction in industrial processes are leading to a technological change in the area of pig iron production. In future, pig iron will be produced by using the direct reduction process instead of the blast furnace process. Direct reduction plants are currently operated with natural gas, this is to be replaced by hydrogen in the future in order to meet the climate targets. Within this work, the influence of hydrogen-containing atmospheres on currently used refractory materials from the Al2O3-SiO2 system was investigated. An experiment was developed to simulate the corrosion of refractory materials in the laboratory under realistic test conditions. Taking into account the atmosphere, the temperature and the sample material, a variety of practical corrosion tests were carried out. By applying a comprehensive analysis strategy, relevant corrosion effects on the materials were subsequently described as a result of the gas composition. The test temperature was in the range of 716 °C < T < 1150 °C. Physical and chemical-mineralogical tests were used to investigate the corrosion effects. In addition, the intensity of the corrosion effects was evaluated based on the gas compositions used. Pure hydrogen atmospheres in particular led to strong gas corrosion, while the presence of water vapor inhibited the chemical reactions. The mixture of methane and hydrogen can create an aggressive H2 / CO atmosphere, which also can lead to the formation of solid carbon. This phenomenon changes the possible causes of damage to refractory material; the crystallization pressure of carbon inside the structure of the refractory can also contribute to material failure. Furthermore, the corrosion reactions could be described by coupling imaging analysis methods and element determination. It was shown that, in contrast to the general opinion in the state of the art, there was not exclusively a decrease in SiO2-amount. Several reactions took place in the investigated, industrially used materials, which led to the local chemical attack of SiO2 (silicate glass phase) and caused a parallel crystallization of cristobalite. The chemical attack of hydrogen on the silicate glass phase can be defined as the primary corrosion reaction in the range of 716 °C < T < 1150 °C in a pure hydrogen atmosphere. In addition, the reaction kinetics as a function of temperature were experimentally investigated and described. Based on these analyses, material properties can be defined that are particularly suitable for the future use of defined refractory qualities within reduction processes.
Zweiunddreißigste Ordnung zur Änderung der Prüfungsordnung für die Prüfung im lehramtsbezogenen Bachelorstudiengang an der Universität Koblenz
Achtundzwanzigste Ordnung zur Änderung der Prüfungsordnung für die Prüfung in den Masterstudiengängen für das Lehramt an Grundschulen, das Lehramt an Realschulen plus sowie das Lehramt an Gymnasien an der Universität Koblenz
Neunundzwanzigste Ordnung zur Änderung der Ordnung für die Prüfung im lehramtsbezogenen Zertifikatsstudiengang (Erweiterungsprüfung) an der Universität Koblenz und der Hochschule Koblenz
Vierte Ordnung zur Änderung der Gemeinsamen Prüfungsordnung für die Bachelor- und Masterstudiengänge des Fachbereichs Informatik an der Universität Koblenz
Gemeinsame Prüfungsordnung für den Masterstudiengang Master of Engineering „Ceramic Science and Engineering“ an der Hochschule Koblenz und der Universität Koblenz (Kooperativer Masterstudiengang)
Wahlordnung der Studierendenschaft der Universität Koblenz
Satzung zur Festsetzung von Zulassungszahlen an der Universität Koblenz für das Studienjahr 2024/2025
Satzung zur Festsetzung der Normwerte für den Ausbildungsaufwand (Curricularnormwerte) der Universität Koblenz
Zweite Ordnung zur Änderung Ordnung für die Prüfung im Masterstudiengang Applied Physics an der Hochschule Koblenz und der Universität Koblenz (Kooperativer Masterstudiengang)
The findings of this study demonstrate that the Random Forest (RF) algorithm provided the most accurate predictions in comparison with other boosting machine learning algorithms. Key drivers of energy consumption identified through XAI techniques such as SHAP and LIME include energy star rating, facility type, and floor area. These XAI methods helped enhance the interpretability of the models, making them more accessible for non-expert users, such as building managers and policymakers. By leveraging machine learning and XAI, this research provides a transparent and actionable framework for optimizing building energy efficiency and supporting sustainable energy management.
In the realm of education, the timely identification of students who need further support to succeed in their respective courses, plays a pivotal role in fostering aca- demic success and preventing potential setbacks. This thesis thus aims to contribute to this critical area by focusing on the development of predictive models for the early detection of at-risk students in their academic journey. The primary dataset used for this thesis is provided by kaggle, encompassing diverse student informa- tion, including demographic, socio-economic factors, and academic performance categorized into three different classes, presenting an imbalanced nature that poses a significant challenge.
Thus the primary objectives of this thesis are to address the problem of imbal- anced data, explore and assess the performance of multiple classification methods such as, logistic regression, decision tress, random forests and support vector ma- chines (SVM), neural networks, and create a comprehensive end-to-end processing pipeline which includes the systematic steps of balancing the data, model training and evaluation. Additionally the developed pipeline is tested on two additional datasets to assess its generalizability and robustness. This research aims to provide a comprehensive understanding of addressing the challenges of imbalanced data and how different classification methods and regression can be optimally applied to early detection of at-risk students. The findings are expected to aid educational institutions in supporting their students and enhancing academic success through timely interventions.
