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This thesis has succeeded in the implementation of N-heterocyclic imino ligands into several novel low-valent silicon compounds with unique electronic and structural features that were utilized to demonstrate prior unprecedented reactivities in the activation of small molecules, such as dihydrogen, ammonia or carbon dioxide. Additionally, with the first reported examples of room temperature stable, three-coordinate and neutral acyclic silanones, the eagerly-awaited dream of isolable silicon analogues of ketones was fulfilled. Im Rahmen dieser Arbeit ist es gelungen, N-heterocyclische Imino-Liganden in mehrere neuartige niedervalente Siliciumverbindungen mit einzigartigen elektronischen und strukturellen Eigenschaften zu integrieren und diese einzusetzen, um bislang unbekannte Reaktivitäten in der Aktivierung kleiner Moleküle wie Wasserstoff, Ammoniak oder Kohlenstoffdioxid zu ermöglichen. Mit den ersten bekannten Beispielen für raumtemperaturstabile, dreifach-koordinierte und neutrale acyclische Silanone konnte zudem der lang ersehnte Traum von isolierbaren Silicium-Analoga von Ketonen verwirklicht werden.

Computerized classification of student answers offers the possibility of instant feedback and improved learning. Open response (OR) questions provide greater insight into student thinking and understanding than more constrained multiple choice (MC) questions, but development of automated classifiers is more difficult, often requiring training a machine learning system with many human-classified answers. Here we explore a novel intermediate constraint question format called WordBytes (WB) where students assemble one-sentence answers to two different college evolutionary biology questions by choosing, then ordering, fixed tiles containing words and phrases. We found WB allowed students to construct hundreds to thousands of different answers ([?]20 tiles), with multiple ways to express correct and incorrect answers with different misconceptions. We found humans could specify rules for an automated WB grader that could accurately classify answers as correct/incorrect with Cohen's kappa [?] 0.88, near the measured intra-rater reliability of two human graders and the performance of machine classification of OR answers (Nehm et al., 2012). Finer-grained classification to identify the specific misconception had lower accuracy (Cohen's kappa < 0.75), which could be improved either by using a machine learner or revising the rules, but both would require considerably more development effort. Our results indicate that WB may allow rapid development of automated correct/incorrect answer classification without collecting and hand-grading hundreds of student answers., The file is in PDF format. If your computer does not recognize it, simply download the file and then open it with your browser.

The recent next generation science standards in the United States have emphasized learning about complex systems as a core feature of science learning. Over the past 15 years, a number of educational tools and theories have been investigated to help students learn about complex systems; but surprisingly, little research has been devoted to identifying the supports that teachers need to teach about complex systems in the classroom. In this paper, we aim to address this gap in the literature. We describe a 2-year professional development study in which we gathered data on teachers’ abilities and perceptions regarding the delivery of computer-supported complex systems curricula. We present results across the 2 years of the project and demonstrate the need for particular instructional supports to improve implementation efforts, including providing differentiated opportunities to build expertise and addressing teacher beliefs about whether computational-model construction belongs in the science classroom. Results from students’ classroom experiences and learning over the 2 years are offered to further illustrate the impact of these instructional supports., National Science Foundation (U.S.). Discovery Research K– 12 (Grant 1019228)

We present a curriculum and instruction framework for computer-supported teaching and learning about complex systems in high school science classrooms. This work responds to a need in K-12 science education research and practice for the articulation of design features for classroom instruction that can address the Next Generation Science Standards (NGSS) recently launched in the USA. We outline the features of the framework, including curricular relevance, cognitively rich pedagogies, computational tools for teaching and learning, and the development of content expertise, and provide examples of how the framework is translated into practice. We follow this up with evidence from a preliminary study conducted with 10 teachers and 361 students, aimed at understanding the extent to which students learned from the activities. Results demonstrated gains in students’ complex systems understanding and biology content knowledge. In interviews, students identified influences of various aspects of the curriculum and instruction framework on their learning., National Science Foundation (U.S.) (1019228)