How Career and Technical Education Teachers’ Attitudes and Perceptions of Students With Disabilities Influence Inclusion in Career and Technical Education Courses

This qualitative case study investigated how the attitudes and perceptions of Career and Technical Education (CTE) teachers toward students with disabilities influenced inclusion in CTE courses. The purpose of this study was to explore how positive or negative experiences of CTE teachers toward students with disabilities impacted the number of students accessing CTE courses. Fifteen high school CTE teachers, along with a focus group of six high school CTE teachers were interviewed. Results of this study revealed that despite positive attitudes toward inclusion in CTE courses, CTE teachers felt unsupported by special education and reported they felt CTE was being used as a dumping ground by counselors placing students with disabilities into any CTE course to fill students’ schedules. Lack of professional development by special education to provide support to CTE teachers led to frustration. Additionally, study findings indicated that without the skills to educate students with specific disabilities, CTE teachers awarded passing grades of 70, even if the students had not completed the work earning a passing grade. It is recommended further research is needed to investigate the postsecondary outcomes of students with disabilities who were given credit for a CTE course and the rate of success for postsecondary education and employment

How Career and Technical Education Teachers’ Attitudes and Perceptions of Students With Disabilities Influence Inclusion in Career and Technical Education Courses

This qualitative case study investigated how the attitudes and perceptions of Career and Technical Education (CTE) teachers toward students with disabilities influenced inclusion in CTE courses. The purpose of this study was to explore how positive or negative experiences of CTE teachers toward students with disabilities impacted the number of students accessing CTE courses. Fifteen high school CTE teachers, along with a focus group of six high school CTE teachers were interviewed. Results of this study revealed that despite positive attitudes toward inclusion in CTE courses, CTE teachers felt unsupported by special education and reported they felt CTE was being used as a dumping ground by counselors placing students with disabilities into any CTE course to fill students’ schedules. Lack of professional development by special education to provide support to CTE teachers led to frustration. Additionally, study findings indicated that without the skills to educate students with specific disabilities, CTE teachers awarded passing grades of 70, even if the students had not completed the work earning a passing grade. It is recommended further research is needed to investigate the postsecondary outcomes of students with disabilities who were given credit for a CTE course and the rate of success for postsecondary education and employment

