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INFOGRAPHIC: The Growth of Distance Learning


Posted on March 23, 2012 by Course Hero

For those whom the classroom model was too limiting, online learning has opened up new opportunities to pursue higher education. Distance learning has grown in popularity as the Internet and better technology have enabled increasingly dynamic and engaging learning experiences. Our latest infographic takes a look at the growth of distance learning over the last decade and examines which demographics have benefitted the most from online courseware. Check it out!

Quality in infomal learning

Back to basics: Lessons learned from the mobile Web

5 Tips for Managing Millennial Employees

Michael A. Olguin

Motivating the millennial generation is key to business success. Here are five tips to inspire and motivate the new working class.

Given that more than 60 percent of my staff is comprised of millennials, I feel abundantly qualified to share what I have learned over the last 20 years in business when it comes to motivating these unique individuals. Unlike Baby Boomers or Gen X employees, motivating millennials requires that you make them feel empowered but also involved in the decision-making process.

Before we get too far down the road, it’s important to clearly define the term millennial. By most definitions, millennials were born between 1982 and 1996. These individuals’ personalities were shaped by the personal technology era combined with parental guidance that was nurturing to a fault (i.e., an “everyone gets a trophy” mentality). These two influences created a sometimes confounding workforce that is at best difficult to understand and at worst entitled.

But don’t allow your lack of understanding millennials get the better of you. Instead, consider the following five tips, which will make managing them easier while engaging them more in your company, brand or department.

1. Reinforce the positives

Millennials need constant affirmation and positive reinforcement in order to feel like they are doing a good job. Thus, on a regular basis managers should tell their millennial staffers that they appreciated their input, liked their thinking or were effective in their execution. This will make them feel needed and valued.

2. Recognize that each person is different and must be managed differently

Like any group, not all millennials are the same. Therefore, it’s important to not implement a “one size fits all” approach to managing them. One millennial might like constant direction while another prefers to do it all by themselves. The rule of thumb is millennials want to believe that you understand them and are not going to try and “old school” them with the ways something used to be done when you were a young executive.

3. Be flexible

Millenials by nature don’t really like rules. They grew up in an environment where parents asked their opinions, allowed them to make decisions, and rarely pushed something on them that they didn’t like. As a result, current college recruits are not used to the rigidity of most workplaces. If you press too hard on them to comply with the company’s position on things like hours or attire, you could very easily find yourself losing a good employee.

4. Allow as much ownership as possible

The best way to handle a millennial’s feelings of entitlement is to provide them with a lot of responsibility. This doesn’t necessarily mean handing them an entire project, but clearly defining areas that they can own so they can flex their knowledge, expertise and decision-making ability. When doing so, you will find them embracing not only the work, but you as a manager and the company overall.

5. Don’t be vague

Millennials are not good at interpreting what you meant and rarely succeed when put into a situation to “wing it” themselves. Though they want responsibility and authority, they are uncomfortable without having some sort of framework for the task at hand. The best scenario is good instructions and a lot of flexibility in how you get there.

Though managing millennials can certainly be challenging—particularly for managers who have no experience in this area—it is not a lost cause and can yield many benefits as they are smart, creative, tech-savvy and resourceful workers. However, since they grew up in the “everyone gets a trophy” culture, you must be extremely sensitive to what makes them tick or you might find them moving back in with mom and dad, who still think they are too young to be committing to a career!

Read more:

7 Steps to Building Your Dream Team Epic Fail: 3 Ways to Come Back Stronger One Trait to Look for in Every Hire

Michael A. Olguin is the president of Formula PR, a national public relations boutique with offices in New York, Los Angeles and San Diego. With over 25 years of experience, he has represented such high-profile brands as Newcastle, Kashi, and ESPN. @FormulaPR

Which Social Network Should You Use — and When? [INFOGRAPHIC]

