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Take Advantage of Two Education Tax Credits

The American Opportunity credit and the Lifetime Learning tax credit can make higher education costs more affordable.

The American Opportunity credit covers up to $2,500 of undergraduate costs

Who can claim the credit, which expenses are covered, income phase-out rule, credit is partially refundable, the lifetime learning tax credit covers up to $2,000 of undergraduate and graduate school costs, eligibility rules and qualified expenses, claiming the credits.

Key Takeaways

• The American Opportunity credit provides up to $2,500 in tax credit for qualified undergraduate education expenses, while the Lifetime Learning credit provides up to $2,000 for both undergraduate and graduate qualified school expenses.
• Both credits can cover tuition and mandatory enrollment fees, but only the American Opportunity credit covers books and course materials.
• The American Opportunity credit can be claimed for up to four years of undergraduate education, and the student has to be enrolled in a program leading to an associate or bachelor's degree, or qualifying credential.
• The income phase-out range for the American Opportunity credit is $80,000 to $90,000 for unmarried individuals, and $160,000 to $180,000 for married couples filing jointly.

In 2009, Congress replaced the well-known Hope Scholarship credit with the more generous American Opportunity credit.

The American Opportunity credit equals

• 100% of the first $2,000 of a student’s qualified education expenses, plus
• 25% of the next $2,000, and
• the maximum annual credit is $2,500.

You can claim the American Opportunity credit for qualified education expenses you pay for a dependent child as well as for expenses you pay for yourself or your spouse. If you have several students in your family, you can claim multiple credits based on the expenses of each student.

• For example, if you have three kids in college, you can claim up to $7,500 ($2,500 x 3) in American Opportunity credits.

The credit is not allowed for a student who has completed the first four years of post-secondary education as of the beginning of the year. So, if your child completed less than four years of college as of January 1, 2023, you can claim the credit on your 2023 return.

• You can only claim the credit for a year during which the student carries at least a half-time course load for a minimum of one semester beginning in that year.
• Additionally, the student must be enrolled in a program that leads to an associate or bachelor's degree or some other recognized credential.

Expenses covered by the credit include:

• mandatory enrollment fees
• the cost of books
• course materials

Room and board do not count as qualified expenses nor do optional fees to cover things like student health insurance , athletics and other activities.

To qualify, students must attend an eligible institution. Almost all accredited public, nonprofit and for-profit postsecondary schools (including many trade schools) fit this description. To make sure a school is eligible, go to fafsa.gov and verify that it has a Federal School Code.

TurboTax Tip: The American Opportunity credit is partially refundable, with 40% being refundable even if the taxpayer doesn't owe any federal income tax.

The American Opportunity credit is phased out if your modified adjusted gross income (MAGI) exceeds certain levels. (MAGI is adjusted gross income plus certain tax-free income from sources outside the United States.)

• The MAGI phase-out range for unmarried individuals is $80,000 to $90,000.
• The MAGI phase-out range for married couples filing jointly is $160,000 to $180,000.
• Regardless of your income, you are not eligible if you use married filing separate status.

Your American Opportunity credit is 40% refundable. That means a portion of the credit will be refunded to you even if you don’t owe any federal income tax. Here’s how it works.

• Say your American Opportunity credit is $2,500.
• The refundable portion is $1,000 ($2,500 x 40%).
• That amount is treated as a payment on your tax return (as if you had the $1,000 withheld from your wages).
• The remaining $1,500 ($2,500 x 60%) is a nonrefundable credit that provides a benefit to you only if you owe federal income taxes.

If you don’t owe any federal income tax because of deductions and other credits, the entire $1,000 refundable credit counts as a tax overpayment and is refunded to you.

For example, if you owe $1,900 in taxes,

• The nonrefundable $1,500 portion of the credit is used first to reduce your tax bill to $400.
• Then the first $400 of the refundable credit is used to lower your tax bill to zero.
• Finally, the last $600 of the refundable credit is paid to you as a tax refund.

If your federal income tax bill is $4,500, the $1,500 nonrefundable portion of the credit reduces your tax bill to $3,000. Then the $1,000 refundable credit further reduces your tax bill to $2,000.

The rules for the Lifetime Learning tax credit are unchanged from prior years. As before, the credit is:

• 20% of up to $10,000 of qualified education expenses
• The maximum credit is $2,000 before any phase-outs

The Lifetime Learning tax credit can help cover undergraduate costs for a student who is not eligible for the American Opportunity credit because they're carrying a limited course load or already have four years of college credit. Additionally, the Lifetime Learning credit can also help cover the cost of graduate school and of courses taken to maintain or improve job skills.

You can claim the Lifetime Learning credit for qualified education expenses you pay for a dependent child as well as for yourself or your spouse.

• The maximum amount of covered expenses is $10,000 no matter how many students you have.
• This translates into a $2,000 maximum credit ($10,000 X 20%).

Qualified expenses include tuition and mandatory enrollment fees at an eligible institution. Books and course materials can also count, but only if you are required to purchase them directly from the school. Other expenses, such as optional fees and room and board, do not qualify.

Warning: You can’t claim both the American Opportunity credit and the Lifetime Learning credit for the same student for the same year. However, you can potentially claim the American Opportunity credit for one or more students and the Lifetime credit for up to $10,000 of qualified expenses for other students in your family.

Like the American Opportunity credit, the Lifetime Learning credit is phased out if your modified adjusted gross income (MAGI) exceeds certain (much lower) levels.

The rules for these credits can be tricky—especially when it comes to handling the refundable portion of the American Opportunity credit. TurboTax will show you which education credits will get you the best tax advantage, do all the calculations and complete all the forms for you. Just answer some simple questions and let TurboTax take care of the rest.

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~37% of taxpayers qualify.  Form 1040 + limited credits only .

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The above article is intended to provide generalized financial information designed to educate a broad segment of the public; it does not give personalized tax, investment, legal, or other business and professional advice. Before taking any action, you should always seek the assistance of a professional who knows your particular situation for advice on taxes, your investments, the law, or any other business and professional matters that affect you and/or your business.

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Massachusetts has made community college tuition-free. What will that mean for enrollment, quality, and workforce development?

HKS education expert David Deming says more people could be attracted to a community college education, but cautions about diluting the quality of the programs. 

Massachusetts recently joined the ranks of states and other localities that have made community colleges tuition-free. David Deming , the Isabelle and Scott Black Professor of Political Economy and HKS faculty co-director of the Project on Workforce , is an economist whose research focuses on higher education and the future of the labor market. We asked him about the growing national movement to eliminate community college tuition and how this effort may be reshaping the country’s workforce development policies.

Q: The idea of free community college really gained traction over the last decade, with nearly 30 states now offering a variety of different tuition-free community college plans. As a scholar who has examined the critical role community colleges play in workforce development, what impact have these plans had?

The idea of free community college is not new. In fact, community colleges in many states were free until the 1960s. Even some four-year schools like the CUNY system in New York City and the University of California system were free until about 50 years ago. Ronald Reagan was elected governor of California in 1966 on a platform of charging tuition to UC students in the name of fiscal responsibility. 

The basic problem is that colleges have been victims of their own success. A generation ago, 20% of people went to college. Now it’s more than 60%, and the population has expanded substantially as well. The promise of free college is just a lot more expensive than it used to be. 

The modern free community college movement really started in 2014 when Republican Governor Bill Haslam of Tennessee signed the Tennessee Promise Scholarship Act of 2014, which provided free tuition and mentoring for recent high school graduates to attend any community college or college of applied technology in the state. A bunch of other states followed Tennessee’s lead, and today about half of all states have some form of tuition-free college program. 

Most programs are still too recent to look at longer-run impacts like degree completion and earnings. One exception is a study of an early version of the Tennessee program in Knox county , which found that free community college increases associates degree completion by more than 20%. However, it found no significant impacts on BA completion or on short-run earnings, although the participants were still relatively young to look at labor market impacts. 

