education

9 Tricky Education Terms You Should Understand

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Learning standards. Kindergarten readiness. Achievement gap. Personalized learning. Universal preschool. Can you define the terms buzzing around the education world? Jargon can be overwhelming—even for the experts. And language can be further complicated by disagreements on (or misunderstandings of) what terms mean, politics, and different definitions that depend on context.

Whether you’re an educator or education expert, a parent or family member of a student, or a voter hoping to learn more about local schools, a clear understanding of education terms, and the issues surrounding them, can help in many ways.

As education experts ourselves, QIP employees encounter a lot of jargon, much of which has the potential to cause confusion. Here are a few tricky education terms we’ve seen lately, along with descriptions of what they mean—and don’t mean.

Education and Career Pathways: Maps for Learning and Job Success

Recent statistics show a mismatch between the skills secondary and postsecondary students are acquiring and the rapidly changing needs of industry. In June 2018, the Bureau of Labor Statistics reported that U.S. job openings had increased to 6.6 million, while the number of unemployed people was down to 6.3 million. According to the 2017 ExcelinEd white paper Putting Career and Technical Education to Work for Students, “Many of these open positions offer middle- and higher-wage salaries, as well as opportunities for continued training and advancement by employers, but they go unfilled due to a lack of appropriately skilled workers who have completed aligned programs of study.” Pathways data—data that help students navigate through different points in their education and career trajectories—can help solve this problem. These data define not just the routes to success (i.e., to the desired destination), but also the milestones along the way.

It is clear from these reports that current students and education providers could use better alignments to the most promising opportunities in higher education and the workforce. At the macro level, we see gaps between what students are learning and what they need to learn to transition into the college programs of study and work positions that are available. At the micro level, a student’s skill gap in any area (e.g., proportional reasoning) becomes a roadblock for learning further skills that depend on that prerequisite understanding or ability (e.g., operations with fractions, word problems, and physical science applications). The lack of well-defined education pathways data—and the failure to use the information that is currently available—is limiting opportunities for students, employees, and employers.

Four kinds of education and career pathways

There are four kinds of pathways that serve different purposes:

  • Competency pathways define recommended sequences of learning. They show prerequisite and post-requisite relationships between competencies. Competencies can include skills, knowledge, dispositions, or practices.
  • Content pathways define sequences of learning resources or learning experiences.
  • Credential pathways define sequences of credentials that build an individual's qualifications. These pathways often include “stackable” credentials that can help a person qualify for a different and potentially higher-paying job, by adding qualifications to those he/she already has. (See also this explanation of stackable credentials from the U.S. Department of Labor.)
  • Career pathways define a series of structured and connected education programs and support services that enable students, often while working, to advance over time to better jobs with higher levels of education and training. (See also this explanation of career pathways from the Career Ladders Project and this definition from ExelinEd.)

Visualizing pathways as a map

Although the four kinds of pathways have different purposes, their structure looks the same. In each case, the information can be visualized as a map. Points of interest on the map, called milestones, can represent

  • a competency (e.g., a skill, piece of knowledge, disposition, or practice);
  • content (e.g., a learning resource or program);
  • a credential (e.g., a qualification or degree); or
  • a career opportunity (e.g., an internship or job).
Figure        SEQ Figure \* ARABIC     1      . A pathways map has milestones (which are like points of interest on a street map) connected by paths (which are like road segments on a street map).

Figure 1. A pathways map has milestones (which are like points of interest on a street map) connected by paths (which are like road segments on a street map).

While these different types of milestones can all be points in a pathways map, the metadata for each will be different, depending on type. For instance, a credential milestone will have different metadata properties than a competency milestone.

A path is a connector between two milestones. Paths, similar to road segments on a street map, represent recommended ways someone can navigate from point A to point B. On a pathways map, a path shows how to get to a slightly more advanced milestone via its prerequisite milestone. Figure 1 shows the relationship between two milestones and a path.

Figure 2. A pathways map can have multiple routes (which are also called routes on a street map). The route in blue represents one of many education/career possibilities in nursing.

Figure 2. A pathways map can have multiple routes (which are also called routes on a street map). The route in blue represents one of many education/career possibilities in nursing.

A pathways map can be formed by connecting many milestones and paths. People can then select routes based on interests and needs. A career pathways map in nursing, for instance, may have several possible routes. There could be an entry-point milestone of a high school diploma, with two paths leading from there, one to a Licensed Practical Nurse (LPN) qualification and another to an Associate Degree in Nursing (ADN) to qualify as a Registered Nurse (RN). Another path could lead from the LPN to the RN. The LPN and RN could each have a path to a Bachelor of Science in Nursing (BSN). All of this creates many possible routes and destinations (illustrated in figure 2). Additional routes could be created, thus expanding the map, by adding paths from the BSN to graduate degree qualifications for other positions in health care.

