Little rigorous evidence exists documenting the relationship between digital media literacy and people’s ability to distinguish between low- and high-quality news online

Keywords: Social Sciences , Political Sciences

Even under ideal conditions, most people struggle to reliably evaluate the quality of information they encounter online because they lack the skills and contextual knowledge required to effectively distinguish between high- and low-quality news content.

Once you have a large enough training model, you can begin to accurately predict how different types of individuals will behave over time

Google, Facebook, Amazon and Microsoft have spearheaded the surveillance market transformation, placing themselves at the top tier of the epistemic hierarchy. They know everything about you and you know nothing about them. You don’t even know what they know about you.

It became very easy to access the private information of anyone due to the existence of smartphones and millions of applications that people download without reading anything about the privacy settings of those applications

So people should be more aware of this issue and start getting educated about it to protect themselves and their privacy and their rights.

I am happy with how my project turned out and you can check out my fully detailed lessons, resources, and unit plan here on Google Classroom

Keywords: eci 832 , major project , common sense , digital citizenship , digital identity , digital literacy , fake news , goals , graduates , major project , online resources , reflections

I’ve been fact-checking everything lately.

A major hurdle for (non-verbal) human communication = how to connect with students without physical presence


Many of us have felt simply overwhelmed with trying to learn these new systems, and decide which are really worth our time to learn. The other night we watched the first online video from my little girl’s elementary school teacher.  She looked completely strung out and had posted it at 8:30 pm. You could tell that she had more than likely spent the last two days getting a crash course through Google hangouts, google sites, Loom, video conferencing, Nearpod and even the creation of take home packets.  And, with this new online world everything seems so urgent. I’m seemingly just going from one email, or online message to the next as I try to keep connected with my students. I worry that if I miss that email or message for help, my student may not log on again. As teachers, we are having to learn how to balance efficiently learning new online systems, while also not allowing it to consume our lives. Here are some quick tips for choosing what is worth your time and best for your students

Teach what you feel comfortable teaching, then just be a parent

Keywords: news, business news

It’s OK to say, ‘I don’t know, but let’s look this up together,’ find it on the internet or check back in their notes,” he said. “The key to teaching is being honest and being calm. Tension and stress in education only goes so far before the kid snaps.

How to Data Science : How to NOT Get Confused by the Confusion Matrix and other Classification metrics

classification , data science , evaluation

Panda Data Science

by Jepp Bautista

I will try my best to help you understand how to understand the most important evaluation metrics in machine learning. In this blog we will talk about all the terminologies related to a binary confusion matrix, precision, recall and many more. By the end of this blog I hope that you will never be confused by the confusion matrix or any other metrics in classification.

Very few mathematical mumbo-jumbos are present in this blog, unlike my previous blogs which you should also have a look at.

The Confusion Matrix

The confusion matrix (also called error matrix), is a visual representation of the performance of a classification model. It is a table with different combinations of a “predicted” class and an “actual” class. Below is a representation of the confusion matrix:

Actual “Positive” Actual “Negative”
Predicted “Positive” True Positive False Positive
Predicted “Negative” False Negative True Negative


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