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The feedback between vocational education and training (VET) and the labour market can provide important input for the review and renewal of qualifications. A feedback loop that is based on learning outcomes helps provide deeper insights into what is required on the labour market, what is offered in training provisions and assessed at the end of a learning programme. The aim of this study is to contribute to strengthening the quality and relevance of qualifications and completing the feedback loop between education and the labour market. It examines methods of collecting data on the match/mismatch between qualifications and labour market requirements, including analysis of how achieved learning outcomes are applied and perceived in the labour market (for example methods of collecting the experience of employers with holders of these qualifications). This report addresses the following two questions: (1) which data already exist in the countries, providing insight into the relevance of qualifications to employees, employers and other labour market stakeholders?; and (2) how can survey methodology be designed to systematically capture the experiences and appreciations of employers as regards the content and profile of qualifications? To what extent, based on limited testing, can scalability of the methodology be achieved?

A number of national and international organizations have compiled lists of grand challenges to unify the efforts of scholars and practitioners in a field. Unified efforts increase the possibility of creating meaningful and lasting progress. In this paper we share ten grand challenges that were identified through an examination of the assessment literature and a national survey. Each of the grand challenges are described. The four challenges which were identified as being of greatest concern to the assessment community are currently being addressed by the Grand Challenges in Assessment Project for possible collective solutions.

This paper describes a method for pooling grouped, ordered-categorical data across multiple waves to improve small-sample heteroskedastic ordered probit (HETOP) estimates of latent distributional parameters. We illustrate the method with aggregate proficiency data reporting the number of students in schools or districts scoring in each of a small number of ordered "proficiency" levels. HETOP models can be used to estimate means and standard deviations of the underlying (latent) test score distributions, but may yield biased or very imprecise estimates when group sample sizes are small. A simulation study demonstrates that pooled HETOP models can reduce the bias and sampling error of standard deviation estimates when group sample sizes are small. An analysis of real test score data suggests the pooled models are likely to improve estimates in applied contexts.

Responsible Operations is intended to help chart library community engagement with data science, machine learning, and artificial intelligence (AI) and was developed in partnership with an advisory group and a landscape group comprised of more than 70 librarians and professionals from universities, libraries, museums, archives, and other organizations. This research agenda presents an interdependent set of technical, organizational, and social challenges to be addressed en route to library operationalization of data science, machine learning, and AI. Challenges are organized across seven areas of investigation: (1) Committing to Responsible Operations; (2) Description and Discovery; (3) Shared Methods and Data; (4) Machine-Actionable Collections; (5) Workforce Development; (6) Data Science Services; (7) Sustaining Interprofessional and Interdisciplinary Collaboration. Organizations can use Responsible Operations to make a case for addressing challenges, and the recommendations provide an excellent starting place for discussion and action.

Education researchers use surveys widely. Yet, critics question respondents' ability to provide high-quality responses. As schools increasingly use student surveys to drive policymaking, respondents' (lack of) motivation to provide quality responses may threaten the wisdom of using surveys for data-based decision-making. To better understand student satisficing (suboptimal responding on surveys) and its impact on data quality, we examined the pervasiveness and impact of this practice on a large-scale social-emotional learning survey administered to 409,721 students in grades 2-12. Findings indicated that despite the prevalence of satisficing in our sample, its impact on data quality appeared more modest than anticipated. We conclude by providing an accessible approach for defining and calculating satisficing for researchers, practitioners, and policymakers working with large-scale datasets.

Recent advances in computational linguistics and the social sciences have created new opportunities for the education research community to analyze relevant large-scale text data. However, the take-up of these advances in education research is still nascent. In this paper, we review the recent automated text methods relevant to educational processes and determinants. We discuss both lexical-based and supervised methods, which expand the scale of text that researchers can analyze, as well as unsupervised methods, which allow researchers to discover new themes in their data. To illustrate these methods, we analyze the text interactions from a field experiment in the discussion forums of online classes. Our application shows that respondents provide less assistance and discuss slightly different topics with the randomized female posters, but respond with similar levels of positive and negative sentiment. These results demonstrate that combining qualitative coding with machine learning techniques can provide for a rich understanding of text-based interactions.