Test grid camera

Everything You Wanted to Know About Data Mining but Were Afraid to Ask

A guide to what data mining is, how it works, and why it’s important. Big data is everywhere we look these days. Businesses are falling all over themselves to hire ‘data scientists,’ privacy advocates are concerned about personal data and control, and technologists and entrepreneurs scramble to find new ways to collect, control and monetize data. We know that data is powerful and valuable. But how? This article is an attempt to explain how data mining works and why you should care about it. Because when we think about how our data is being used, it is crucial to understand the power of this practice. Without data mining, when you give someone access to information about you, all they know is what you have told them. With data mining, they know what you have told them and can guess a great deal more. Put another way, data mining allows companies and governments to use the information you provide to reveal more than you think.  Data mining allows companies and governments to use the information you provide to reveal more than you think.To most of us data mining goes something like this: tons of data is collected, then quant wizards work their arcane magic, and then they know all of this amazing stuff. But, how? And what types of things can they know? Here is the truth: despite the fact that the specific technical functioning of data mining algorithms is quite complex — they are a black box unless you are a professional statistician or computer scientist — the uses and capabilities of these approaches are, in fact, quite comprehensible and intuitive. For the most part, data mining tells us about very large and complex data sets, the kinds of information that would be readily apparent about small and simple things. For example, it can tell us that “one of these things is not like the other” a la Sesame Street or it can show us categories and then sort things into pre-determined categories. But what’s simple with 5 datapoints is not so simple with 5 billion datapoints. And these days, there’s always more data. We gather far more of it then we can digest. Nearly every transaction or interaction leaves a data signature that someone somewhere is capturing and storing. This is, of course, true on the internet; but, ubiquitous computing and digitization has made it increasingly true about our lives away from our computers (do we still have those?). The sheer scale of this data has far exceeded human sense-making capabilities. At these scales patterns are often too subtle and relationships too complex or multi-dimensional to observe by simply looking at the data. Data mining is a means of automating part this process to detect interpretable patterns; it helps us see the forest without getting lost in the trees. Discovering information from data takes two major forms: description and prediction. At the scale we are talking about, it is hard to know what the data shows. Data mining is used to simplify and summarize the data in a manner that we can understand, and then allow us to infer things about specific cases based on the patterns we have observed. Of course, specific applications of data mining methods are limited by the data and computing power available, and are tailored for specific needs and goals. However, there are several main types of pattern detection that are commonly used. These general forms illustrate what data mining can do. Anomaly detection : in a large data set it is possible to get a picture of what the data tends to look like in a typical case. Statistics can be used to determine if something is notably different from this pattern. For instance, the IRS could model typical tax returns and use anomaly detection to identify specific returns that differ from this for review and audit. Association learning: This is the type of data mining that drives the Amazon recommendation system. For instance, this might reveal that customers who bought a cocktail shaker and a cocktail recipe book also often buy martini glasses. These types of findings are often used for targeting coupons/deals or advertising. Similarly, this form of data mining (albeit a quite complex version) is behind Netflix movie recommendations. Cluster detection: one type of pattern recognition that is particularly useful is recognizing distinct clusters or sub-categories within the data. Without data mining, an analyst would have to look at the data and decide on a set of categories which they believe captures the relevant distinctions between apparent groups in the data. This would risk missing important categories. With data mining it is possible to let the data itself determine the groups. This is one of the black-box type of algorithms that are hard to understand. But in a simple example – again with purchasing behavior – we can imagine that the purchasing habits of different hobbyists would look quite different from each other: gardeners, fishermen and model airplane enthusiasts would all be quite distinct. Machine learning algorithms can detect all of the different subgroups within a dataset that differ significantly from each other. Classification: If an existing structure is already known, data mining can be used to classify new cases into these pre-determined categories. Learning from a large set of pre-classified examples, algorithms can detect persistent systemic differences between items in each group and apply these rules to new classification problems. Spam filters are a great example of this – large sets of emails that have been identified as spam have enabled filters to notice differences in word usage between legitimate and spam messages, and classify incoming messages according to these rules with a high degree of accuracy. Regression: Data mining can be used to construct predictive models based on many variables. Facebook, for example, might be interested in predicting future engagement for a user based on past behavior. Factors like the amount of personal information shared, number of photos tagged, friend requests initiated or accepted, comments, likes etc. could all be included in such a model. Over time, this model could be honed to include or weight things differently as Facebook compares how the predictions differ from observed behavior. Ultimately these findings could be used to guide design in order to encourage more of the behaviors that seem to lead to increased engagement over time. The patterns detected and structures revealed by the descriptive data mining are then often applied to predict other aspects of the data. Amazon offers a useful example of how descriptive findings are used for prediction. The (hypothetical) association between cocktail shaker and martini glass purchases, for instance, could be used, along with many other similar associations, as part of a model predicting the likelihood that a particular user will make a particular purchase. This model could match all such associations with a user’s purchasing history, and predict which products they are most likely to purchase. Amazon can then serve ads based on what that user is most likely to buy. Data mining, in this way, can grant immense inferential power. If an algorithm can correctly classify a case into known category based on limited data, it is possible to estimate a wide-range of other information about that case based on the properties of all the other cases in that category. This may sound dry, but it is how most successful Internet companies make their money and from where they draw their power. Image: Reuters. via…

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