Enriching open-world knowledge graphs with expressive negative statements

Machine knowledge about entities and their relationships has been a long-standing goal for AI researchers. Over the last 15 years, thousands of public knowledge graphs have been automatically constructed from various web sources. They are crucial for use cases such as search engines. Yet, existing web-scale knowledge graphs focus on collecting positive statements, and store very little to no negatives. Due to their incompleteness, the truth of absent information remains unknown, which compromises the usability of the knowledge graph. In this dissertation: First, I make the case for selective materialization of salient negative statements in open-world knowledge graphs. Second, I present our methods to automatically infer them from encyclopedic and commonsense knowledge graphs, by locally inferring closed-world topics from reference comparable entities. I then discuss our evaluation fin-dings on metrics such as correctness and salience. Finally, I conclude with open challenges and future opportunities.Machine knowledge about entities and their relationships has been a long-standing goal for AI researchers. Over the last 15 years, thousands of public knowledge graphs have been automatically constructed from various web sources. They are crucial for use cases such as search engines. Yet, existing web-scale knowledge graphs focus on collecting positive statements, and store very little to no negatives. Due to their incompleteness, the truth of absent information remains unknown, which compromises the usability of the knowledge graph. In this dissertation: First, I make the case for selective materialization of salient negative statements in open-world knowledge graphs. Second, I present our methods to automatically infer them from encyclopedic and commonsense knowledge graphs, by locally inferring closed-world topics from reference comparable entities. I then discuss our evaluation fin-dings on metrics such as correctness and salience. Finally, I conclude with open challenges and future opportunities.Wissensgraphen über Entitäten und ihre Attribute sind eine wichtige Komponente vieler KI-Anwendungen. Wissensgraphen im Webmaßstab speichern fast nur positive Aussagen und übersehen negative Aussagen. Aufgrund der Unvollständigkeit von Open-World-Wissensgraphen werden fehlende Aussagen als unbekannt und nicht als falsch betrachtet. Diese Dissertation plädiert dafür, Wissensgraphen mit informativen Aussagen anzureichern, die nicht gelten, und so ihren Mehrwert für Anwendungen wie die Beantwortung von Fragen und die Zusammenfassung von Entitäten zu verbessern. Mit potenziell Milliarden negativer Aussagen von Kandidaten bewältigen wir vier Hauptherausforderungen. 1. Korrektheit (oder Plausibilität) negativer Aussagen: Unter der Open-World-Annahme (OWA) reicht es nicht aus, zu prüfen, ob ein negativer Kandidat im Wissensgraphen nicht explizit als positiv angegeben ist, da es sich möglicherweise um eine fehlende Aussage handeln kann. Von entscheidender Bedeutung sind Methoden zur Prüfung großer Kandidatengruppen, und zur Beseitigung falsch positiver Ergebnisse. 2. Bedeutung negativer Aussagen: Die Menge korrekter negativer Aussagen ist sehr groß, aber voller trivialer oder unsinniger Aussagen, z. B. “Eine Katze kann keine Daten speichern.”. Es sind Methoden zur Quantifizierung der Aussagekraft von Negativen erforderlich. 3. Abdeckung der Themen: Abhängig von der Datenquelle und den Methoden zum Abrufen von Kandidaten erhalten einige Themen oder Entitäten in demWissensgraphen möglicherweise keine negativen Kandidaten. Methoden müssen die Fähigkeit gewährleisten, Negative über fast jede bestehende Entität zu entdecken. 4. Komplexe negative Aussagen: In manchen Fällen erfordert das Ausdrücken einer Negation mehr als ein Wissensgraphen-Tripel. Beispielsweise ist “Einstein hat keine Ausbildung erhalten” eine inkorrekte Negation, aber “Einstein hat keine Ausbildung an einer US-amerikanischen Universität erhalten” ist korrekt. Es werden Methoden zur Erzeugung komplexer Negationen benötigt. Diese Dissertation geht diese Herausforderungen wie folgt an. 1. Wir plädieren zunächst für die selektive Materialisierung negativer Aussagen über Entitäten in enzyklopädischen (gut kanonisierten) Open-World-Wissensgraphen, und definieren formal drei Arten negativer Aussagen: fundiert, universell abwesend und konditionierte negative Aussagen. Wir stellen die Peer-basierte Negationsinferenz-Methode vor, um Listen hervorstechender Negationen über Entitäten zu erstellen. Die Methode berechnet relevante Peers für eine bestimmte Eingabeentität und verwendet ihre positiven Eigenschaften, um Erwartungen für die Eingabeentität festzulegen. Eine Erwartung, die nicht erfüllt ist, ist ein unmittelbar negativer Kandidat und wird dann anhand von Häufigkeits-, Wichtigkeits- und Unerwartetheitsmetriken bewertet. 2. Wir schlagen die Methode musterbasierte Abfrageprotokollextraktion vor, um hervorstechende Negationen aus umfangreichen Textquellen zu extrahieren. Diese Methode extrahiert hervorstechende Negationen über eine Entität, indem sie große Korpora, z.B., die Anfrageprotokolle von Suchmaschinen, unter Verwendung einiger handgefertigter Muster mit negativen Schlüsselwörtern sammelt. 3. Wir führen die UnCommonsense-Methode ein, um hervorstechende negative Phrasen über alltägliche Konzepte in weniger kanonisierten commonsense-KGs zu generieren. Diese Methode ist für die Negationsinferenz, Prüfung und Einstufung kurzer Phrasen in natürlicher Sprache konzipiert. Sie berechnet vergleichbare Konzepte für ein bestimmtes Zielkonzept, leitet aus dem Vergleich ihrer positiven Kandidaten Negationen ab, und prüft diese Kandidaten im Vergleich zum Wissensgraphen selbst, sowie mit Sprachmodellen (LMs) als externer Wissensquelle. Schließlich werden die Kandidaten mithilfe semantischer Ähnlichkeitserkennungshäufigkeitsmaßen eingestuft. 4. Um die Exploration unserer Methoden und ihrer Ergebnisse zu erleichtern, implementieren wir zwei Prototypensysteme. In Wikinegata wird ein System zur Präsentation der Peer-basierten Methode entwickelt, mit dem Benutzer negative Aussagen über 500K Entitäten aus 11 Klassen untersuchen und verschiedene Parameter der Peer-basierten Inferenzmethode anpassen können. Sie können den Wissensgraphen auch mithilfe einer Suchmaske mit negierten Prädikaten befragen. Im UnCommonsense-System können Benutzer genau prüfen, was die Methode bei jedem Schritt hervorbringt, sowie Negationen zu 8K alltäglichen Konzepten durchsuchen. Darüber hinaus erstellen wir mithilfe der Peer-basierten Negationsinferenzmethode den ersten groß angelegten Datensatz zu Demografie und Ausreißern in Interessengemeinschaften und zeigen dessen Nützlichkeit in Anwendungsfällen wie der Identifizierung unterrepräsentierter Gruppen. 5. Wir veröffentlichen alle in diesen Projekten erstellten Datensätze und Quellcodes unter https://www.mpi-inf.mpg.de/negation-in-kbs und https://www.mpi-inf.mpg.de/Uncommonsense

