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A design study was conducted to test a machine learning (ML)-enabled automated feedback system developed to support students' revision of scientific arguments using data from published sources and simulations. This paper focuses on three simulation-based scientific argumentation tasks called Trap, Aquifer, and Supply. These tasks were part of an online science curriculum module addressing groundwater systems for secondary school students. ML was used to develop automated scoring models for students' argumentation texts as well as to explore emerging patterns between students' simulation interactions and argumentation scores. The study occurred as we were developing the first version of "simulation feedback" to augment the existing "argument feedback." We studied two cohorts of students who used argument only (AO) feedback (n = 164) versus argument and simulation (AS) feedback (n = 179). We investigated how AO and AS students interacted with simulations and wrote and revised their scientific arguments before and after receiving their respective feedback. Overall, the same percentages of students (49% each) revised their arguments after feedback, and their revised arguments received significantly higher scores for both feedback conditions, p < 0.001. Significantly greater numbers of AS students (36% across three tasks) reran the simulations after feedback as compared with the AO students (5%), p < 0.001. For AS students who reran the simulation, their simulation scores increased for the Trap task, p < 0.001, and for the Aquifer task, p < 0.01. AO students who did not receive simulation feedback but reran the simulations increased simulation scores only for the Trap task, p < 0.05. For the Trap and Aquifer tasks, students who increased simulation scores were more likely to increase argument scores in their revisions than those who did not increase simulation scores or did not revisit simulations at all after simulation feedback was provided. This pattern was not found for the Supply task. Based on these findings, we discuss strengths and weaknesses of the current automated feedback design, in particular the use of ML.

This study addresses the measurement of high school students' epistemic knowledge associated with scientific experimentation (EKSE) which concerns how scientific experimentation generates knowledge and why that knowledge is justified. Based on philosophical, educational standards, and literature analyses, an EKSE construct is characterized as (1) underlying students' decisions and reasoning elicited during experiment design, data collection/measurement, and data analysis/interpretation and (2) progressing in five levels of sophistication: no information, nascent, dogmatic, contextualized, and reflective. This study psychometrically examined the structure of the EKSE construct using the construct modeling approach. An instrument was designed to ask students decisions and reasoning encountered while carrying out the activities embedded in experimental inquiry. High school students took the instrument as a pretest and a posttest to a physics curriculum where they learned to carry out independent scientific experiments. Results indicate that (1) all decisions and reasoning taking place during experiment design, data collection/measurement, and data analysis/interpretation contribute to the EKSE construct, (2) epistemic knowledge develops in the hypothesized order, (3) scientifically aligned decisions are likely to occur when students' epistemic knowledge is at or higher than the dogmatic level, and (4) students' EKSE can improve after engaging in a curriculum that encourages independent scientific experimentation with materials. Implications of these results and further research are discussed. [ABSTRACT FROM AUTHOR]

With the current interest in the rare-earth tellurides as high temperature charge density wave materials, a greater understanding of the physics of these systems is needed, particularly in the case of the ditellurides. We report a detailed study of the band structure of LaTe_2 in the charge density wave state using high-resolution angle resolved photoemission spectroscopy (ARPES). From thiswork we hope to provide insights into the successes and weaknesses of past theoretical study as well as helping to clear up prior ambiguities by providing an experimental basis for future work inthe tellurides.

Angle-resolved photoemission measurements of divalent hexaborides reveals a >1 eV X-point gap between the valence and conduction bands, in contradiction to the band overlap assumed in several models of their novel ferromagnetism. While the global ARPES band structure and gap size observed are consistent with the results of bulk-sensitive soft x-ray absorption and emission boron K-edge spectroscopy, the surface-sensitive photoemission measurements also show a variation with cation, surface and time of the position of the surface chemical potential in the band structure.

A dramatic temperature dependent enhancement of U 5f spectral weight at EF is observed in angle-resolved photoemission measurements of URu_2Si_2 at the center of an X-point hole-pocket. Comparison of this temperature dependent behavior for excitation both at and below the U 5d->5f resonant threshold is presented.

Surface-related effects in angle-resolved valence spectra of CeRu_2Si_2 at the Ce 4d->$5f resonance threshold for different cleaved surfaces are presented and compared to angle-integrated valence spectra and to bulk-sensitive Ce 3d-edge valence spectra. Evidence that Ce 4d-edge photoemission spectroscopy on ideal cleaved surfaces is dominated by bulk Ce 4f states is presented.

Boron K-edge soft x-ray emission and absorption are used to address the fundamental question of whether divalent hexaborides are intrinsic semimetals or defect-doped bandgap insulators. These bulk sensitive measurements, complementary and consistent with surface-sensitive angle-resolved photoemission experiments, confirm the existence of a bulk band gap and the location of the chemical potential at the bottom of the conduction band.