The bottom line is that these programs clearly increase community college enrollment. Whether they have broader positive economic impacts is still uncertain.

Q: Is tuition the biggest hurdle for students trying to access community colleges across the country? What other hurdles exist?

The main reason so many states have enacted free community college plans is that they don’t cost very much taxpayer money. Why? Tuition is already very low, and more importantly, most states (including Massachusetts) have enacted so-called “last dollar” plans that only kick in after students apply for and receive federal financial aid. To put some numbers to it—average community college tuition in Massachusetts is about $4,500. The maximum Federal Pell Grant is $7,395. Since most community college students are low- or middle-income, and many are young and financially independent, they would already have gone for free even without the new Massachusetts plan.  

In my view this makes the Massachusetts plan more valuable, because the advertising value of “free community college” will get many people in the door who would have gone for free anyway. Now they know before they apply for aid, rather than having to fill out a long form and wait for a month or more. These folks are busy and have complicated lives. The administrative burden of applying for aid looms just as large as the financial cost, but knowing that it will be free at the end of the day could make more people take the plunge.

“The primary risk of free community college is that we will focus too much on price, and not enough on quality. … The risk is that the fiscal burden of free tuition will cause legislators to cut appropriations over time.”

David deming, q: is there a concern that these tuition-free programs might inadvertently undermine enrollment numbers and the subsequent viability of some four-year colleges.

Potentially. The evidence I’ve seen suggests that it does divert some students away from four-year colleges, but that the net impact is positive. This is partly because of the advertising value I mentioned above, but also because some community college students who enroll on the promise of free tuition will end up transferring to a four-year school down the line.

The big losers are private institutions, who will lose students to the promise of free tuition in the public sector. In my view this is a good thing, not because private institutions are per se bad (I teach at one!), but because the competitors to community colleges are typically large online for-profit schools that charge high tuition and deliver poor value for money.

Q: Massachusetts was relatively slow in making community college free. In fact, Tennessee has been a national trendsetter in this movement, with policymakers flocking to the state to see how community colleges there attract and retain students. How was this significant expansion of education spending in a relatively conservative state such as Tennessee possible?

Like many other states, the Tennessee Promise program was funded primarily by proceeds from the state lottery. In Massachusetts, the legislative deal that established free community college also allowed the Massachusetts state lottery to move online, which they expect will increase sales and revenue. Some people think lotteries are exploitative, but lawmakers typically think the tradeoff is worth it.

Q: Back in Massachusetts, what steps should policymakers and community college administrators take to ensure the rollout of this tuition-free initiative is a success?

The primary risk of free community college is that we will focus too much on price, and not enough on quality. Community college tuition may be about $4,500 per year in Massachusetts, but the true resource cost of the education is more than double that amount. In other words, the cost of paying the salaries of community college instructors, student support, building maintenance, and everything else is much higher than what can be funded by tuition. The gap is made up by state legislative appropriations—essentially, the taxpayers fund a transfer of tax revenue each year directly to community colleges. Students were paying $4,500 per year to get a $10,000 per year education.

The risk is that the fiscal burden of free tuition will cause legislators to cut appropriations over time. Concretely, this would mean larger classes, less student support, and a lower quality education all around. A $10,000 per year education would become a $5,000 per year education, even if the price is still “free.”

We know from many studies that spending increases graduation rates, and the best programs in dynamic fields like nursing and technology are often more expensive to maintain. So, the main risk is that free community college will become lower quality and stop being a good deal for students, who, after all, are still foregoing a lot of income to attend.

Banner image: a professor delivers a lecture to students at Bunker Hill Community College in Boston. Photo by Josh Reynolds/AP Photos. Faculty portrait by Martha Stewart.

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Apprenticeships multiply the pathways to education, jobs, or both, new harvard report finds, legacy of privilege: david deming and raj chetty on how elite college admissions policies affect who gains power and prestige, putting cross-sector skills to work for a post-pandemic labor force.

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https://educationhub.blog.gov.uk/2024/08/20/gcse-results-day-2024-number-grading-system/

GCSE results day 2024: Everything you need to know including the number grading system

Thousands of students across the country will soon be finding out their GCSE results and thinking about the next steps in their education.   

Here we explain everything you need to know about the big day, from when results day is, to the current 9-1 grading scale, to what your options are if your results aren’t what you’re expecting.  

When is GCSE results day 2024?  

GCSE results day will be taking place on Thursday the 22 August.     

The results will be made available to schools on Wednesday and available to pick up from your school by 8am on Thursday morning.  

Schools will issue their own instructions on how and when to collect your results.   

When did we change to a number grading scale?  

The shift to the numerical grading system was introduced in England in 2017 firstly in English language, English literature, and maths.  

By 2020 all subjects were shifted to number grades. This means anyone with GCSE results from 2017-2020 will have a combination of both letters and numbers.  

The numerical grading system was to signal more challenging GCSEs and to better differentiate between students’ abilities - particularly at higher grades between the A *-C grades. There only used to be 4 grades between A* and C, now with the numerical grading scale there are 6.  

What do the number grades mean?  

The grades are ranked from 1, the lowest, to 9, the highest.  

The grades don’t exactly translate, but the two grading scales meet at three points as illustrated below.  

The bottom of grade 7 is aligned with the bottom of grade A, while the bottom of grade 4 is aligned to the bottom of grade C.    

Meanwhile, the bottom of grade 1 is aligned to the bottom of grade G.  

What to do if your results weren’t what you were expecting?  

If your results weren’t what you were expecting, firstly don’t panic. You have options.  

First things first, speak to your school or college – they could be flexible on entry requirements if you’ve just missed your grades.   

They’ll also be able to give you the best tailored advice on whether re-sitting while studying for your next qualifications is a possibility.   

If you’re really unhappy with your results you can enter to resit all GCSE subjects in summer 2025. You can also take autumn exams in GCSE English language and maths.  

Speak to your sixth form or college to decide when it’s the best time for you to resit a GCSE exam.  

Look for other courses with different grade requirements     

Entry requirements vary depending on the college and course. Ask your school for advice, and call your college or another one in your area to see if there’s a space on a course you’re interested in.    

Consider an apprenticeship    

Apprenticeships combine a practical training job with study too. They’re open to you if you’re 16 or over, living in England, and not in full time education.  

As an apprentice you’ll be a paid employee, have the opportunity to work alongside experienced staff, gain job-specific skills, and get time set aside for training and study related to your role.   

You can find out more about how to apply here .  

Talk to a National Careers Service (NCS) adviser    

The National Career Service is a free resource that can help you with your career planning. Give them a call to discuss potential routes into higher education, further education, or the workplace.   

Whatever your results, if you want to find out more about all your education and training options, as well as get practical advice about your exam results, visit the  National Careers Service page  and Skills for Careers to explore your study and work choices.   

You may also be interested in:

• Results day 2024: What's next after picking up your A level, T level and VTQ results?
• When is results day 2024? GCSEs, A levels, T Levels and VTQs

Tags: GCSE grade equivalent , gcse number grades , GCSE results , gcse results day 2024 , gsce grades old and new , new gcse grades

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• Open access
• Published: 25 August 2024

Optimizing a national examination for medical undergraduates via modern automated test assembly approaches

• Lingling Xu 1 ,
• Zhehan Jiang 1 ,
• Fen Cai 1 ,
• Jinying Ouyang 1 ,
• Hanyu Liu 1 &
• Ting Cai 1  

BMC Medical Education volume  24 , Article number:  919 ( 2024 ) Cite this article

Metrics details

Automated test assembly (ATA) represents a modern methodology that employs data science optimization on computer platforms to automatically create test form, thereby significantly improving the efficiency and accuracy of test assembly procedures. In the realm of medical education, large-scale high-stakes assessments often necessitate lengthy tests, leading to elevated costs in various dimensions (such as examinee fatigue and expenses associated with item development). This study aims to augment the design of the medical education assessments by leveraging modern ATA approaches.

