arXiv subjects

Viranga Perera

Publications and source records attributed to Viranga Perera.

4 recordsLinked to original sources

arXiv2024

Group Dynamics in Inquiry-based Labs: Gender Inequities and the Efficacy of Partner Agreements

Recent studies provide evidence that social constructivist pedagogical methods such as active learning, interactive engagement, and inquiry-based learning, while pedagogically more effective, can enable inequities in the classroom. By conducting a quantitative empirical examination of gender-inequitable group dynamics in two inquiry-based physics labs, we extend results of previous work. Using a survey on group work preferences and video recordings of lab sessions, we find similar patterns of gendered role-taking noted in prior studies. These results are not reducible to differences in students' preferences. We find that an intervention which employed partner agreement forms, with the goal of reducing inequities, had a positive impact on students' engagement with equipment during a first-semester lab course. Our work will inform implementation of more effective interventions in the future and emphasizes challenges faced by instructors who are dedicated to both research-based pedagogical practices and efforts to promote diversity, equity, and inclusion in their classrooms.

physics.ed-ph↗

arXiv2021

Using Machine Learning to Predict Engineering Technology Students' Success with Computer Aided Design

Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students. To encourage CAD programs to build in assistance to students, we used data generated from students using a free, open source CAD software called Aladdin to demonstrate how student data combined with machine learning techniques can predict how well a particular student will perform in a design task. We challenged students to design a house that consumed zero net energy as part of an introductory engineering technology undergraduate course. Using data from 128 students, along with the scikit-learn Python machine learning library, we tested our models using both total counts of design actions and sequences of design actions as inputs. We found that our models using early design sequence actions are particularly valuable for prediction. Our logistic regression model achieved a >60% chance of predicting if a student would succeed in designing a zero net energy house. Our results suggest that it would be feasible for Aladdin to provide useful feedback to students when they are approximately halfway through their design. Further improvements to these models could lead to earlier predictions and thus provide students feedback sooner to enhance their learning.

cs.LG↗

arXiv2018

Effect of Re-impacting Debris on the Solidification of the Lunar Magma Ocean

Anorthosites that comprise the bulk of the lunar crust are believed to have formed during solidification of a Lunar Magma Ocean (LMO) in which these rocks would have floated to the surface. This early flotation crust would have formed a thermal blanket over the remaining LMO, prolonging solidification. Geochronology of lunar anorthosites indicates a long timescale of LMO cooling, or re-melting and re-crystallization in one or more late events. To better interpret this geochronology, we model LMO solidification in a scenario where the Moon is being continuously bombarded by returning projectiles released from the Moon-forming giant impact. More than one lunar mass of material escaped the Earth-Moon system onto heliocentric orbits following the giant impact, much of it to come back on returning orbits for a period of 100 Myr. If large enough, these projectiles would have punctured holes in the nascent floatation crust of the Moon, exposing the LMO to space and causing more rapid cooling. We model these scenarios using a thermal evolution model of the Moon that allows for production (by cratering) and evolution (solidification and infill) of holes in the flotation crust that insulates the LMO. For effective hole production, solidification of the magma ocean can be significantly expedited, decreasing the cooling time by more than a factor of 5. If hole production is inefficient, but shock conversion of projectile kinetic energy to thermal energy is efficient, then LMO solidification can be somewhat prolonged, lengthening the cooling time by 50% or more.

astro-ph.EP↗

arXiv2016

The spherical Brazil Nut Effect and its significance to asteroids

Many asteroids are likely rubble-piles that are a collection of smaller objects held together by gravity and possibly cohesion. These asteroids are seismically shaken by impacts, which leads to excitation of their constituent particles. As a result it has been suggested that their surfaces and sub-surface interiors may be governed by a size sorting mechanism known as the Brazil Nut Effect. We study the behavior of a model asteroid that is a spherical, self-gravitating aggregate with a binary size-distribution of particles under the action of applied seismic shaking. We find that above a seismic threshold, larger particles rise to the surface when friction is present, in agreement with previous studies that focussed on cylindrical and rectangular box configurations. Unlike previous works we also find that size sorting takes place even with zero friction, though the presence of friction does aid the sorting process above the seismic threshold. Additionally we find that while strong size sorting can take place near the surface, the innermost regions remain unsorted under even the most vigorous shaking.

astro-ph.EP↗