Returns to education have received much attention from scholars as well as policy makers and the media. It is a well-established fact that educational attainment improves employability and wages and that university graduates better than their less educated counterparts. However, the educational expansion rises the importance of the horizontal dimension in explaining social stratification. The field of study has proved to be an important factor for graduates' labor market performance in many countries. This paper discusses the differences in the labor market outcomes in early careers of graduates of various disciplines in Poland where the topic has been hotly debated for many years but remained under researched. We use data from the second measurement of the recently established Polish National Graduate Tracking System (ELA). The system uses administrative data exported from the national register of students and graduates as well as monthly records on employment status and earnings fetched from the Social Insurance Institution registers. The data cover entire population of recent higher education graduates in Poland and enables tracking for twenty-one months after graduation. We focus particularly on the fields of Science, Technology, Engineering, and Mathematics which are presumed to lead to the best labor market positions. We demonstrate that, contrary to popular opinion, only some of the STEM disciplines give their graduates an edge on the labor market while the rest struggle to achieve results comparable with those of graduates of Humanities and Social Sciences.

The field of learning analytics holds considerable promise for higher education, with reports of successful uses now emerging in selected institutions. At the same time, critics have expressed concerns regarding privacy, ethics, and intrusions into teachers' pedagogy. Without attentive planning, higher-education professionals applying learning analytics may inadvertently undermine their institutions' core teaching and learning missions. The authors offer a framework for moving forward with learning analytics, organized around three principles: (a) Institutions should take the lead in their conversations with vendors, emphasizing the distinctive values of higher education; (b) Learning analytics data should be balanced with other forms of evidence that analytics cannot capture, especially participant experiences; and (c) Successful implementations will leave room for adaptations by people on the ground--to notice what is working and integrate the tools into their practices. Only by empowering students, faculty, and staff can these tools fulfill their potential in higher education.

Community college transfer students encounter challenges progressing toward a bachelor's degree, leading to widespread transfer credit loss. This in turn may lower students' chances of credential completion and increase the time and costs for students, their families, and taxpayers. In this study we review three definitions of credit transfer inefficiency--"credit transferability," "credit applicability," and "excess credits among completers"--focusing on the last to examine why students who start at a community college and transfer to a four-year institution so often end up with excess credits that do not count toward a bachelor's degree. To shed light on credit transfer inefficiency, we examine the course-taking behaviors of community college transfer students who earn bachelor's degrees with numerous excess credits compared with transfer students who earn bachelor's degrees with few excess credits. We employ data-mining techniques to analyze student transcripts from two state systems, enabling us to examine a large number of variables that could explain the variation in students' excess credits at graduation. These variables include not only student demographics but also the types and timing of courses taken. Overall, we find more excess credits associated with several factors, including taking larger proportions of 100- and 200-level courses and smaller proportions of 300-level courses throughout students' progression toward completion, and taking 100-level courses in any subject--and specifically 100-level math courses--immediately after transferring to a four-year institution. Findings suggest that institutions could help students reduce credit transfer inefficiency by encouraging them to explore and choose a bachelor's degree major early on so they can take the required lower division (100- and 200-level) courses at the community college, thereby enabling them to take mostly upper division 300- and 400-level courses in their desired major field once they transfer to a four-year institution.

