Assessing Visual Science Literacy Using Functional Near-Infrared Spectroscopy with an Artificial Neural Network

The primary barrier to understanding visual and abstract information in STEM fields is representational competence the ability to generate, transform, analyze and explain representations. The relationship is known between the foundational visual literacy and the domain specific science literacy, however how science literacy is a function of science learning is still not well understood despite investigation across many fields. To support the improvement of students’ representational competence and promote learning in science, identification of visualization skills is necessary. This project details the development of an artificial neural network (ANN) capable of measuring and modeling visual science literacy (VSL) via neurological measurements using functional near infrared spectrometry (fNIRS). The developed model has the capacity to classify levels of scientific visual literacy allowing educators and curriculum designers the ability to create more targeted and immersive classroom resources such as virtual reality, to enhance the fundamental visual tools in science.

Learning with Friction—Students’ Gestures and Enactment in Relation to a GeoGebra Simulation

The present study contributes to the understanding of physics students’ representational competence by examining specific bodily practices (e.g. gestures, enactment) of students’ interaction and constructions of representations in relation to a digital learning environment. We present and analyse video data of upper-secondary school students’ interaction with a GeoGebra simulation of friction. Our analysis is based on the assumption that, in a collaborative learning environment, students use their bodies as means of dealing with interpretational problems, and that exploring students’ gestures and enactment can be used to analyse their sensemaking processes. This study shows that specific features of the simulation—features connected with microscopic aspects of friction—triggered students to ask what-if and why questions and consequently, to learn about the representation. During this sense-making process, students improvised their own representations to make their ideas more explicit. The findings extend current research on students’ representational competence by bringing attention to the role of students’ generation of improvised representations in the processes of learning with and about representations.

Modeling the Theory-Form: Beyond the Elements of Observational Postulation

We formalize a theory of the subject by sketching a pragmatic functional hierarchy of sapient cognition. Our expanded framework attempts to articulate a normative understanding of discursive cognition by demarcating its functional propriety within a naturalist rejoinder, seeing in the functional development of cognition from pre-discursive to discursive abilities an increase and refinement in representational competence found in non-intentional systems. We therein explain how sapient cognitive systems not only engage in patterns of material and formal inference to map intensional relations between phenomena in nature through theoretical and practical reasonings, but also engage in practices of theoretical construction and systematic integration through techniques of formalization that make the unity of nature and thought progressively intelligible. We trace the development of mind in its representational function from barren discriminatory capacities, shared with inanimate objects, to complex theory-forming systematizing conceptual abilities enabling agents to theoretically map and intervene upon the world of which they are part, and to embed the informational indexes they register from the environment that makes globally explicit the objective modal structure of the world.

Chemistry instructors’ intentions toward developing, teaching, and assessing student representational competence skills

Representational competence is one's ability to use disciplinary representations for learning, communicating, and problem-solving. These skills are at the heart of engagement in scientific practices and were recognized by the ACS Examinations Institute as one of ten anchoring concepts. Despite the important role that representational competence plays in student success in chemistry and the considerable number of investigations into students’ ability to reason with representations, very few studies have examined chemistry instructors’ approaches toward developing student representational competence. This study interviewed thirteen chemistry instructors from eleven different universities across the US about their intentions to develop, teach, and assess student representational competence skills. We found that most instructors do not aim to help students develop any representational competence skills. At the same time, participants’ descriptions of their instructional and assessment practices revealed that, without realizing it, most are likely to teach and assess several representational competence skills in their courses. A closer examination of these skills revealed a focus on lower-level representational competence skills (e.g., the ability to interpret and generate representations) and a lack of a focus on higher-level meta-representational competence skills (e.g., the ability to describe affordances and limitations of representations). Finally, some instructors reported self-awareness about their lack of knowledge about effective teaching about representations and the majority expressed a desire for professional development opportunities to learn about differences in how experts and novices conceptualize representations, about evidence-based practices for teaching about representations, and about how to assess student mastery of representational competence skills. This study holds clear implications for informing chemistry instructors’ professional development initiatives. Such training needs to help instructors take cognizance of relevant theories of learning (e.g., constructivism, dual-coding theory, information processing model, Johnstone's triangle), and the key factors affecting students’ ability to reason with representations, as well as foster awareness of representational competence skills and how to support students in learning with representations.