Sociohydrologic Systems Thinking: An Analysis of Undergraduate Students’ Operationalization and Modeling of Coupled Human-Water Systems

One of the keys to science and environmental literacy is systems thinking. Learning how to think about the interactions between systems, the far-reaching effects of a system, and the dynamic nature of systems are all critical outcomes of science learning. However, students need support to develop systems thinking skills in undergraduate geoscience classrooms. While systems thinking-focused instruction has the potential to benefit student learning, gaps exist in our understanding of students’ use of systems thinking to operationalize and model SHS, as well as their metacognitive evaluation of systems thinking. To address this need, we have designed, implemented, refined, and studied an introductory-level, interdisciplinary course focused on coupled human-water, or sociohydrologic, systems. Data for this study comes from three consecutive iterations of the course and involves student models and explanations for a socio-hydrologic issue (n = 163). To analyze this data, we counted themed features of the drawn models and applied an operationalization rubric to the written responses. Analyses of the written explanations reveal statistically-significant differences between underlying categories of systems thinking (F(5, 768) = 401.6, p \u3c 0.05). Students were best able to operationalize their systems thinking about problem identification (M = 2.22, SD = 0.73) as compared to unintended consequences (M = 1.43, SD = 1.11). Student-generated systems thinking models revealed statistically significant differences between system components, patterns, and mechanisms, F(2, 132) = 3.06, p \u3c 0.05. Students focused most strongly on system components (M = 13.54, SD = 7.15) as compared to related processes or mechanisms. Qualitative data demonstrated three types of model limitation including scope/scale, temporal, and specific components/mechanisms/patterns excluded. These findings have implications for supporting systems thinking in undergraduate geoscience classrooms, as well as insight into links between these two skills

Sociohydrologic Systems Thinking: An Analysis of Undergraduate Students’ Operationalization and Modeling of Coupled Human-Water Systems

One of the keys to science and environmental literacy is systems thinking. Learning how to think about the interactions between systems, the far-reaching effects of a system, and the dynamic nature of systems are all critical outcomes of science learning. However, students need support to develop systems thinking skills in undergraduate geoscience classrooms. While systems thinking-focused instruction has the potential to benefit student learning, gaps exist in our understanding of students’ use of systems thinking to operationalize and model SHS, as well as their metacognitive evaluation of systems thinking. To address this need, we have designed, implemented, refined, and studied an introductory-level, interdisciplinary course focused on coupled human-water, or sociohydrologic, systems. Data for this study comes from three consecutive iterations of the course and involves student models and explanations for a socio-hydrologic issue (n = 163). To analyze this data, we counted themed features of the drawn models and applied an operationalization rubric to the written responses. Analyses of the written explanations reveal statistically-significant differences between underlying categories of systems thinking (F(5, 768) = 401.6, p \u3c 0.05). Students were best able to operationalize their systems thinking about problem identification (M = 2.22, SD = 0.73) as compared to unintended consequences (M = 1.43, SD = 1.11). Student-generated systems thinking models revealed statistically significant differences between system components, patterns, and mechanisms, F(2, 132) = 3.06, p \u3c 0.05. Students focused most strongly on system components (M = 13.54, SD = 7.15) as compared to related processes or mechanisms. Qualitative data demonstrated three types of model limitation including scope/scale, temporal, and specific components/mechanisms/patterns excluded. These findings have implications for supporting systems thinking in undergraduate geoscience classrooms, as well as insight into links between these two skills

When Does Disengagement Correlate with Performance in Spoken Dialog Computer Tutoring?

In this paper we investigate how student disengagement relates to two performance metrics in a spoken dialog computer tutoring corpus, both when disengagement is measured through manual annotation by a trained human judge, and also when disengagement is measured through automatic annotation by the system based on a machine learning model. First, we investigate whether manually labeled overall disengagement and six different disengagement types are predictive of learning and user satisfaction in the corpus. Our results show that although students’ percentage of overall disengaged turns negatively correlates both with the amount they learn and their user satisfaction, the individual types of disengagement correlate differently: some negatively correlate with learning and user satisfaction, while others don’t correlate with eithermetric at all. Moreover, these relationships change somewhat depending on student prerequisite knowledge level. Furthermore, using multiple disengagement types to predict learning improves predictive power. Overall, these manual label-based results suggest that although adapting to disengagement should improve both student learning and user satisfaction in computer tutoring, maximizing performance requires the system to detect and respond differently based on disengagement type. Next, we present an approach to automatically detecting and responding to user disengagement types based on their differing correlations with correctness. Investigation of ourmachine learningmodel of user disengagement shows that its automatic labels negatively correlate with both performance metrics in the same way as the manual labels. The similarity of the correlations across the manual and automatic labels suggests that the automatic labels are a reasonable substitute for the manual labels. Moreover, the significant negative correlations themselves suggest that redesigning ITSPOKE to automatically detect and respond to disengagement has the potential to remediate disengagement and thereby improve performance, even in the presence of noise introduced by the automatic detection process

Exploring affect-context dependencies for adaptive system development

We use χ2 to investigate the context dependency of student affect in our computer tutoring dialogues, targeting uncertainty in student answers in 3 automatically monitorable contexts. Our results show significant dependencies between uncertain answers and specific contexts. Identification and analysis of these dependencies is our first step in developing an adaptive version of our dialogue system.

