The SpeechRater engine processes each response with an automated speech recognition system specially adapted for use with nonnative English. Based on the output of this system, natural language processing (NLP) and speech-processing algorithms are used to calculate a set of features that define a "profile" of the speech on a number of linguistic dimensions, including fluency, pronunciation, vocabulary usage, grammatical complexity and prosody. A model of speaking proficiency is then applied to these features in order to assign a final score to the response.
Newsletter archives are here
A Comment to Jenny Mackness
I want to begin this newsletter with a comment I made on a post by Jenny Mackness. In it, I lay out my thinking for this module, and I thought it was worth sharing more widely:
My views about badges haven’t changed. But I’ve had mixed motives this week in the course.
– first, I did want to issue badges with this course, because I haven’t done it before. This meant learning a lot more about badges than I already knew (and for me, that typically means learning them down to the details of how to create them in software, which I’ve done).
– so, second, no small part of this first item has leaked into the course content itself, including the assignment. Badges are just one small thing; there is the core idea of giving recognition (in a distributed digital system) I want to capture.
– third, I have a bunch of blockchain algorithms I wrote last March that are just sitting there not doing anything, so I wanted to also write the course badges to a blockchain. This also ties them to the course graph – which i will return to by the end of the course, bringing us full circle back to data
– fourth, I want to explore the idea of automated assessment. In my ideal world, people do their assignment, block it, my aggregator picks up the post, assesses it, and automatically awards the appropriate badge (I might need to depend on hashtags for the first iteration of this). But I’m running out of time to make this work this week (the delays in making things work are a major motivation for wanting to do a second run of E-Learning 3.0)
– fifth, I wanted to tie all this back to competencies – tasks as demonstrations of competencies as criteria for badges (which leads to the suggestion that, with sufficiently advanced software, you simply describe the competencies you need, and then the software identifies evidence of it in unstructured performance)
– and finally (for this course) sixth, I want this all to be managed in one’s own *personal* learning environment, such that the course (the MOOC version of gRSShopper) is only a facilitator if this.
So you can see how, in the end, badges don’t play a particularly important role, but conceptually, they play an important role in getting from here to there.
Toward Automated Assessment
The resources in this newsletter tell a story. There are probably too many to read, but at least look at the summaries in order to get a sense of the direction things are headed. The evidence is overwhelming (and it would have been possible to multiply examples after examples). We are in the process of building society-wide automated competency recognition systems. These are already being developed for training, for compliance, for civic justice, and for credit and insurance assessment.
So far - as Matthias Melcher suggests - the only people not benefiting are the learners themselves, with their own data. And that's what can and must change.
The extensive use of Data Mining and, particularly, Text Mining can greatly improve speed and quality of competence assessment, making it less human-biased at the same time. This approach should improve business processes and significantly decrease HR expenses. Several researchers and developers work in the field of applying modern Data Science approaches to the field of Competence Management.
AI Engine analyzes individual learner performance against key competencies across entire program – automating individual “learner fingerprints”. Assessment data categorized by block, assessment type, skills, body systems, threads, competencies, and EPAs. Learner dashboards provide progress snapshots toward mastery of key competencies throughout the education journey.
Our framework comprises key video clips extraction, trade recognition and worker competency evaluation. Trade recognition is a new proposed method through analyzing the dynamic spatiotemporal relevance between workers and non-worker objects. We also improved the identification results by analyzing, comparing, and matching multiple face images of each worker obtained from videos. The experimental results demonstrate the reliability and accuracy of our deep learning-based method to detect workers who are carrying out work for which they are not certified to facilitate safety inspection and supervision.
In this paper, we make a practical approach to automated credibility assessment on Twitter. We describe the process behind the design of an automated classi- fier for information credibility assessment. As an addition, we propose practical implementation of TwitterBOT, a tool which is able to score submitted tweets while working in the native Twitter interface.
Predictim, an online service that uses “advanced artificial intelligence” to assess a babysitter’s personality, and aimed its scanners at one candidate’s thousands of Facebook, Twitter and Instagram posts. The system offered an automated “risk rating” of the 24-year-old woman, saying she was at a “very low risk” of being a drug abuser. But it gave a slightly higher risk assessment — a 2 out of 5 — for bullying, harassment, being “disrespectful” and having a “bad attitude.”
The plan is to link public and private data on financial and social behavior across China, use the data to evaluate behavior of individuals and organizations, and punish or reward them according to certain agreed upon standards of appropriate conduct.
Through the use of cognitive computing tools like machine learning, predictive analytics, robotics processing automation, and both image recognition and natural language processing, underwriting is becoming less manual and more automated. Providers of the tools offer novel ways for underwriters to better gauge risk, set premiums, save time, become more efficient and lower loss ratios.
Continue reading →
This Week's Task
Create a free account on a Badge service (several are listed in the resources for this module). Then:
- create a badge
- award it to yourself.
- use a blog post on your blog as the 'evidence' for awarding yourself the badge
- place the badge on the blog post.
BadgesAs is always the case, it's easier to talk about doing something than it is to do it. Thus has been the case with course badges. I've been working on it for a couple of days, learning a lot in the process, but making little headway. But I've done just enough to know I'll be able to make it work. So there will be badges.