We use longitudinal data on all high school students in Washington State, including postsecondary education and workforce outcomes, to investigate predictors of intermediate and postsecondary outcomes for students with disabilities. We pay particular attention to career and technical education (CTE) enrollment and the extent of inclusion in general education classrooms, as prior research suggests these factors may be particularly important in influencing the outcomes of students with disabilities. We estimate models that compare students with other students within the same school district, who are receiving special education services for the same disability, and have similar baseline measures of academic performance and other demographic information. We find generally weak relationships between CTE enrollment in any particular grade and intermediate and postsecondary outcomes for students with disabilities, though we replicate earlier findings that students with disabilities who are enrolled in a "concentration" of CTE courses have higher rates of employment after graduation than students with disabilities who are similar in other observable ways but are enrolled in fewer CTE courses. We also find consistently strong evidence that students with disabilities who spend more time in general education classrooms experience better outcomes--fewer absences, higher academic performance, higher rates of grade progression and on-time graduation, and higher rates of college attendance and employment--than students with disabilities who are similar in other observable ways but spend less time in general education classrooms.

This working paper is the first publication coming out of a project investigating the role of vocational education and training (VET) in developing digital skills in the Australian workforce, using two sectors as case studies--Transport and Logistics, and Public Safety and Correctional Services. The study employs a mixed method approach, combining both qualitative and quantitative analyses. It involves industry training package content analysis, content extraction and analysis from online job vacancy advertisements, and key industry interviews, as well as a quantitative employer survey. In the online job vacancy analysis, a total of 1,708 job advertisements covering 74 occupations/job titles were analysed to explore digital skills requirements. In addition, a detailed content analysis was conducted of 11 training packages, with a specific focus on the qualifications for these occupations. In this analysis, 758 units of competency were analysed to examine how and the extent to which digital skills provision is embedded into qualifications. Findings showed that only a small number mentioned digital skills as a requirement. The training packages for these industries contained significant digital training content but with the majority of these units of competency occurring as electives. These findings raise questions about whether employers are making assumptions about the digital skills of potential employees.

The digital revolution and automation are accelerating changes in the labour market and in workplace skills, changes that are further affected by fluctuations in international and regional economic cycles and employment opportunity. These factors pose a universal policy challenge for all advanced economies and governments. In the workplace, people seek to acquire contemporary and relevant skills to gain employment and retain transferable skills to maintain employment. The central purpose of this paper is to investigate how other nations or regions are dealing with these issues. What approaches are they taking to understanding the mix and dynamics of the skills attained by individuals and, more broadly, the totality of skills that in aggregate constitute a highly capable and adaptable labour force, one that supports firm viability and greater national productivity. This research has examined a range of initiatives and approaches being developed or in use in selected countries, including the United States, Singapore and New Zealand, and agencies/organisations; for example, the European Commission and the Skills for the Information Age Foundation. In doing so, it showcases the good practices used to ensure that occupational-level skills information remains current and widely accessible. [For "Identifying Work Skills: International Case Summaries. Support Document," see ED579875.]

We use longitudinal data from Washington State to provide estimates of the extent to which performance on the edTPA, a performance-based, subject-specific assessment of teacher candidates, is predictive of the likelihood of employment in the teacher workforce and value-added measures of teacher effectiveness. While edTPA scores are highly predictive of employment in the state's public teaching workforce, evidence on the relationship between edTPA scores and teaching effectiveness is more mixed. Specifically, continuous edTPA scores are a significant predictor of student mathematics achievement in some specifications, but when we consider that the edTPA is a binary screen of teaching effectiveness (i.e., pass/fail), we find that passing the edTPA is significantly predictive of teacher effectiveness in reading but not in mathematics. We also find that Hispanic candidates in Washington were more than three times more likely to fail the edTPA after it became consequential in the state than non-Hispanic White candidates. Supplementary tables are appended.