Cultivating Water Literacy in STEM Education: Undergraduates’ Socio-Scientific Reasoning about Socio-Hydrologic Issues

Water-literate individuals effectively reason about the hydrologic concepts that underlie socio-hydrological issues (SHI), but functional water literacy also requires concomitant reasoning about the societal, non-hydrological aspects of SHI. Therefore, this study explored the potential for the socio-scientific reasoning construct (SSR), which includes consideration of the complexity of issues, the perspectives of stakeholders involved, the need for ongoing inquiry, skepticism about information sources, and the affordances of science toward the resolution of the issue, to aid undergraduates in acquiring such reasoning skills. In this fixed, embedded mixed methods study (N = 91), we found SHI to hold great potential as meaningful contexts for the development of water literacy, and that SSR is a viable and useful construct for better understanding undergraduates’ reasoning about the hydrological and non-hydrological aspects of SHI. The breadth of reasoning sources to which participants referred and the depth of the SSR they exhibited in justifying those sources varied within and between the dimensions of SSR. A number of participants’ SSR was highly limited. Implications for operationalizing, measuring, and describing undergraduate students’ SSR, as well as for supporting its development for use in research and the classroom, are discussed

Dynamical Evolution of Globular Cluster Systems formed in Galaxy Mergers: Deep HST/ACS Imaging of Old and Intermediate-Age Globular Clusters in NGC 3610

(ABRIDGED) The ACS camera on board the Hubble Space Telescope has been used to obtain deep images of the giant elliptical galaxy NGC 3610, a well-established dissipative galaxy merger remnant. These observations supersede previous WFPC2 images which revealed the presence of a population of metal-rich globular clusters (GCs) of intermediate age (~1.5-4 Gyr). We detect a total of 580 GC candidates, 46% more than from the previous WFPC2 images. The new photometry strengthens the significance of the previously found bimodality of the color distribution of GCs. Peak colors in V-I are 0.93 +/-0.01 and 1.09 +/- 0.01 for the blue and red subpopulations, respectively. The luminosity function (LF) of the inner 50% of the metal-rich (`red') population of GCs differs markedly from that of the outer 50%. In particular, the LF of the inner 50% of the red GCs shows a flattening consistent with a turnover that is about 1.0 mag fainter than the turnover of the blue GC LF. This is consistent with predictions of recent models of GC disruption for the age range mentioned above and for metallicities that are consistent with the peak color of the red GCs as predicted by population synthesis models. We determine the specific frequency of GCs in NGC 3610 and find a present-day value of S_N = 1.4 +/- 0.6. We estimate that this value will increase to S_N = 3.8 +/- 1.7 at an age of 10 Gyr, which is consistent with typical S_N values for `normal' ellipticals. Our findings constitute further evidence in support of the notion that metal-rich GC populations formed during major mergers involving gas-rich galaxies can evolve dynamically (through disruption processes) into the red, metal-rich GC populations that are ubiquitous in `normal' giant ellipticals.Comment: 15 pages, 14 figures, 4 tables. Accepted for publication in The Astronomical Journal. Figure 6 somewhat degraded to adhere to astro-ph rule

Application of Emotional Design to the Form Redesign of a Midwifery Training Aid

This paper focuses on the form redesign of a midwifery training aid. The training aid needed to represent a pregnant woman as well as having an appearance suitable for a medical device. The redesign was informed by Donald Norman’s Emotional Design Theory in order to explore the design form to combine functionality and technology, as well to gain attention and elicit positive emotional responses from the user. The redesigned prototype was realised using 3D printing and other rapid prototyping technologies. The prototype was exhibited at an international exhibition and feedback from medical simulation experts indicated that the design form was appropriate for the intended purpose

β-Catenin is a pH sensor with decreased stability at higher intracellular pH.

β-Catenin functions as an adherens junction protein for cell-cell adhesion and as a signaling protein. β-catenin function is dependent on its stability, which is regulated by protein-protein interactions that stabilize β-catenin or target it for proteasome-mediated degradation. In this study, we show that β-catenin stability is regulated by intracellular pH (pHi) dynamics, with decreased stability at higher pHi in both mammalian cells and Drosophila melanogaster β-Catenin degradation requires phosphorylation of N-terminal residues for recognition by the E3 ligase β-TrCP. While β-catenin phosphorylation was pH independent, higher pHi induced increased β-TrCP binding and decreased β-catenin stability. An evolutionarily conserved histidine in β-catenin (found in the β-TrCP DSGIHS destruction motif) is required for pH-dependent binding to β-TrCP. Expressing a cancer-associated H36R-β-catenin mutant in the Drosophila eye was sufficient to induce Wnt signaling and produced pronounced tumors not seen with other oncogenic β-catenin alleles. We identify pHi dynamics as a previously unrecognized regulator of β-catenin stability, functioning in coincidence with phosphorylation