Designing Better Scaffolding in Teaching Complex Systems with Graphical Simulations

Complex systems are an important topic in science education today, but they are usually difficult for secondary-level students to learn. Although graphic simulations have many advantages in teaching complex systems, scaffolding is a critical factor for effective learning. This dissertation study was conducted around two complementary research questions on scaffolding: (1) How can we chunk and sequence learning activities in teaching complex systems? (2) How can we help students make connections among system levels across learning activities (level bridging)? With a sample of 123 seventh-graders, this study employed a 3x2 experimental design that factored sequencing methods (independent variable 1; three levels) with level-bridging scaffolding (independent variable 2; two levels) and compared the effectiveness of each combination. The study measured two dependent variables: (1) knowledge integration (i.e., integrating and connecting content-specific normative concepts and providing coherent scientific explanations); (2) understanding of the deep causal structure (i.e., being able to grasp and transfer the causal knowledge of a complex system). The study used a computer-based simulation environment as the research platform to teach the ideal gas law as a system. The ideal gas law is an emergent chemical system that has three levels: (1) experiential macro level (EM)(e.g., an aerosol can explodes when it is thrown into the fire); (2) abstract macro level (AM) (i.e., the relationships among temperature, pressure and volume); (3) micro level (Mi) (i.e., molecular activity). The sequencing methods of these levels were manipulated by changing the order in which they were delivered with three possibilities: (1) EM-AM-Mi; (2) Mi-AM-EM; (3) AM-Mi-EM. The level-bridging scaffolding variable was manipulated on two aspects: (1) inserting inter-level questions among learning activities; (2) two simulations dynamically linked in the final learning activity. Addressing the first research question, the Experiential macro-Abstract macro-Micro (EM-AM-Mi) sequencing method, following the "concrete to abstract" principle, produced better knowledge integration while the Micro-Abstract macro-Experiential macro (Mi-AM-EM) sequencing method, congruent with the causal direction of the emergent system, produced better understanding of the deep causal structure only when level-bridging scaffolding was provided. The Abstract macro-Micro-Experiential macro (AM-Mi-EM) sequencing method produced worse performance in general, because it did not follow the "concrete to abstract" principle, nor did it align with the causal structure of the emergent system. As to the second research question, the results showed that level-bridging scaffolding was important for both knowledge integration and understanding of the causal structure in learning the ideal gas law system

Computational Biology and Chemistry

The use of computers and software tools in biochemistry (biology) has led to a deep revolution in basic sciences and medicine. Bioinformatics and systems biology are the direct results of this revolution. With the involvement of computers, software tools, and internet services in scientific disciplines comprising biology and chemistry, new terms, technologies, and methodologies appeared and established. Bioinformatic software tools, versatile databases, and easy internet access resulted in the occurrence of computational biology and chemistry. Today, we have new types of surveys and laboratories including “in silico studies” and “dry labs” in which bioinformaticians conduct their investigations to gain invaluable outcomes. These features have led to 3-dimensioned illustrations of different molecules and complexes to get a better understanding of nature

BIOINFORMATIC TOOLS FOR NEXT GENERATION GENOMICS

New sequencing strategies have redefined the concept of \u201chigh-throughput sequencing\u201d and many companies, researchers, and recent reviews use the term \u201cNext-Generation Sequencing\u201d (NGS) instead of high-throughput sequencing. These advances have introduced a new era in genomics and bioinformatics\u2060\u2060. During my years as PhD student I have developed various software, algorithms and procedures for the analysis of Nest Generation sequencing data required for distinct biological research projects and collaborations in which our research group was involved. The tools and algorithms are thus presented in their appropriate biological contexts. Initially I dedicated myself to the development of scripts and pipelines which were used to assemble and annotate the mitochondrial genome of the model plant Vitis vinifera. The sequence was subsequently used as a reference to study the RNA editing of mitochondrial transcripts, using data produced by the Illumina and SOLiD platforms. I subsequently developed a new approach and a new software package for the detection of of relatively small indels between a donor and a reference genome, using NGS paired-end (PE) data and machine learning algorithms. I was able to show that, suitable Paired End data, contrary to previous assertions, can be used to detect, with high confidence, very small indels in low complexity genomic contexts. Finally I participated in a project aimed at the reconstruction of the genomic sequences of 2 distinct strains of the biotechnologically relevant fungus Fusarium. In this context I performed the sequence assembly to obtain the initial contigs and devised and implemented a new scaffolding algorithm which has proved to be particularly efficient

The mitochondrial calcium uniporter regulator 1 (MCUR1) matrix domain is a self-associated multimer sensitive to divalent cations

