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Jennifer Kotler

Publications and source records attributed to Jennifer Kotler.

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Overview of the New Hubble Spectroscopic Legacy Archive

The new Hubble Spectroscopic Legacy Archive (HSLA) provides coadded spectra of individual targets that have been observed with the Cosmic Origins Spectrograph (COS) and the Space Telescope Imaging Spectrograph (STIS) over their operating lifetime. HSLA uses data available in the Mikulski Archive for Space Telescopes (MAST). It automatically produces coadds whenever new data become publicly available or when there is newly recalibrated data. HSLA defines individual targets by their associated coordinates, accounting for proper motions, and uses SIMBAD, NED and the Phase II observing proposals to obtain astronomical classifications for each object. Coadded spectra are produced for each observing mode. In the case of COS far-ultraviolet observations there is one coadded spectrum for each lifetime position (LP). Additionally, a spectrum spanning the entire wavelength range covered by the observations is produced by abutting the spectra from a selection of individual modes. For each individual target, HSLA also provides a human-readable metadata file with key information that can be used in searches or for further exploration of the data. The HSLA project also makes the code used for coadding spectra publicly available along with several other tools (using Jupyter notebooks) for custom coaddition required in special cases. In this report we will describe the main components of HSLA and provide a brief description of how the data and metadata can be accessed.

astro-ph.IM

Design and Evaluation of a Tutor Platform for Personalized Vocabulary Learning

This paper presents our experiences in designing, implementing, and piloting an intelligent vocabulary learning tutor. The design builds on several intelligent tutoring design concepts, including graph-based knowledge representation, learner modeling, and adaptive learning content and assessment exposition. Specifically, we design a novel phased learner model approach to enable systematic exposure to words during vocabulary instruction. We also built an example application over the tutor platform that uses a learning activity involving videos and an assessment activity involving word to picture/image association. More importantly, the tutor adapts to the significant variation in children's knowledge at the beginning of kindergarten, and evolves the application at the speed of each individual learner. A pilot study with 180 kindergarten learners allowed the tutor to collect various kinds of activity information suitable for insights and interventions both at an individual- and class-level. The effort also demonstrates that we can do A/B testing for a variety of hypotheses at scale with such a framework.

cs.AI