Graduation Year

2022

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Biology (Integrative Biology)

Major Professor

Luanna Prevost, Ph.D.

Committee Member

Stephen Deban, Ph.D.

Committee Member

David Lewis, Ph.D.

Committee Member

Jeffrey Raker, Ph.D.

Keywords

Arrows, Machine learning, Scaffolding, Surface Features, Systems

Abstract

To better prepare undergraduate students for current and future biological challenges, scientists, educators and researchers in the Vision and Change report recommended five core conceptual areas essential for the improvement of biological literacy, one of which is biological systems. Systems are an identified core concept that may help promote biological literacy. One example of a system that students have difficulty understanding is an ecological food web, which consists of parts or components interacting with one another to perform a given phenomenon. The intricacies of this system tend to confuse students and can produce naïve conceptions that could hinder future learning and application of more complex ecological phenomena. My research focused on the development of formative written constructive response assessment tools and predictive computer models that could provide insight into student understanding of ecological food web systems.

In chapter two, I developed scaffolded assessment question prompts in order to reveal student understanding of direct and indirect relationships within food webs. Scaffolded question prompts can guide students through the process of answering questions through more structured problem solving and can foster learning and understanding of complex biological systems. My research goal was to examine how scaffolding in assessment question prompts may be used to examine student understanding and potential naïve conceptions related to food webs and the core concept of systems. Four formative written constructed responses assessments were administered to students in a General Biology course at a large southeastern public university. Half the students randomly received a scaffolded version while the other half received an unscaffolded version. Responses were coded and a Chi-square analysis was used to compare scaffolded to unscaffolded versions. My results demonstrated that students provided richer discussions with which to garner a clear understanding of their knowledge for the scaffolded than the unscaffolded version. These results also demonstrated how the more robust explanations in scaffolded responses allowed for in depth analysis to determine how students think about and apply knowledge of food web systems. My results suggested that students may benefit from scaffolded question prompts when presented with formative written assessments. Instructors can utilize scaffolded assessments to assess student knowledge regarding food webs and how students utilize tracing of relationships between parts or components to analyze changes within food webs. Instructors can also use this to identify naïve conceptions held by students in order to address and correct them prior to higher stakes assessments.

In chapter three, I investigated how question surface features affect student responses to ecological food web formative assessment questions. Surface features can serve as tools to retrieve knowledge that can activate certain schema in a student’s working memory and has been shown to influence student responses. Varying the surface features of a question may provide insight into how much influence they impart on student responses and whether responses are expressed differently for various surface features versus others. My research goal was to examine how varying surface features (concrete vs. abstract) in assessment question prompts may be used to examine student understanding and potential naïve conceptions related to food web systems. Four scaffolded questions with varying surface features (letters, pictures, common names) were administered to 160 in a General Biology course at a large southeastern public university over two semesters. Each of the surface features was randomly assigned to one third of the class. Student responses were human coded and analyzed for differences between concrete (pictures and common names) vs. abstract (letters) features and then between the two concrete features using Chi-square analysis. My results demonstrated a difference between concrete and abstract surface feature influence on student responses with picture concrete surface features having the greatest effect on responses. My findings suggested that the usage of concrete surface features, specifically, in assessment can lead to more discussion of scientifically accurate responses and less discussion of inaccurate or naïve conceptions. The use of concrete features allowed students to retrieve previous knowledge on predator-prey relationships in food webs and apply this knowledge to various population dynamic changes within the food web itself. By using picture concrete surface features, instructors can explain feeding relationships between organisms and promote learning of other concepts contained within food webs (such as direct and indirect relationships, prey switching, competition, etc.) and correct potential naïve conceptions that students may come in with to establish a solid foundation with which to build on in future biological courses.

In chapter four, I investigated student interpretation and application of visual representations, specifically arrows, in food web population dynamics. Visual representations are key to understanding and organizing complex biological information and can improve student conceptual learning through promotion of knowledge recall and problem solving. However, in order to interpret visuals, students need to be aware of the various symbols and features contained within the visual but the meaning of these symbols and features are not always clear to students. A lack of understanding of the symbols and features of visuals can possibly block the student’s ability to interpret the information contained within the visual representation. Arrows have been shown to mean different things in different biological systems and have been deemed confusing to understand for students. My research goal was to examine how students interpret the meaning and direction of arrows in ecological food webs along with how they apply this knowledge in direct and indirect effect relationships contained within a food web. Students in a General Biology course at a large southeastern public university were given four scaffolded questions with abstract letter surface features and responses were human coded. An arrow coding category was correlated with all other categories using the Phi correlation coefficient. Five students whose written responses demonstrated arrow reversal were interviewed to determine their interpretation and application of arrows in food web systems. My results demonstrated that a small portion of student responses misinterpreted the direction of arrows. These responses correlated very strongly with both incorrect and correct directions of change observed for direct relationships. Results from interviews demonstrated that students misinterpreted the direction of arrows as pointing from a predator to its prey and that students utilized this misinterpretation to “trace” through designated food chains in a step-by-step manner from one organism to the next. Students also pulled knowledge from other areas of biology and chemistry to reinforce their usage of arrows as a single step-by-step advancement through each organism in a food chain or web. My results suggested that students interpret arrows as being steps in a process or path and may be influenced by previous instruction in biology or other science areas on arrows and their meaning and translate this into interpretation of ecological food chains and webs in which arrows mean something different and are interpreted differently. The usage of arrows in ecological food chains and webs, if interpreted and applied incorrectly, could lead to incorrect directions of change based on direct relationships between a predator and its prey. Instructors should teach students to deconstruct visual representations, their parts and the interaction between these parts. Instructors should clearly explain the meanings of arrows when encountered for any biological concept that utilizes them. Instructors should also reinforce that not all concepts and topics in biology utilize the same meaning or interaction of the arrows.

In chapter five, I developed automated machine learning scoring models to facilitate the evaluation of written formative responses based on direct and indirect relationships of food web systems. Automated scoring through machine learning provides a quick turnaround in providing feedback to instructors and students to support learning and inform pedagogy. Machines are used to analyze patterns in CR written student responses and “learn” from these patterns to build predictive models that can be utilized to assess future responses. Automated scoring can result in models that can code at the same level that a human coder would be able to. My research objective was to build predictive automated scoring models that could mimic human scoring of ecological direct and indirect relationship food web system questions. Four scaffolded questions with abstract letter features were administered to General Biology students across two semesters at a large southeastern public university. Machine scoring was used to build two predictive models that could analyze student thinking about direct and indirect relationships in food web systems with high interrater reliability agreement to human scoring. My results demonstrated that the predictive models for both direct and indirect relationship food web questions was capable of achieving high agreement to human scoring for most categories. Instructors can utilize the feedback generated by models to promote further learning of a concept or to clear up any misconceptions, both of which can promote the correct understanding of the concept. This feedback can also assist instructors in revising pedagogy, and developing assessment to measure student performance of a particular concept more accurately over time.

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