Students Output Quotes

You became conscious of precisely what you unconsciously intended to say only when you said it. You modify your speech depending on wether you are talking to child, a colleague, a student, or a dean. Not consiously, most probably. Paradoxically, speech is usually considered the case of conscious behavior - behavior for which we hold people responsoble. Certainly, it require consciousness: you cannot have a conversation while in deep sleep or in coma. Nevertheless, the activities that organize your speech output are not conscious activities. Speaking is a highly skilled business, relyling on uncounscious knowledge of precisely what to say and how.

One overly simplistic idea is that we can improve student performance just by giving financial incentives to parents, teachers, or kids. Unfortunately, there is little evidence that such incentives are effective, but nuances matter. For example, one intriguing finding by Roland Fryer suggests that rewarding students for inputs (such as doing their homework) rather than outputs (such as their grades) is effective. I find this result intuitively appealing because the students most in need do not know how to become better students. It makes sense to reward them for doing things that educators believe are effective.

Merrill Swain and Sharon Lapkin (2002), who have investigated sociocultural explanations for second language learning in Canadian French immersion programmes. Their work has its origins in Swain’s comprehensible output hypothesis and the notion that when learners have to produce language, they must pay more attention to how meaning is expressed through language than they ordinarily do for the comprehension of language. Swain (1985) first proposed the comprehensible output hypothesis based on the observation that French immersion students were considerably weaker in their spoken and written production than in their reading and listening comprehension. She advocated more opportunities for learners to engage in verbal production (i.e. output) in French immersion classrooms.

It may seem paradoxical to claim that stress, a physiological mechanism vital to life, is a cause of illness. To resolve this apparent contradiction, we must differentiate between acute stress and chronic stress. Acute stress is the immediate, short-term body response to threat. Chronic stress is activation of the stress mechanisms over long periods of time when a person is exposed to stressors that cannot be escaped either because she does not recognize them or because she has no control over them. Discharges of nervous system, hormonal output and immune changes constitute the flight-or-fight reactions that help us survive immediate danger. These biological responses are adaptive in the emergencies for which nature designed them. But the same stress responses, triggered chronically and without resolution, produce harm and even permanent damage. Chronically high cortisol levels destroy tissue. Chronically elevated adrenalin levels raise the blood pressure and damage the heart. There is extensive documentation of the inhibiting effect of chronic stress on the immune system. In one study, the activity of immune cells called natural killer (NK) cells were compared in two groups: spousal caregivers of people with Alzheimer’s disease, and age- and health-matched controls. NK cells are front-line troops in the fight against infections and against cancer, having the capacity to attack invading micro-organisms and to destroy cells with malignant mutations. The NK cell functioning of the caregivers was significantly suppressed, even in those whose spouses had died as long as three years previously. The caregivers who reported lower levels of social support also showed the greatest depression in immune activity — just as the loneliest medical students had the most impaired immune systems under the stress of examinations. Another study of caregivers assessed the efficacy of immunization against influenza. In this study 80 per cent among the non-stressed control group developed immunity against the virus, but only 20 per cent of the Alzheimer caregivers were able to do so. The stress of unremitting caregiving inhibited the immune system and left people susceptible to influenza. Research has also shown stress-related delays in tissue repair. The wounds of Alzheimer caregivers took an average of nine days longer to heal than those of controls. Higher levels of stress cause higher cortisol output via the HPA axis, and cortisol inhibits the activity of the inflammatory cells involved in wound healing. Dental students had a wound deliberately inflicted on their hard palates while they were facing immunology exams and again during vacation. In all of them the wound healed more quickly in the summer. Under stress, their white blood cells produced less of a substance essential to healing. The oft-observed relationship between stress, impaired immunity and illness has given rise to the concept of “diseases of adaptation,” a phrase of Hans Selye’s. The flight-or-fight response, it is argued, was indispensable in an era when early human beings had to confront a natural world of predators and other dangers. In civilized society, however, the flight-fight reaction is triggered in situations where it is neither necessary nor helpful, since we no longer face the same mortal threats to existence. The body’s physiological stress mechanisms are often triggered inappropriately, leading to disease. There is another way to look at it. The flight-or-fight alarm reaction exists today for the same purpose evolution originally assigned to it: to enable us to survive. What has happened is that we have lost touch with the gut feelings designed to be our warning system. The body mounts a stress response, but the mind is unaware of the threat. We keep ourselves in physiologically stressful situations, with only a dim awareness of distress or no awareness at all.

Less is more. “A few extremely well-chosen objectives,” Grove wrote, “impart a clear message about what we say ‘yes’ to and what we say ‘no’ to.” A limit of three to five OKRs per cycle leads companies, teams, and individuals to choose what matters most. In general, each objective should be tied to five or fewer key results. (See chapter 4, “Superpower #1: Focus and Commit to Priorities.”) Set goals from the bottom up. To promote engagement, teams and individuals should be encouraged to create roughly half of their own OKRs, in consultation with managers. When all goals are set top-down, motivation is corroded. (See chapter 7, “Superpower #2: Align and Connect for Teamwork.”) No dictating. OKRs are a cooperative social contract to establish priorities and define how progress will be measured. Even after company objectives are closed to debate, their key results continue to be negotiated. Collective agreement is essential to maximum goal achievement. (See chapter 7, “Superpower #2: Align and Connect for Teamwork.”) Stay flexible. If the climate has changed and an objective no longer seems practical or relevant as written, key results can be modified or even discarded mid-cycle. (See chapter 10, “Superpower #3: Track for Accountability.”) Dare to fail. “Output will tend to be greater,” Grove wrote, “when everybody strives for a level of achievement beyond [their] immediate grasp. . . . Such goal-setting is extremely important if what you want is peak performance from yourself and your subordinates.” While certain operational objectives must be met in full, aspirational OKRs should be uncomfortable and possibly unattainable. “Stretched goals,” as Grove called them, push organizations to new heights. (See chapter 12, “Superpower #4: Stretch for Amazing.”) A tool, not a weapon. The OKR system, Grove wrote, “is meant to pace a person—to put a stopwatch in his own hand so he can gauge his own performance. It is not a legal document upon which to base a performance review.” To encourage risk taking and prevent sandbagging, OKRs and bonuses are best kept separate. (See chapter 15, “Continuous Performance Management: OKRs and CFRs.”) Be patient; be resolute. Every process requires trial and error. As Grove told his iOPEC students, Intel “stumbled a lot of times” after adopting OKRs: “We didn’t fully understand the principal purpose of it. And we are kind of doing better with it as time goes on.” An organization may need up to four or five quarterly cycles to fully embrace the system, and even more than that to build mature goal muscle.

In a stunning 1971 paper, Twenty Things to Do with a Computer, Seymour Papert and Logo co-creator Cynthia Solomon proposed educative computer-based projects for kids. They included composing music, controlling puppets, programming, movie making, mathematical modeling, and a host of other projects that schools should aspire to more than 40 years later. Papert and Solomon also made the case for 1:1 computing and stressed the three game changers discussed later in this book. The school computer should have a large number of output ports to allow the computer to switch lights on and off, start tape recorders, actuate slide projectors and start and stop all manner of little machines. There should also be input ports to allow signals to be sent to the computer. In our image of a school computation laboratory, an important role is played by numerous “controller ports” which allow any student to plug any device into the computer… The laboratory will have a supply of motors, solenoids, relays, sense devices of various kids, etc. Using them, the students will be able to invent and build an endless variety of cybernetic systems.

depletion and climate change. For the older generation it’s easy to misunderstand the word ‘student’ or ‘graduate’: to my contemporaries, at college in the 1980s, it meant somebody engaged in a liberal, academic education, often with hours of free time to dream, protest, play in a rock band or do research. Today’s undergraduates have been tested every month of their lives, from kindergarten to high school. They are the measured inputs and outputs of a commercialized global higher education market worth $1.2 trillion a year—excluding the USA. Their free time is minimal: precarious part-time jobs are essential to their existence, so that they are a key part of the modern workforce. Plus they have become a vital asset for the financial system. In 2006, Citigroup alone made $220 million clear profit from its student loan book.2

For many people, writing is a way to clarify their thoughts and communicate their deepest understandings. For others, writing is a barrier to communicating, a seemingly endless gauntlet of rules and restrictions, a daunting maze of grammar and structures. For some challenging students, the expectation to write across the curriculum is overwhelming, not so much an invitation to share as a minefield to cross. The expectation to write and write and write provokes shutdowns and conflicts. For these students, we offered a writing plan with two significant goals: 1) allowing the student to continue to receive direct instruction to improve written output, and 2) allowing the student to demonstrate understanding across the curriculum in ways other than writing.

12.2. The transformed variable has equal variances across the two groups (Levene’s test, p = .119), and the t-test statistic is –1.308 (df = 85, p = .194). Thus, the differences in pollution between watersheds in the East and Midwest are not significant. (The negative sign of the t-test statistic, –1.308, merely reflects the order of the groups for calculating the difference: the testing variable has a larger value in the Midwest than in the East. Reversing the order of the groups results in a positive sign.) Table 12.2 Independent-Samples T-Test: Output For comparison, results for the untransformed variable are shown as well. The untransformed variable has unequal variances across the two groups (Levene’s test, p = .036), and the t-test statistic is –1.801 (df = 80.6, p =.075). Although this result also shows that differences are insignificant, the level of significance is higher; there are instances in which using nonnormal variables could lead to rejecting the null hypothesis. While our finding of insignificant differences is indeed robust, analysts cannot know this in advance. Thus, analysts will need to deal with nonnormality. Variable transformation is one approach to the problem of nonnormality, but transforming variables can be a time-intensive and somewhat artful activity. The search for alternatives has led many analysts to consider nonparametric methods. TWO T-TEST VARIATIONS Paired-Samples T-Test Analysts often use the paired t-test when applying before and after tests to assess student or client progress. Paired t-tests are used when analysts have a dependent rather than an independent sample (see the third t-test assumption, described earlier in this chapter). The paired-samples t-test tests the null hypothesis that the mean difference between the before and after test scores is zero. Consider the following data from Table 12.3. Table 12.3 Paired-Samples Data The mean “before” score is 3.39, and the mean “after” score is 3.87; the mean difference is 0.54. The paired t-test tests the null hypothesis by testing whether the mean of the difference variable (“difference”) is zero. The paired t-test test statistic is calculated as where D is the difference between before and after measurements, and sD is the standard deviation of these differences. Regarding t-test assumptions, the variables are continuous, and the issue of heterogeneity (unequal variances) is moot because this test involves only one variable, D; no Levene’s test statistics are produced. We do test the normality of D and find that it is normally distributed (Shapiro-Wilk = .925, p = .402). Thus, the assumptions are satisfied. We proceed with testing whether the difference between before and after scores is statistically significant. We find that the paired t-test yields a t-test statistic of 2.43, which is significant at the 5 percent level (df = 9, p = .038 < .05).17 Hence, we conclude that the increase between the before and after scores is significant at the 5 percent level.18 One-Sample T-Test Finally, the one-sample t-test tests whether the mean of a single variable is different from a prespecified value (norm). For example, suppose we want to know whether the mean of the before group in Table 12.3 is different from the value of, say, 3.5? Testing against a norm is akin to the purpose of the chi-square goodness-of-fit test described in Chapter 11, but here we are dealing with a continuous variable rather than a categorical one, and we are testing the mean rather than its distribution. The one-sample t-test assumes that the single variable is continuous and normally distributed. As with the paired t-test, the issue of heterogeneity is moot because there is only one variable. The Shapiro-Wilk test shows that the variable “before” is normal (.917, p = .336). The one-sample t-test statistic for testing against the test value of 3.5 is –0.515 (df = 9, p = .619 > .05). Hence, the mean of 3.39 is not significantly

Research on comprehension-based approaches to second language acquisition shows that learners can make considerable progress if they have sustained exposure to language they understand. The evidence also suggests, however, that comprehension-based activities may best be seen as an excellent way to begin learning and as a supplement to other kinds of learning for more advanced learners. Comprehension of meaningful language is the foundation of language acquisition. Active listening and reading for meaning are valuable components of classroom teachers’ pedagogical practices. Nevertheless, considerable research and experience challenge the hypothesis that comprehensible input is enough. VanPatten’s research showed that forcing students to rely on specific linguistic features in order to interpret meaning increased the chances that they would be able to use these features in their own second language production. Another response to comprehension-based approaches is Merrill Swain’s (1985) comprehensible output hypothesis. She argues that it is when students have to produce language that they begin to see the limitations of their interlanguage (see Chapter 4). However, as we will see in the discussion of the ‘Let’s talk’ proposal, if learners are in situations where their teachers and classmates understand them without difficulty, they may need additional help in overcoming those limitations.

