Training self-regulated learning skills with video modeling examples: Do task-selection skills transfer?

Self-assessment and task-selection skills are crucial in self-regulated learning situations in which students can choose their own tasks. Prior research suggested that training with video modeling examples, in which another person (the model) demonstrates and explains the cyclical process of problem-solving task performance, self-assessment, and task-selection, is effective for improving adolescents’ problem-solving posttest performance after self-regulated learning. In these examples, the models used a specific task-selection algorithm in which perceived mental effort and self-assessed performance scores were combined to determine the complexity and support level of the next task, selected from a task database. In the present study we aimed to replicate prior findings and to investigate whether transfer of task-selection skills would be facilitated even more by a more general, heuristic task-selection training than the task-specific algorithm. Transfer of task-selection skills was assessed by having students select a new task in another domain for a fictitious peer student. Results showed that both heuristic and algorithmic training of self-assessment and task-selection skills improved problem-solving posttest performance after a self-regulated learning phase, as well as transfer of task-selection skills. Heurist

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Effects of self-assessment feedback on self-assessment and task-selection accuracy

Effective self-regulated learning in settings in which students can decide what tasks to work on, requires accurate self-assessment (i.e., a judgment of own level of performance) as well as accurate task selection (i.e., choosing a subsequent task that fits the current level of performance). Because self-assessment accuracy is often low, task-selection accuracy suffers as well and, consequently, self-regulated learning can lead to suboptimal learning outcomes. Recent studies have shown that a training with video modeling examples enhanced self-assessment accuracy on problem-solving tasks, but the training was not equally effective for every student and, overall, there was room for further improvement in self-assessment accuracy. Therefore, we investigated whether training with video examples followed by feedback focused on self-assessment accuracy would improve subsequent self-assessment and task-selection accuracy in the absence of the feedback. Experiment 1 showed, contrary to our hypothesis, that self-assessment feedback led to less accurate future self-assessments. In Experiment 2, we provided students with feedback focused on self-assessment accuracy plus information on the correct answers, or feedback focused on self-assessment accuracy, plus the correct answers and the opportunity to contrast those with their own answers. Again, however, we found no beneficial effect of feedback on subsequent self-assessment accuracy. In sum, we found no evidence that feedback on self-assessment accuracy improves subsequent accuracy. Therefore, future research should address other ways improving accuracy, for instance by taking into account the cues upon which students base their self-assessments

Cognitive load, cues, and task selection in learning probability calculus

Abstract presented at the 8th Cognitive Load Theory Conference, Fort Collins, Colorado, USA, June 15th - 17th, 2015

Effects of performance feedback valence on perceptions of invested mental effort

2016 Elsevier Ltd.We investigated whether the valence of performance feedback provided after a task, would affect participants\u27 perceptions of how much mental effort they invested in that same task. In three experiments, we presented participants with problem-solving tasks and manipulated the presence and valence of feedback between conditions (no, positive, or negative feedback valence), prior to asking them to rate how much mental effort they invested in solving that problem. Across the three experiments-with different problem-solving tasks and participant populations-we found that subjective ratings of effort investment were significantly higher after negative than after positive feedback; ratings given without feedback fell in between. These findings show that feedback valence alters perceived effort investment (possibly via task perceptions or affect), which can be problematic when effort is measured as an indicator of cognitive load. Therefore, it seems advisable to measure mental effort directly after each task, before giving feedback on performance

Effects of self-assessment feedback on self-assessment and task-selection accuracy

Effective self-regulated learning in settings in which students can decide what tasks to work on, requires accurate self-assessment (i.e., a judgment of own level of performance) as well as accurate task selection (i.e., choosing a subsequent task that fits the current level of performance). Because self-assessment accuracy is often low, task-selection accuracy suffers as well and, consequently, self-regulated learning can lead to suboptimal learning outcomes. Recent studies have shown that a training with video modeling examples enhanced self-assessment accuracy on problem-solving tasks, but the training was not equally effective for every student and, overall, there was room for further improvement in self-assessment accuracy. Therefore, we investigated whether training with video examples followed by feedback focused on self-assessment accuracy would improve subsequent self-assessment and task-selection accuracy in the absence of the feedback. Experiment 1 showed, contrary to our hypothesis, that self-assessment feedback led to less accurate future self-assessments. In Experiment 2, we provided students with feedback focused on self-assessment accuracy plus information on the correct answers, or feedback focused on self-assessment accuracy, plus the correct answers and the opportunity to contrast those with their own answers. Again, however, we found no beneficial effect of feedback on subsequent self-assessment accuracy. In sum, we found no evidence that feedback on self-assessment accuracy improves subsequent accuracy. Therefore, future research should address other ways improving accuracy, for instance by taking into account the cues upon which students base their self-assessments