Waching daily Jul 31 2017

Data is valuable.

Data is a new commodity.

Data keeps whole enterprises going.

Data translates into money.

It makes people rich.

It makes people bankrupt.

So whenever we're thinking about data, we're thinking about something that has increasingly

value across the economies in our world today.

That's one of the reasons why we have to take it very seriously, why we have to build ethical

codes, and why we have to think about legal responsibilities.

Information is power.

Data is a form of wealth.

Learning is no exception to that.

I mean, let's think a little bit about some of the potential commercial values of education

data, for example.

If I have access to detailed information about your courses, about your learning, your educational

level, your subject, your interest, there's all sorts of things that I could sell to you

with that information.

I could sell you books, for example.

I could sell you services.

I could use that information for a job search agency to target you for advertisements, for

example.

I could use a whole lot of information of that sort to a commercial process.

Equally of course if I'm a public organisation like a government, I've got interest in that

information for predicting labour market needs.

It would help me to build an understanding of the future tax base of the country, because

I could predict your earning levels.

So lots and lots of people are interested in educational data for a whole variety of

reasons across a spectrum, from pure commercial gain, through to the public interest of managing

information for the public good.

That's one of the reasons why we have to take it so seriously.

When we consider what we need to do about putting in place appropriate ethical codes

for learning analytics, it's as well to start with the principle that all of the data is

originally owned by the individual who generates it.

And of course in order to do anything with that data the individual has to surrender

some of those rights to let another person or people actually use that data in some way.

As soon as that act of surrender actually happens, we've got an ethical and legal set

of considerations.

The legal considerations are the obligations for data protection, the ethical considerations

are the way that we deal with the person who owns that data.

So, the reason why we have to have an ethical code is so that we can be sure to fulfil our

ethical responsibilities towards the original owner of information who has surrendered their

right over it to us to make use of.

So that's the starting point for any one of those codes.

Clearly it involves informed consent.

You have to know why that data is being used, you have to give your permission in an informed

way, but increasingly informed consent is not sufficient.

We all give informed consent every day for everyday devices that we use, and these are

very well-known cases.

So, for example, every time you use Facebook, every time you use Google, you have given

informed consent somewhere along the line for them to use your information for the purposes

that they have, but you won't necessarily recall that.

So an ethical code of practice goes way beyond informed consent and guides us in how we should

behave.

The sorts of things we'd expect to see in an ethical code of practice will also relate

to our purpose in education; our purpose in education is to provide the ability for the

learner to learn in a beneficial way for them.

Education is about improving individual people's prospects, it's about improving society as

a whole.

So first of all our Code of Ethics will embody in some way or another, that goal of personal

advancement, that goal of societal benefit.

And we build ethical codes around those principles.

A further important point of course, is that an ethical code cannot be built without appropriate

consultation.

You can't sit in a darkened room by yourself and assume that you have enough knowledge

to build that ethical code.

So we will need to consult with appropriate organisations, appropriate individuals, in

building that ethical code, and get their approval and support for it.

Which, again, is why Jisc started off by talking to the National Union of Students about the

whole process of learning analytics and about building an ethical code.

The ethical code, once constructed, must be easily interpreted and communicated, it must

be available to people to use, and it must be appropriately broad for institutions to

adapt it to their own particular circumstances and their own particular needs.

But at the end of the day, an ethical code is about ensuring best practice in the use

of learning analytics data.

In parallel to ethical considerations of course, we have legal considerations, and they speak

to each other.

The purpose of legal considerations over the use of data is to reinforce ethical considerations,

and to provide the sorts of protections that everybody needs in their everyday lives.

Now, most of us know, as citizens, about the way in which our personal information can

be abused, for example, for advertising.

Most of us become enraged by spam emails that we don't want, by cold calls to sell us products

we put the phone down.

We feel that's a violation of our privacy.

And so legal protections have been put in place, and are increasingly putting in place,

to protect our rights over our personal data.

And any institution is subject to those data protection obligations.

Now this obviously applies to our educational data as well.

So any college or university is very aware of their data protection obligations, and

will take very significant precautions to make sure that they're on the right side of

the law in ensuring that those are in place.

So as we design learning analytics systems for institutions, as we roll them out across

our colleges and our universities, we are always mindful of the needs for data protection.

