Measuring social presence in online-based learning: An exploratory path analysis using log data and social network analysis

Recent work in computer vision has shown that common im-age datasets contain a non-trivial amount of near-duplicateimages. For instance CIFAR-10 has 3.3% overlap betweentrain and test images (Barz & Denzler, 2019). This results inan over-reporting of the generalization performance of ma-chine learning systems.

Chatti notes that Connectivism misses some concepts, which are crucial for learning, such as reflection, learning from failures, error detection and correction, and inquiry. He introduces the Learning as a Network (LaaN) theory which builds upon connectivism, complexity theory, and double-loop learning. LaaN starts from the learner and views learning as the continuous creation of a personal knowledge network (PKN).[18]

people reading the same book at the same time, exploring the same ideas…Norms around signalling you're interested in something, and the extent of your interest, would go far

One of the biggest challenges is a very human one – how do we get everyone to work with their camera switched on.

Particularly striking in 1971 was his call for advanced technology to support "learning webs": The operation of a peer-matching network would be simple. The user would identify himself by name and address and describe the activity for which he sought a peer. A computer would send him back the names and addresses of all those who had inserted the same description. It is amazing that such a simple utility has never been used on a broad scale for publicly valued activity.

Workplace Learning in Informal Networks

import all the necessary libraries into our notebook. LibROSA and SciPy are the Python libraries used for processing audio signals. import os import librosa #for audio processing import IPython.display as ipd import matplotlib.pyplot as plt import numpy as np from scipy.io import wavfile #for audio processing import warnings warnings.filterwarnings("ignore") view raw modules.py hosted with ❤ by GitHub View the code on <a href="https://gist.github.com/aravindpai/eb40aeca0266e95c128e49823dacaab9">Gist</a>. Data Exploration and Visualization Data Exploration and Visualization helps us to understand the data as well as pre-processing steps in a better way. 

TensorFlow recently released the Speech Commands Datasets. It includes 65,000 one-second long utterances of 30 short words, by thousands of different people. We’ll build a speech recognition system that understands simple spoken commands. You can download the dataset from here.

Learn how to Build your own Speech-to-Text Model (using Python) Aravind Pai, July 15, 2019 Login to Bookmark this article (adsbygoogle = window.adsbygoogle || []).push({}); Overview Learn how to build your very own speech-to-text model using Python in this article The ability to weave deep learning skills with NLP is a coveted one in the industry; add this to your skillset today We will use a real-world dataset and build this speech-to-text model so get ready to use your Python skills!

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Use Keras if you need a deep learning library that: Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). Supports both convolutional networks and recurrent networks, as well as combinations of the two. Runs seamlessly on CPU and GPU. Read the documentation at Keras.io. Keras is compatible with: Python 2.7-3.6.

Compared with neural networks configured by a pure grid search,we find that random search over the same domain is able to find models that are as good or betterwithin a small fraction of the computation time.

Throughout the past two decades, he has been conducting research in the fields of psychology of learning and hybrid neural network (in particular, applying these models to research on human skill acquisition). Specifically, he has worked on the integrated effect of "top-down" and "bottom-up" learning in human skill acquisition,[1][2] in a variety of task domains, for example, navigation tasks,[3] reasoning tasks, and implicit learning tasks.[4] This inclusion of bottom-up learning processes has been revolutionary in cognitive psychology, because most previous models of learning had focused exclusively on top-down learning (whereas human learning clearly happens in both directions). This research has culminated with the development of an integrated cognitive architecture that can be used to provide a qualitative and quantitative explanation of empirical psychological learning data. The model, CLARION, is a hybrid neural network that can be used to simulate problem solving and social interactions as well. More importantly, CLARION was the first psychological model that proposed an explanation for the "bottom-up learning" mechanisms present in human skill acquisition: His numerous papers on the subject have brought attention to this neglected area in cognitive psychology.

Rather than framing everything at the course level, we should be deploying these technologies for the individual.26

Rather than selecting a single organization to lead the network, consider a spoke-and-hub or constellation model that empowers teams of organizations to act as “network hubs” for different sectors of the network. The best candidates for these hubs are intermediary organizations that act in the best interests of the network, allowing other network members to focus on their core mission and programmatic activities. Hub organizations play several roles. As conveners, they bring people together and build the field. As catalysts, they invest money and resources to get new ideas off the ground or help exciting projects to develop. As communicators, hub organizations enhance networks members’ ability to tell their story effectively and efficiently, internally and externally. As champions, hubs lift up the accomplishments of network actors, regionally, nationally, and internationally. And, as coordinators, hub organizations connect the dots, recommend priorities for the network, and connect those priorities to national resources.

If we write that out as equations, we get:

n and d

Learning is considered a “. . . knowledge creation process . . . not only knowledge consumption.”

Enter the Daily Mail website, MailOnline, and CNN online. These sites display news stories with the main points of the story displayed as bullet points that are written independently of the text. “Of key importance is that these summary points are abstractive and do not simply copy sentences from the documents,” say Hermann and co.