Essay grading software criterion
Through features. 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 algorithm , such 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.
Sample Essay Rubric for Elementary Teachers
Once the model is created, then it can predict the scores for new essays. We would take a new 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 to 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 assessment , and 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. The AES would tell you how you did on each of the rubric dimensions which are customizable by the instructor. 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 both , but more to lead into a discussion of how, when, and why AES should be deployed.
I personally have learned a lot of lessons in both developing and applying AES algorithms.
Critics: Rubrics are a disservice to students
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? In the same vein as the point above, AES is useful in some domains, and can given students accurate scores and rubric feedback. However, AES cannot give detailed feedback like an instructor or peer can.
Automated essay scoring - Wikipedia
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. If the tools are built properly, it will be possible to evaluate all these options, and figure out which one, if any, has the most value for students.
AES is useless when the power is in the hands of researchers and programmers although it does make us feel important. The real people who need to shape and implement these technologies are teachers and students, and they need the power to define how the AES looks and works.
AES is a semi-shadow world to a lot of people, and that may be partially by design. The less we tell people about how things are done, the more valuable and important we become. But just open source is not enough.
We need to discuss what the code is doing, build up documentation around it, and most critically, allow people to contribute to it, to make it truly useful. The Hewlett Foundation, and particularly Vic Vuchic , have done some great work here, and I hope it is continued. Algorithms can estimate their own error rates how many papers they grade correctly vs incorrectly. At edX, these error rates are displayed to teachers, so that teachers can make the machine learning models better if they want to. Giving teachers and students as much information as possible within an AES system is key. Algorithms are fun and exciting, but learning tools are only useful if they help students, well, learn.
The most important thing in this is usability. Can a student quickly digest and use their feedback? Can a teacher quickly create a new problem and deliver it to students? It is actually pretty easy to implement an algorithm. It is hard to put the things in place around it to allow students to succeed. I would even venture to say that once you get a certain level of accuracy in your algorithm, improving usability should become the primary goal.
Does a user have to manually read a ton of essays into a command line or GUI program think Microsoft office? How do students get papers into the system? At edX, everything is a web-based tool, and students can write papers and receive feedback entirely through a web interface. Teachers can create problems that use AES in a few clicks, and can grade student papers through a web interface.
Can we grade uploaded videos? How about pictures or songs? This can be done with peer and teacher grading, but AES needs to be extended to work with alternative media as technology advances. I alluded earlier to several large assessment companies participating in the Kaggle essay scoring competition.
The Carnegie Mellon CMU tool is and was open source, but crucially, it does not appear to be open information or open contribution edit: Elijah Mayfield points out in the comments that the Carnegie Mellon tool is on bitbucket , and is open contribution.
However, there were advantages on both sides, as vendors got to talk to the Hewlett Foundation about the data several times. Competitors and vendors were ranked by quadratic weighted kappa QWK , which measures how closely the predicted scores from the models matched up with human scores higher kappas are better.
We can summarize the performance with this excellent charts from Christopher Hefele :. We can see that the top six competition participants did better in terms of accuracy than all of the vendors. I have discussed before what I think of accuracy as the sole metric for AES success, so take this with a bit of salt. The main reason I show this is to illustrate that open competition, with a fair target, can lead to very unexpected results and breakthroughs.
Passwords are case sensitive. Learn more. Students: Have more opportunities to practice writing at their own pace, get immediate feedback and revise essays based on the feedback. Administrators: Can make data driven decisions and easily monitor district, school and classroom writing performance.