This work asks one simple question: "how reliable is the method used by the National Centre for Vocational Education Research (NCVER) to estimate projected rates of VET program completion?" In other words, how well do early projections align with actual completion rates some years later? Completion rates are simple to calculate with a cohort of students who start together in a very short program with a defined end date. The context in vocational education and training (VET) is, however, far more complex. Program lengths vary and may span several years, students commence at different times and many study part-time. Waiting for all students to complete or "drop out" of their training before calculating an actual completion rate gives a reliable answer, but is somewhat impractical. This paper summarises the key findings from a technical review of the validity of the method long used by NCVER in estimating projected completion rates for government-funded VET programs. This analysis required the interrogation of large longitudinal data sets with tens of millions of enrolments over multiple years. The outcomes are revealing because of ever-high interest in completion rates as measures of the efficiency and effectiveness of the VET sector. Key findings include the following: (1) The method long used by NCVER for estimating VET program completion rates using data from the National VET Provider Collection is shown to be reliable and aligns well with actual rates of completion for historical estimates; (2) Given that it takes a number of years for actual rates of completion to stabilise, the method is well suited for inclusion as part of any method of assessing completion rates, where the projected completion rate method is used to estimate rates for the most recent years and actual rates used for prior years; (3) The technical review has also shown that the current predictive method can be improved by defining a program's commencing year as the year it first appears in the National VET Provider Collection rather than using the commencing flag variable; and (4) It is anticipated that the incorporation of unique student identifiers into any preferred methodology, and its extension to total VET activity, can be phased in from the collection of 2017 training activity. "The Current Method for Projecting Rates of Completion: A Working Example" is appended.

Most public school teachers in the United States are enrolled in defined benefit (DB) pension plans. Using administrative micro data from four states, combined with national pension funding data, we show these plans have accumulated substantial unfunded liabilities -- effectively debt -- owing to previous plan operations. On average across 49 state plans, an amount that exceeds 10 percent of current teachers' earnings is being set aside to pay for previously-accrued pension liabilities. To the extent that the costs of the unfunded liabilities drag on teacher compensation, they may exacerbate problems of teacher recruitment and retention. We briefly discuss three policy changes that could end or reduce the accumulation of unfunded liabilities in educator pension plans: (1) transition teachers to defined-contribution retirement plans; (2) transition teachers to cash-balance retirement plans; and (3) tighten the link between funding and benefit formulas within the current defined-benefit structure.

We find an individual's rank within their reference group has effects on later objective outcomes. To evaluate the impact of local rank, we use a large administrative dataset tracking over two million students in England from primary through to secondary school. Academic rank within primary school has sizable, robust and significant effects on later achievement in secondary school, conditional on national test scores. Moreover we find boys gain four times more in later test scores from being top compared to girls. We provide evidence for a mechanism using matched survey data, which shows that rank affects an individual's self-concept. The paper discusses other potential channels but concludes that malleable non-cognitive skills such as confidence and belief in own ability are most likely to generate these results. We put forward a basic model where rank effects costs and effort allocation when faced with multiple tasks. We believe this is the first large-scale study to show large and robust effects of rank position on objective outcomes of that have consequences in the labour market. The following are appended: (1) Model; (2) Peer Effects; and (3) Measurement Error in Test Scores. Five tables are also included in the appendices.

