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Past research shows that teachers benefit immensely from reflecting on their classroom practices. At the same time, adaptive and artificially intelligent (AI) tutors are shown to be highly effective for students, especially when teachers are involved in supporting students’ learning. Yet, there is little research on how to support teachers to reflect on their practices around AI tutors. We posit that analytics built on multimodal data from the classroom (e.g., teacher position, student-AI interaction) would be beneficial in providing effective scaffolding and evidence for teachers’ collaborative reflection on human-AI hybrid teaching. To better understand the design opportunities and constraints of a future tool for teacher reflection, we conducted storyboarding sessions with seven in-service teachers. Our analysis revealed that certain modalities (e.g., position v. video) might be more beneficial and less constrained than others in identifying reflection-worthy moments and trends. We discuss teachers’ needs for reflection in classrooms with AI tutors and their boundaries in using multimodal analytics. 

Sc 3 Mn 3 Al 7 Si 5 is a rare example of a correlated metal in which the Mn moments form a kagome lattice. The absence of magnetic ordering to the lowest temperatures suggests that geometrical frustration of magnetic interactions may lead to strong magnetic fluctuations. We have performed inelastic neutron scattering measurements on Sc 3 Mn 3 Al 7 Si 5 , finding that phonon scattering dominates for energies from ∼20–50 meV. These results are in good agreement with ab initio calculations of the phonon dispersions and densities of states, and as well reproduce the measured specific heat. A weak magnetic signal was detected at energies less than ∼10 meV, present only at the lowest temperatures. The magnetic signal is broad and quasielastic, as expected for metallic paramagnets. 

Sc 3 Mn 3 Al 7 Si 5 is a rare example of a correlated metal in which the Mn moments form a kagome lattice. The absence of magnetic ordering to the lowest temperatures suggests that geometrical frustration of magnetic interactions may lead to strong magnetic fluctuations. We have performed inelastic neutron scattering measurements on Sc 3 Mn 3 Al 7 Si 5 , finding that phonon scattering dominates for energies from ∼20–50 meV. These results are in good agreement with ab initio calculations of the phonon dispersions and densities of states, and as well reproduce the measured specific heat. A weak magnetic signal was detected at energies less than ∼10 meV, present only at the lowest temperatures. The magnetic signal is broad and quasielastic, as expected for metallic paramagnets 

Self-healing triboelectric nanogenerators (SH-TENGs) with fast self-healing, high output performance, and wearing comfort have wide and promising applications in wearable electronic devices. This work presents a high-performance hydrogel-based SH-TENG, which consists of a high dielectric triboelectric layer (HDTL), a self-healing hydrogel electrode layer (SHEL), and a physical cross-linking layer (PCLL). Carbon nanotubes (CNTs), obtained by a chemical vapor deposition (CVD) method, were added into polydimethylsiloxane (PDMS) to produce the HDTL. Compared with pure PDMS, the short-circuit transferred charge (44 nC) and the open circuit voltage (132 V) are doubled for PDMS with 0.01 wt% CNTs. Glycerin, polydopamine particles (PDAP) and graphene were added to poly (vinyl alcohol) (PVA) to prepare the self-healing hydrogel electrode layer. SHEL can physically self-heal in ~1 min when exposed to air. The self-healing efficiency reaches up to 98%. The PCLL is made of poly(methylhydrosiloxane) (PMHS) and PDMS. It forms a good physical bond between the hydrophilic hydrogel and hydrophobic PDMS layers. The electric output performance of the SH-TENG can reach 94% of the undamaged one in 1 min. The SH-TENG (6 × 6 cm2) exhibits good stability and superior electrical performance, enabling it to power 37 LEDs simultaneously.