Note that, unlike a street map, a pathways map is unidirectional. While people commonly travel from point A to point B and then back to point A, they do not travel from a more advanced milestone to its prerequisite. Of course, people may need to relearn a prerequisite they either missed or forgot in order to advance; they may also decide to double back and change routes. But they will never begin at a master-level job and move from there to a basic internship in the same field, or start by learning differential equations before moving on to addition and subtraction.

More information about education and career pathways

QIP team members are working with teams from edtech initiatives (such as those mentioned in my recent EdSurge article on initiatives working on learner navigation) to help define standards for pathways data that will serve all levels of education, training, and careers. I will be facilitating a session on this topic at the upcoming National Defense Industry Association (NDIA) iFEST conference in Alexandria, Virginia, on August 27–29. See also my video Demystifying Pathways Data on YouTube for another look at education and career pathways.

Jim Goodell (@jgoodell2is Senior Analyst at QIP. He works on connections between education sciences, policy, practice, and personalized/optimized learning. He wrote Turning ‘Google Maps for Education’ From Metaphor to Reality for EdSurge. Learn more about Jim here.

What To Expect at the STATS-DC Education Data Conference

One of the most exciting conferences in the realm of education data is the NCES STATS-DC Data Conference. If your interests and work involve education statistics, this is a great opportunity for learning and networking. STATS-DC attracts approximately 800 to 900 attendees, and there are multiple simultaneous sessions.

STATS-DC in a nutshell

The annual STATS-DC conference is sponsored by the National Center for Education Statistics (NCES), the statistical agency of the U.S. Department of Education. “STATS-DC” is not an acronym, but a shortening of the word statistics, plus a mention of Washington, D.C., where the conference takes place. This year’s theme is “Visualizing the Future of Education through Data.” The 2018 conference will be held during three consecutive days in late July.

One important feature of STATS-DC is that U.S. Department of Education offices provide updates and training on federal policies and activities that affect data collection and reporting. Another highlight is presentations by state and local education agency personnel who work directly with data collection and reporting, as well as by experts from other organizations who share strategies and ideas involving education statistics. Finally, since the conference draws participants and presenters from diverse locations—including a variety of specialists in education and data—it offers great networking opportunities.

Twelve kinds of presentation topics

Many presentations occur simultaneously at STATS-DC—typically 10 presentations at once in 10 rooms. To decide which you are interested in attending, refer to the 2018 Agenda at a Glance, available on the IES conference web page (also provided on paper in the conference registration packet). The Agenda at a Glance color-codes presentations by topic. There are 12 topics, and each presentation is assigned to one of them, based on its content:

  • CCD: The Common Core of Data is a national database that contains information collected from public elementary and secondary schools.
  • Data Collection: Federal, state, and local agencies collect data about education—a large logistical operation.
  • Data Linking Beyond K-12: Linking data from K-12 to early learning, higher education, and workforce provides information used to support students.
  • Data Management: Collecting, storing, and using data requires governance, oversight, and procedures.
  • Data Privacy: When personal information is collected, privacy and security concerns are paramount.
  • Data Quality: It is important that data are as accurate and precise as possible.
  • Data Standards: The education data community is forming common standards, or understandings, about what terms mean and how they are used.
  • Data Use (Analytical): Analysts use data for analyses such as time series, for academic research, and in many other ways.
  • Data Use (Instructional): Educators use data to improve teaching and learning.
  • Fiscal Data: Data on finances can help agencies, districts, and schools plan budgets and use resources efficiently.
  • SLDS: The Statewide Longitudinal Data System Grant Program provides grants and resources for the development and expansion of student-level state data systems.
  • Other: Some presentations may not fall into any of the above categories.

To decide which presentations you would like to attend, you may also wish to read abstracts. Abstracts offer more detailed information about each presentation than is available in the Agenda at a Glance and are available on the Agenda tab of the NCES STATS-DC web page. The Agenda will not be included on paper in the registration packet. However, it is posted online in both HTML and PDF formats, and complimentary Wi-Fi will be available to conference participants in the meeting space.

Making plans to attend STATS-DC

QIP staff will be attending STATS-DC this year, as we have every year for many years. We are currently preparing for the event as described in our recent blog post about how to maximize the benefits of a professional conference. If you are a member of the education statistics community, are interested in learning more about education data, or are attending for another reason, we look forward to seeing you there.