This report analyses how learning supports labour market transitions and career changes of adult workers across five countries (Denmark, Germany, Spain, France and Italy). To make the most of career and labour market opportunities, individuals have to rely on their own resources and their agency but also know how to navigate the institutional context. To make successful labour market transitions, individuals need to have a sense of direction, they need to learn about opportunities, and they need to learn skills. Education and training has a special role in supporting adult workers in their careers, providing them with the competences, qualifications and, in some cases, with the self-confidence needed for successful transition. Guidance services tailored to individual needs can help individuals find appropriate career trajectories in their search for suitable job opportunities, and can foster relational, emotional, cognitive and practical learning. [This report is the result of a team effort. Cedefop is grateful to Alan Brown, Jenny Bimrose and Barbara Merrill from the University of Warwick (Institute for Employment Research and Centre for Lifelong Learning) for coordinating the research teams and contributing to and editing project reports. The interviews were carried out and the country reports prepared by country teams: Massimo Tomassini from the Faculty of Education Science, University of Roma Tre and Silvia Zanazzi from the Faculty of Education Science, University of Roma La Sapienza (Italy); Rie Thomsen, Ida Juul and Pia Cort from Aarhus University (Denmark); Esther Oliver and Lena de Botton from the University of Barcelona and Itxaso Tellado from University of Vic (Spain); M'Hamed Dif from the University of Strasbourg (BETA-Céreq Alsace), Rachel Mulvey from the University of East London and Sophie Perdrix from the University of Lausanne (France); as well as Simone Haasler and Barbara Rinken from the University of Bremen (Germany). Cedefop experts Antje Barabasch and Giovanni Russo coordinated the study, under the supervision of the Head of Area Pascaline Descy, with a valuable contribution from Pedro Moreno Da Fonseca who reviewed this publication. The work was carried out under Cedefop's service contract No AO/RPA/GRUSSO-ABARA/Narrative learning transitions/015/11.]

We study the link between parental selection and children criminality in a new context. After the fall of the Berlin Wall, East Germany experienced an unprecedented temporary drop in fertility driven by economic uncertainty. We exploit this natural experiment to estimate that the children from these (smaller) cohorts are 40 percent more likely to commit crimes. We show that women who gave birth at this period were negatively selected. Investigation of the underlying mechanisms reveals that emotional attachment and risk attitudes play important roles in the fertility-crime relationship. Finally, results for siblings support a causal interpretation of our findings. [This research was partially funded by a grant from the Graduate School of Business and Economics (GSBE), Maastricht University.]

In recent years, "continuous improvement" has become a popular catchphrase in the field of education. However, while continuous improvement has become commonplace and well-documented in other industries, such as healthcare and manufacturing, little is known about how this work has manifested itself in education. This white paper attempts to map the landscape of this terrain by identifying and describing organizations engaged in continuous improvement, and by highlighting commonalities and differences among them. The findings classify three types of organizations engaged in continuous improvement: those focused on instructional improvement at the classroom level; those concentrating on system-wide improvement; and those addressing collective impact. Each type is described in turn and illustrated by an organizational case study. Through the analysis, six common themes that characterize all three types of organizations (e.g., leadership and strategy, communication and engagement, organizational infrastructure, methodology, data collection and analysis, and building capacity) are enumerated. This white paper makes four concluding observations. First, the three case studies provide evidence of organizations conducting continuous improvement work in the field of education, albeit at different levels and in different ways. Second, entry points to continuous improvement work are not mutually exclusive, but are nested and, hence, mutually informative and comparative. Third, continuous improvement is not synonymous with improving all organizational processes simultaneously; rather, research and learning cycles are iterative and gradual in nature. Fourth, despite being both iterative and gradual, it is imperative that improvement work is planned and undertaken in a rigorous, thoughtful, and transparent fashion. The following are appended: (1) Selected Continuous Improvement Methodologies; (2) Baldrige Education Criteria for Performance Excellence Framework; (3) Examples of Improvement Artifacts from the School District of Menomonee Falls, Wisconsin; (4) Examples of Improvement Artifacts from Montgomery County Public Schools, Maryland; and (5) Examples of Improvement Artifacts from Strive Partnership Cincinnati.

Over the past fifteen years, new types of "professional practice" doctorates in fields ranging from nursing to bioethics have increased exponentially, from near zero to over 500 programs in at least a dozen fields in the U.S. today. This growth raises many policy questions. For example, do doctorate holders serve their clients and organizations more effectively? How do new credential requirements affect access to these professions? How are they shaping institutional missions, pressures, and resource allocation? Using national data and case studies, this paper examines the forces driving the growth of new types of professional practice doctorates and their impacts on higher education institutions, especially those that had not previously offered doctorates.

