Oakes and Saunders address four questions: 1) Are textbooks and curriculum materials essential to students’ education in California? 2) Do all students have access to these resources? 3) Has the State failed to correct (or even contributed to) gaps or inequalities in access to instructional materials? 4) Are there policies the State could follow that would lead to better access? Drawing both on empirical research and the analysis of current state policy, Oakes and Saunders show that, although textbooks and instructional materials are fundamentally important to students’ education, many California students do not have access to them. At many schools, instructional materials inadequacies converge with problems in staffing and facilities. Schools serving low-income students, English language learners, and/or non-white student majorities are most plagued by inadequacies in instructional materials. Furthermore, by not mandating that students be provided with instructional materials, and by not investigating their availability through existing oversight mechanisms, the State has contributed to shortages and disparities in access. An analysis of other states’ policies, however, shows that the State could take measures that would insure students access to sufficient and appropriate textbooks and instructional materials.

Type Ia Supernovae provided the first strong evidence of dark energy and are still an important tool for measuring the accelerated expansion of the universe. However, future improvements will be limited by systematic uncertainties in our use of Type Ia supernovae as standard candles. Using Type Ia supernovae for cosmology relies on our ability to standardize their absolute magnitudes, but this relies on imperfect models of supernova spectra time series. This thesis is focused on using data from the Nearby Supernova Factory both to understand current sources of uncertainty in standardizing Type Ia supernovae and to develop techniques that can be used to limit uncertainty in future analyses.

Part I of this thesis is concerned with studying systematic errors that occur in the current generation of supernova lightcurve models. Lightcurve models are used to fit photometric supernova data, generating a small number of parameters that can then be used to `correct' the supernova magnitude to a standard value. The analysis presented here estimates systematic errors due to K-corrections that occur when using such lightcurve models to fit high-redshift supernovae. These errors occur when the spectral template underlying the lightcurve fitter poorly matches the actual supernova spectral energy distribution and also if the model fit is dependent on the spectral coverage of the photometric filters used to observe the supernova.

In order to quantify this effect, synthetic photometry is performed on artificially redshifted spectrophotometric data from the Nearby Supernova Factory, simulating having photometric data for the same supernovae with a range of filter positions. The resulting lightcurves are fit with a conventional lightcurve fitter and the variation is measured in the resulting standardized magnitudes. We find significant variation in the measurements of the same supernovae placed at different redshifts regardless of filters used, which causes dispersion greater than $\sim 0.05$ mag for measurements of photometry using Sloan-like filters and a bias that corresponds to a 0.03 shift in $w$ when applied to an outside data set. We also test the effect of population changes at high redshift and measure the resulting bias for the average of the supernova magnitudes. Lastly, methods are discussed for mitigating bias and dispersion due to K-corrections.

Part II presents an alternative to current lightcurve models. The supernovae from the Nearby Supernova Factory are used to develop an empirical model that will be able to more fully describe the variety of supernova behavior. The spectrophotometric time series for over 200 supernova are used to fit linear principal components that will be able to be used as a lightcurve model. This is done by first using Gaussian Processes to model the true spectral time series for each supernova and make a prediction for it on a regular grid in time and wavelength space. Once this has been done, principal components are calculated that describe the full set of supernovae using a method that incorporates the variance in the data. K-fold cross-validation is used to determine how many components best describe the full population without over-training on noise in the data. In the final version of the analysis, three different models are presented: one simple model that can be compared to the current generation of lightcurve models; one model that is the best for performing a linear standardization of supernova magnitudes following current standardization methods, at least when spectra for the supernova are available; and one complex model that provides the most complete model of spectral time series for the full population.

As part of an ongoing "component building" (Slavin, 1984) program of research designed to estimate the effects of several individual components of a Spanish-to-English language arts transition program (Saunders, O'Brien, Lennon, & McLean, 1998), an experiment tested the effects of two instructional components—literature logs and instructional conversations—on the story comprehension and thematic understanding of upper-elementary-grade students. Five teachers and 116 fourth and fifth graders participated in the study. Slightly more than half the students were English learners completing their first or second year of English language arts. Teachers had completed one year of literature log and instructional conversation training. Students were randomly assigned to one of four treatment conditions: literature logs only, instructional conversations only, literature logs plus instructional conversations, and control. Posttests found significant differences among treatment groups. Students in the instructional conversation and the literature log plus instructional conversation groups scored significantly higher than the control group on story comprehension. Moreover, students in all three experimental groups were significantly more likely to demonstrate an understanding of the story themes than students in the control group. The combined effects of literature logs and instructional conversations on students' essays about a story's theme varied by language proficiency: For limited English proficient students, the combined effects of literature logs and instructional conversations were greater than the effects of either treatment condition alone. For fluent English proficient students, however, the combined effects were not significantly greater than the effect of one treatment condition or the other.