To register and access details about the event, visit the NCES web page on 2018 STATS-DC. If you are unable to attend STATS-DC in 2018 but are interested in attending in a future year, check for updates about future conferences on the IES web page on conferences, workshop/training, and technical assistance.

Big Data in Education: Researchers’ Responsibilities

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While big data’s growing influence has impacted our lives across a spectrum of issues, it also has created many questions and concerns, particularly among education researchers.

Big data allows researchers to uncover patterns in data that might be otherwise invisible. This has led to several powerful advances, such as better treatments for disease, improvements in agriculture, and more timely and effective responses to natural disasters. The benefits of big data have even been highlighted in popular media, such as in the movie Moneyball, which dramatizes how the pioneering use of large datasets helped a general manager assemble a winning baseball team.

But the rise of big data has also prompted many to note its potential negative consequences. Within education, researchers have identified not only benefits to using big data, but also legitimate concerns. As they do with all data, education researchers have a responsibility to focus on both the integrity of their research using big data and on clear communications about this research to the public. Further, their communications with the public should focus not just on the research itself and its useful possibilities, but also on the precautions they are taking to ensure that the rise of big data does not negatively affect the education community.

Big data, defined

The term “big data” refers to very large and complex datasets—those datasets that have been described as “defying traditional data-processing applications” (National Academy of Education, 2017). Modern technologies allow us to capture information in previously unforeseen ways and transform it into digital data. This has resulted in datasets that are much larger and more complicated than anything seen before. From a research standpoint, big data changes data collection from an often lengthy and painstaking process to one that can happen nearly automatically, given the right connections to sources.

Big data in education: improving teaching and learning

Big data in education tends to fall into two major categories: administrative data and learning process data. Combining digital data from these two areas in innovative ways can allow researchers to identify patterns or correlations that may otherwise go unnoticed.

  • Administrative data can be demographic, behavioral, and achievement data and may include items such as attendance records, transcripts, and test scores.
  • Learning process data are continuous records of students’ behaviors and may include online assessments, keystrokes, or time latencies (e.g., the time it takes a student to respond to a question).

Innovative data analyses can lead to useful solutions to problems in schools and classrooms, uncover potential inequities in learning opportunities, and zero in on students’ needs in ways that reveal how to personalize learning more effectively. The overarching goal of this data collection and analysis is to expand possibilities for teaching and learning—including how to meet individual students’ needs.

Big data in education: legitimate concerns

Education researchers have raised some legitimate concerns about big data. While they recognize that big data has many exciting possibilities, researchers have also identified some potential problems with its use—or misuse. These concerns tend to fall into three main categories: misinterpretation, inappropriate use, and data privacy and security.

  • Misinterpretation concerns center on the possibility that studies using big data may be misunderstood by readers—especially if the studies are distilled or simplified before reaching the public—and that these misinterpretations could lead to inaccurate decisionmaking.
  • Inappropriate use concerns suggest that the public nature and accessibility of some big data may lead to people using the data in ways that were not intended and that defy accepted research standards.
  • Data privacy and security concerns are based on concerns that individuals’ personal information may not be properly protected, which could lead to data breaches or other inadvertent disclosures of private information.

As the education field continues to move toward greater use of big data, each of these issues should be specifically and consistently addressed. This can be accomplished through strong data governance, research standards, and other precautionary measures.

Researchers’ responsibilities: communication with the public

Education researchers must think not just about the research on big data, but also about how the public is receiving and reacting to this research. Public discussion of big data is frequently negative and inaccurate. Unlike the measured considerations of big data presented in academic articles, much of the communication about education-related big data to the public has encouraged skepticism and fear. It is not surprising that many parents and other stakeholders have developed negative views, given the frequent headlines that tout the “big dangers” of big data. The public less frequently encounters news that describes the potentially positive aspects of this education information or the clear standards that are in place to protect the privacy of personal information.

At the same time, researchers should work to ensure that members of the education community understand the legitimate concerns about big data and what we can all do to avoid or mitigate problems that may arise from misinterpretation, inappropriate use, and data privacy and security issues. Walking the fine line between explaining the intricacies of this difficult topic and communicating concisely and clearly is something education researchers must strive to master.

Big data is indeed a problem if it is used ineffectively, inappropriately, or by individuals without a requisite level of comprehension of the complexities of the subject. But that is true of all research data. Data, in various forms, can reveal that something has happened, that a phenomenon exists, or that variables appear to have a relationship, but data cannot on their own reveal why. It is the responsibility of researchers—especially those in the public sphere—to provide the lenses that make research relevant and comprehensible to varied audiences, from parents and teachers to administrators and elected officials.