Ethics isn't just about collecting that data in the first place.

It's about the extent of data we collect, it's the way that we analyse it, the way that

we report it.

So the ethical code starts of course with the institution relating to the individual,

making sure they understand why we're doing it, making sure they understand what information

we're collecting about them.

But there are certain other ethical principles that we need to apply.

So it makes sense to apply a principle of minimality, for example, when we collect information;

we shouldn't be collecting information that is utterly unnecessary for our purposes.

We should confine ourselves to collecting data that is appropriate for learning analytics,

and appropriate for better understanding how learning happens.

So, for example, if we are collecting information from a student of their swiping into the institution,

if that swipe card is a smart card that also contains information about their purchasing

patterns in the cafeteria, is it ethical to be collecting that information as well?

Does it have anything to do with our understanding of their learning experience?

So a principle of collecting only appropriate data.

Similarly, when we are analysing that data, are we analysing it appropriately to our needs?

Are we producing reports that are relevant to education?

Are we confining ourselves to the purposes we've set for ourselves?

Are those purposes transparent?

Are people aware of what we're doing?

And then ethics also extends to interventions.

Are we always intervening in a way that is thoughtful and appropriate?

So, for example, it could very well, on an early alert system, be unethical to alert

a student directly, because if you're alerting a student directly without a human intervention

that they might be at risk, you could do more harm, by persuading them that they lack the

ability to be at the institution.

By persuading them that they did the right thing in leaving.

It will be unethical to make that intervention without a considered appraisal of the consequences

of your intervention.

So being ethical is really about being thoughtful, and about holding in place the ultimate objective

of learning analytics, which is to improve the quality of the student experience, and

the quality of the learning environment.

All of these elements need to come together into a code of practice and so one of the

first things that Jisc did, when we started on the learning analytics journey, was to

consult and to build a code of practice, which is readily available to all practitioners

to use.

For more infomation >> Jisc Learning Analytics: The need for a code of ethics - Duration: 10:00.

-------------------------------------------

Deadline for free/reduced school lunch sign-ups - Duration: 1:12.

For more infomation >> Deadline for free/reduced school lunch sign-ups - Duration: 1:12.

-------------------------------------------

5 Proven Trading Tips For An Aspiring Trader - Duration: 9:01.

hi traders it's Bruce banks and today we're going to talk about five rules for

an aspiring trader these are traders who are looking to get

into it and are just getting the feel of what trading is about and there's a few

really key things that every trader who's getting into this should know so

the first one is never add to a losing position I'm sure you've heard this

before but this one is incredibly important now let's take a look here and

say that we have a long bias on this market you know the trader through his

analysis has decided that he wants to go along here and he wants to go along here

and he thinks the markets gonna go up market has been trading up recently but

there are a few warning signs that you know as you test though your message

depending what they you what you use you're going to see like for me big

warning sign right here is you see that we have a second low here that broke

this previous low so it kind of broke the uptrend we're going to ignore that

sign you know we're talking about a new trader here says like I want to take a

long position on this goes long on the market and as the market goes down like

you can see here we have a sharp down in the market this is quite a sharp move so

most likely news related as the mark goes down they get a little bit worried

now say the market goes down and they see it bottomed all the way out they

don't have a stop level and you let the market go all the way down human emotion

kicks in there and that's one of the things we're always fighting with this

traders and as the market starts to go up think oh I'm going to have to do so

much to earn them my money back what if I the markets going back up now I can

just go ahead and say I'm just gonna add another position on here so I'll earn

the money back and you know I can average it down so I don't even need the

mark to reach what it previously was when I entered back here all I need to

do is have the market just reach even below that level and I'll average down

and that's such a big mistake because if the trade is not gone in the direction

that you originally anticipated you need to just basically get out of the trade

there's no reason to ever average down by adding on positions

to a losing trade because you can see what would happen is they would add it

on and again they would have just lost even more because they have two

positions on now both losing money and that's how a lot of new traders end up

bleeding a lot of money into the market you know what you have to have your

trade idea and go for it and that goes into rule number 2 using hard stop loss

levels. now we already briefly touched on why taking along here is a little bit

risky but you know say this trader has an idea it's like you know I really

really want to take this trade I believe the markets going to go up through all

of their analysis whatever strategy they're using and they take the trade

and the most important thing is you have to take a level just draw a line when

you enter your trade before you enter that trade you have to find a level and

you're just like okay this is going to be my heart stop loss level this is

going to be my heart stuff here you know you have to choose whatever level that

you choose you have to choose that hard stop loss level and is especially

important for aspiring traders we're just getting into it that level is

golden that's just as important of a level as your entry you have to have

your hard stop loss level because the thing is you can take a losing trade but

a losing trade that is just open-ended without a hard stop loss level can just

end up costing you more and more money it's an open pit so a hard stop loss

level is incredibly important number three is understand that trading is a

long-term plan you're going to have losing trades and they're going to have

winning trades you know you'll have down days and you will have updates you know

just because this trader took this long trade says okay I'm going to take it and

roll with it and he took a loss on the trade if he used a proper like stop-loss

level where as soon as the trade went against the direction that he was

betting on he got out of the trade and you know he cut his losses as soon as

possible you know he can take another trade he doesn't have an open trade

bleeding there and yes I understand that that's just a losing trade you chock

that up in the pile of that's a loser and you go for your next trade which

could be a winner number four is trade with one to two percent risk of your

capital per trade so that's total capital this is a really good rule that

you'll hear quoted quite often because it limits the amount that you can lose

on every trade by so much you know if you have a ten thousand dollar account

you can't trade like a trader who has a hundred thousand dollar account you

might have to scale your position sizing down to meet your actual trades I've got

a video on this that'll pop up in the link above or it will be linked below

but it's a really good rule because the whole thing is if you take a losing

trade it's not account crippling that's the most important thing about the one

to two percent rule is the fact that you can take multiple trades that are losers

in a row which if you're a trader you have to be willing to accept will happen

but as they happen it's not that big of a deal because you're always going for

winning trades like a better winning percentage so losing 1% losing two

percent of your account size on a single trade is not that big of a problem so no

matter even if you make a complete blunder of mistake that you can look

back on and say oh yeah I understand why I shouldn't make that trade it's still

it's not that big of your account size that it took away and you'll end up

being able to take more trades that way by limiting how much risk you take per

trade and that takes us into 0.5 back tests any strategy that you want to use

before you use real money after our traders spent a good amount of time in

the market they get a bit of marketing tuition you understand when you start to

see higher highs higher lows you start to see points of resistance points of

support one thing that is key for all trading strategies is the fact that you

back test it you can have a simple strategy saying that say if you get two

up bars like we will make up the strategy right now if I get two up bars

in a row of equal size I take a long grade if I

get two down bars of equal size in a row I take a short trade and what you can do

is you can quickly and easily test that theory so you look for that on the chart

and say okay well I'm going to test that theory right now look for two up trades

equal here so I see two equal here that'll be an upgrade I see two equal

here roughly that would be a down trade I look for and that's the only ones I'm

quickly seeing right now we'll test that theory should be along so we enter here

and yeah possibly could work see you enter here oh no stop to sell right away

so we've tested that theory that's back test that's a super simple back test

that's a terrible back test but I'm saying that what you need to do is you

need to be able to take whatever theory you have that's why I've tested the

highs and lows theory and I really do like trading with highs and lows but

you'll have to take that theory that you have and back test it religiously

because back testing is basically playing with free money you know you're

not actually playing with real money they have there's amazing back testing

software out there as well where you can use it and you can test your theories

before you apply your real money it's such a easy thing to do because you're

not only you're learning the market at the same time you know if you're an

aspiring trader you're a new trader you really want to have a better

understanding of the market you won't have a better understanding of how the

market moves when there's news announcements that are upcoming or when

there's or during the open of every day you want to have a better understanding

of the market and back testing allows you to not only understand the potential

trading strategy that you're going to use but understand the market as a whole

so it's really important to back test every strategy that was five rules for

an aspiring trader and I hope you got some value out of this as always this is

Bruce Bank saying enjoy trading

For more infomation >> 5 Proven Trading Tips For An Aspiring Trader - Duration: 9:01.

-------------------------------------------

Learn colors and Shapes with Stacking Colours for Children Colors Rings Games 3D For Kids - Duration: 4:54.

Learn colors and Shapes with Stacking Colours for Children

For more infomation >> Learn colors and Shapes with Stacking Colours for Children Colors Rings Games 3D For Kids - Duration: 4:54.

-------------------------------------------

Jisc Learning Analytics: The Jisc code of practice for learning analytics - Duration: 13:31.

So what then should a code of ethics be?

What should it do?

Well of course we have to have an ethical approach to what we do, but we equally don't

want to put up prohibitive barriers that actually stop us doing the work that we want to do,

stop us doing sensible learning analytics.

So an ethics code needs to be a living, breathing document; something that is evolving all the

time.

Jisc put in place a code of practice at a very early stage of our journey in learning

analytics, but it is a living document, subject to amendments, something that we need to reinterpret

as our practice develops.

So what then are the major headings, the major subject areas that should be in a code of

practice?

So firstly responsibility.

Learning analytics collects sources of data from a wide range of departments within a

complex organisation like a university and a college.

People involved range from people who are at the rock face of collecting data, through

to people who analyse that data, record it and pass it on.

So a first key area for any code of ethics is to be very clear and transparent about

where that responsibility lies.

The code of ethics needs to identify who is responsible, define the extent of that responsibility,

and be quite clear and unambiguous about it, so that that responsibility can be mapped

on to a particular person's job responsibilities and professional obligations.

Transparency.

Well transparency is about openness and honesty.

We expect transparency in any public organisation or in any organisation that holds information

about us.

So transparency is about being very clear and open about what we're doing, so the person

that ultimately owns the data is very clear about where it's being used and where it's

going.

So a code of ethics should define what that means in terms of an institution.

How much are we making people aware of what we're doing, aware of our responsibilities,

aware of our obligations and their rights?

Consent.

Consent is getting more complicated.

So informed consent is essential for many legal processes in terms of data protection,

and is also generally accepted as an ethical thing to do.

So, in other words, we have to make sure that the students from whom we're collecting the

information have given a form of consent.

But consent is getting more and more legally complicated because there is a recognition

that consent is also about power.

So, for example, you can't really say that a person has given unconstrained consent if

a condition for them joining the university in the first place is they tick the box to

surrender their data.

So this is an area that's quite volatile and under consideration.

So we would expect a code of ethics translated into practice to certainly mention informed

consent.

But this is one of those areas that's alive and changing and we need to make sure that

we're on top of those changes as they develop.

Privacy.

Everybody has a right to privacy.

Now privacy here, when we're considering ethics and practice, needs to be differentiated from

de-identification.

So de-identification is really important when it comes to releasing personal information

to different levels within an organisation.

But de-identification is not in itself a substitute for privacy.

So when a student necessarily releases their personal data to their teacher, for example,

who has to have access to their individual sets of data, that teacher has a significant

number of privacy responsibilities in terms of both ethics and practice.

A lot of data is sensitive.

So, for example, a teacher might need to know about a person's disabilities.

But that right to know doesn't extend their right to share that information without consent

to others.

People need to be sensitive to that.

People need to be sensitive to the political environment on key indicators of identity,

for example, that can compromise a person in the world.

So if we're not ever mindful of privacy, we are likely to violate the trust that we have

with an individual, which is essential for learning analytics.

Validity.

Validity is about a cluster of issues.

It obviously includes accuracy; we want our information to be accurate.

Inaccurate information is simply wrong.

But it's more than just accuracy.

It's about recognising the danger of gaps in the data set, for example, which might

have implications.

It's about recognising that inappropriate correlations can be made that imply a cause-effect

relationship.

If those get into a system, then the learning analytics system lacks validity and we come

to false conclusions.

So validity extends the concept of accuracy into the process of analysis and the process

of reporting.

The reason why it's an ethical concern, that underlines a code of practice, is that invalid

data can have very severe consequences.

Very clearly an invalid data record can have very serious consequences for an individual

student.

If the mark is incorrect, the student can fail the degree.

So there's obviously a key interest in validity there.

But validity also extends to the public implications that we draw from information.

So at the opposite end of the spectrum, if we take something like the Teaching Excellence

Framework, for example, that depends on the validity of the data sets that originate in

individual institutions.

If those aren't valid then the public is going to draw quite wrong conclusions about the

implications of the relative value of different institutions, and potential students might

make inappropriate choices in what are some of the most important investments in their

lives.

So validity goes to the heart of the trust that we can expect in the system and the value

of that system both to individuals, organisations, but also to society at large.

