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When it comes to special education, teachers are the cornerstone of children's development. A special education teacher (SET) who possesses correct perceptions to teach students with special education needs (SENs) in inclusive education and ability to maintain balance that works best for children with physical and emotional disabilities are expected to thrive. This requires several skills that are entirely different from traditional teaching, often depicted as ‘chalk and talk’ – keeping control and making decisions about what is the right thing to do for students. This trait is just an illustration of many myopic characteristics that will no longer work well for both SENs and regular students. An effective SET in the twenty-first century must be able to adapt and respond rapidly to the changing society and is more likely to be open to learning. These character qualities are typical among millennials and Generation Z or Alpha, who are more prone to be digital native, that will future-proof them in the upcoming era of ubiquitous technology. As such, new generation SETs are significant precursor to create a new special education system that responds well to the challenges encountered by the pervasive future ahead.

Though test-based accountability policies seek to redress educational inequities, their underlying theories of action treat inequality as a technical problem rather than a political one: data point educators toward ameliorative actions without forcing them to confront systemic inequities that contribute to achievement disparities. To highlight the problematic nature of this tension, the purpose of this paper is to identify key problems with the techno-rational logic of accountability policies and reflect on the ways in which they influence teachers’ data-use practices.

This paper illustrates the data use practices of a workgroup of sixth-grade math educators. Their meeting represents a “best case” of commonplace practice: during a full-day professional development session, they used data from a standardized district benchmark assessment with support from an expert instructional leader. This sociolinguistic analysis examines episodes of data reasoning to understand the links between the educators’ interpretations and instructional decisions.

This paper identifies three primary issues with test-based accountability policies: reducing complex constructs to quantitative variables, valuing remediation over instructional improvement, and enacting faith in instrument validity. At the same time, possibilities for equitable instruction were foreclosed, as teachers analyzed data in ways that gave little consideration of students’ cultural identities or funds of knowledge.

Test-based accountability policies do not compel educators to use data to address the deeper issues of equity, thereby inadvertently reinforcing biased systems and positioning students from marginalized backgrounds at an educational disadvantage.

This paper fulfills a need to critically examine the ways in which test-based accountability policies influence educators’ data-use practices.

Basis risk – the risk that payoffs of a hedging instrument do not correspond to the underlying exposures – is cited as a primary concern for implementing weather data, we investigate several dimensions of weather basis risk in the U.S. corn market. Results suggest that while geographic basis risk can be significant, it should not preclude the use of geographic cross‐hedging, particularly with temperature as opposed to precipitation derivatives. Risk reduction is appreciable and the degree to which geographic basis risk impedes effective hedging diminishes as spatial aggregation in the risk exposure and hedging instrument increases.

Holbrook Working (1949) discovered that the percentage change in futures prices seemed to be largelyrandom. This led Paul Samuelson (1965) to develop the Efficient Market Hypothesis (EMH) which claims that the current spot and futures1 prices fully reflect all relevant information. Furthermore, because the future flow of information cannot be anticipated, price changes will not be serially correlated. These papers linked the notion of randomness of price changes to informational efficiency. From that point on, a major part of the empirical studies of asset markets has been the application of time series analysis to asset prices, in order to evaluate whether the price changes are random and whether futures prices reflect all available information. As the statistical tests became more sophisticated, the number of empirical studies increased and the results became more contradictory and difficult to interpret. An economic theorist can only be bemused by contemplating the empirical/econometric studies in the finance literature.

The purpose of this paper is to provide an effective solution for a complex planning problem encountered in heavy industry. The problem entails selecting a set of projects to produce from a larger set of solicited projects and simultaneously scheduling their production to maximize profit. Each project has a due window inside of which, if accepted, it must be shipped. Additionally, there is a limited inventory buffer where lots produced early are stored. Because scheduling affects which projects may be selected and vice-versa, this is a particularly difficult combinatorial optimization problem.

The authors develop an algorithm based on the Metaheuristic for Randomized Priority Search (Meta-RaPS) as well as a greedy heuristic and an integer programming (IP) model. The authors then perform computational experiments on a large set of benchmark problems over a wide range of characteristics to compare the performance of each method in terms of solution quality and time required.

The paper shows that this problem is very difficult to solve using IP, with even small instances unable to be solved optimally. The paper then shows that both proposed algorithms will in seconds often outperform IP by a large margin. Meta-RaPS is particularly robust, consistently producing the best or very near-best solutions.