Key findings demonstrates the robustness of SVM SMOTE balancing technique acro- ss the datasets used in this study, where it consistently achieved best results when combined with various models, particularly highlighting the success of the combi- nation of Random Forest model with SVM SMOTE, and Decision tree model with SVM SMOTE in achieving notable accuracy rates. This emphasizes the adaptability of the balancing techniques employed, providing a strong foundation for predictive intervention educational settings.
Die vorliegende Dissertation hat sich unter dem Titel >>„Woher soll ich denn vorher wissen, ob ich den Job liebe?“ – Eine qualitative Längsschnitt-Studie zur Differenzierung der Phasenstruktur vorliegender Modelle beruflicher Orientierung anhand von Jugendlichen aus drei kontrastierenden institutionellen Ausgangslagen<< dem Erkenntnisinteresse gewidmet, Modelle zur beruflichen Orientierung anhand eines ethnografischen Zugangs aus der Perspektive unterschiedlicher institutioneller Settngs weiterzuentwickeln, um der Komplexität gegenwärtiger Lebenswelten besser gerecht zu werden. Dabei wurde unter Betrachtung von Berufsorientierungsaspekten als Forschungsgegenstand sowie unter Betrachtung der Gestalt der Berufsorientierung als Forschungsfeld der Fragestellung nachgegangen, inwiefern sich das in der Debatte zentrale Sechs-Phasenmodell der Berufswahl nach Herzog et. al. (2006) im Licht ethnografischer Forschung, welche die Perspektiven Jugendlicher eines allgemeinbildenden Gymnasiums, eines beruflichen Gymnasiums sowie eines Freiwilligen Sozialen Jahres umfasst, ausdifferenzieren lässt. Dabei wurde die Grounded Theory-Methodologie als Forschungsstil zugrunde gelegt, der wiederum im Rahmen der Datenerhebung die Ethnografie, im Rahmen der Datenanalyse die Grounded Theory untergeordnet wurde.
Als zentrales Untersuchungsergebnis lässt sich zum einen aufführen, dass das bestehende Berufswahlmodell deutlich modifiziert, ausdifferenziert und folglich erweitert werden konnte. Mit dieser Erweiterung ist eine Spezifikation dahingehend verbunden, dass viele Komponenten und Ebenen hinzugekommen sind, die bei dem ursprünglichen Modell nicht bedacht waren, im Gegenzug jedoch auch solche Komponenten gestrichen und als irrelevant deklariert wurden, die sich anhand der Untersuchungsdaten nicht zu bestätigen wussten. So konnte ein Neun-Phasenmodell der Berufsorientierung in Gestalt eines Ablaufdiagramms
entstehen, das den Prozessverlauf beruflicher Orientierung anschaulich in Phasen eingebettet darzustellen vermag. Dabei ist es gelungen, das Modell derart auszugestalten, dass es institutionsübergreifend anwendbar und nicht lediglich auf eine bestimmte institutionelle Ausgangslage beschränkt ist. Zum anderen kann statuiert werden, dass das vorliegende neunphasige Modell vor allem Phasen der Orientierungslosigkeit und der Desorientierung, des Entscheidungsaufschubs, der Überprüfung und der Überbrückung sowie der Um- und Neuorientierung explizit zu nutzen weiß, um diejenigen Situationen zu berücksichtigen, die den beruflichen Orientierungsprozess der Untersuchungsteilnehmenden aus allen drei
institutionellen Ausgangslagen maßgeblich geprägt haben.
Assessing ChatGPT’s Performance in Analyzing Students’ Sentiments: A Case Study in Course Feedback
(2024)
The emergence of large language models (LLMs) like ChatGPT has impacted fields such as education, transforming natural language processing (NLP) tasks like sentiment analysis. Transformers form the foundation of LLMs, with BERT, XLNet, and GPT as key examples. ChatGPT, developed by OpenAI, is a state-of-the-art model and its ability in natural language tasks makes it a potential tool in sentiment analysis. This thesis reviews current sentiment analysis methods and examines ChatGPT’s ability to analyze sentiments across three labels (Negative, Neutral, Positive) and five labels (Very Negative, Negative, Neutral, Positive, Very Positive) on a dataset of student course reviews. Its performance is compared with fine tuned state-of-the-art models like BERT, XLNet, bart-large-mnli, and RoBERTa-large-mnli using quantitative metrics. With the help of 7 prompting techniques which are ways to instruct ChatGPT, this work also analyzed how well it understands complex linguistic nuances in the given texts using qualitative metrics. BERT and XLNet outperform ChatGPT mainly due to their bidirectional nature, which allows them to understand the full context of a sentence, not just left to right. This, combined with fine-tuning, helps them capture patterns and nuances better. ChatGPT, as a general purpose, open-domain model, processes text unidirectionally, which can limit its context understanding. Despite this, ChatGPT performed comparably to XLNet and BERT in three-label scenarios and outperformed others. Fine-tuned models excelled in five label cases. Moreover, it has shown impressive knowledge of the language. Chain-of-Thought (CoT) was the most effective technique for prompting with step by step instructions. ChatGPT showed promising performance in correctness, consistency, relevance, and robustness, except for detecting Irony. As education evolves with diverse learning environments, effective feedback analysis becomes increasingly valuable. Addressing ChatGPT’s limitations and leveraging its strengths could enhance personalized learning through better sentiment analysis.