To achieve the objective, a four-step process employing psychometric methodologies was used to calibrate and analyze the item pool of the Standardized Competence Test for Clinical Medicine Undergraduates (SCTCMU), a nationwide summative test comprising 300 multiple-choice questions (MCQ) in China. Subsequently, two modern ATA approaches were employed to determine the optimal item combination, accounting for both statistical and content requirements specified in the test blueprint. The qualities of the assembled test form, generated using modern ATA approaches, underwent meticulous evaluation.

Through an exploration of the psychometric properties of the SCTCMU as a foundational step, the evaluation revealed commendable quality in the item properties. Furthermore, the evaluation of the quality of assembled test form using modern ATA approaches indicated the ability to ascertain the optimal test length within the predefined measurement precision. Specifically, this investigation demonstrates that the application of modern ATA approaches can substantially reduce the test length of assembled test form, while simultaneously maintaining the required statistical and content standards specified in the test blueprint.

Conclusions

This study harnessed modern ATA approaches to facilitate the automatic construction of test form, thereby significantly enhancing the efficiency and precision of test assembly procedures. The utilization of modern ATA approaches offers medical educators a valuable tool to enhance the efficiency and cost-effectiveness of medical education assessment.

Peer Review reports

Introduction

Assessments serve a wide array of vital functions: they exert a significant influence on learning, offer feedback on the efficacy of educational and training programs, and consequently contribute to patient safety [ 1 ]. Prior to the mid-twentieth century, medical education, licensure, and certification assessments primarily consisted of essays or oral evaluations [ 1 , 2 ]. During that era, assessment-derived evaluations often proved to be subjective, arbitrary, and lacked reproducibility [ 1 ]. Subsequently, Multiple-choice question (MCQ) examinations emerged in large-scale high-stakes assessments [ 3 ]. Meanwhile, several alternative assessment methods, such as performance assessments, were also devised owing to the growing complexity of medical education assessments [ 4 ]. However, large-scale high-stakes assessments using primarily MCQs are prevalent in medical education and are designed to objectively measure student performance in complex areas of medical knowledge. This approach is heavily utilizated in medical licensure and certification examinations, such as the United States Medical Licensing Examination and the National Board of Medical Examiners subject examination.

To ensure that large-scale high-stakes assessments effectively demonstrate a student’s grasp of clinical knowledge, tests should exhibit objectivity, reproducibility (reliability), and validity for the intended purpose. Furthermore, they should garner acceptance from both examinees and examiners, integrate a learning-enhancing component, and maintain cost-efficiency [ 5 ]. From an educational measurement theory standpoint, an excessive number of items in an examination not only escalates item development costs but also results in unnecessary item exposure within the item bank [ 6 , 7 ]. Additionally, this practice may increase respondent burden, diminish response quality and willingness to complete the examination [ 8 ]. Such an approach not only elevates the risk of test fatigue and performance impairment but also amplifies the time and effort invested by test takers, thereby further increasing examination costs. Compared with other disciplines, existing medical education qualifications and certification examinations reveal that large-scale high-stakes assessments often include a substantial number of test items [ 9 , 10 ]. Therefore, it is crucial to explore novel methodologies for optimizing test items in the field, ensuring precise measurement of target skills/attributes and reducing expenses associated with test development and administration, without imposing excessive burdens on examinees.

As an indispensable component of educational assessment, measurement theories offer frameworks and approaches to the entire process of a test; the mainstream ones include classic test theory (CTT) [ 11 , 12 , 13 ], generalizability theory (G-theory) [ 14 , 15 , 16 ], and item response theory (IRT) [ 17 , 18 ]. The extensive utilization of IRT in the past decades underscores its significance in the development and analysis of large-scale high-stakes assessments in medical education. The statistical models of IRT have been used to analyze item quality, cut-off score reliability, dimensionality, and examinees’ scores of many large-scale high-stakes assessments [ 10 , 19 , 20 , 21 ].

In tandem with the IRT’s advancements, the arduous process of manually selecting items for test form generation has been revolutionized with the introduction of automated test assembly (ATA). The conventional test assembly method heavily relies on the experiential insights of test developers, a process that varies among developers and is likely to result in unreliable and suboptimal test assembly decisions. In contrast, the modern ATA approach employs computer algorithms to automate the construction of test form, greatly enhancing efficiency and accuracy [ 22 , 23 ]. Modern ATA approaches aim to align the difficulty of test items with examinees’ ability levels under the IRT framework [ 24 ]. This minimizes the disparity in measurement precision between assembled test form and the desired test, while satisfying the specifications outlined in the test blueprint. Realizing these benefits, the research committee of the Standardized Competence Test for Clinical Medicine Undergraduates (SCTCMU) in China would investigate the use of modern ATA approaches to enhance the design.

Developed and administered jointly by the National Medical Examination Center, and the National Center for Health Professions Education Development in China, the SCTCMU functions as a nationwide assessment for evaluating students’ learning outcomes in basic and clinical medical sciences at the end of the fourth year of their five-year undergraduate studies in clinical medicine [ 10 ]. Examinees whose scores meet the pass score will be admitted to the clinical placement stage to further improve their clinical skills. It consists of two components: clinical skills assessed through objective structured clinical examinations (OSCEs) and medical knowledge assessed through computerized MCQs, which are the target of the present paper. The medical knowledge part comprises 300 dichotomously scored MCQs, each of which contains four distractors and a key answer.

The sheer magnitude of China’s population, the largest in the world, contributes to elevated costs associated with test administration. For example, more than 25,000 examinees registered for the Spring 2022 SCTCMU administration. Moreover, the application of modern ATA approaches for optimizing test items in the medical education field can amplify its utility, alleviating testing burdens for a greater absolute number of examinees. Fueled by these pragmatic necessities, modern ATA approaches were utilized to enhance the design of the SCTCMU by estimating the optimal test length, all the while adhering to the requisites of the test blueprint and the prescribed measurement precision.

This paper aims to apply modern ATA approaches to the SCTCMU to reduce the test length of assembled test form, while simultaneously upholding the requisite statistical and content standards that are outlined in the test blueprint. To achieve the objective, four fundamental issues were addressed in order to satisfy the cycle of a typical ATA research agenda: (a) investigating psychometric properties and deriving calibrated item parameters from an authentic SCTCMU item pool, (b) constructing test form using modern ATA approaches while adhering to a predetermined level of measurement precision, (c) evaluating the quality of the assembled test form in order to determine the optimal test length within the designated content and statistical requirements, and (d) providing practical insights and guidance for the design and quality assurance of medical education test form generated using ATA approaches.

Overall workflow

Figure  1 visually outlines the three pivotal components involved in this optimization process: item pool preparation, test assembly, and quality evaluation of the assembled test form. The item pool for this study comprises items extracted from the Spring 2022 SCTCMU. During the item pool preparation phase, a four-step process employing psychometric methodologies was employed to evaluate the item parameters of the Spring 2022 SCTCMU. Subsequently, various modern ATA approaches were used to identify the most suitable item combination, ensuring conformity with predefined constraints like alignment with the test blueprint, while achieving the desired measurement precision for pass-fail determinations. Ultimately, the attributes of the assembled test form, achieved through modern ATA approaches, were evaluated based on several criteria encompassing test length, reliability, validity, measurement precision, and adherence to the non-statistical aspects of the blueprint. These evaluation criteria will be elaborated upon in the subsequent sections.

The overall framework for utilizing modern ATA approaches to optimize medical education assessments

Item pool preparation

To conduct the psychometric evaluation of SCTCMU in item pool preparation, a 4-step flow covering essential tasks was adopted, as seen in Fig.  2 . Without statistical and/or mathematical context, one can regard a step as a prerequisite for its successor, and rules of thumb for all necessary statistics (i.e., thresholds) are also provided in the figure. Step 1 is self-evident: obtaining the target dataset is a foundation for further analysis. In the second step, the unidimensionality assumption should be satisfied so that IRT models can be constructed. Like all statistical modeling procedures, Step 3 drives researchers to check the appropriateness of the model, including which candidate model performs the best based on model-fit statistics, as well as evaluating the quantitative values for the model fit. After confirming the use of IRT, the final step is extracting item parameter information yielded by the selected model. Details about each point listed in the steps are described below.