It is important for education researchers to make clear that they are using the same stringent research standards for big data analysis that they have adhered to with previous types of data. Additionally, they must communicate to the public that they are regularly discussing the potential hazards of big data and routinely updating methodologies and security protocols as projects and analyses become increasingly complex. The clearest path to public trust in the research process is via straightforward and detailed communication.

Bridget Thomas (@DrBridgeQIP) is Senior Education Researcher at QIP and Adjunct Professor at George Mason University. Her work focuses on early childhood policy and translating research for multiple audiences.

The World Is Changing . . . What’s Your Resolution?

We’ve recently welcomed the year 2018 into our lives. With it will come new experiences, lots of growth, and many changes. Some of us made personal New Year’s resolutions as we contemplated the preceding year as well as looked to the year ahead. January is also a great month to make resolutions for your 2018 work life.

How do you hope to grow as a professional this year? If you haven’t yet identified a work-related resolution, you could try thinking in terms of the ways you want, or need, to change in response to shifting circumstances around you. Since the world is constantly evolving, it’s important to adapt and keep abreast of new trends and technologies.

Also keep in mind that not all resolutions have to be related to completing discrete tasks or learning specific skills (though they can be, of course). For example, consider Maya Angelou’s advice: “If you don't like something, change it. If you can't change it, change your attitude.” Whatever you do, however, don’t remain the same in the face of the rapid transformations our world is experiencing.

The majority of what we do at QIP involves ensuring that education data are as accurate, available, and useful as possible. Therefore, in 2018 we will be thinking about how we can improve data operations, quality, accessibility, and use. We will also be seeking to improve our professional capabilities to support these goals—for example, by focusing on being effective teammates, learning new technological skills, and showing leadership and initiative at work.

Here’s to both continuing to improve education data and developing ourselves professionally this year—sound and reasonable resolutions for 2018. At QIP, we’re proud of what we do because improving education data directly impacts the quality  of education . . . and education, more than any other field, sows seeds that can change our world for the better. We wish to all a wonderful 2018 that’s filled with positive growth and exciting transformations.

[Source of quotation: https://www.theguardian.com/books/2014/may/28/maya-angelou-in-fifteen-quotes]

Thank You, Lunch Ladies and All Who Work to Help Others

“A thank you can change a life.” This assertion was shared by Jarrett Krosoczka during his 2014 TED Talk “Why Lunch Ladies are Heroes.” Krosoczka is the author of the Lunch Lady graphic novel series, and his talk and novels focus on the school lunch ladies who play an essential, but often overlooked, role in the education system. This reminded us of education data—essential for everyday district and school operations (such as planning, evaluation, and improvement), but often overlooked relative to other aspects of the education enterprise.

As emphasized in Krosoczka’s talk, lunch ladies do a lot more than serve chicken nuggets. More than 30 million kids participate in school lunch programs every day—that’s more than 5 billion lunches made each school year. In one district in Kentucky, where 67 percent of the students relied on schools to serve them meals each day during the school year, the lunch ladies retrofitted a school bus so they could feed 500 kids a day over the summer, when the children might not otherwise receive regular meals. Thank you, lunch ladies, for making the effort, and thank you to members of the data community for collecting the data that drives education decisionmaking and action.

Krosoczka’s presentation made us think about the unsung heroes in our lives. In the context of QIP, sometimes some of our staff become very busy with a large, important assignment. When we finish a big project, it is natural that we should thank the folks who worked on it—for example, the management planners, content leads, reviewers, editors, and graphic designers. But we sometimes forget to thank the people who didn’t work on that particular project, but carried the weight of all the other projects—also extremely important—that needed to be completed at that time, allowing others to focus on the large assignment. We want to take this opportunity to say thank you to everyone on the QIP staff—no matter which projects you happen to be involved in.

There are also many people beyond the work environment who deserve thanks. As Krosoczka states, “Before a child can learn, their belly needs to be full.” Who feeds your belly, literally or figuratively? Who helped you along your journey, but wasn’t noticed, or thanked, often enough because they were quiet and humble or, like the lunch ladies, didn’t have a high-profile position, even though their contributions to your life were real and meaningful?

Late November is a great time to share gratitude. Perhaps we should all reach out to the “lunch ladies” in our lives and share a heartfelt thank you. As shown in Krosoczka’s TED Talk, it can mean the world to someone, especially if they are feeling otherwise unnoticed or undervalued in our busy world.

[Source of TED Talk: https://www.ted.com/talks/jarrett_krosoczka_why_lunch_ladies_are_heroes]