Education Portal for Students in India

Many students leave the schools because of the inability to deal with the academic pressure and debilitating anxiety. Many parents have high expectations about their children they never consider the abilities and interests of the children.
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Why do most of the students choose to drop out?

3. College social life – Did you know that 13% of college students drop out because of the social dynamics at college? Campus life can put a lot of pressure on students to fit into new social norms. For some, this can feel like re-living high school drama all over or worse feeling like it’s impossible to live up to your peers academically.

Many non-traditional college students who return to college after years away are older than the general student population. This can also contribute to feelings of being an outsider who doesn’t fit into the college social scene. We offer an alternative! At Accelerated Pathways, social interaction is online and on your terms.

You get to collaborate with teachers, classmates and academic counselors in ways that suit your learning and social style. This lets you put your education—not social dynamics—front and center. You’ll never be forced into an awkward situation that makes you feel distracted from your studies, scared to ask a question or socially uneasy about your learning experience.
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Why should you stay in school?

Graduating high school improves your skills – Staying in school is a great way to develop essential skills that can benefit you in college, in the workplace, and even in your personal life. This can include study habits, time management skills, interpersonal skills, critical thinking, communication skills, and more.
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What is a dropout student?

Dropout. / (ˈdrɒpˌaʊt) / noun. a student who fails to complete a school or college course.
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What state has the highest dropout rate?

County-Level Findings on High School Drop Out Rates in America – Over 9 million children live in the lowest-ranked counties (bottom 25%), and they are facing huge challenges to growing up safe and secure. Nationwide, 15% of high school students failed to graduate on time during the 2016-2017 school year.

Iowa had the lowest percentage of students not graduating on time, with a rate of 9%, closely followed by New Jersey at 9.5%. The states with the highest percentage of students not graduating on time were New Mexico (28.9%) and Oregon (23.3%). On-time graduation rates are lowest in Wheeler County, Oregon, where 74% of children fail to complete high school on time.

Compared to Page County, Virginia – where only 0.4% of students fail to graduate on time – children in Wheeler County are 185 times more likely to miss out on education. Dozens of counties across 14 states reportedly have on-time graduation rates of 100% (although almost all of them are less-populous rural areas with small numbers of children).
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Who drops out of school the most?

Dropout Rate by Race – Between 2010 and 2019, there is a considerable improvement in high school student retention as there has been a decrease in the high school dropout rate by year for all races except Pacific Islanders (NCES, 2021). The rates, however, remain high for people of color.

In particular, American Indian/Alaska Native high school students have the highest high school dropout rate at 9.6% (NCES, 2021). This is much higher compared to the overall average dropout rate of 5.1% (NCES, 2021). According to the National Center for Education Statistics, the demographic breakdown of high school students who drop out are as follows: Source: National Center for Education Statistics A 2019 research by the United Way of King County provide some insight into why students of color are more likely to drop out than their white peers,

They cited that as majority of teachers in public schools are white, students of color simply do not see themselves in their teachers. In addition, many families belonging to ethnic minorities are low- to middle-income households, making it difficult for the students to get access to the technologies and resources necessary to succeed academically.
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Which students are most likely to drop out?

Graduation Rates – College graduation rates show that only 14.7% of those enrolled in bachelor’s degrees and 37.5% of associate’s degree-enrollees finish their studies in six years, (EDI, 2021). In this case, American students tend to either delay their studies or drop out completely—with the latter option being more appealing to older enrollees.

• College freshmen make up 30% of the total dropout rate (ThinkImpact, 2021).
• In 2019, less than half of Americans aged between 25-30 had credentials that exceed a high-school diploma, as only four million aged over 25 received “some” college credit (EDI, 2021).
• In four-year institutions, 56% of students tend to drop out after six years (What to Become, 2021).
• Students aged between 24-29 are most likely to drop out of four-year colleges, as 52.5% of them have already left without a degree (What to Become, 2021).
• Only 30% of these dropouts re-enroll in college to finish their degree (EDI, 2021).

Source: National Center for Education Statistics
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Is a dropout a failure?

School failure occurs when students are not able to complete their work with a passing grade. Truancy is an unexcused absence from school. A dropout happens when the student gives up and withdraws from school without completing the requirements for graduation.
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Is dropping out of high school bad?

