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The current study measures the extent to which students’ self-regulated learning tactics and learning outcomes change as the result of a deliberate, data-driven improvement in the learning design of mastery-based online learning modules. In the original design, students were required to attempt the assessment once before being allowed to access the learning material. The improved design gave students the choice to skip the first attempt and access the learning material directly. Student learning tactics were measured using a multi-level clustering and process mining algorithm, and a quasi-experiment design was implemented to remove or reduce differences in extraneous factors, including content being covered, time of implementation, and naturally occurring fluctuations in student learning tactics. The analysis suggests that most students who chose to skip the first attempt were effectively self-regulating their learning and were thus successful in learning from the instructional materials. Students who would have failed the first attempt were much more likely to skip it than those who would have passed the first attempt. The new design also resulted in a small improvement in learning outcome and median learning time. The study demonstrates the creation of a closed loop between learning design and learning analytics: first, using learning analytics to inform improvements to the learning design, then assessing the effectiveness and impact of the improvements. 

How animals respond to repeatedly applied stimuli, and how animals respond to mechanical stimuli in particular, are important questions in behavioral neuroscience. We study adaptation to repeated mechanical agitation using theDrosophilalarva. Vertical vibration stimuli elicit a discrete set of responses in crawling larvae: continuation, pause, turn, and reversal. Through high-throughput larva tracking, we characterize how the likelihood of each response depends on vibration intensity and on the timing of repeated vibration pulses. By examining transitions between behavioral states at the population and individual levels, we investigate how the animals habituate to the stimulus patterns. We identify time constants associated with desensitization to prolonged vibration, with re-sensitization during removal of a stimulus, and additional layers of habituation that operate in the overall response. Known memory-deficient mutants exhibit distinct behavior profiles and habituation time constants. An analogous simple electrical circuit suggests possible neural and molecular processes behind adaptive behavior. 

We show that an analogy between crowding in fluid and jammed phases of hard spheres captures the density dependence of the kissing number for a family of numerically generated jammed states. We extend this analogy to jams of mixtures of hard spheres in d = 3 dimensions and, thus, obtain an estimate of the random close packing volume fraction, ϕRCP, as a function of size polydispersity. We first consider mixtures of particle sizes with discrete distributions. For binary systems, we show agreement between our predictions and simulations using both our own results and results reported in previous studies, as well as agreement with recent experiments from the literature. We then apply our approach to systems with continuous polydispersity using three different particle size distributions, namely, the log-normal, Gamma, and truncated power-law distributions. In all cases, we observe agreement between our theoretical findings and numerical results up to rather large polydispersities for all particle size distributions when using as reference our own simulations and results from the literature. In particular, we find ϕRCP to increase monotonically with the relative standard deviation, sσ, of the distribution and to saturate at a value that always remains below 1. A perturbative expansion yields a closed-form expression for ϕRCP that quantitatively captures a distribution-independent regime for sσ < 0.5. Beyond that regime, we show that the gradual loss in agreement is tied to the growth of the skewness of size distributions. 

Success in online and blended courses requires engaging in self-regulated learning (SRL), especially for challenging STEM disciplines, such as physics. This involves students planning how they will navigate course assignments and activities, setting goals for completion, monitoring their progress and content understanding, and reflecting on how they completed each assignment. Based on Winne & Hadwin’s COPES model, SRL is a series of events that temporally unfold during learning, impacted by changing internal and external factors, such as goal orientation and content difficulty. Thus, as goal orientation and content difficulty change throughout a course, so might students’ use of SRL processes. This paper studies how students’ SRL behavior and achievement goal orientation change over time in a large ( N  = 250) college introductory level physics course taught online. Students’ achievement goal orientation was measured by repeated administration of the achievement goals questionnaire-revised (AGQ-R). Students’ SRL behavior was measured by analyzing their clickstream event traces interacting with online learning modules via a combination of trace clustering and process mining. Event traces were first divided into groups similar in nature using agglomerative clustering, with similarity between traces determined based on a set of derived characteristics most reflective of students’ SRL processes. We then generated causal nets for each cluster of traces via process mining and interpreted the underlying behavior and strategy of each causal net according to the COPES SRL framework. We then measured the frequency at which students adopted each causal net and assessed whether the adoption of different causal nets was associated with responses to the AGQ-R. By repeating the analysis for three sets of online learning modules assigned at the beginning, middle, and end of the semester, we examined how the frequency of each causal net changed over time, and how the change correlated with changes to the AGQ-R responses. Results have implications for measuring the temporal nature of SRL during online learning, as well as the factors impacting the use of SRL processes in an online physics course. Results also provide guidance for developing online instructional materials that foster effective SRL for students with different motivational profiles. 

Abstract Metal foam is light in weight and exhibits an excellent impact-absorbing capability. Laser forming has emerged as a promising process in shaping metal foam plates into desired geometry. While the feasibility and shaping mechanism has been studied, the effect of the laser forming process on the mechanical properties and the energy-absorbing behavior in particular of the formed foam parts has not been well understood. This study comparatively investigated such effect on as-received and laser-formed closed-cell aluminum alloy foam. In quasi-static compression tests, attention paid to the changes in the elastic region. Imperfections near the laser-irradiated surface were closely examined and used to help elucidate the similarities and differences in as-received and laser-formed specimens. Similarly, from the impact tests, differences in deformation and specific energy absorption were focused on, while relative density distribution and evolution of foam specimens were numerically investigated.