Nuclear magnetic resonance (NMR) spectroscopy is vital to synthesis and provides rich problem-solving opportunities for organic chemistry students. However, little is known about 1H NMR spectroscopy instruction or how students use spectral features in solving. The goal of this dissertation research was to examine how students learn about and solve 1H NMR spectroscopy problems. Organic chemistry textbooks were analyzed for the ways in which spectral features were introduced and incorporated into worked examples and practice problems. Spectral features like the number of signals and chemical shift were covered by problems more frequently, while integration was covered least. Think-aloud interviews were completed to identify the operators students utilized in their problem-solving processes, and extra credit problem sets were designed and administered to students at three different universities to examine whether students could correctly perform each individual type of operator. While students could perform operators, it was unclear if students knew how and when to use the operators. To fill this knowledge gap, multiple choice assessment questions were developed and administered to students at three different large universities. Coding schemes were developed to identify and describe students’ use of task features and inferences, and regression analyses were completed to discern which areas of reasoning led to success in solving. A majority of students did not identify using any critical spectral features in written explanations. Regression analyses revealed that the inferences students made, and not the task features they paid attention to, were most significantly associated with success in structural predictions; a majority of students made solely correct inferences in their reasoning explanations. When a mixture of correct and incorrect inferences were made, a majority of those students were unable to answer the questions correctly. These findings suggest that students may know enough to solve simple 1H NMR spectroscopy problems, but may lack knowledge about specific spectral features which could impact overall solving success. Students may require considerable support in deciphering the critical features in 1H NMR spectroscopy problems and developing robust, correct inferences across all spectral features.

Across the United States, Human Anatomy and Physiology (HA&P) courses typically have some of the highest withdrawal and failure rates on college campuses. These high enrollment course typically serve as gate-keepers for those individuals with aspirations of entering the medical field. In light of the growing national shortage of healthcare professionals, there is a pressing need to improve the state of HA&P education at a national scale. The goal of this dissertation is to understand why undergraduate students struggle to succeed in HA&P courses. I leveraged multiple frameworks from biology education research, physics education research, and cognitive psychology to understand the source of student difficulty in HA&P. I used a mixed-methods approach to unpack how students reason about the complex phenomena covered in HA&P classes. The data presented here suggest student difficulties in HA&P are not the product of a culmination of individual conceptual difficulties. Rather, this work suggests students have difficulty reasoning with the many complex systems that are at the heart of HA&P curriculum. Students appear to frame these complex systems in a manner that activates reasoning strategies that are often in conflict with course goals. The findings from this work advocate for a dynamic view of student cognition that recognizes the implications of context features on student reasoning of complex systems.

This thesis is about knowing in biology in higher education and research. The gene concept is used as an example of knowledge content that is common to both biological research and education. The purpose is to study how knowing about the gene is expressed in different forms of knowledge contexts at the university. This is important to study in order to understand documented learning problems regarding the gene concept but also to better understand the relation between knowledge in research and teaching. Knowledge has to be transformed to become an educational content, a process that is of special interest within the field of Didaktik. The thesis is based on three qualitative case studies. Study I is an analysis of a textbook in biology. The purpose is to examine the content as presented to the students to see how its structure may contribute to the problems students have. How does the gene concept function as a scientific representation and at the same time as an object for learning in a biology college textbook? A phenomenographic approach is used to study implicit variation in gene concept use when the textbook treats different sub disciplines. The results show conceptual differences between them. The different categories of the gene found–as a trait, an information structure, an actor in the cell, a regulator in embryonic development or as a marker for evolutionary change–mean that we deal with different phenomena. The gene as an object is ascribed different functions and furthermore these functions are intermingled in the textbook. Since, in the textbook, these conceptual differences are not articulated, they likely are a source of confusion when learning about genes. Study II examines the gene concept use in a scientific context, as exemplified by five research articles from a scientific journal. Using an adaptation of Hirst’s criteria for forms of knowledge, the study characterizes how the scientific contexts for the gene concept use vary. What kinds of different gene concept use in these contexts can be discerned? When comparing the articles, it becomes evident that the gene concept is used to answer different kinds of questions. The meanings of the gene concept are connected to various knowledge projects, their purposes and the methods used. Shifts of methodologies and questions entail a concept that escapes single definitions and “slides around” in meanings. These contextual transformations and associated content leaps are here referred to as epistemic drift. Study III follows an integrative research project in biology.  What are the characteristic content conditions for knowledge development? What different ways in using the gene concept can be distinguished? By using the analytic methodology developed in study II, the scientific contexts are categorized according to their knowledge project, methods used and conceptual contexts. The results show that the gene concept meanings and the content vary in focus, are more or less explicitly formulated, or possible to formulate, and consist of different skills. One didactic conclusion is that by being more overt about the conditions for problem solving within a specific subdisciplin (i.e. fruitful questions to ask, knowledge needed to answer them, and methods available), students may be given opportunities to get a broader perspective on what it means to know biology. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript.</p><p> </p>

<p dir="ltr">In 2011, the American Association for the Advancement of Science (AAAS) published a report titled “Vision & Change: A Call to Action” that called for reform in undergraduate biology education. The report proposed core competencies that educators should target so students are graduating ready to tackle 21st-century challenges. Of these core competencies is the ability to reason quantitatively, which includes graphing. However, undergraduate biology students struggle with applying essential graph knowledge. The following dissertation project addresses these challenges by exploring two graphing tasks: constructing versus evaluating graphs. We primarily focused on introductory biology students' reasoning practices in applying graph knowledge between these two tasks. As such, we used a digital performance-based assessment tool, <i>GraphSmarts</i>, to analyze students' graphing choices and their justifications in an ecology-based scenario. Chapter 2 discusses the findings of these analyses (n=301), which revealed a disconnect in graph knowledge application between students' graph construction and evaluation skills. While students tend to create basic bar graphs when constructing graphs, they prefer more sophisticated representations, such as bar graphs with averages and error bars, during evaluation tasks—suggesting that the framing of a task influences students' application of graph knowledge between their recognition of effective data representation and their ability to produce such graphs independently. While insightful, we needed to explore ‘why’ this variation exists. Chapter 3 explores the root of this variation through student interviews (n=12). Students would complete the two tasks, followed by questions that help clarify their thought processes. Through the lens of the Conceptual Dynamics framework and the Dynamic Mental Construct model, the study identified two critical cognitive patterns, ‘mode-switching’ and ‘mode-stability.’ Results reaffirm the context-dependent nature of students' graphing knowledge and the influence of task framing on their reasoning processes, as seen in Chapter 2. Results from this project can inform recommendations that biology educators can consider, including 1) having students conduct multiple types of graphing tasks beyond construction, 2) teaching statistical features more explicitly by integrating them into course content, and 3) encouraging students to reflect on their graphing practices. That would be expected to address these instructional needs and foster characteristics of quantitative reasoning and graphing that transfer out of biology. Future directions on this work include exploring other standard graphing tools (Excel, R studio) on graph knowledge, examining the transferability of graphing skills across biological sub-disciplines, and developing targeted interventions for gaps in students' graphing competencies across various graphing tasks. Overall, the work contributes toward developing evidence-based instructional strategies that will be supportive in cultivating competent, robust quantitative reasoning and graphing skills among undergraduate biology students.</p>