Introduction – High school dropout is associated with negative individual and social consequences. For example, dropping out of high school can lead to long-term economic hardships that can weaken health and family functioning. Dropout is typically viewed as the result of long-held vulnerabilities such as learning problems.

But little is known about the immediate circumstances triggering high school dropout. Taking into account adolescents’ circumstances at the time of dropping out and comparing those circumstances with similar students who do not drop out could illuminate why dropout sometimes occurs in the absence of long-term vulnerabilities or why vulnerable adolescents drop out at a specific point in time.

This comparison might also help explain why only a fraction of vulnerable adolescents actually drop out. The goal of this study is to examine whether recent exposure to stressful life events precipitate high school dropout over and above, or in interaction with, previous vulnerabilities.

The authors used data from a case–control study of Canadian adolescents recruited from 12 disadvantaged public schools. Students were divided into three equal-sized groups: those who had just dropped out of high school (referred to as “dropouts”); students with a similar academic profile and family background as dropouts (“matched at-risk students”); and students with scores on the risk index that were close to their school’s average (“average, not-at-risk students”).

After a recent dropout was identified and interviewed, a matched at-risk student was interviewed, as was an average, not-at-risk student. During the interview, the students were asked about all the stressful situations that they had experienced in the past year.
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What is the point of dropout?

Dropout layers have been the go-to method to reduce the overfitting of neural networks. It is the underworld king of regularisation in the modern era of deep learning. – In this era of deep learning, almost every data scientist must have used the dropout layer at some moment in their career of building neural networks. Figure 0: Indian Jharokhe, dropping out some light (Image by Author) If you have similar questions regarding dropout layers, then you are in the correct place. In this blog, you will discover the intricacies behind the famous dropout layers. After completing this blog, you would be comfortable answering different queries related to dropout and if you are more of an innovative person, you might come up with a more advanced version of dropout layers.

Introduction: The problem it tries to solveWhat is a dropout?How does it solve the problem?Dropout ImplementationDropout during InferenceHow it was conceivedTensorflow implementationConclusion

So before diving deep into its world, let’s address the first question. What is the problem that we are trying to solve? The deep neural networks have different architectures, sometimes shallow, sometimes very deep trying to generalise on the given dataset.

But, in this pursuit of trying too hard to learn different features from the dataset, they sometimes learn the statistical noise in the dataset. This definitely improves the model performance on the training dataset but fails massively on new data points (test dataset). This is the problem of overfitting.

To tackle this problem we have various regularisation techniques that penalise the weights of the network but this wasn’t enough. The best way to reduce overfitting or the best way to regularise a fixed-size model is to get the average predictions from all possible settings of the parameters and aggregate the final output.

1. But, this becomes too computationally expensive and isn’t feasible for a real-time inference/prediction.
2. The other way is inspired by the ensemble techniques (such as AdaBoost, XGBoost, and Random Forest) where we use multiple neural networks of different architectures.
3. But this requires multiple models to be trained and stored, which over time becomes a huge challenge as the networks grow deeper.

So, we have a great solution known as Dropout Layers. Figure 1: Dropout applied to a Standard Neural Network (Image by Nitish ) The term “dropout” refers to dropping out the nodes (input and hidden layer) in a neural network (as seen in Figure 1). All the forward and backwards connections with a dropped node are temporarily removed, thus creating a new network architecture out of the parent network.

The nodes are dropped by a dropout probability of p. Let’s try to understand with a given input x: to the fully connected layer. We have a dropout layer with probability p = 0.2 (or keep probability = 0.8). During the forward propagation (training) from the input x, 20% of the nodes would be dropped, i.e.

the x could become or and so on. Similarly, it applied to the hidden layers. For instance, if the hidden layers have 1000 neurons (nodes) and a dropout is applied with drop probability = 0.5, then 500 neurons would be randomly dropped in every iteration (batch).

Generally, for the input layers, the keep probability, i.e.1- drop probability, is closer to 1, 0.8 being the best as suggested by the authors. For the hidden layers, the greater the drop probability more sparse the model, where 0.5 is the most optimised keep probability, that states dropping 50% of the nodes.