Mitochondria are primarily appreciated for the generation of adenosine triphosphate (ATP), a chemical store of energy required by all cells. These organelles, however, also play key roles in apoptosis, autophagy and shaping cytosolic calcium (Ca2+) signaling via Ca2+ uptake into the mitochondrial matrix. This Ca2+ uptake is mediated chiefly via the mitochondrial Ca2+ uniporter (MCU), an inner mitochondrial membrane protein that oligomerizes to form a Ca2+ selective pore. MCU is regulated by several protein binding partners, including the recently identified MCU regulator-1 (MCUR1). MCUR1 stabilizes a higher order MCU heterocomplex through interactions with MCU and other protein regulators. I hypothesize that the evolutionarily conserved matrix region of MCUR1 contains domains vital for protein and ion interactions which regulate MCU complex formation and function. My biophysical characterization of the MCUR1 matrix domain which includes the coiled-coil domains (i.e. residues 161-338) revealed that this conserved region forms a highly a-helical and self-associated multimer that is conformationally sensitive to divalent cations. Additionally, my solution nuclear magnetic resonance spectroscopy-driven structural elucidation of the MCUR1 matrix region which excludes the coiled-coil domains (i.e. residues 161-209) revealed that this region of MCUR1 forms a compact triple helix which is structurally homologous to the HdeB acid stress chaperone protein despite very low sequence identity. These findings represent the first structural data on MCUR1 and provide a strong framework for future functional studies to assess the significance of MCUR1 oligomerization and ion sensitivity on MCU heterocomplex assembly and activity which has been implicated in numerous cancers as well as metabolic, neurodegenerative and cardiovascular disorders

Glycosaminoglycans: What Remains To Be Deciphered?

Glycosaminoglycans (GAGs) are complex polysaccharides exhibiting a vast structural diversity and fulfilling various functions mediated by thousands of interactions in the extracellular matrix, at the cell surface, and within the cells where they have been detected in the nucleus. It is known that the chemical groups attached to GAGs and GAG conformations comprise “glycocodes” that are not yet fully deciphered. The molecular context also matters for GAG structures and functions, and the influence of the structure and functions of the proteoglycan core proteins on sulfated GAGs and vice versa warrants further investigation. The lack of dedicated bioinformatic tools for mining GAG data sets contributes to a partial characterization of the structural and functional landscape and interactions of GAGs. These pending issues will benefit from the development of new approaches reviewed here, namely (i) the synthesis of GAG oligosaccharides to build large and diverse GAG libraries, (ii) GAG analysis and sequencing by mass spectrometry (e.g., ion mobility-mass spectrometry), gas-phase infrared spectroscopy, recognition tunnelling nanopores, and molecular modeling to identify bioactive GAG sequences, biophysical methods to investigate binding interfaces, and to expand our knowledge and understanding of glycocodes governing GAG molecular recognition, and (iii) artificial intelligence for in-depth investigation of GAGomic data sets and their integration with proteomics

In-Situ Educational Research from Concept to Classroom Implementation: A Multiple Paper Dissertation

An educational researcher sought to collaborate with a classroom instructor to introduce problem-based learning as a new teaching intervention. First, a classroom instructor was approached to consider how a problem-based learning instructional approach might fit with their existing curriculum plan. The researcher and the classroom teacher used a discussion framework to decide together how to best design a professional learning course meant to prepare the teacher to use the new techniques in their classroom. The teacher took the professional learning course and subsequently designed his own problem-based learning course. That course was then delivered to undergraduate students in a college senior thermo-fluids lab course. Quantitative and qualitative data describe how students recognized the connection between the lab course and their perceptions of a future career as engineers. Preliminary findings suggest the researcher and teacher professional learning codesign process contributed positively to the classroom teachers developing and delivering their own PBL course that was perceived by students to contribute positively to their content knowledge, motivation and perception of their future career as engineers

The effect of a professional development program on subject advisors' PCK on the energy concept

Teacher effectiveness is argued to depend on sound pedagogical content knowledge (PCK) at topic level referred to as topic specific (PCK). The development of sound content knowledge for in-service science teachers through professional development (PTD) workshops has been a focus for the South African department of education over the years. The aim of the study was to explore the effect of a PTD programme on the development of subject advisors’ PCK of the energy concept. The rationale for the selection of the energy concept was based on the central role played by the energy concept as a cross-cutting concept in science. The study sought to provide answers to the following main research question: How does a PTD workshop develop the quality of physical science subject advisors’ TSPCK of the energy concept? The study followed a qualitative research approach, based on the post-positivist paradigm and a case study design. A conceptual framework adapted from Mavhunga (2014) and Gess-Newsome (2015) was used which links PCK to five components through which transformation emerges. A sample of fifteen physical science subject advisors from a province in South Africa was conveniently sampled and they completed the pre- and post-assessment CoRes. The participants’ written CoRes were then scored using an expert CoRe and rubric that were designed by the researcher. The validity of the expert CoRe and the rubric were aided by the use of the Rasch model. The model indicated reversed thresholds for some prompts and the researcher had to adjust the rubric until all the prompts indicated ordered thresholds before the final scoring process. To aid to the trustworthiness of the data collected, the researcher employed triangulation, data collection involved a semi-structured interview, document analysis of the workshop study manual and data from the CoRes. Data was interpreted and analysed using content analysis and the results suggested that the subject advisors’ PCK of the energy concept improved after the PTD workshop. The improvement was more noticeable in the TSPCK components that were addressed during the workshop. It is however apparent from the analysis and interpretation that teachers’ TSPCK of the energy concept may be improved through PTD workshops.Dissertation (MEd)--University of Pretoria, 2018.Science, Mathematics and Technology EducationMEdUnrestricte

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings