AI News, Machine Learning

Machine Learning

To eliminate outlier grades for homeworks and quizzes, the lowest grade is replaced by the second lowest grade when grades are cumulated at the end of the semester.

Bonus points are not real points and are not summed up for the final grade, but they can nudge somebody to a higher grade who is right on the boundary.

This means that you will get to read and comment on other students work, which provides input for the TA's and the professor to determine the project grades.

VAM’s instability can result from differences in the characteristics of students assigned to particular teachers in a particular year, from small samples of students (made even less representative in schools serving disadvantaged students by high rates of student mobility), from other influences on student learning both inside and outside school, and from tests that are poorly lined up with the curriculum teachers are expected to cover, or that do not measure the full range of achievement of students in the class.

A research summary concludes that while students overall lose an average of about one month in reading achievement over the summer, lower-income students lose significantly more, and middle-income students may actually gain in reading proficiency over the summer, creating a widening achievement gap.

For these and other reasons, even when methods are used to adjust statistically for student demographic factors and school differences, teachers have been found to receive lower “effectiveness” scores when they teach new English learners, special education students, and low-income students than when they teach more affluent and educationally advantaged students.

Research shows that an excessive focus on basic math and reading scores can lead to narrowing and over-simplifying the curriculum to only the subjects and formats that are tested, reducing the attention to science, history, the arts, civics, and foreign language, as well as to writing, research, and more complex problem-solving tasks.

Adopting an invalid teacher evaluation system and tying it to rewards and sanctions is likely to lead to inaccurate personnel decisions and to demoralize teachers, causing talented teachers to avoid high-needs students and schools, or to leave the profession entirely, and discouraging potentially effective teachers from entering it.

Some believe that the prospect of higher pay for better performance will attract more effective teachers to the profession and that a flexible pay scale, based in part on test-based measures of effectiveness, will reduce the attrition of more qualified teachers whose commitment to teaching will be strengthened by the prospect of greater financial rewards for success.

A recent careful econometric study of the causal effects of NCLB concluded that during the NCLB years, there were noticeable gains for students overall in fourth grade math achievement, smaller gains in eighth grade math achievement, but no gains at all in fourth or eighth grade reading achievement.

The study concludes, “The lack of any effect in reading, and the fact that the policy appears to have generated only modestly larger impacts among disadvantaged subgroups in math (and thus only made minimal headway in closing achievement gaps), suggests that, to date, the impact of NCLB has fallen short of its extraordinarily ambitious, eponymous goals.”1 Such findings provide little support for the view that test-based incentives for schools or individual teachers are likely to improve achievement, or for the expectation that such incentives for individual teachers will suffice to produce gains in student learning.

Management experts warn against significant use of quantitative measures for making salary or bonus decisions.2 The national economic catastrophe that resulted from tying Wall Street employees’ compensation to short-term gains rather than to longer-term (but more difficult-to-measure) goals is a particularly stark example of a system design to be avoided.

Other human service sectors, public and private, have also experimented with rewarding professional employees by simple measures of performance, with comparably unfortunate results.3 In both the United States and Great Britain, governments have attempted to rank cardiac surgeons by their patients’ survival rates, only to find that they had created incentives for surgeons to turn away the sickest patients.

third reason for skepticism is that in practice, and especially in the current tight fiscal environment, performance rewards are likely to come mostly from the redistribution of already-appropriated teacher compensation funds, and thus are not likely to be accompanied by a significant increase in average teacher salaries (unless public funds are supplemented by substantial new money from foundations, as is currently the situation in Washington, D.C.).

Not only are they subject to errors of various kinds—we describe these in more detail below—but they are narrow measures of what students know and can do, relying largely on multiple-choice items that do not evaluate students’ communication skills, depth of knowledge and understanding, or critical thinking and performance abilities.

These tests are unlike the more challenging open-ended examinations used in high-achieving nations in the world.{{4 }}Indeed, U.S. scores on international exams that assess more complex skills dropped from 2000 to 2006,{{5 }}even while state and local test scores were climbing, driven upward by the pressures of test-based accountability.

Without going that far, the now widespread practice of giving students intense preparation for state tests—often to the neglect of knowledge and skills that are important aspects of the curriculum but beyond what tests cover—has in many cases invalidated the tests as accurate measures of the broader domain of knowledge that the tests are supposed to measure.