The purpose of this Issue Paper is to provide a framework and justification for effective ways that teachers can collect and use assessment data to make instructional decisions. This framework is provided as an indication of what effective linking of assessment data to instructional decisions "ought" to look like--rather than a summary or survey of current practices. The framework and respective Innovation Configuration for Linking Assessment and Instruction in Teacher Preparation and Professional Development (provided in the Appendix, pages 31-34) are primarily designed to provide a blueprint for preservice teacher preparation; however, they also may be used as an evaluation rubric or development guide for inservice professional development. Although many schools and districts may not currently have in place the practices discussed in this Issue Paper, these practices are strongly endorsed by the requirements of the 2002 reauthorization of the Elementary and Secondary Education Act (ESEA)--also known as the No Child Left Behind Act--and the competitive grants to states that were made available through the Race to the Top Fund. This paper begins with a discussion of why assessment and instruction should be linked. It continues with an overview of the innovation configuration, describing essential components in preservice and inservice teacher training to identify the skills and competencies that teachers need to make sound decisions about using assessment information to improve instruction. Next, the major points within the innovation configuration are provided, with a rationale for their importance and elaboration of some of their core characteristics. Last, recommendations are provided regarding how the components of the innovation configuration might be included in teacher preparation and professional development practices. (Contains 2 figures.)

This paper examines whether teachers in schools serving students from high-poverty backgrounds are as effective as teachers in schools with more advantaged students. The question is important. Teachers are recognized as the most important school factor affecting student achievement, and the achievement gap between disadvantaged students and their better off peers is large and persistent. Using student-level microdata from 2000-2001 to 2004-2005 from Florida and North Carolina, the authors compare the effectiveness of teachers in high-poverty elementary schools (greater than 70% FRL students) with that of teachers in lower-poverty elementary schools (less than 70% FRL students). The results show that the average effectiveness of teachers in high-poverty schools is in general less than teachers in other schools, but only slightly, and not in all comparisons. The authors also find differences in within-school-type variation in teacher effectiveness in nearly every comparison. These differences are largely driven by the longer tail at the bottom of the teacher effectiveness distribution in high-poverty schools. Teachers at the top of the effectiveness distribution are very similar across school settings. The observed differences in teacher quality between high-poverty and lower-poverty schools are not due to differences in the observed characteristics of teachers, such as experience, certification status and educational attainment. Rather, they appear to arise from differences in the marginal return or payoff from increases in a characteristic. In particular, the gain in productivity from increased experience is much stronger in lower-poverty schools. The lower return to experience in high-poverty schools does not appear to be a result of differences in the quality of teachers who leave teaching or who switch schools. Rather, it may be the case that the effect of experience on teacher productivity may depend on the setting in which the experience is acquired. If there are positive spillovers among teachers that depend on teacher quality (ie. teacher "peer effects") or if exposure to challenging student populations lessens the future productivity of teachers (i.e. leads to "burn out"), teachers in schools serving large proportions of low-income students may simply not improve much as time goes by. These findings suggest that solutions to the achievement gap between high and lower-poverty schools may be complex. Changing the quality of new recruits or importing teachers with good credentials into high-poverty schools may not be sufficient. Rather, the findings suggest that measures that induce highly effective teachers to move to high-poverty schools and which promote an environment in which teachers' skills will improve over time are more likely to be successful. An appendix is included. (Contains 8 figures, 15 tables and 14 footnotes.) [This is an updated version of Working Paper 41.]

