Enterprise AI and the importance of Text slides Speaker:
Machine learning predicts everything!
All we got was the sentence I really like solving problems. Machine learning is very useful. But how would a computer do the same thing?
Some of the features we might extract: This is a very simple example, but it gives you a good idea of what features are. Features allow us to represent text, which a machine does not understand, as numbers, which it does understand.
We can then tell a machine learning algorithmsuch as a random forest, or a linear regression, that a certain sequence of features means that the teacher gave the student a 2, another sequence of features means that the teacher gave the student a 0, and so on.
This trains the algorithm, and gives us a model. Once the model is created, then it can predict the scores for new essays.
We would take a Machine scoring essay essay, turn it into a sequence of features, and then ask our model to score it for us. As you can see, what the model is trying to do is mimic the human scorer. The model is figuring out how an expert human scorer grades an essay, and then trying Machine scoring essay apply that same criteria to other essays.
More on this is beyond the scope here, but I recently did a talk about AES, and also have a tutorial on my blog, both of which I highly encourage you to check out if you are interested.
Here is a diagram of how we grade essays and constructed responses at edX: So, when a student answers a question, it goes to any or all of self, peer, and AES to be scored.
Written feedback from peer assessmentand rubric feedback from all three assessments are displayed to the student.
It is completely up to the instructor how each problem is scored, and how the rubric looks. Here is specifically how the AES works: The main difference between this and the generic workflow I showed you before is that edX allows teachers to regrade essays that AES has scored poorly.
A low confidence indicates that the machine learning model does not know how to score a given essay well. We show student papers that AES has already graded to the teacher, in order of lowest confidence to highest.
This is called active learning. The AES will give the student feedback on how many points they scored for each category of the rubric. I show you this example less to discuss the strengths and weaknesses of the edX system it has bothbut more to lead into a discussion of how, when, and why AES should be deployed.
Lessons of application I personally have learned a lot of lessons in both developing and applying AES algorithms. Below are some, in no particular order.
I talk about the edX system a lot, because I have a lot of recent experience with it. The goal is to maximize student learning and limited teacher resources time in a way that is flexible, and under the control of the subject expert teacher. But scale can also play a big part in the classroom.
Can a teacher grade 10 drafts per student per week? However, AES cannot give detailed feedback like an instructor or peer can. You should evaluate your options and see how you can best use AES. Maybe it works for certain questions.
Maybe you can grade tests with AES. Maybe it is good for grading first drafts. Maybe you should combine it with small group discussions or peer scoring.Course materials, exam information, and professional development opportunities for AP teachers and coordinators.
Professionals Against Machine Scoring Of Student Essays In High-Stakes Assessment We call for schools, colleges, and educational assessment programs to stop using computer scoring of student essays written during high-stakes tests. Machines, an international, peer-reviewed Open Access journal.
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