As policy makers attach more incentives and sanctions to the tests, scores are more likely to increase without actually improving students’ broader knowledge and understanding.6 Statisticians, psychometricians, and economists who have studied the use of test scores for high-stakes teacher evaluation, including its most sophisticated form, value-added modeling (VAM), mostly concur that such use should be pursued only with great caution.

research team at RAND has cautioned that: The estimates from VAM modeling of achievement will often be too imprecise to support some of the desired inferences.8 and, The research base is currently insufficient to support the use of VAM for high-stakes decisions about individual teachers or schools.9 Henry Braun, then of the Educational Testing Service, concluded in his review of VAM research: VAM results should not serve as the sole or principal basis for making consequential decisions about teachers.

We still lack sufficient understanding of how seriously the different technical problems threaten the validity of such interpretations.10 In a letter to the Department of Education, commenting on the Department’s proposal to use student achievement to evaluate teachers, the Board on Testing and Assessment of the National Research Council of the National Academy of Sciences wrote: …VAM estimates of teacher effectiveness should not be used to make operational decisions because such estimates are far too unstable to be considered fair or reliable.11 And a recent report of a workshop conducted jointly by the National Research Council and the National Academy of Education concluded: Value-added methods involve complex statistical models applied to test data of varying quality.

Despite a substantial amount of research over the last decade and a half, overcoming these challenges has proven to be very difficult, and many questions remain unanswered…12 Among the concerns raised by researchers are the prospects that value-added methods can misidentify both successful and unsuccessful teachers and, because of their instability and failure to disentangle other influences on learning, can create confusion about the relative sources of influence on student achievement.

These challenges arise because of the influence of student socioeconomic advantage or disadvantage on learning, measurement error and instability, the nonrandom sorting of teachers across schools and of students to teachers in classrooms within schools, and the difficulty of disentangling the contributions of multiple teachers over time to students’ learning.

Social scientists have long recognized that student test scores are heavily influenced by socioeconomic factors such as parents’ education and home literacy environment, family resources, student health, family mobility, and the influence of neighborhood peers, and of classmates who may be relatively more advantaged or disadvantaged.

Thus, teachers working in affluent suburban districts would almost always look more effective than teachers in urban districts if the achievement scores of their students were interpreted directly as a measure of effectiveness.13 New statistical techniques, called value-added modeling (VAM), are intended to resolve the problem of socio-economic (and other) differences by adjusting for students’ prior achievement and demographic characteristics (usually only their income-based eligibility for the subsidized lunch program, and their race or Hispanic ethnicity).14 These techniques measure the gains that students make and then compare these gains to those of students whose measured background characteristics and initial test scores were similar, concluding that those who made greater gains must have had more effective teachers.

and over growth measures (that simply compare the average student scores of a teacher in one year to the same students’ scores when they were in an earlier grade the previous year).15 Status measures primarily reflect the higher or lower achievement with which students entered a teacher’s classroom at the beginning of the year rather than the contribution of the teacher in the current year.

Even when student demographic characteristics are taken into account, the value-added measures are too unstable (i.e., vary widely) across time, across the classes that teachers teach, and across tests that are used to evaluate instruction, to be used for the high-stakes purposes of evaluating teachers.16 Because education is both a cumulative and a complex process, it is impossible fully to distinguish the influences of students’ other teachers as well as school conditions on their apparent learning, let alone their out-of-school learning experiences at home, with peers, at museums and libraries, in summer programs, on-line, and in the community.

This possibility cannot be ruled out entirely, but some studies control for cross-school variability and at least one study has examined the same teachers with different populations of students, showing that these teachers consistently appeared to be more effective when they taught more academically advanced students, fewer English language learners, and fewer low-income students.20 This finding suggests that VAM cannot control completely for differences in students’ characteristics or starting points.21 Teachers who have chosen to teach in schools serving more affluent students may appear to be more effective simply because they have students with more home and school supports for their prior and current learning, and not because they are better teachers.

Although VAM attempts to address the differences in student populations in different schools and classrooms by controlling statistically for students’ prior achievement and demographic characteristics, this “solution” assumes that the socioeconomic disadvantages that affect children’s test scores do not also affect the rates at which they show progress—or the validity with which traditional tests measure their learning gains (a particular issue for English language learners and students with disabilities).

