AI computing involves two phases: training and inference. Training requires computers that can process enormous amounts of data. For example, getting an AI system to recognize what’s in photographs requires a computer to sort through billions of labeled photos to create a model. That model is used in the second step to infer, or identify, what’s in a specific photo.
Intel already sells its Nervana chips for training and inference to data centers packed with servers, computing infrastructure that often powers services at AI-heavy companies such as Google and Facebook. Intel is now shipping its larger, more expensive and power-hungry Nervana NNP-T chips for training and its smaller NNP-I chips for inference, the chipmaker announced.
Moment also makes filter adapters for screw-in 62mm filters, such as polarizers, which can help reduce reflections on water or boost the blues in the sky. Filter adapters also let you use professional-quality square Lee Filters, which slide into a holder connected to the adapter via a 62mm adapter ring.
In September, “three members of the U.S. House of Representatives introduced a version of the Digital Equity Act of 2019…The proposal would authorize up to $250M a year in funding for state and community digital inclusion efforts.”https://t.co/y7M2VHjdYI via @netinclusion
Rubrics: online scoring guides to evaluate students’ work.
Annotations: notes or comments added digitally to essays and other assignments.
Audio: a sound file of your voice giving feedback on students’ work.
Video: a recorded file of you offering feedback either as a “talking head,” a screencast, or a mix of both.
Peer review: online systems in which students review one another’s work.
Two main types of feedback — formative and summative — work together in that process but have different purposes. Formative feedback occurs during the learning process and is used to monitor progress. Summative feedback happens at the end of a lesson or a unit and is used to evaluate the achievement of the learning outcomes.
Good feedback should be: Frequent, Specific, Balanced, Timely
“It’s no secret that students today face the ultimate paradox—the same tools they need to use to complete their work can also provide their biggest distractions from completing work.” How can we help them manage this struggle? https://t.co/xmaOzXpvfU
According to the Pew Research Center, 72 percent of teenagers check their phones as soon as they get up (and so do 58 percent of their parents), and 45 percent of teenagers feel as though they are online on a nearly constant basis. Interestingly, and importantly, over half of U.S. teenagers feel as though they spend too much time on their cell phones.
Research on intrinsic motivation focuses on the importance of autonomy, competency and relatedness in classroom and school culture.
According to one Common Sense Media report, called Social Media, Social Life, 57 percent of students believe social media use often distracts them when they should be doing homework. In some ways, the first wave of digital citizenship education faltered by blocking distractions from school networks and telling students what to do, rather than effectively encouraging them to develop their own intrinsic motivation around making better choices online and in real life.
Research also suggests that setting high expectations and standards for students can act as a catalyst for improving student motivation, and that a sense of belonging and connectedness in school leads to improved academic self-efficacy and more positive learning experiences.
Educators and teachers who step back and come from a place of curiosity, compassion and empathy (rather than fear, anger and frustration) are better poised to deal with issues related to technology and wellness.
Rienties and his team linked 151 modules (courses) and 111,256 students with students’ behaviour, satisfaction and performance at the Open University UK, using multiple regression models.
There is little correlation between student course evaluations and student performance
The design of the course matters
Student feedback on the quality of a course is really important but it is more useful as a conversation between students and instructors/designers than as a quantitative ranking of the quality of a course. In fact using learner satisfaction as a way to rank teaching is highly misleading. Learner satisfaction encompasses a very wide range of factors as well as the teaching of a particular course.
this research provides quantitative evidence of the importance of learning design in online and distance teaching. Good design leads to better learning outcomes. We need a shift in the power balance between university and college subject experts and learning designers resulting in the latter being treated as at least equals in the teaching process.
#K12 schools need to start teaching students at a young age how to use the internet responsibly. Luckily, there are resources available to help them. #NCSAMhttps://t.co/iM2wPBTuR1