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This work-in-progress research paper describes a study of different categorical data coding procedures for machine learning(ML) in engineering education. Often left out of methodology sections, preprocessing steps in data analysis can have important ramifications on project outcomes. In this study, we applied three different coding schemes (i.e., scalar conversion, one-hot encoding, and binary) for the categorical variable of Race across three different ML models (i.e., Neural Network, Random Forest, and Naive Bayes classifiers) looking at the four standard measures of ML classification models (i.e., accuracy, precision, recall, and F1-score). Results showed that, in general, the coding scheme did not affect predictive outcomes as much as ML model type did. However, one-hot encoding – the strategy of transforming a categorical variable with k possible values to k binary nodes, a common practice in educational research – does not work well with a Naive Bayes classifier model. Our results indicate that such sensitivity studies at the beginning of ML modeling projects are necessary. Future work includes performing a full range of sensitivity studies on our complete, grant-funded project dataset that has been collected, and publishing our findings. 

STEM undergraduate instructors teaching remote courses often use traditional lecture-based instruction, despite evidence that active learning methods improve student engagement and learning outcomes. One simple way to use active learning online is to incorporate exploratory learning. In exploratory learning, students explore a novel activity (e.g., problem solving) before a lecture on the underlying concepts and procedures. This method has been shown to improve learning outcomes during in-person courses, without requiring the entire course to be restructured. The current study examined whether the benefits of exploratory learning extend to a remote undergraduate physics lesson, taught synchronously online. Undergraduate physics students (N = 78) completed a physics problem-solving activity either before instruction (explore-first condition) or after (instruct-first condition). Students then completed a learning assessment of the problem-solving procedures and underlying concepts. Despite lower accuracy on the learning activity, students in the explore-first condition demonstrated better understanding on the assessment, compared to students in the instruct-first condition. This finding suggests that exploratory learning can serve as productive failure in online courses, challenging students but improving learning, compared to the more widely-used lecture-then-practice method. 

This WIP paper presents new research on exploratory learning, an educational technique that reverses the order of standard lecture-based instruction techniques. In exploratory learning, students are presented with a novel activity first, followed by instruction. Exploratory learning has been observed to benefit student learning in foundational math and science courses such as calculus, physics, and statistics; however, it has yet to be applied to engineering topics such as programming. In two studies, we tested the effectiveness of exploratory learning in the programming unit of a first-year undergraduate engineering course. We designed a new activity to help students learn about different python error types, ensuring that it would be suitable for exploration. Then we implemented two different orders (the traditional instruct-first versus exploratory learning’s explore-first) across the six sections of the course. In Study 1 (N=406), we did not detect a difference between the instruct-first and explore-first conditions. In Study 2 (N=411), we added more scaffolding to the activity. Students who received the traditional order of instruction followed by the activity scored significantly higher on the assessment. These findings contradict the exploratory learning benefits typically shown, shedding light on potential boundary conditions to this effect. 

This study examined the difficulty introduced by spaced retrieval practice in Calculus I for undergraduate engineering students. Spaced retrieval practice is an instructional technique in which students engage in multiple recall exercises on the same topic with intermittent temporal delays in between. Spacing out retrieval practice increases the difficulty of the exercises, reducing student performance on them. However, empirical research indicates that spaced retrieval practice is associated with improvements in students’ long-term memory for the retrieved information. The short-term costs and long-term benefits of spaced retrieval practice is an example of desirable difficulty, when more difficult exercises during the early stages of learning result in longer-lasting memory [1]. With support from the National Science Foundation (NSF), we sought to address: Does spacing decrease performance on retrieval practice exercises in an engineering mathematics course? Results showed that student performance was significantly lower for questions in the spaced condition than questions in the massed condition, indicating that we successfully increased the difficulty of the questions by spacing them out over time. Future work will assess final quiz performance to determine whether spacing improved long-term course performance, i.e., whether the difficulty imposed by spacing was desirable. 

ABSTRACT We present Hubble Space Telescope optical images, Keck-OSIRIS near-infrared (NIR) integral field spectroscopy data cubes and Keck-Near InfraRed Camera-2 (NIRC2) NIR images of nova V5668 Sgr from 2016 to 2019. The observations indicate enhanced emission at the polar caps and equatorial torus for low-ionization lines, and enhanced high-ionization emission lines only at the polar caps. The radial velocities are compatible with a homogeneous expansion velocity of v = 590 km s−1 and a system inclination angle of 24°. These values were used to estimate an expansion parallax distance of 1200 ± 400 pc. The NIRC2 data indicate the presence of dust in 2016 and 2017, but no dust emission could be detected in 2019. The observational data were used for assembling 3D photoionization models of the ejecta. The model results indicate that the central source has a temperature of 1.88 × 105 K and a luminosity of 1.6 × 1035 erg s−1 in August of 2017 (2.4 yr post eruption), and that the shell has a mass of 6.3 × 10−5 M⊙. The models also suggest anisotropy of the ionizing flux, possibly by the contribution from a luminous accretion disc. 

The extent of increasing anthropogenic impacts on large marine vertebrates partly depends on the animals’ movement patterns. Effective conservation requires identification of the key drivers of movement including intrinsic properties and extrinsic constraints associated with the dynamic nature of the environments the animals inhabit. However, the relative importance of intrinsic versus extrinsic factors remains elusive. We analyze a global dataset of ∼2.8 million locations from >2,600 tracked individuals across 50 marine vertebrates evolutionarily separated by millions of years and using different locomotion modes (fly, swim, walk/paddle). Strikingly, movement patterns show a remarkable convergence, being strongly conserved across species and independent of body length and mass, despite these traits ranging over 10 orders of magnitude among the species studied. This represents a fundamental difference between marine and terrestrial vertebrates not previously identified, likely linked to the reduced costs of locomotion in water. Movement patterns were primarily explained by the interaction between species-specific traits and the habitat(s) they move through, resulting in complex movement patterns when moving close to coasts compared with more predictable patterns when moving in open oceans. This distinct difference may be associated with greater complexity within coastal microhabitats, highlighting a critical role of preferred habitat in shaping marine vertebrate global movements. Efforts to develop understanding of the characteristics of vertebrate movement should consider the habitat(s) through which they move to identify how movement patterns will alter with forecasted severe ocean changes, such as reduced Arctic sea ice cover, sea level rise, and declining oxygen content.