Indeed, it is just as reasonable to expect that “learning begets learning”: students at the top of the distribution could find it easier to make gains, because they have more knowledge and skills they can utilize to acquire additional knowledge and skills and, because they are independent learners, they may be able to learn as easily from less effective teachers as from more effective ones.

The pattern of results on any given test could also be affected by whether the test has a high “ceiling”—that is, whether there is considerable room at the top of the scale for tests to detect the growth of students who are already high-achievers—or whether it has a low “floor”—that is, whether skills are assessed along a sufficiently long continuum for low-achieving students’ abilities to be measured accurately in order to show gains that may occur below the grade-level standard.22 Furthermore, students who have fewer out-of-school supports for their learning have been found to experience significant summer learning loss between the time they leave school in June and the time they return in the fall.

In any event, teacher effectiveness measures continue to be highly unstable, whether or not they are estimated using school fixed effects.23 Nonrandom sorting of students to teachers within schools: A comparable statistical problem arises for teachers within schools, in that teachers’ value-added scores are affected by differences in the types of students who happen to be in their classrooms.

Statistical models cannot fully adjust for the fact that some teachers will have a disproportionate number of students who may be exceptionally difficult to teach (students with poorer attendance, who have become homeless, who have severe problems at home, who come into or leave the classroom during the year due to family moves, etc.) or whose scores on traditional tests are frequently not valid (e.g., those who have special education needs or who are English language learners).

Surprisingly, it finds that students’ fifth grade teachers appear to be good predictors of students’ fourth grade test scores.24 Inasmuch as a student’s later fifth grade teacher cannot possibly have influenced that student’s fourth grade performance, this curious result can only mean that students are systematically grouped into fifth grade classrooms based on their fourth grade performance.

In a careful modeling exercise designed to account for the various factors, a recent study by researchers at Mathematica Policy Research, commissioned and published by the Institute of Education Sciences of the U.S. Department of Education, concludes that the errors are sufficiently large to lead to the misclassification of many teachers.25 The Mathematica models, which apply to teachers in the upper elementary grades, are based on two standard approaches to value-added modeling, with the key elements of each calibrated with data on typical test score gains, class sizes, and the number of teachers in a typical school or district.

Researchers have found that teachers’ effectiveness ratings differ from class to class, from year to year, and from test to test, even when these are within the same content area.26 Teachers also look very different in their measured effectiveness when different statistical methods are used.27 Teachers’ value-added scores and rankings are most unstable at the upper and lower ends of the scale, where they are most likely to be used to allocate performance pay or to dismiss teachers believed to be ineffective.28 Because of the range of influences on student learning, many studies have confirmed that estimates of teacher effectiveness are highly unstable.

One study examining two consecutive years of data showed, for example, that across five large urban districts, among teachers who were ranked in the bottom 20% of effectiveness in the first year, fewer than a third were in that bottom group the next year, and another third moved all the way up to the top 40%.

Among those who were ranked in the top 20% in the first year, only a third were similarly ranked a year later, while a comparable proportion had moved to the bottom 40%.29 Another study confirmed that big changes from one year to the next are quite likely, with year-to-year correlations of estimated teacher quality ranging from only 0.2 to 0.4.30 This means that only about 4% to 16% of the variation in a teacher’s value-added ranking in one year can be predicted from his or her rating in the previous year.

These patterns, which held true in every district and state under study, suggest that there is not a stable construct measured by value-added measures that can readily be called “teacher effectiveness.” That a teacher who appears to be very effective (or ineffective) in one year might have a dramatically different result the following year, runs counter to most people’s notions that the true quality of a teacher is likely to change very little over time.

Once teachers in schools or classrooms with more transient student populations understand that their VAM estimates will be based only on the subset of students for whom complete data are available and usable, they will have incentives to spend disproportionately more time with students who have prior-year data or who pass a longevity threshold, and less time with students who arrive mid-year and who may be more in need of individualized instruction.

And such response to incentives is not unprecedented: an unintended incentive created by NCLB caused many schools and teachers to focus greater effort on children whose test scores were just below proficiency cutoffs and whose small improvements would have great consequences for describing a school’s progress, while paying less attention to children who were either far above or far below those cutoffs.31 As noted above, even in a more stable community, the number of students in a given teacher’s class is often too small to support reliable conclusions about teacher effectiveness.