Step 1: data collection

The items were obtained from the raw responses of 300 MCQs from 25,154 examinees in the Spring 2022 SCTCMU administration and were used as the source data. It is based on the content specified in the National Chinese Medical Qualification Examination Standards to determine the scope of content, and the summary of content and corresponding proportions in each subject regarding medical knowledge are shown in Tab. S1 of Supplementary Material 1 .

Step 2: test model hypothesis

Unidimensionality is a critical assumption for conducting IRT analysis [ 25 ]. Factor-analytic methods are frequently used to assess whether a one-dimensional construct underlies the examination. Unidimensionality is confirmed when the ratio of the first eigenvalue to the second eigenvalue is equal to or greater than 4, and when the first factor accounts for more than 20% of the total variance [ 26 ].

Step 3: model-data fit and model selection

In the framework of IRT, an appropriate model is required for parameter estimation. In this study, three commonly used dichotomous IRT models were considered: the two-parameter logistic model (2PLM) [ 27 ], the three-parameter logistic model (3PLM) [ 28 ], and the Rasch model [ 29 ]. The optimal IRT model was selected for further analysis based on the following test-level model fitting indices: -2 log-likelihood (-2LL) [ 30 ], Akaike information criterion (AIC) [ 31 ], and Bayesian information criterion (BIC) [ 32 ], and the G 2 statistic [ 33 ]. Lower values of these indices indicate a better model fit. Additionally, the goodness-of-fit test ( G 2 statistic) was not statistically significant, suggesting that the selected model provided a good fit to the data.

Step 4: item parameters analysis

Item parameters, such as item discrimination and item difficulty, are essential in constructing test form. These parameters can be evaluated using specific cutoff values. Items with discrimination values below 0.8 should be excluded [ 25 , 34 ]. Baker [ 35 ] further categorizes the discriminative ability of an item as none (0), very low (0.01 to 0.34), low (0.35 to 0.64), moderate (0.65 to 1.34), high (1.35 to 1.69), very high (above 1.70), and perfect (+ infinite) based on its discrimination value. Furthermore, according to Steinberg and Thissen [ 36 ], the item difficulty parameter should fall within the range of -3 to 3.

The steps for using psychometric methods in item pool preparation

Test assembly using modern ATA approaches

General framework of test assembly.

The objective of test assembly is to minimize the difference in measurement precision between the assembled test form and target test while meeting the test blueprint requirements, termed non-statistical constraints. When using modern ATA approaches for test assembly, one needs to generate target test information functions (TIF) (“targets”), which determine the ability estimation precision (i.e., score precision), and the “constraints,” which indicate the qualitative and nonstatistical test specifications in some way. In this study, drawing on the previous studies [ 37 , 38 , 39 ], we set the measurement precision values through TIF at the cut scores (i.e., the ability value corresponding to an expected test score of 180 on the test characteristic curve) of 25 during the test assembly process. Additionally, the SCTCMU content specialists provide test blueprints that outline the required test sections, test majors, and content representation in terms of topics.

ATA algorithms

The modern ATA algorithms can be classified into heuristic algorithms (e.g., normalized weighted absolute deviation heuristic, maximum priority index (MPI), and weighted deviations model), mixed-integer programming (MIP) algorithms, and machine learning algorithms (e.g., Bayesian optimization algorithm, deep learning algorithm, and simulated annealing algorithm). The reasons why MPI and MIP algorithms were selected for this study are threefold. First, extensive literature has demonstrated the widespread application of MIP and MPI algorithms in multiple fields of educational and psychological research [ 40 , 41 , 42 ]. Second, unlike the ones such as pure machine learning approaches, MPI and MIP algorithms are capable of effectively handling complex IRT models and accommodating both statistical and non-statistical constraints, allowing the item combination procedures in accordance with IRT principles. Third, numerous studies have successfully employed these algorithms in practical settings, such as the large-scale Chinese Proficiency Tests [ 43 ] and the Comprehensive Osteopathic Medical Licensing Examination of the United States [ 44 ]. The evidence for the maturity, suitability, and generalizability of these algorithms establishes a solid foundation for optimizing the design of medical education assessments. A comprehensive review of how these two approaches are utilized in test assembly is provided below.

MPI algorithm

The MPI algorithm is used to control both the statistical and non-statistical constraints [ 45 ]. The test assembly processing flow of MPI algorithm is shown in Fig. 3 . Suppose that we need to select the item from the item pool that most closely matches the target TIF value at the cut score while satisfying K constraints, then the MPI index for the candidate item i in the item pool is given by

where \({I}_{i}\) represents the Fisher information of item i evaluated at the ability value of the cut score, \({f}_{k}\) measures the scaled “quate left” of constraint k ; and \({q}_{ik}\) indicated whether item i is related to constraint k ( \({q}_{ik}=1\) if constraint k is relevant to item i , otherwise \({q}_{ik}=0\) ). Each constraint k is associated with a weight \({w}_{k}\) . Generally, more important constraints are assigned larger weights.

Suppose constraint k is a content constraint that requires the test to have \({B}_{k}\) items from a certain content area, and so far \({b}_{k}\) items have been selected. The resulting scaled ‘quota left’ is calculated as \({f}_{k}=\frac{{B}_{k-}{b}_{k}}{{B}_{k}}\) . Using Eq. ( 1 ), we can compute the priority index (PI) for every available item in the pool, and the item with the largest PI, instead of the largest Fisher information, will be chosen for the test assembly. The process continues until the selected items satisfy the target measurement accuracy at the cut score.

MIP algorithm

Compared to the MPI algorithm, the MIP algorithm can strictly satisfy many non-statistical constraints (e.g., content specifications and item exposure rate) when building the test form [ 46 ]. The test assembly processing flow of MIP algorithm also is shown in Fig. 3 . A MIP model consists of an objective function and multiple constraints, both of which are defined in the form of linear formulation. The goal of the MIP algorithm is to select available items from the item pool so that the difference between the test information based on the selected items and the target information at the anchored ability point (i.e., cut score), \({\theta}_{q}\) , is minimized, while also meeting the non-statistical constraints. Specifically, items are selected for test assembly based on the following equation,

Subject to: nonstatistical constraints and \({x}_{i}\in\:\left\{\text{0,1}\right\},k=1,\dots,N\) .

Two approaches utilizing in the test assembly

Evaluation criteria

Drawing on previous studies relating to modern ATA approaches [ 43 , 46 , 47 ], the evaluation of modern ATA approaches for optimizing medical education assessments in this study was conducted using the following measures. Firstly, the test length of the assembled test form was recorded. Secondly, the Cronbach’s alpha was used to evaluate the reliability, and the criterion-related validity and content validity were computed. Thirdly, the value of TIF at the cut score was also recorded to assess the measurement accuracy. Fourthly, the non-statistical constraint violation rate ( \(\bar{V}\) ) was used to evaluate the non-statistical constraints from the test blueprint in the modern ATA approaches for optimizing medical education assessments [ 48 ]. A smaller \(\bar{V}\) indicates that there were fewer violated constraints from the test blueprint in the constructed test form.

Item pool analysis

In this study, the ratio between the first eigenvalue and the second eigenvalue was 6.562 (i.e., greater than 4), and the first factor explained 23.203% of the total variance (i.e., higher than 20%). Therefore, the items in the current item pool were considered unidimensional and suitable for the next phase of analysis. In order to find a suitable IRT model to fit the dataset, three commonly used dichotomous IRT models were applied in R software, including the Rasch model, the 2PLM, and the 3PLM. Obviously, as seen in Table  1 , the 3PLM had the best fit among these models; therefore, this model was chosen for the subsequent analysis. Furthermore, the item parameter estimates of the 3PLM indicated that most items had moderate to high discrimination power. The difficulty parameters showed an approximately normal distribution and were sufficiently broad to cover a wide range of student abilities. The chances of guesses were negligible.

