Let's just put it out there. December is exhausting for teachers. The weather grows colder and (at least here in Oregon) wetter. Students are anxious -- whether it's a buzzing excitement for vacation or a sense of dread that some kids feel in homes that are unsafe during the holidays. They're tired of redirecting behaviors and tired of the mid-year pressure of the test and simply tired of the sheer energy it takes to be a teacher. It's no wonder that so many teachers begin playing holiday movies around this time of year. They want to create a sense of fun and escape and enjoyment, and a motion picture promises exactly that. So, please don't read this post as a slam on teachers showing movies before the break. If this is a part of a positive classroom culture, keep doing it. This isn't meant to be a guilt trip or a rant or a

Data visualisation helps understanding data represented by multiple variables, also called features, stored in a large matrix where individuals are stored in lines and variable values in columns. These data structures are frequently called multidimensional spaces.In this paper, we illustrate ways of employing the visual results of multidimensional projection algorithms to understand and fine-tune the parameters of their mathematical framework. Some of the common mathematical common to these approaches are Laplacian matrices, Euclidian distance, Cosine distance, and statistical methods such as Kullback-Leibler divergence, employed to fit probability distributions and reduce dimensions. Two of the relevant algorithms in the data visualisation field are t-distributed stochastic neighbourhood embedding (t-SNE) and Least-Square Projection (LSP). These algorithms can be used to understand several ranges of mathematical functions including their impact on datasets. In this article, mathematical parameters of underlying techniques such as Principal Component Analysis (PCA) behind t-SNE and mesh reconstruction methods behind LSP are adjusted to reflect the properties afforded by the mathematical formulation. The results, supported by illustrative methods of the processes of LSP and t-SNE, are meant to inspire students in understanding the mathematics behind such methods, in order to apply them in effective data analysis tasks in multiple applications.

Imagine a stuffed animal that can record children and transmit the recording to their parents. If the child is getting bullied at school, the parent will find out. But is it ethical to record one's own child without their knowledge or consent? Does it matter how old the children are? Eamon Marchant, a STEM teacher and technology coordinator at Whitney High School in Cerritos, Calif., presents quandaries like this to his students all the time.

Imagine a stuffed animal that can record children and transmit the recording to their parents. If the child is getting bullied at school, the parent will find out. But is it ethical to record one's own child without their knowledge or consent? Does it matter how old the children are? Eamon Marchant, a STEM teacher and technology coordinator at Whitney High School in Cerritos, Calif., presents quandaries like this to his students all the time.

Artificial intelligence is an increasingly prevalent part of our everyday lives. From live-updating, turn-by-turn driving directions to responsive voice-controlled digital assistants--all in the palms of our hands--we are constantly interacting with computer programming where machines learn from experience and adjust to new data to perform human-like tasks. For children growing up right now, AI will undoubtedly be a part of their future lives and jobs. So, it's critical that students understand computational thinking and know how machine learning works. "It's important that kids leave our classrooms with real-world knowledge and industry-standard software and technical experience under their belt," says Teresa Blizman-Schmitt, a sixth through eighth grade computer science and business education teacher in Vernon, CT.

Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes. Using flexible machine learning methods in the counterfactual framework is a promising approach to address challenges due to complex individual characteristics, to which treatments need to be tailored. To evaluate the operating characteristics of recent survival machine learning methods for the estimation of TEH and inform better practice, we carry out a comprehensive simulation study representing a variety of confounded heterogeneous survival treatment effect settings and varying degrees of covariate overlap. Our results indicate that the nonparametric Bayesian Additive Regression Trees within the framework of accelerated failure time model (AFT-BART-NP) consistently carries the best performance, both in terms of bias and precision. Moreover, the credible interval estimators from AFT-BART-NP provide close to nominal frequentist coverage for the individual survival treatment effect when the covariate overlap is at least moderate. Under lack of overlap, where accurate estimation of the average treatment effect becomes challenging, the credible intervals from AFT-BART-NP still provide nominal frequentist coverage among units near the centroid of the propensity score distribution. Finally, we demonstrate the application of these machine learning methods through a comprehensive case study examining the heterogeneous survival effects of two radiotherapy approaches for localized high-risk prostate cancer.

As these teachers have come to understand, you don't have to be technically minded to introduce your students to the important concepts behind artificial intelligence. When computer science teacher Sharon Harrison wanted to introduce her eighth graders to the basic idea of artificial intelligence, she had them try out an online chatbot called Akinator, which asks the user questions to determine what historic or fictional character he or she is thinking of. In some instances, the students marveled at how quickly the program could figure out the answer. "Sometimes it would guess in three or four guesses, and we'd say, 'How on earth was it able to do that?'" From there, the discussion in this elective class at the University of Chicago Laboratory Schools examined how responses to the chatbot could be sabotaged -- by responding to the questions incorrectly, and thereby "damage the integrity of the program."

That student with the heart of the world was born, My data burns the same stars in a window. No human would ever mistake these lines for award-winning verse. If they're a bit fanciful and lack coherence, that's because they were composed by a computer algorithm trained on more than 20 million words of 19th century poetry. The seed word in this case was "student." The resulting word salad makes it clear that as powerful as AI is, it still can't create like a human.

Nisha Rataria remembers the moment that she understood the power of technology to significantly improve a child's learning and comprehension. As a teacher at the public Vidhya Nagar Primary School in Ahmedabad, Gujarat, India, Rataria teaches students from across the spectrum – bright, struggling, poor and middle class. A few years ago, her school implemented an artificial-intelligence based education program called EnglishHelper that provides a suite of tools to help children learn to speak, read and write English. Many of her students, who she says could not even recognize the alphabet, could now read English with some confidence. By the end of the 2019-2020 school year, EnglishHelper and ReadToMe could be used by nearly 20 million students worldwide.