Thus, testing expert Daniel Koretz concludes that “because of the need for vertically scaled tests, value-added systems may be even more incomplete than some status or cohort-to-cohort systems.”32 It is often quite difficult to match particular students to individual teachers, even if data systems eventually permit such matching, and to unerringly attribute student achievement to a specific teacher.

Indeed, researchers have found that three-fourths of schools identified as being in the bottom 20% of all schools, based on the scores of students during the school year, would not be so identified if differences in learning outside of school were taken into account.34 Similar conclusions apply to the bottom 5% of all schools.35 Another recent study showed that two-thirds of the difference between the ninth grade test scores of high and low socioeconomic status students can be traced to summer learning differences over the elementary years.36 A research summary concluded that while students overall lose an average of about one month in reading achievement over the summer, lower-income students lose significantly more, and middle-income students may actually gain in reading proficiency over the summer, creating a widening achievement gap.37 Teachers who teach a greater share of lower-income students are disadvantaged by summer learning loss in estimates of their effectiveness that are calculated in terms of gains in their students’ test scores from the previous year.

To do so, schools would have to administer high stakes tests twice a year, once in the fall and once in the spring.38 While this approach would be preferable in some ways to attempting to measure value-added from one year to the next, fall and spring testing would force schools to devote even more time to testing for accountability purposes, and would set up incentives for teachers to game the value-added measures.

However commonplace it might be under current systems for teachers to respond rationally to incentives by artificially inflating end-of-year scores by drill, test preparation activities, or teaching to the test, it would be so much easier for teachers to inflate their value-added ratings by discouraging students’ high performance on a September test, if only by not making the same extraordinary efforts to boost scores in the fall that they make in the spring.

The need, mentioned above, to have test results ready early enough in the year to influence not only instruction but also teacher personnel decisions is inconsistent with fall to spring testing, because the two tests must be spaced far enough apart in the year to produce plausibly meaningful information about teacher effects.

Most teachers will already have had their contracts renewed and received their classroom assignments by this time.39 Although the various reasons to be skeptical about the use of student test scores to evaluate teachers, along with the many conceptual and practical limitations of empirical value added measures, might suffice by themselves to make one wary of the move to test-based evaluation of teachers, they take on even greater significance in light of the potential for large negative effects of such an approach.

This narrowing takes the form both of reallocations of effort between the subject areas covered in a full grade-level curriculum, and of reallocations of effort within subject areas themselves.40 The tests most likely to be used in any test-based teacher evaluation program are those that are currently required under NCLB, or that will be required under its reauthorized version.

In practice, therefore, evaluating teachers by their students’ test scores means evaluating teachers only by students’ basic math and/or reading skills, to the detriment of other knowledge, skills, and experiences that young people need to become effective participants in a democratic society and contributors to a productive economy.

Thus, for elementary (and some middle-school) teachers who are responsible for all (or most) curricular areas, evaluation by student test scores creates incentives to diminish instruction in history, the sciences, the arts, music, foreign language, health and physical education, civics, ethics and character, all of which we expect children to learn.

This shift was most pronounced in districts where schools were most likely to face sanctions—districts with schools serving low-income and minority children.41 Such pressures to narrow the curriculum will certainly increase if sanctions for low test scores are toughened to include the loss of pay or employment for individual teachers.

(If teachers are found wanting, administrators should know this before designing staff development programs or renewing teacher contracts for the following school year.) As a result, standardized annual exams, if usable for high-stakes teacher or school evaluation purposes, typically include no or very few extended-writing or problem-solving items, and therefore do not measure conceptual understanding, communication, scientific investigation, technology and real-world applications, or a host of other critically important skills.

Not surprisingly, several states have eliminated or reduced the number of writing and problem-solving items from their standardized exams since the implementation of NCLB.42 Although some reasoning and other advanced skills can be tested with multiple-choice questions, most cannot be, so teachers who are evaluated by students’ scores on multiple-choice exams have incentives to teach only lower level, procedural skills that can easily be tested.

Although specific questions may vary from year to year, great variation in the format of test questions is not practical because the expense of developing and field-testing significantly different exams each year is too costly and would undermine statistical equating procedures used to ensure the comparability of tests from one year to the next.

Similarly, if teachers know they will be evaluated by their students’ scores on a test that predictably asks questions about triangles and rectangles, teachers skilled in preparing students for calculations involving these shapes may fail to devote much time to polygons, an equally important but somewhat more difficult topic in the overall math curriculum.