Evaluation the quality of assembled test form using modern ATA approaches

Test length, reliability, and validity.

As shown in Table  2 , the test length of the assembled test, generated using the MPI algorithm, was lower than that of the test assembled using the MIP algorithm. The Cronbach’s alpha coefficient for the test assembled using the MPI algorithm was 0.946, while that for the MIP algorithm was 0.937. Moreover, the assembled test generated using the MPI and MIP algorithms exhibited strong criterion-related validity, as the total score of the assembled test form had a significant correlation of 0.953 and 0.971 ( p  < .001) with the total SCTCMU score. Our analysis confirmed that the assembled test had good content validity, as detailed in the following points: (1) the content coverage of the assembled test remains consistent with that of the item bank, both containing 4 content areas that met the requirements of the test blueprints (see Table  2 ). (2) By comparing the content distribution in the item bank over the past three years, we identified a consistent pattern (see Tab. S2 in Supplementary Material  1 ). This indicates that the content distribution of the item bank used in this study is reasonable. Our study also found that the content distribution of the assembled test exhibited similarities to that of the item bank (see Table  2 ). These findings collectively suggest that the assembled test using modern ATA approaches demonstrates robust reliability and validity.

Measurement accuracy and non-statistical constraints

As shown in Table  3 , the actual TIF value at the cut score of the assembled test form closely approached the maximum TIF value, indicating a high level of measurement precision for the assembled test form near the cut score. The test form generated using the MIP algorithm fully meets the predefined requirements for non-statistical constraints from test blueprint. Additionally, the fulfillment of non-statistical constraints in the assembled test form, generated using the MIP algorithm, is superior to those generated using the MPI algorithm.

The present study focuses on optimizing the design of medical education assessments through the application of modern ATA approaches. A four-step process, employing psychometric methodologies, was utilized to calibrate and analyze the item pool of the SCTCMU. The evaluation demonstrated the commendable quality of item properties. Subsequently, two modern ATA approaches (i.e., MPI and MIP algorithms) were employed to determine the optimal item combination, accounting for both statistical and content requirements specified in the test blueprint. The qualities of the assembled test form, generated using modern ATA approaches, underwent meticulous evaluation. Results from this investigation indicated the ability to ascertain the optimal test length within the predefined measurement precision. Overall, the utilization of these modern ATA approaches significantly reduced the test length among the assembled test form while satisfying both the statistical and content prerequisites outlined in the test blueprint.

Another significant finding of this study is that the test length of the assembled test form, generated using the MPI algorithm, was comparatively lower than those assembled using the MIP algorithm. One potential explanation for this disparity could be due to the weight assigned to non-statistical constraints within the MPI algorithm. Future research could explore the impact of adjusting the weight of non-statistical constraints within the MPI algorithm to ascertain the optimal number of test items required for assembly. Additionally, the measurement accuracy outcomes from the MPI algorithm are generally similar to those from the MIP algorithm. While the MIP algorithm guarantees the satisfaction of all non-statistical constraints, the MPI algorithm may lead to minor nonstatistical constraint violations. The flawless non-statistical constraint adherence of the MIP algorithm is due to the global optimization objective within MIP models and the substantial number of items in each non-statistical constraint category within the item banks. Nevertheless, the MIP algorithm may encounter infeasibility problems if the item bank has a limited number of items in each category. These results offer valuable insights into selecting a specific modern ATA approach for optimizing the design of medical education assessments. While MIP algorithms are recommended when non-statistical constraint satisfaction is prioritized, caution must be exercised regarding potential infeasibility concerns. On the other hand, MPI algorithms are suitable for those seeking a more balanced and cost-efficient test assembly, given their heuristic nature and ease of implementation.

Leveraging modern psychometric methods rooted in IRT has proven beneficial for the calibration and analysis of item pools in medical education assessments. IRT, by providing standard error of measurement for various ability levels, facilitates the construction of large-scale high-stakes assessments to optimize measurement precision at the pass-fail threshold. Furthermore, metric calibration enables the establishment of item banks, ensuring continuity and equity in examination standards. However, certain considerations merit attention in practice. Different IRT models exist for varying data types and testing scenarios. For instance, the Rasch model, 2PLM, and 3PLM described herein are applicable for dichotomous scoring items, while the multifaceted Rasch model addresses cases where the same examinee is scored by multiple judges, as in an OSCE. The range of item parameters used for evaluating item pool quality, such as discrimination and difficulty parameters, serves an informational purpose and should be interpreted within the context of the assessment’s objectives. For instance, certain items assessing critical content mastery may be retained, even if their discrimination parameter is relatively modest, in the context of criterion-referenced assessments.

In the field of medical education, to create the items needed for a large-scale high-stakes assessment, extensive MCQ development is required. Item development is an expensive process because the cost of developing a single item often ranges from US$1500 to US$2000 [ 6 ]. Given this estimate, it is easy to see how the costs of item development quickly escalate. Moreover, administering a multitude of items leads to greater respondent burden, which can reduce the quality of an examinee’s responses and/or willingness to take the test altogether [ 8 ]. To address these issues, this study harnessed IRT-based ATA approaches to facilitate the automatic construction of test form, thereby significantly enhancing the efficiency and precision of test assembly procedures. The utilization of modern ATA approaches offers medical educators a valuable tool and indicates vital implications for medical education assessment. Firstly, for test administration, our approach can effectively reduce the number of required test items, resulting in significant cost savings associated with test development. Secondly, for examinees, the shorter duration effectively prevents them from making mistakes due to fatigue, and it also means a lower error tolerance rate and a higher demand on their overall ability. Thirdly, for educational policy and practice in other regions or disciplines, these changes could inspire policymakers in other districts to adapt their assessment systems for better student experiences and outcomes. Additionally, it may motivate educators to reassess testing methods, favoring more effective, targeted assessments aligned with learning outcomes.

Despite the promising results, the present investigation could be further enhanced in several ways. First, the item bank was consistent with the assumed yet verified structure of unidimensionality under a series of criteria in accordance with the requirements of the proposed approach. However, if multidimensional feedback is demanded, the study should consider using multidimensional item response theory (MIRT), such as bifactor IRT [ 49 ], to accommodate the need for subdomain assessment. To reiterate, the dimensionality is both theoretical and empirical, meaning that even if subdomains are assumed to exist, they should be evidenced by data and modeling results. That said, the general flow of fitting MIRT models should be conducted and validated prior to the multidimensional ATA. Second, the utilization of modern ATA approaches involves complex computing processes that require a certain amount of computing power. Each assembling iteration is costly, while elements such as the sample size, the number of items, and the complexity of modeling and constraints dramatically increase the computational costs. In the present study, parallel computation from a high-end workstation was utilized to reduce the computing time. However, it still consumed substantial time to achieve the results. Future studies should explore more strategies for boosting the speed to deliver timely results for the test/exam organizer. Third, blueprints are not always static; test/exam organizers may revise and update their blueprints from time to time, meaning the constraints should be adjusted. While this study evaluated the adaptability of MIP and MPI algorithms to such constraints, further research could explore the effects of the number of constraints in test blueprints on test assembly. Lastly, the number of items highly correlate with the session time, which should be reduced to align with the test length. Future studies can consider match the time information from the log files of the examination administration server, and incorporate the information to determine the session time.

In the present study, we investigated the utilization of modern ATA approaches for optimizing the design of medical education assessments. This investigation demonstrates that the application of IRT-based ATA approaches can substantially reduce the test length of assembled test form, while simultaneously adhering to the requisite statistical and content standards outlined in the test blueprint. Our findings suggest that the utilization of modern ATA approaches offers medical educators a valuable tool to enhance the efficiency and cost-effectiveness of medical education assessments, such as reducing respondent burden and saving costs associated with test development.