One of the lightning rods in the discourse over teacher pay has been the question of "how much" teachers should be paid. What the "how much" debate does not directly address, however, is the question of "how" teachers should be paid. This paper attempts to help lay groundwork for a better understanding of what exactly teachers want in terms of how they should be paid. Nontraditional teacher pay (NTTP), which is considered here to be pay systems that diverge from exclusive reliance on the traditional salary schedule, continues to be a high-profile and controversial issue. Often lost in the debate over NTTP is a thorough assessment of what teachers "actually prefer" in a pay system, which has important implications for teacher recruitment, retention, and motivation. While teachers are sometimes broadly described as resisting NTTP, the truth is much more nuanced. Using data collected in 2005 from over 2,500 unionized teachers in a single state, this paper explores: (1) whether teachers favor or oppose four different bonus-based NTTP systems, and the demographic and attitudinal characteristics associated with these preferences; (2) levels of teacher support for traditional (i.e., education, service) and nontraditional (e.g., standardized test score) criteria for salary increases, as well as teacher characteristics related to this support; and (3) changes over time in NTTP preferences, as additional survey data from six years earlier allow for a unique opportunity to examine possible cohort versus service effects. Some of the main findings are the following: (1) Teachers supported an emphasis on education and service as the basis for salary increases, preferring these criteria to student test scores and performance evaluation; (2) Merit-based bonus plans were the least favored of the four bonus plans presented, as only 28% of teachers favored their adoption; and (3) Evidence indicated that the average teacher becomes less NTTP-friendly over time, growing less likely to favor the adoption of the bonus-based NTTP plans or to support the use of NTTP criteria in salary increases. (Contains 5 figures, 6 tables, and 11 endnotes.)

In July 2009, President Barack Obama set out a bold higher education agenda for his administration and promised that the U.S. would once again lead the world in college degree attainment. Given the nation's current level of college completion, it is reasonable to wonder whether such ambitions are feasible. While there is a sense that the country needs to recreate the "Golden Age" of American higher education, where high completion rates were the norm, few have bothered to ask whether this era was actually as golden as the conventional wisdom would suggest. In one of the few efforts to examine this question, John R. Thelin, research professor at the Education Policy Studies School at the University of Kentucky and author of "A History of American Higher Education" (Johns Hopkins University Press, 2004), reevaluates the idyllic image of university life in an earlier period and uncovers the historical roots of America's "attrition tradition." Thelin finds that not only did university students often drop out at a high rate in the early 1900s, but also that college attrition was largely ignored until the last few decades. If we are to tackle the challenge of raising graduation rates in an era of increased access--a strikingly modern goal--it will require fine-grained, institution-level analysis, Thelin argues, in addition to significant investments in improved data systems for America's colleges and universities. Using detailed cohort tracking data and a seasoned historical perspective on the origins of today's "war on attrition," this AEI working paper should give pause to ambitious completion promises and prod university leaders to reflect on their own performance data to map a better course for serving students. As Thelin notes, without an accurate sense of how far we have come in our higher education aspirations--and how difficult and costly it has been to get there--we cannot strategically plot the road ahead. (Contains 1 figure, and 39 endnotes.)

The Australian Vocational Education and Training Management Information Statistical Standard (AVETMISS) for vocational education and training (VET) providers (referred to as "the Standard") is currently being reviewed by the National Centre for Vocational Education Research (NCVER). The Standard is the framework that underpins the National VET Provider Collection. It provides consistency in data definitions, which ensures accurate data for use in national data collections, where information is compiled from many different sources. This discussion paper is the first step in the review. It provides a framework for feedback and identifies key issues for consideration. There are many issues associated with the specification of a Standard, such as what information is to be collected, from whom and how often. Decisions about implementation of the AVETMIS Standard are made by a range of different bodies and, while the focus of this paper is primarily on the information to be collected, comments on the timing and coverage are also welcome. Two appendices are included. (Contains 1 figure.)

Proposition 209 banned the use of racial preferences in admissions at public colleges in California. We analyze unique data for all applicants and enrollees within the University of California (UC) system before and after Prop 209. After Prop 209, graduation rates increased by 4.4%. We present evidence that certain institutions are better at graduating more-prepared students while other institutions are better at graduating less-prepared students and that these matching effects are particularly important for the bottom tail of the qualification distribution. We find that Prop 209 led to a more efficient sorting of minority students and the sorting effects explain over 20% of the graduation rate increase. Further, universities appear to have responded to Prop 209 by investing more in their students, explaining between 30-45% of the graduation rate increase. An appendix contains: (1) Table 9: Characteristics of UC Applicants, Admits, and Enrollees by Race, Pre-Prop 209 and Change Post Prop 209; and (2) Table 10: Estimates Using the Baseline Method for Under-Represented Minorities, Whites and Asian Americans.