So how does dropout solves the problem of overfitting? In the overfitting problem, the model learns the statistical noise. To be precise, the main motive of training is to decrease the loss function, given all the units (neurons). So in overfitting, a unit may change in a way that fixes up the mistakes of the other units.

1. This leads to complex co-adaptations, which in turn leads to the overfitting problem because this complex co-adaptation fails to generalise on the unseen dataset.
2. Now, if we use dropout, it prevents these units to fix up the mistake of other units, thus preventing co-adaptation, as in every iteration the presence of a unit is highly unreliable.

So by randomly dropping a few units (nodes), it forces the layers to take more or less responsibility for the input by taking a probabilistic approach. This ensures that the model is getting generalised and hence reducing the overfitting problem. Figure 2: (a) Hidden layer features without dropout; (b) Hidden layer features with dropout (Image by Nitish ) From figure 2, we can easily make out that the hidden layer with dropout is learning more of the generalised features than the co-adaptations in the layer without dropout. Figure 3: (a) A unit (neuron) during training is present with a probability p and is connected to the next layer with weights ‘ w ‘ ; (b) A unit during inference/prediction is always present and is connected to the next layer with weights, ‘ pw ‘ (Image by Nitish ) In the original implementation of the dropout layer, during training, a unit (node/neuron) in a layer is selected with a keep probability (1-drop probability). Figure 4: Forward propagation of a standard neural network (Image by Nitish ) where: z: denote the vector of output from layer (l + 1) before activation y: denote the vector of outputs from layer l w: weight of the layer l b: bias of the layer l Further, with the activation function, z is transformed into the output for layer (l+1). Figure 5: Forward propagation of a layer with dropout (Image by Nitish ) So before we calculate z, the input to the layer is sampled and multiplied element-wise with the independent Bernoulli variables. r denotes the Bernoulli random variables each of which has a probability p of being 1. Figure 6: Comparison of the dropout network with the standard network for a given layer during forward propagation (Image by Nitish ) Now, we know the dropout works mathematically but what happens during the inference/prediction? Do we use the network with dropout or do we remove the dropout during inference? This is one of the most important concepts of dropout which very few data scientists are aware of.

According to the original implementation (Figure 3b) during the inference, we do not use a dropout layer. This means that all the units are considered during the prediction step. But, because of taking all the units/neurons from a layer, the final weights will be larger than expected and to deal with this problem, weights are first scaled by the chosen dropout rate.

With this, the network would be able to make accurate predictions. To be more precise, if a unit is retained with probability p during training, the outgoing weights of that unit are multiplied by p during the prediction stage. According to Geoffrey Hinton, one of the authors of “Dropout: A Simple Way to Prevent Neural Networks from Overfitting” there were a set of events that inspired the fundamental dropout.

The analogy with Google Brain is that it should be big because it learns a large ensemble of models. In neural networks, it is not a very efficient use of hardware since the same features would need to be invented separately by different models. This is when the idea of using the same subset of neurons was discovered.Bank Teller: In those days, the tellers keep changing regularly and it must be because it would require cooperation between the employees to successfully defraud the bank. This implanted the idea of randomly selecting different neurons such that with every iteration there is a different set of neurons used. This would ensure that neurons are unable to learn the co-adaptations and prevent overfitting, similar to preventing the conspiracies in the bank.Sexual Reproduction: It involves talking half of the genes of one parent and half of the other, adding a very small amount of random mutation, to produce an offspring. This creates a mixed ability of the genes and makes them more robust. This can be linked to a dropout which is used to break co-adaptations (adds randomness just like a gene mutation).

Isn’t the entire journey fascinating? The main motive to introduce the idea of how the dropout was conceived is to motivate the readers and explore the world around them and relate it to the working principles of several other neural networks. It would definitely give birth to many such innovations.

If we follow the original implementation, we need to multiply the weights with the dropout probability during the prediction stage. Just to remove any processing during this stage, we have an implementation known as ” inverse dropout”. The intention of multiplying weights with dropout probability is to ensure that the final weights are of the same scale, thus the predictions are correct.