In English, state standards typically include skills such as learning how to use a library and select appropriate books, give an oral presentation, use multiple sources of information to research a question and prepare a written argument, or write a letter to the editor in response to a newspaper article.

Reading proficiency includes the ability to interpret written words by placing them in the context of broader background knowledge.46 Because children come to school with such wide variation in their background knowledge, test developers attempt to avoid unfairness by developing standardized exams using short, highly simplified texts.47 Test questions call for literal meaning – identifying the main idea, picking out details, getting events in the right order—but without requiring inferential or critical reading abilities that are an essential part of proficient reading.

It is relatively easy for teachers to prepare students for such tests by drilling them in the mechanics of reading, but this behavior does not necessarily make them good readers.48 Children prepared for tests that sample only small parts of the curriculum and that focus excessively on mechanics are likely to learn test-taking skills in place of mathematical reasoning and reading for comprehension.

We can confirm that some score inflation has systematically taken place because the improvement in test scores of students reported by states on their high-stakes tests used for NCLB or state accountability typically far exceeds the improvement in test scores in math and reading on the NAEP.49 Because no school can anticipate far in advance that it will be asked to participate in the NAEP sample, nor which students in the school will be tested, and because no consequences for the school or teachers follow from high or low NAEP scores, teachers have neither the ability nor the incentive to teach narrowly to expected test topics.

In addition, because there is no time pressure to produce results with fast electronic scoring, NAEP can use a variety of question formats including multiple-choice, constructed response, and extended open-ended responses.50 NAEP also is able to sample many more topics from a grade’s usual curriculum because in any subject it assesses, NAEP uses several test booklets that cover different aspects of the curriculum, with overall results calculated by combining scores of students who have been given different booklets.

Thus, when scores on state tests used for accountability rise rapidly (as has typically been the case), while scores on NAEP exams for the same subjects and grades rise slowly or not at all, we can be reasonably certain that instruction was focused on the fewer topics and item types covered by the state tests, while topics and formats not covered on state tests, but covered on NAEP, were shortchanged.51 Another confirmation of score inflation comes from the Programme for International Student Assessment (PISA), a set of exams given to samples of 15-year-old students in over 60 industrialized and developing nations.

Even if they show that monetary incentives for teachers lead to higher scores in reading and math, we will still not know whether the higher scores were achieved by superior instruction or by more drill and test preparation, and whether the students of these teachers would perform equally well on tests for which they did not have specific preparation.

In one recent study, economists found that peer learning among small groups of teachers was the most powerful predictor of improved student achievement over time.{{53 }}Another recent study found that students achieve more in mathematics and reading when they attend schools characterized by higher levels of teacher collaboration for school improvement.54 To the extent that teachers are given incentives to pursue individual monetary rewards by posting greater test score gains than their peers, teachers may also have incentives to cease collaborating.

Their interest becomes self-interest, not the interest of students, and their instructional strategies may distort and undermine their school’s broader goals.55 To enhance productive collaboration among all of a school’s staff for the purpose of raising overall student scores, group (school-wide) incentives are preferred to incentives that attempt to distinguish among teachers.

Except at the very bottom of the teacher quality distribution where test-based evaluation could result in termination, individual incentives will have little impact on teachers who are aware they are less effective (and who therefore expect they will have little chance of getting a bonus) or teachers who are aware they are stronger (and who therefore expect to get a bonus without additional effort).

Studies in fields outside education have also documented that when incentive systems require employees to compete with one another for a fixed pot of monetary reward, collaboration declines and client outcomes suffer.56 On the other hand, with group incentives, everyone has a stronger incentive to be productive and to help others to be productive as well.57 A

We noted above that an individual incentive system that rewards teachers for their students’ mathematics and reading scores can result in narrowing the curriculum, both by reducing attention paid to non-tested curricular areas, and by focusing attention on the specific math and reading topics and skills most likely to be tested.

Recent survey data reveal that accountability pressures are associated with higher attrition and reduced morale, especially among teachers in high-need schools.58 Although such survey data are limited, anecdotes abound regarding the demoralization of apparently dedicated and talented teachers, as test-based accountability intensifies.