The Meta-RaPS algorithm developed enables companies facing this problem to achieve higher profits through improved decision making. Moreover, this algorithm is relatively easy to implement.

This research provides an effective solution for a difficult combinatorial optimization problem encountered in heavy industry which has not been previously addressed in the literature.

The purpose of this paper is to explore upper secondary school students’ voices on how information and communication technology (ICT) could structure and support their everyday activities and time at school.

In all, 11 group interviews were conducted with a total of 46 students from three upper secondary schools. NVivo PRO 11 was used for a qualitative content analysis.

The results show that ICT plays a central role in the students’ schooling, not in terms of “state-of-the-art” technology, but rather as “state-of-the-actual”, by for example supporting the writing process and for peer support, digital documentation and storage.

A relatively small number of students in three schools and three specific programmes make generalisations difficult.

Students’ perspectives on the “state-of-the-actual” could influence teachers’ use of ICT in education, their professional development activities and the development of an in-school ICT infrastructure.

The study could lead to a better understanding of students’ expectations and use of ICT at school and in everyday life.

The originality of this paper is the focus on students’ voices about how the basic use and functionality of ICT could structure and support their everyday activities at school.

The purpose of this paper is to test for bubbles in the US hard red spring (HRS) wheat market from 2004 to 2014, with particular focus on 2007-2008 when the market experienced record-high price volatility.

The authors apply a recently developed bubble testing procedure to cash, rolling nearby futures contract, and individual futures contract prices of HRS wheat sampled at daily, weekly, and monthly frequencies. Two critical value (CV) sequences are derived to date-stamp bubbles, one from Monte Carlo simulations, and the other from recursive wild bootstrap procedure.

The authors find that regardless of the price series adopted, sampling frequency chosen, or CVs used, bubbles account for only a small fraction of the HRS wheat price behavior during 2004-2014. However, much sharper differences are detected regarding the key policy question of bubble behavior during 2007-2008. Individual futures contract prices during this period suggest only a minimal number of bubble days, while rolling nearby futures and cash prices indicate bubbles lasting much longer. Since theory suggests that prices for individual futures contracts are more likely to provide a clearer test of bubble components, the authors conclude there is little evidence that the spike in spring wheat prices to $25 per bushel in 2007-2008 was a bubble.

This paper is the first in the literature to examine the sensitivity of bubble testing to different types of data, sampling frequencies, and inference procedures.

Between 2007 and 2013, public procurement systems in Latin America and the Caribbean underwent a critical reform process. However, not much is known about the elements of reform and their effect on the public sector. In order to assess this gap in knowledge, this study used an institutional evaluation multivariate tool developed and carried out by the Inter-American Development Bank called PRODEV. Based on a sample panel of countries, effects on the development of public procurement systems were calculated. The results indicated that, of the three main areas of procurement reform evaluated, the creation of a procurement agency2 had the largest impact. At the same time, evidence was found that backed the hypothesis that creating these agencies had a positive effect on the perception of public sector performance.

This paper aims to address the limitations in designing educational approaches that apply critical approaches to data literacy, given the obscure nature of digital platforms, which leave youth unable to develop discourses that challenge dominant narratives about the role of data in their lives. The purpose of this study is to propose and evaluate a critical data literacy approach that empowers youth to engage with data from a sociocultural perspective using a speculative participatory research approach that affords opportunities to develop alternative discourses.

This is a multiple-case study that involves five alternative schools in Uruguay which implemented the Nayah-Irú curriculum over ten weeks leading to the development of six distinct research projects about the materialization of data in youth lives. The curriculum incorporates an alternate reality game (ARG) to engage youth in critical data literacy, based on the principles of Youth Participatory Action Research (YPAR) epistemology and Speculative Civic Literacies.

The findings of this study highlight the integration of speculative storytelling and real-life experiences in developing alternative discourses about datafication. The analysis revealed instances of discursive closure among the youth, but through the curriculum's speculative fiction elements, such as the narrative of Nayah-Irú, emotional connections were formed, leading to increased engagement, critical inquiry, and problem framing.

The study conducted on the Nayah-Irú curriculum shows its effectiveness in engaging youth and educators in critical data literacy by affording opportunities for youth to engage in the analysis of their personal data literacies in an alternative world. Bringing speculative approaches to data literacy can open new avenues for exploring data literacy with youth in ways that center their voices and help them overcome different forms of discursive closure.

This study offers new insights into critical data literacy education blending youth participatory action research epistemologies with a speculative literacies framework to support youth in developing alternative discourses regarding the role of data in their lives.