Data availability

The datasets analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. Requests to access these datasets should be directed to ZJ, [email protected].

Abbreviations

Automated Test Assembly

Standardized Competence Test for Clinical Medicine Undergraduates

Multiple-Choice Question

Classic Test Theory

Generalizability Theory

Item Response Theory

Objective Structured Clinical Examination

Two-Parameter Logistic Model

Three-Parameter Logistic Model

-2 Log-Likelihood

Akaike’s Information Criteria

Bayesian Information Criterion

Test Information Functions

Maximum Priority Index

Mixed-Integer Programming

Root Mean Square Deviation

Non-Statistical Constraint Violation Rate

Multidimensional Item Response Theory

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Acknowledgements

The authors would like to thank the editor and anonymous reviewers for their suggestions, and are very grateful to all the individual participants who involved in this study.

This work was supported by National Natural Science Foundation of China for Young Scholars under Grant 72104006 and 72304019, Peking University Health Science Center under Grant BMU2021YJ010, National Medical Examination Center of China for the project Examination Standards and Content Designs of National Medical Licensing Examination, China Postdoctoral Science Foundation under Grant 2023M740082, and Peking University Health Science Center Medical Education Research Funding Project 2023YB24.

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ZJ and LX developed the study concept and drafted the manuscript. ZJ conducted the literature review and discussion. LX and FC performed the data analysis. FC and HL implemented the data interpretation. JO and TC were involved in drafting and revising the manuscript. All authors contributed to the article and approved the submitted version.

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Xu, L., Jiang, Z., Cai, F. et al. Optimizing a national examination for medical undergraduates via modern automated test assembly approaches. BMC Med Educ 24 , 919 (2024). https://doi.org/10.1186/s12909-024-05905-1

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Education Savings Bond Program: What it is, How it Works

Yarilet Perez is an experienced multimedia journalist and fact-checker with a Master of Science in Journalism. She has worked in multiple cities covering breaking news, politics, education, and more. Her expertise is in personal finance and investing, and real estate.

An education savings bond program lets qualified taxpayers exempt all or a portion of interest earned upon redemption of eligible  savings bonds  from their annual gross income. The bonds become tax exempt when their owners use both the principal and interest to pay for higher education at qualified institutions, either attended by themselves, their spouses, or their dependents.

Key Takeaways

• An education savings bond program lets taxpayers exempt some or all of the interest earned upon redemption of eligible savings bonds from their annual gross income.  
• The bond owner must be at least 24 years of age when the bond is purchased for it to qualify for this program.
• The bond must be used solely for tuition related expenses, such as lab fees and college courses.
• The bonds cannot be used to pay for textbooks, room and board, or sports programs.

To take advantage of this program, investors must comply with the following rules:

• The bond owner must be 24 years of age or older at the time of purchase. If a parent buys the bond and puts it in the name of a child who is under 24 years of age, the bond does not qualify.
• When savings bonds are redeemed, all funds must be used to pay off higher education expenses for the owners, their spouses, or their dependents. The Internal Revenue Service only recognizes payments made to qualified institutions where the U.S. Department of Education has established student-aid programs.
• Funds can only be used towards tuition-related expenses, including lab fees and degree-required courses. The funds may not be used to cover the costs of board, books, or recreational activities.
• Funds from the redeemed bonds can also be used to make tax-free contributions to a  Coverdell Education Savings Account .
• Eligible education expenses must be incurred during the same tax year as the bond's redemption.
• Any nontaxable education payments, education aid, or tax-free scholarships must be subtracted from eligible expenses.
• If the total proceeds from the bonds equal less than the amount of eligible expenses, all of the interest accrued on the bond remains tax-free. But if the bond proceeds exceed the eligible expense amount, the amount of tax-exempt interest is subject to a prorated reduction.
• The amount of tax exempt interest is based on the owner's  modified adjusted gross income (MAGI). If the owner's MAGI reaches a certain threshold, they may not be eligible for this program. For joint tax filers in 2020, that threshold was $153,550. For single filers, the MAGI threshold was $97,350. Married owners are required to file joint taxes in order to receive the exemption.
• All payments made with bond proceeds must be reported to the IRS along with detailed receipts. It is also necessary to maintain itemized records of all redeemed bonds. Filers must use forms the IRS specifically designed for this purpose.

To qualify for this program, the savings bonds must be Series EE or Series I bonds issued after 1989.

Internal Revenue Service. " Publication 970 (2020), Tax Benefits for Education ."

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Normally, the interest you earn on your savings bonds becomes part of your gross income for tax purposes. Under certain conditions, though, you can avoid taxes on the interest by using it to pay for higher education.

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Series EE or I savings bonds issued after 1989.

They must be registered with you as the owner. If you are married, they may be registered with you and your spouse as owners.

Important: Note the age restriction in the next section. The owner of the bond must be 24 years or older when the bond is issued. Therefore, a bond registered with a child as owner will not qualify even years later when the child is ready for college. If you want to buy savings bonds to later get this tax exclusion for a child's higher education, you must register the bonds with yourself, or yourself and your spouse, as owners.

What other restrictions apply?

You can take the tax exclusion if you meet all of these conditions:

• You were 24 years old or older before the bonds were issued.
• Your modified adjusted gross income is less than the cut-off amount that the IRS sets for the year in which you want to take the exclusion. The cut-off amount may change each year. You can find the current cut-off amount on IRS Form 8815 .
• You cash the qualifying savings bonds in the same tax year for which you are claiming the exclusion.
• You paid qualified higher education expenses to an eligible institution that same tax year. (The instructions that come with IRS Form 8815 explain both "qualified expenses" and "eligible institution." They also tell you what records you must keep.)
• The expenses were for yourself, your spouse, or someone you list as a dependent on your federal income tax return.
• You file your IRS tax return with any status EXCEPT married filing separately.

Where can I find more information?

IRS Form 8815 gives details and instructions.

How do I get the tax exclusion?

If you meet all the conditions, fill out IRS Form 8815 and submit it with your tax return.

The 13 disability categories under IDEA

How do kids qualify for special education? Learn about the 13 disability categories and other important details about Individualized Education Programs (IEPs).

By Julie Rawe

Expert reviewed by Rayma Griffin, MA, MEd

Updated April 9, 2024

What are the 13 disability categories in special education ? And why do they matter?

To qualify for services, kids need to have a disability that impacts their schooling. The Individuals with Disabilities Education Act (IDEA) groups disabilities into 13 categories. But this doesn’t mean the law only covers 13 disabilities . Some of the categories cover a wide range of challenges.

IDEA disability categories

To get an Individualized Education Program (IEP), kids need to meet the requirements for at least one category. Keep reading to learn about the 13 disability categories and why all of them require finding that the disability “adversely affects” a child’s education.

1. Specific learning disability (SLD)

This category covers a wide range of learning challenges. These include differences that make it hard to read, write, listen, speak, reason, or do math. Here are some common examples of specific learning disabilities (SLD):

Dyscalculia

Written expression disorder (you may also hear this referred to as dysgraphia)

This is by far the most common category in special education. The numbers vary a bit from year to year. But students with learning disabilities tend to make up about a third of all students who have IEPs. In the 2020–21 school year, around 35 percent of students who had IEPs qualified under this category.

2. Speech or language impairment

This is the second most common category in special education. A lot of kids have IEPs for speech impediments. Common examples include lisping and stuttering.

Language disorders can be covered in this category too. Or they can be covered in the learning disability category. These disorders make it hard for kids to understand words or express themselves.

3. Other health impairment

This is another commonly used category. It covers a wide range of conditions that may limit a child’s strength, energy, or alertness. One example is ADHD . Many kids who qualify for an IEP under this category have attention deficits.

Other examples in this category include epilepsy, sickle cell anemia, and Tourette syndrome.

4. Autism spectrum disorder (ASD)

ASD is a common developmental disability. It affects social and communication skills. It can also impact behavior.

5. Intellectual disability

This category covers below-average intellectual ability. Kids with Down syndrome often qualify for special education under this category.