In inverse dropout, this step is performed during the training itself. At the training time, all the weights that remain after the dropout operation is multiplied by the inverse of keep probability, i.e. w * (1/p). To gain mathematical proof of why both operations are similar on the layer weights, I recommend going through a blog by Lei Mao,

Finally!! We have covered the in-depth analysis of the dropout layers that we use with almost all the neural networks. Dropouts can be used with most types of neural networks. It is a great tool to reduce overfitting in a model. It is far better than the available regularisation methods and can also be combined with max-norm normalisation which provides a significant boost over just using dropout.

In the upcoming blogs, we would learn more about such basic layers which are used in almost all networks. Batch normalisation, layer normalisation, and attention layers to name a few. Nitish Srivastava, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf Jason Brownlee, A Gentle Introduction to Dropout for Regularizing Deep Neural Networks, https://machinelearningmastery.com/dropout-for-regularizing-deep-neural-networks/ Lei Mao, Dropout Explained, https://leimao.github.io/blog/Dropout-Explained/#:~:text=During%20inference%20time%2C%20dropout%20does,were%20multiplied%20by%20pkeep%20,
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What is the dropout rate for Harvard?

What Is the Graduation Rate at Harvard? – According to the most recent data available from the National Center for Education Statistics, Harvard’s graduation rate is 98%. The vast majority of Harvard University students complete their degree programs within 4-6 years.
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What is the successful dropout rate?

The Glamorized Dropout – The college dropout rate due to COVID-19 paints a bleak picture of diminished trajectories. But the story of highly successful college dropouts provides hope. Through the years the way college dropouts are viewed by society has shifted.

The narrative of Silicon Valley, with its big businesses and fortune, altered the negative perception towards students that chose not to have a college degree. Not all dropouts, however, have a success story. This narrative becomes problematic when we disregard the other factors that enabled a dropout to become an extraordinary achiever.

A 2017 study on the factors that contribute to high educational and occupational achievement revealed that among 11,745 US leaders which include CEOs, politicians, federal judges, business leaders, multi-millionaires, and billionaires, 94% attended college, and not just an ordinary college because 50% attended a value college,

1. This research emphasizes that the successful dropout entrepreneur is not a pervasive phenomenon.
2. Based on these numbers, the college dropout success rate is only at around 6%.
3. There is no guarantee of financial success if one chooses to leave school and pursue an interest that could possibly be translated into a scalable business.

In addition to talent, networks and elite education are equally important as identified by Wai and Rindermann (2017). Moreover, there are a lot of factors at play – inborn and fixed personality traits, ambition, drive, access to resources, and flexibility – that would put someone at an ideal place to become an entrepreneur minus the college degree.
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How much does dropout cost?

How Much Does Dropout Cost Per Month? – After a three-day free trial, Dropout costs $5.99 per month or $59.99 per year. You can browse the content for free before signing up, and you can give a subscription to a friend. This is reasonably priced for what you get, especially since everything is ad-free. (Credit: PCMag)
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Are gifted students more likely to drop out?

Specific risks – However, the following risks are listed in The Social and Emotional Development of Gifted Children :

• frustration, irritability, anxiety, tedium, and social isolation (p.11)
• intense social isolation and stress among those with IQ greater than 160 (p.14)
• difficulty making friends due to advanced concept of friendship, mostly among those less than age 10 (p.23)
• de-motivation, low self-esteem, and social rejection among the exceptionally gifted (p.26)
• emotional awareness beyond their ability to control (p.34)
• difficulty with peer relations proportional to their IQ (p.35)
• loneliness, anxieties, phobias, interpersonal problems, fear of failure, and perfectionism (p.43)
• underachievement for social acceptance (p.64)
• lack of resilience reinforced by easy work and well-intentioned but misguided praise (p.65)
• increasing perfectionism throughout school years among girls (p.75)
• fear of failure and risk avoidance due to perfectionism (p.75)
• depression among creatively gifted (p.93)

There is a cause-and-effect relationship between the unmet learning needs of gifted students and the above risks. “Research indicates that many of the emotional and social difficulties gifted students experience disappear when their educational climates are adapted to their level and pace of learning.” Linda Kreger Silverman enumerates these additional risks:

• refusal to do routine, repetitive assignments
• inappropriate criticism of others
• lack of awareness of impact on others
• difficulty accepting criticism
• hiding talents to fit in with peers
• nonconformity and resistance to authority
• poor study habits

Further, there exists anecdotal evidence of truancy problems with gifted children, who sometimes miss school because of disengagement, and worse, fear of bullying. In 1999, legislation was introduced in Colorado to recognize gifted students as at-risk, with truancy as a factor, but the bill did not become law.