Large Language Models in Finance: A Survey

Recent advances in large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance. In this paper, we provide a practical survey focused on two key aspects of utilizing LLMs for financial tasks: existing solutions and guidance for adoption. First, we review current approaches employing LLMs in finance, including leveraging pretrained models via zero-shot or few-shot learning, fine-tuning on domain-specific data, and training custom LLMs from scratch. We summarize key models and evaluate their performance improvements on financial natural language processing tasks. Second, we propose a decision framework to guide financial professionals in selecting the appropriate LLM solution based on their use case constraints around data, compute, and performance needs. The framework provides a pathway from lightweight experimentation to heavy investment in customized LLMs. Lastly, we discuss limitations and challenges around leveraging LLMs in financial applications. Overall, this survey aims to synthesize the state-of-the-art and provide a roadmap for responsibly applying LLMs to advance financial AI.Comment: Accepted by 4th ACM International Conference on AI in Finance (ICAIF-23) https://ai-finance.or

What Happens When the Green New Deal Meets the Old Green Laws?

The multi-faceted infrastructure goals of the Green New Deal will be impossible to achieve in the desired time frames if the existing federal, state, and local siting and environmental protection statutory regimes are applied. Business, labor, property rights, environmental protection, and social justice interests will use them to grind the Green New Deal to a snail\u27s pace. Using the renewable energy transition as the infrastructure case study, this Essay is a call to arms for the need to design New Green Laws for the Green New Deal. Part I briefly summarizes what we are learning about the pace and magnitude of climate change impacts and the need for rapid and robust mitigation and adaptation responses. Part II demonstrates the magnitude and urgency of new renewable energy infrastructure needed to fulfill Green New Deal goals. Part III points to the intensity of pushback that renewable energy has faced under existing siting and environmental protection laws. Part IV uses the Texas wind power experience to argue that mobilizing the Green New Deal energy transition will require resolving significant trade- offs regarding environmental protection, property rights, process, and sovereignty. Ultimately, for the Green New Deal to succeed in its renewable energy (and other) infrastructure agendas, siting and environmental protection regulatory regimes will need to tolerate more streamlined, top- down, preemptive processes, as well as extensive use of eminent domain powers, which necessarily will require new ways of satisfying demands for distributive justice and public participation