Read more: Executive Summary

]]>Traditionally, Latina/o students in the K-20 pipeline -- not unlike those at UTEP -- have had to contend with deficit notions surrounding their academic performance and achievement. This deficit thinking has placed emphasis on students' deficiencies -- whether in terms of language, cognition, or motivation, among other factors -- rather than the structural conditions, such as inequitable funding for schools, that have tended to contribute to the persistent under-achievement of certain groups (Valencia, 2010).

As a challenge to deficit explanations of Latina/o student academic under-achievement, the recent 10-year student success framework adopted by UTEP, known as the UTEP Edge, advocates an asset-based approach to working with students both inside and outside of the classroom. Drawing on educational research as well community development literature, these asset-based pedagogical approaches emphasize students' individual and collective strengths, skills, and capacities as the starting point for learning and engagement. Such approaches do not claim to resolve the systemic conditions that contribute to persistent inequities experienced by minoritized students in the K-20 pipeline; rather, they are focused on reconfiguring teaching and learning to promote equity at the classroom level.

This paper provides an outline of the conceptual underpinnings of an asset-based framework for teaching and learning (ABTL), highlights key characteristics of ABTL with culturally and linguistically diverse learners, and provides examples of ABTL in the classroom, across disciplines.

]]>Traditional engineering approach assumes that we know the probability distributions of measurement errors; however, in practice, we often only have partial information about these distributions. In some cases, we only know the upper bounds on the measurement errors; in such cases, the only thing we know about the actual value of each measured quantity is that it is somewhere in the corresponding interval. Interval computation estimates the range of possible values of the desired quantity under such interval uncertainty.

In other situations, in addition to the intervals, we also have partial information about the probabilities. In this paper, we describe how to solve this problem in the linearized case, what is computable and what is feasibly computable in the general case, and, somewhat surprisingly, how physics ideas -- that initial conditions are not abnormal, that every theory is only approximate -- can help with the corresponding computations.

]]>