Paul Graham is the founder of Y Combinator, one of the most successful and sought-after startup accelerators in the tech world. Graham has invested in several blockbuster companies, including AirBNB and Dropbox, both of which are valued in the billions at the time of this writing. After investing in hundreds of companies and considering thousands more, Paul Graham has perfected the art of identifying promising startups. His methods may surprise you. In an interview, Graham highlighted two key strategies: Favoring people over product Favoring determination over intelligence What’s most essential for a successful startup? Graham: The founders. We’ve learned in the six years of doing Y Combinator to look at the founders—not the business ideas—because the earlier you invest, the more you’re investing in the people. When Bill Gates was starting Microsoft, the idea that he had then involved a small-time microcomputer called the Altair. That didn’t seem very promising, so you had to see that this 19-year-old kid was going places. What do you look for? Graham: Determination. When we started, we thought we were looking for smart people, but it turned out that intelligence was not as important as we expected. If you imagine someone with 100 percent determination and 100 percent intelligence, you can discard a lot of intelligence before they stop succeeding. But if you start discarding determination, you very quickly get an ineffectual and perpetual grad student.[74] Your intelligence doesn’t matter as much as you think it does. If you’re reading this book, you’re probably more than capable. Your ideas don’t matter much, either. What matters most—by far, is your perseverance. Stop worrying about your mental aptitude. Stop worrying about the viability of the project you’re considering. Stop worrying about all the other big decisions keeping you up at night. Instead, focus on relentlessly grinding away at your passion until something incredible happens. Your potential output is governed by your mindset, not your mind itself.

It is unnecessary, he added, to pressure students to produce speech or writing in the second language before they are ready, because “output” contributes nothing. It is the result of second language acquisition, not the cause. In fact, putting pressure on children to speak or write can be counterproductive, increasing stress and raising the affective filter.

Third, the Comprehension Hypothesis, on which Krashen’s concept of sheltering is based, holds that input is what matters in second language acquisition—not output. As noted above, forcing students to “produce” a second language can be counterproductive. Output per se does not contribute directly to language acquisition, and forcing speech before students have acquired enough language to express their meaning tends to create anxiety and embarrassment, thereby raising the “affective filter” that keeps input from getting through.

SIOP Feature 21 requires activities that integrate all language skills (reading, writing, listening, speaking)—that is, forced output in English for all students, beginning at the earliest stages of acquiring the language.

iTranslate by Sonico Mobile iTranslate is a great free app that does language translation. If you're a student of languages or you do a lot of international travel, you'll definitely want to have this one on your Kindle Fire. The app does a magnificent job of combining voice recognition with voice output, so you can speak and see your language. The app will translate words, phrases, and entire sentences into any one of more than 50 languages.

Cultivate new hobbies and watch new areas of your brain explode into creative output.

Nongrade measures of educational output—like students taking Advanced Placement classes or tests, or kids applying to college—have trended upward, along with labor productivity in other sectors. It’s a twisted system that aspires to train every student for “A” work, then calls it a crisis when the distribution shifts in that direction.

Much more than accumulations of books, the best libraries are hotspots and organs of civilisation; magical places in which students, scholars, curators, philanthropists, artists, pranksters and flirts come together and make something marvellous. Yet none of these descriptions fits comfortably in the arid, clinical, neo-liberal, managerial paradigm of inputs and outputs and outcomes. And therein lies a problem. Throughout most of the modern world, that very paradigm guides how public funds are spent. The inputs for libraries (books, librarians, capital) are easy enough to identify, and to count. But what are the ‘outputs’ of a library, and how might the ‘outcomes’ be measured? The ‘performance’ of libraries resists evaluation as much as the ‘customers’ of libraries resist classification.

Future is uncertin and that's why sticking to one plan with no output/growth may lead to disaster, so switch faster before it's too late. And remember you can't change the people's opinion, and circumstances. So believe in yourself that things will be irrespective of circumstances and uncertainity. Keep moving forward and act accordingly.

Choosing an output as an outcome. Shifting to an outcome mindset is harder than it looks. We spend most of our time talking about outputs. So, it’s not surprising that we tend to confuse the two. Even when teams intend to choose an outcome, they often fall into the trap of selecting an output. I see teams set their outcome as “Launch an Android app” instead of “Increase mobile engagement” or “Get to feature parity on the new tech stack” instead of “Transition customer to the new tech stack.” A good place to start is to make sure your outcome represents a number even if you aren’t sure yet how to measure it. But even then, outputs can creep in. I worked with a team that helped students choose university courses who set their outcome as “Increase the number of course reviews on our platform.” When I asked them what the impact of more reviews was, they answered, “More students would see courses with reviews.” That’s not necessarily true. The team could have increased the number of reviews on their platform, but if they all clustered around a small number of courses, or if they were all on courses that students didn’t view, they wouldn’t have an impact. A better outcome is “Increase the number of course views that include reviews.” To shift your outcome from less of an output to more of an outcome, question the impact it will have.