6. Emotional disturbance

This category covers mental health issues. Examples include anxiety disorder, bipolar disorder, and oppositional defiant disorder. (Some emotional or conduct disorders may also be covered under “other health impairment.”)

7. Developmental delay

This category can be used for young kids who are late in meeting developmental milestones like walking and talking.

Different states have different rules about this category. It’s also the only category in IDEA that has an age limit. It can’t be used after age 9.

8. Multiple disabilities

Many kids have more than one disability, such as ADHD and autism. But this category is only used when the combination of disabilities requires a highly specialized approach, such as intellectual disability and blindness. 

9. Hearing impairment, including deafness

This category includes a range of hearing issues that can be permanent or that can change over time. (This category does not include auditory processing disorder , which is considered a learning disability.)

10. Orthopedic impairment

This category covers issues with bones, joints, and muscles. One example is cerebral palsy.

11. Visual impairment, including blindness

This category covers a range of vision problems, including partial sight and blindness. But if eyewear can correct a vision problem, then a child wouldn’t qualify for special education under this category.

12. Traumatic brain injury

This category covers brain injuries that happen at some point after a child is born. These can be caused by things like being shaken as a baby or hitting your head in an accident.

13. Deaf-blindness

This category covers kids with severe hearing and vision loss. Their communication challenges are so unique that programs for just the deaf or blind can’t meet their needs.

What “adversely affects” means

Kids need to have a disability to qualify for special education. But IDEA says schools must also find that the disability “adversely affects” a child’s performance in school. This means it has to have a negative impact on how the student is doing in school.

Learn how schools decide if a child is eligible for special education .

Primary disability category

When kids have more than one disability, it’s a good idea to include all of them in the IEP. This can help get the right services and supports in place.

But the IEP will likely need to list a primary disability category. This is mainly for data-tracking reasons and will not limit the amount or type of services a child receives.

Variations in some states

Depending on where you live, your state may have more than 13 disability categories. For example, some states may split hearing impairment and deafness into two categories.

In most states, a child’s disability category is listed in their IEP. Iowa is the only state that doesn’t do this. (But it still keeps track of disability categories and reports this data to the federal government.)

To learn more about the categories in your state, contact a Parent Training and Information Center . They’re free and there’s at least one in every state.

Podcast: “IEPs: The 13 disability categories”

Listen to a 13-minute episode of “Understood Explains: IEPs.” Special educator Juliana Urtubey explains what you need to know about the 13 disability categories in IDEA.

Explore related topics

Understanding ieps.

The difference between IEPs and 504 plans

Download: Anatomy of an IEP

RFK Jr. as Trump’s health secretary? Here’s what he wants to do

Robert F. Kennedy Jr. is setting aside one ambition and making room for another. 

Kennedy ended his independent presidential campaign Friday and endorsed former President Donald Trump. While announcing his decision, Kennedy said Trump had “asked to enlist me in his administration,” though Kennedy did not specify a role.

On Tuesday, Kennedy’s running mate, Nicole Shanahan, told an interviewer the campaign was weighing whether to “join forces” with Trump and suggested that Kennedy would do an “incredible job” as secretary of health and human services. Trump later told CNN that he “probably would” appoint Kennedy to some role. 

“I didn’t know he was thinking about getting out, but if he is thinking about getting out, certainly I’d be open to it,” Trump said. 

(Sen. JD Vance, R-Ohio, Trump’s vice presidential nominee, said Wednesday that there was no quid pro quo deal to offer Kennedy a Cabinet post in exchange for his endorsement and that any conversations about a future role would be separate.) 

Neither Kennedy nor his campaign responded to requests for comment on just what he would do if he were nominated and approved by the Senate to serve in a position former HHS Secretary Alex Azar described as having “a shocking amount of power by the stroke of a pen,” at the head of a department with a more than $1.5 trillion budget .

By historical comparisons, Kennedy, a famous anti-vaccine advocate and conspiracy theorist, would be an odd pick for HHS secretary. Previous appointees have had varied backgrounds in medicine, government, law and public health. The current secretary, Xavier Becerra, served as attorney general of California.

Kennedy, also an attorney, practiced environmental law and founded Children’s Health Defense, which is now the most well-funded anti-vaccine organization in the country. During the pandemic, he became the purveyor of wild conspiracy theories , often aimed at public health officials in the agencies he now seeks to lead. Kennedy has criticized Dr. Anthony Fauci, former director of the National Institute of Allergy and Infectious Diseases, for Covid’s death toll and said Fauci should be prosecuted if he committed a crime. He has also said the attorney general should force editors of medical journals to publish retracted studies. 

HHS oversees 13 agencies , including the Food and Drug Administration, the Centers for Disease Control and Prevention and the National Institutes of Health. On the campaign trail, in podcasts and in news interviews, Kennedy has described wanting to dismantle those offices and rebuild them with like-minded fringe figures. 

The agencies have become “sock puppets” for the industries they regulate, Kennedy told NBC News in an interview last year, in which he laid out his plans for public health if he were elected president. Faced with another pandemic, Kennedy said, he wouldn’t prioritize the research, manufacture or distribution of vaccines. 

“The priority should be finding treatments that work and building people’s immune systems,” he said, falsely adding that “vaccines have probably caused more deaths than they’ve averted.” He mentioned ivermectin and hydroxychloroquine as treatments — which he says worked against Covid, even though numerous studies say they didn’t.

Kennedy’s campaign has been supported and led by the anti-vaccine movement he helped build. In November, he credited activists at Children’s Health Defense, which he chaired until he took leave to run for president, for boosting his campaign. Accepting an award at the group’s annual conference, he said he would stop the National Institutes of Health from studying infectious diseases, like Covid and measles, and pivot it to studying chronic diseases, like diabetes and obesity. Kennedy believes environmental toxins, a category in which he places childhood vaccines, to be the major threat to public health, rather than infectious disease. 

“I’m going to say to NIH scientists, God bless you all,” Kennedy said at the time. “Thank you for public service. We’re going to give infectious disease a break for about eight years.” 

Dr. Paul Offit, the director of the Vaccine Education Center at Children’s Hospital of Philadelphia and a longtime target of the anti-vaccine movement, said a Kennedy reign over HHS — a department tasked with overseeing health policy, providing and regulating care, sponsoring medical research and training, and communicating with the public during emergencies — would be disastrous.

“He no doubt will try to perform studies that prove his views and thus further weaken America’s trust in vaccines and, no doubt, try to eliminate all mandates,” Offit said. “He said he doesn’t want to study infectious diseases. He would eliminate studies around real problems and gear them toward what he thinks the problems are, independent of what good data show.

“It doesn’t matter whether the data show that he’s wrong; he’s still going to be convinced that he’s right,” Offit continued, referring to Kennedy’s focus on proving the harms of vaccines that have repeatedly been proven to be safe. “In no way would this advance human health.”

In Kennedy’s interview with NBC News last year, he sharply criticized the FDA, the NIH and the CDC and said he would “unravel the corrupt corporate capture of these agencies that turned them predatory, against the American public.” He said he would boot the officials in charge and appoint people who would “turn them back into healing and public health agencies.”

He declined to name names, but he has surrounded himself with those on the fringe of public health. He has praised “brave dissidents,” including discredited vaccine scientist Robert Malone , and Dr. Pierre Kory, who was stripped of his certification by the American Board of Internal Medicine this month for promoting and peddling false cures for Covid. Kennedy posted that doctors like Kory “help clear away the smoke of corporate profiteering so that we can see clearly the causes and solutions to the chronic disease epidemic.”

Last year, candidate Kennedy told a group of anti-vaccine doctors and influencers assembled for a health policy roundtable that he would surround himself with “dissidents.” 

“Have faith and watch what we do,” he said. “I think you’ll be pleased.” 

Brandy Zadrozny is a senior reporter for NBC News. She covers misinformation, extremism and the internet.