Access.

Now remember that learning analytics depends on individual students surrendering their

rights over their personal information, and shifts the responsibility onto the organisation

to use that information appropriately.

So it follows from this that individual students have a right of access to how we're using

that data and the way that we're interpreting it.

And a code of ethics translated into a code of practice must allow for appropriate access.

It doesn't from this follow that a student will have access to everything that an organisation

knows about them or deduces about them.

For example, if the outcome of the analysis might be harmful for the student, the institution

might have a reason not to tell the student that that is their conclusion.

But the code of ethics must translate into practices that allow that to be done on an

everyday basis in a consistent, fair and reasonable manner.

Positive interventions.

Now remember that the key purpose of learning analytics is to improve the student experience

and to improve the learning environment; that word improvement runs through everything that

we do.

So it's ethical to expect that the application of learning analytics will lead to positive

outcomes.

Ethics, and the code of practice that follows from this, needs to ensure that we understand

what those are, and also that it defines what form those interventions will take, as we

move forward.

That will depend on the individual institution.

So the code of ethics will set that out in broad terms, but the individual institution

needs to define what form of interventions it's envisaging, how it's going to put them

in practice, and how always, those are going to lead to positive outcomes that are appropriate

to the mission of that institution in particular.

In turn, positive interventions depend on appropriate resourcing.

So you can't institute a programme of learning analytics, make promises to your students

and make promises to your staff, and then find that you have inadequate resources to

carry out those interventions.

Not only is that betrayal of trust, it's unethical, because promises have been made that can't

be met.

This is why it's essential that the decision to implement learning analytics is made at

the highest level of the institution, to ensure that appropriate resources are put behind

the interventions that will follow.

Don't promise students interventions based on their analytic results, that then as an

institution you can't meet.

Avoiding adverse impact.

This is a really important part of ethical practice that needs to be translated into

the way we behave, our codes of practice.

Clearly monitoring something always runs the risk of the intervention affecting the behaviour,

and whenever we're thinking about how we use learning analytics we've got to be very careful

that people don't respond by trying to game the system, or that we don't induce adverse

behaviour in people by the process of monitoring and reporting that we put in place.

This must be defined in terms of the particular level and the particular application of learning

analytics.

So, for example, if for our educators, our teachers, we set job performance goals that

require very, very specific outcomes, we mustn't create a situation where the use of learning

analytics distorts the value of the teaching simply to achieve the monitored outcomes.

At the organisational level where we have national indicators, for example, such as

the Teaching Excellence Framework, we need to avoid the tendency of institutions to drive

their whole policies towards achieving better scores on these sorts of narrow outcomes.

Now this is a very familiar problem across all aspects of education, whether we're talking

about the way we measure performance in primary schools, or the way that we measure performance

in universities.

And that difficulty, which we're well aware of, goes back into the heart of learning analytics,

and is why it has such an important part to play in a code of practice.

Stewardship is a really important part of any data driven organisation.

It also relates to data literacy.

It also relates to the whole concept of a data culture within in an institution.

Stewardship is our responsibility as the custodians of data.

It may not have originated with us.

It may not belong to us, but when we hold it we have a clear set of responsibilities

for it which extend to making sure that it is used appropriately, making sure it's accurate

and valid, making sure that it is not passed on inappropriately to third parties but equally

making sure that when necessary it is deleted or destroyed.

And those stewardship rules need to be very clearly set out within the code of ethics

and the code of practice.

There's naturally a tendency to think that data is owned or the responsibility of somebody

else, maybe somebody in the IT department or someone in the Registrar's department.

What data stewardship tells us is in our complicated organisations such as colleges and universities,

very many people are data stewards and we need to set out very clearly the ways in which

they should fulfil those responsibilities.

Overall, codes of ethical practice should be living, breathing documents that make sense

in the world.

They should be things that we think about that are sensible.

They shouldn't be prescriptive documents that stop us doing things.

They shouldn't be rules and they shouldn't be regulations.

We want an environment where we're free to innovate, where we're free to put in place

new systems, where we're free to do things that improve the quality of learning.

That's why it's so important that an ethical code is something that is actively discussed,

actively worked on, actively changed to changing circumstances.

At the end of the day it's a document that keeps us honest, keeps us on focus, keeps

us directed towards our primary purposes of doing good.