This made teaching boring for me and was a huge part of why I decided to leave the profession.60 If these anecdotes reflect the feelings of good teachers, then analysis of student test scores may distinguish teachers who are more able to raise test scores, but encourage teachers who are truly more effective to leave the profession.

However, because of the broad agreement by technical experts that student test scores alone are not a sufficiently reliable or valid indicator of teacher effectiveness, any school district that bases a teacher’s dismissal on her students’ test scores is likely to face the prospect of drawn-out and expensive arbitration and/or litigation in which experts will be called to testify, making the district unlikely to prevail.

However, progress has been made over the last two decades in developing standards-based evaluations of teaching practice, and research has found that the use of such evaluations by some districts has not only provided more useful evidence about teaching practice, but has also been associated with student achievement gains and has helped teachers improve their practice and effectiveness.61 Structured performance assessments of teachers like those offered by the National Board for Professional Teaching Standards and the beginning teacher assessment systems in Connecticut and California have also been found to predict teacher’s effectiveness on value-added measures and to support teacher learning.62 These systems for observing teachers’ classroom practice are based on professional teaching standards grounded in research on teaching and learning.

Given the importance of teachers’ collective efforts to improve overall student achievement in a school, an additional component of documenting practice and outcomes should focus on the effectiveness of teacher participation in teams and the contributions they make to school-wide improvement, through work in curriculum development, sharing practices and materials, peer coaching and reciprocal observation, and collegial work with students.

In some districts, peer assistance and review programs—using standards-based evaluations that incorporate evidence of student learning, supported by expert teachers who can offer intensive assistance, and panels of administrators and teachers that oversee personnel decisions—have been successful in coaching teachers, identifying teachers for intervention, providing them assistance, and efficiently counseling out those who do not improve.63 In others, comprehensive systems have been developed for examining teacher performance in concert with evidence about outcomes for purposes of personnel decision making and compensation.64 Given the range of measures currently available for teacher evaluation, and the need for research about their effective implementation and consequences, legislatures should avoid imposing mandated solutions to the complex problem of identifying more and less effective teachers.

What is now necessary is a comprehensive system that gives teachers the guidance and feedback, supportive leadership, and working conditions to improve their performance, and that permits schools to remove persistently ineffective teachers without distorting the entire instructional program by imposing a flawed system of standardized quantification of teacher quality.

There is a well-known decline in relative test scores for low-income and minority students that begins at or just after the fourth grade, when more complex inferential skills and deeper background knowledge begin to play a somewhat larger, though still small role in standardized tests.

Get Started

will then automatically attempt to match each submission to a student in your roster, and you'll be able to manually match any remaining submissions. You

Modern Educayshun

The follow up to #Equality, Modern Educayshun delves into the potential dangers of a hypersensitive culture bred by social media and political correctness.

WWDC 2018 Keynote — Apple

Apple WWDC 2018. Four OS updates. One big day. Take a look at updates for iPhone and iPad, Mac, Apple Watch, and Apple TV. 9:54 — Announcing iOS 12 ...

How to write a good essay

How to write an essay- brief essays and use the principles to expand to longer essays/ even a thesis you might also wish to check the video on Interview ...

CBSE Class XII Exam Toppers Interview 2010

The girls have outdone the boys yet again. Both CBSE toppers are from Chennai - they scored 98 per cent each. Courtesy: CNN IBN.

Imperialism: Crash Course World History #35

In which John Green teaches you about European Imperialism in the 19th century. European powers started to create colonial empires way back in the 16th ...

How To Pick Up Girls And Keep Them: The RSDMax Manifesto

How to pick up girls and keep them: EVERYTHING you need to know compiled in ONE crazy seminar! Get RSD's #1 most-popular product THE NATURAL here: ...

LGR - The Sims 4 Parenthood Review

Gameplay and overview of the fifth game pack for The Sims 4. What are the new items and activities? And much does parenting affect the outcome of sim lives?

Internships & Interview Techniques

We know it's tough to secure a great job in this market, but we're here to help! Presented by Roger Philipp, CPA, CGMA, of Roger CPA Review, this video gives ...

Fuzzy and Techie: A False Divide?

Techie” students who pursue STEM subjects are commonly seen as greater drivers of innovation than “fuzzy” students who pursue the humanities and sciences.

Learn To Speed Read: Read 300% Faster in 15 Minutes

Read Faster: Learn to speed read to increase your reading speed CALCULATE READING SPEED- ...