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Alloy Grade UNS: HH – Higher in Chromium

Alloy HH (25-12) is higher in chromium than MTEK 20-10 and will resist scaling to 2000° F with applications in the 1600 to 1900° F range. For maximum strength above 1500° F, the composition must be balanced to insure a stable austenitic structure. For applications requiring higher ductility at service temperatures, the composition can be balanced to give some ferrite in the structure. This increase in ductility is accompanied by a considerable loss in creep strength. Specification ASTM A447 designates two classes of the alloy: Type I, with some ferrite in the structure, for maximum ductility, and Type II, completely austenitic for maximum strength.

Convection furnace components, grate bars, rabble arms, radiant tubes, and refractory support hardware.

Similar Specifications

Cast UNS: J93503

Wrought UNS: S30900

Wrought Grade: 309

Cast Grade: HH

Cast ASTM: A297, A608

Typical Chemical Composition (% by wt.)

Aluminum: n/a

Carbon: 0.20-0.50

Chromium: 24-28

Manganese: 2

Copper: n/a

Nickel: 11.0-14.0

Minimum Mechanical Properties

Heat Treatment: Not Heat Treated

Explore Other Alloys

Non-Ferrous

Non-ferrous alloys are, literally, materials that are not iron-based. While others may provide non-ferrous materials of other materials, the MetalTek family of non-ferrous alloys are copper-based. In general, these materials deliver high strength, and excellent wear and corrosion resistance. They are often found in marine applications and also bushing, bearing, and gear environments.

Stainless Steel

Stainless steels owe their ability to resist corrosion primarily to the presence of a passive film on their surface. Chromium is chiefly responsible for formation of the passive film. Iron ceases to rust at approximately 12% chromium content and resistance to oxidizing corrosives increases rapidly with chromium content up to approximately 20%.

Specialty Alloys

Specialty Alloys comprise a broad group of materials that deliver properties for more demanding applications, use more exotic or expensive materials, or require special processes to manufacture. Where standard materials may not offer desired performance, specialty alloys (including those that are modified further) may be a sensible selection.

Maximum Education

The Ultimate Guide to Optimal Academics

Maximum Education: The Optimal Guide to Optimal Academics

Maximum education: the #1 amazon best seller by dr steven greene of make the grade, the book and the live workshop.

Time Management Information Management  A Comprehensive Daily Action Plan

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CLICK HERE TO SCHEDULE A MEETING WITH DR GREENE

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The Maximum Education Workshop Series is available for:

• Universities
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• Home School / Private Academies
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Middle School/High School/University Level Students… Turbocharge Your Education Now with Maximum Education

• Interested in boosting your grades?
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The Maximum Education System helps to:

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School is Challenging Enough – Make it Easier.

Students are often overloaded with a wide range of subjects, tests, quizzes and papers and other projects, with different types of assignments, with different teachers with different teaching styles, books to read, websites to log on, grading rubrics to be aware of, classrooms and class schedules, not to mention all of the out of school commitment.

“MAXIMUM EDUCATION makes your educational process easier by providing you with tools and systems –  so you can reach your academic potential.”

Learn and master systematic activities to be the most productive and efficient that you can be.   Save time by having a comprehensive  system that is geared towards SUCCESS.   This is the exact same system that Dr Greene (the Success Doctor) has taught mAke the grAde students to use in their every day success routine that has lead them to “mAke the grAde!”

Students today are overloaded with school work load, extracurricular commitments, sports, volunteer work and more. They struggle to reach their academic potential and often become frustrated with the process. Here is the solution… Maximum Education provides a blueprint for success for students by providing functional tools for time management and prioritization, class note taking, outlines to organize information, test preparation skills and specialized tips for test preparation, standardized tests, college admissions test (ACT, SAT) and term papers. This workbook also offers access to the mAke the grAde community of learners – an interactive group coordinated by Dr Greene – which allows students to share ideas, ask questions, and collaborate.

There is a reason – They have a system!

This is why Dr Greene wrote Maximum Education — To give YOU the tools that you need NOW to help you succeed at all levels of your education. 

The Maximum Education Workshop is available:

CLICK HERE TO SUBSCRIBE TO THE MAXIMUM EDUCATION EMAIL LIST TO GET ONGOING INFORMATION, TEST TAKING TIPS, INFORMATION MANAGEMENT TIPS, STUDY SKILLS TIPS AND YOUR COMPREHENSIVE STUDY PLAN.

Maximum Education… your source for:

• Time management and information management systems.  Learn to manage your time for the short and long term.
• Information management: note taking, outlining, calendaring, test prep and more.
• Putting it all together in a comprehensive study and academic plan including:
• Note taking (4 ways)
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• Test Preparation
• Having a complete overall study plan
• Tips for specific subjects and topics
• Test Prep for SAT / ACT exams
• Term Papers
• Online Education
• A complete plan

 About the Author:

Dr Steven Greene is a lifelong educator.  In 1996 he founded mAke the grAde, a full time and full service tutoring and educational support company.  Dr Greene holds a teaching certificate in Comprehensive Science.

Since its inception mAke the grAde has worked with over 8 000 students and their families to “help them to reach their goals”.  mAke the grAde specializes in test prep for the important SAT/PSAT and ACT college admissions exams, academic support for all levels of Science (Biology, Chemistry, Physics, etc) and Mathematics (Calculus, Pre-calculus, Algebra 1 and 2, Geometry, etc).

In 2015 Dr Greene published ‘Maximum Education’ which provides a straightforward system for students at all levels to manage their time, their information, and to create a comprehensive study plan. The book quickly became a #1 Best Seller on Amazon and continues to sell steadily. Shortly after the release of the book, Dr Greene introduced a series of live and internet based workshops based on the principles in Maximum Education.

Dr Greene continues to teach and tutor full time.  He meets with students in his office in the Philadelphia PA USA area as well as working with students all over the world using an internet based classroom.

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Generate maximum HH:MM from a list of 4 integers [duplicate]

Note: Another question is the duplicate of this one. If I posted this question 6 months earlier than that one, then how can mine be a duplicate?

I have 4 non-negative integers a,b,c,d. All of them are less than or equal to 9. I have to return the maximum time that can be shown on the clock in 24-hour format.

For example: a=9, b=4, c=3, d=1, would yield 19:43

I have so far only been able to come up with brute-force approach which kind of tests all 24-possible combinations. Although this isn't too bad, I was wondering if there are more elegant approaches. All ideas better than brute force are welcome.

Note: It is not a homework question. I got this from an interview prep site but has no solutions there.

3 Answers 3

Generating all 24 permutations would work, but you don't have to do that. Your validation for the brute force method would require you to validate each digit, and you may as well build up the permutation as you validate each digit. Start with big numbers.

• sort numbers in high -> low

iterate over each digit [i] and search through the list until you find the highest number that fits the requirements.

[0] must be <=2

[1] must be <=3 if [0]==2

[2] must be <=5

[3] can be anything

Remove that number from the list, and place it in position [i]

repeat for each digit

Each of those conditions could be expressed as lambda function in a list, making it easy to separate the search loop from the conditions.

• It would be worth to mention that the algorithm should backtrack (restoring the list accordingly) when at a certain stage no digit is available that meets the condition. –  trincot Commented Jan 16, 2017 at 22:40

The key is to sort the data and then apply these simple rules:

• At least one element has to be <= 2
• A second element has to be <= 5
• If there are only two elements meeting the first two rules then one of them must be < 2
• If the element selected for the first value is 2 then the element selected for the second value must be less than 4

The rules are easy to implement by using three counters:

• less_than_3 - this must always be at least 1
• less_than_6 - this must always be at least 2
• less_than_4 - if a == 2 then less_than_4 must be at least 2

Here's a solution in JavaScript that could be further refactored.

You can change time to minutes. Then you can compare it.

//23:59 ==> 23*60+59 ==1439 min //(10a+b)*60+(10c+d)<=1439

This is my code.

Not the answer you're looking for? Browse other questions tagged algorithm sorting or ask your own question .

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