• Lastly, meta-analysis from the paper “Gifted Students Who Drop Out—Who and Why: A Meta-Analytical Review of the Literature” shows two key points.
• First, 4.5% of high school dropouts are gifted, and they leave school in part because of school-related issues.
• One would expect a very few gifted children to drop out, given the ease with which they can excel in school.

According to the Achievement Trap, this problem is even more pronounced among economically disadvantaged children.
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What subject has the highest dropout rate?

These are the degree subjects with the highest drop-out rates: – Computer sciences – 9.2 per cent Business & administrative studies – 8.6 per cent Mass communications & documentation – 7.6 per cent Creative arts & design – 7.6 per cent Subjects allied to medicine – 7.5 per cent Combined subjects – 7.2 per cent Agriculture & related subjects – 7.0 per cent Engineering & technology – 7.0 per cent Architecture, building & planning – 6.9 per cent Biological sciences – 6.8 per cent Education – 6.6 per cent Social studies – 5.8 per cent Law – 5.8 per cent Physical sciences – 4.7 per cent Mathematical sciences – 4.7 per cent Languages – 4.3 per cent Historical & philosophical studies – 4.3 per cent Medicine & dentistry and veterinary science – 1.4 per cent Percentage is calculated by entrants to full-time first degree courses who did not continue into their second year, by subject.
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Why do students dropout of school in Cambodia?

Dropout in Context – A situational analysis was conducted in three high-dropout provinces to identify the factors and conditions associated with dropout, develop a profile of a child at risk of dropping out, and inform intervention selection and design.

Nearly three-quarters of the at-risk students, dropouts, and dropouts’ parents/guardians cited the need to supplement income through household chores or domestic work. About half cited the need to work to earn money.50% of at-risk students and their parents/guardians and 33% of dropouts and parents/guardians cited school-related expenses.

Students also drop out of school for academic reasons:

About one-third of dropouts and one-fifth of at-risk students said they were unable to keep up with their lessons. About 20% of at-risk students and dropouts cite poor academic performance. Chronic absenteeism is a major contributor to dropout. Over half of at-risk students and one-third of dropouts have missed more than 15 consecutive days of school; 80–90% missed up to four days per month.66% of at-risk parents/guardians reported they have kept their child at home when not ill; while 56% of the parents/guardians reported they were not aware of their child’s school attendance. Negative behavior exists: About 16% of child respondents say they have broken school rules. Parents/guardians were twice as likely as their child to say the child did not like school.

Other factors (10% of respondents) cited for dropout were: illness and distance to school. Factors not frequently cited: poor school quality, overage, discouraged by teachers, marriage, and lack of latrines.
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What is a left out vs dropout?

À Left outs are those children who have never been vaccinated or reached (thus remaining unimmunized); à Drop outs are those children who started vaccination but did not complete the schedule (thus remaining partially immunized). Ans.
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What is academic dropout?

Definition. Dropouts are pupils which either no longer attend school, have moved to another school system or have died. The number of dropouts is determined as a ‘residue’.
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Why is it called the college dropout?

16 February 2022, 13:03 | Updated: 16 February 2022, 13:10 When did Kanye West drop out of college? Picture: KanyeWest/Netflix Kanye West’s first album ‘The College Dropout’ came after Ye dropped out of university – here’s the lowdown on his time at college. Watch the official trailer of jeen-yuhs – A Kanye Trilogy on Netflix Kanye West is one of the most successful artists around and decided to commit to music full time after dropping out of university. The ‘Donda’ rapper first rose to fame with his debut album ‘The College Dropout’ – which was named after his own experiences of leaving college to pursue music. Kanye West has dropped his three-part Netflix documentary, Jeen-Yuhs. Picture: Netflix Kanye West released ‘The College Dropout’ in 2004. Picture: KanyeWest
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