As a research and technical assistance organization, CNA Education would be the last to deny the importance of good data and the first to applaud efforts to improve the quality and scope of the data available. Solid data are critical to CNA's mission to further general understanding of the efficacy of specific education reform initiatives and to address the needs of their district-level and state-level customers and constituents for a clearer assessment of their own specific efforts and of the available options for improving their education outcomes. The three papers in this collection ("The State Data Analysis Gap: A Threat to Education Reform" by Arthur Sheekey and Michael Allen; "Getting State Education Data Right: What We Can Learn from Tennessee" by Kyle Southern and Joe Jones; and "The Equitable Distribution of Effective Teachers: Can States Meet the Research Challenges Required for Success?" by Michael Allen) make the strong case, however, that the availability of good data is not all that policymakers and educators need for their decisions. In addition, they need to understand the limitations of their data and how to use and interpret them appropriately. And they must ask--and answer to their satisfaction--a number of conceptual and technical questions that have implications for the validity, interpretation, and very meaning of the data they seek to use.

By definition, youth transitions involve young people moving between school, post-school study and employment. It is a time of flux, as young people try out different school, post-school work and study options. But are those who don't find work immediately likely to make a poor transition? Given that many may well have a spell out of the labour force, everyone needs to understand when this becomes a risk factor. This briefing paper draws on related research and some primary data analysis to consider whether being "at risk" is a permanent or transitory state. It suggests that, rather than counting the numbers of young people who are detached from work, study or other meaningful activities, everyone should focus on those who remain disconnected. It is important to be able to identify who may be most "at risk" of an unsuccessful transition to ensure that targeted and appropriate interventions can be implemented. Young people who accumulate disadvantage through poor literacy and numeracy and who are uninterested in school appear particularly vulnerable. They tend to leave school early and suffer disproportionally in the labour market. If this detachment from work or study continues for an extended period of time, the young person's inability to develop employability skills and their lack of work experience adversely affect their prospects of future employment. This is detrimental not only to the individual but also to the nation's productivity. Therefore, programs which help young people to make smoother and faster transitions into further study or employment are important. (Contains 6 tables, 3 figures and 4 footnotes.)

Learning communities are a popular strategy that community colleges nationwide have embraced in support of developmental students. In a learning community, a cohort of students takes two or more courses linked by integrated themes and assignments that are developed through ongoing faculty collaboration. While the number of learning community programs continues to grow, rigorous studies measuring their effectiveness are limited. To address this need for evidence, the Learning Communities demonstration, launched in 2007, uses random assignment to test models of learning communities at six community colleges: Kingsborough Community College, Queensborough Community College, Hillsborough Community College, Merced College, Houston Community College System, and Community College of Baltimore County. The study is designed to determine: (1) how learning communities can be designed to address the needs of academically underprepared students; (2) the effects of learning communities on student achievement, as measured by test scores, credits earned, and grades; (3) the effects of learning communities on students' persistence in higher education; and (4) what learning communities cost and how these costs compare with the costs of standard college programs for students with low basic skills. Preliminary findings will be available in 2009. This working paper describes the study's design, including a summary of the theoretical and empirical research relevant to learning communities, descriptions of the sites and their learning community models, the random assignment procedures, and plans for data analysis. (Contains 7 tables, 1 figure, and 67 notes.) [This paper was produced by the National Center for Postsecondary Research. It was written with the assistance of Oscar Cerna, Christine Sansone, and Michelle Ware.]