MELISSA MIGUEIS
Want to know more about AI in education?
Learn all about how Learning Analytics and Machine Learning is also used in the field of Education!
Learning analytics involves measuring, collecting, analyzing and reporting data about students. Its purpose is to understand and optimize student learning and education institutions. It’s helpful for teachers, students and institutions.
Here are some ways that teachers use learning analytics:
- Identity student patterns: helps the student receive more support and holistic assessment based on their individual need because the teacher is more informed
- Predict trends in student progress: determine how the student will perform in the future, and make a plan to properly support their needs
- Recommend resources and tools: the teacher can be offered tools that will help the individual student and cater to their needs.
- Track academically weak students: teachers get detailed information about each student so they can see who is struggling. By being able to compare all the students, it’s obvious which students need more attention. This prevents student drop-out or failures.
For example, teachers and colleges can use Moodle, and its plug-ins, such as SmartKlass™. These are two examples of Learning Analytic dashboards. Have you ever noticed when you go on Moodle, you can see various analytics about your course performance? You can see your test results, how they compare to other students’ grades, and your evolution in the class. This is done through learning analytics. These tools help teachers make changes to the curriculum and modify lessons for individual students to help them improve their grades and succeed overall.
These are various levels of learning analytics.
1. Measurement: This level required no complex math. It involves tracking and gathering data and recording values.
2. Data Evaluation: This is where data is evaluated and assessed. Here, you apply high-school level math, such as average, mean, mode and basic statistics, to find all the data.
3. Advanced Evaluation: At this level, college-level math is required. It involves correlation and regression analytics, and applying statistical techniques to know why something has happened. It’s about assessing what is effective, and what is ineffective.
4. Predictive & Prescriptive Analytics: This final level is the most sophisticated level of analytics. It required graduate-level math. Predictive analytics are based on how past patterns can predict future ones. Prescriptive analytics looks at what has been predicted, and how it can be used to optimize the outcome.
You may not know this, but colleges like Dawson, use Learning Analytics to track student drop-up rates for economic reasons. Dawson only receives funds for students who spend 2 years at the institution. If a student extends, or prolongs their studies, the college loses money. That’s why, they use learning analytics to track our studies and collect data.
Another way AI is used in the classroom is through Machine Learning. Pioneer Arthur Samuel defined Machine Learning as a “field of study that gives computers the ability to learn without being explicitly programmed.” This means that it is a program that adjusts itself to the data it receives. You can think of it as a child. When born, a baby doesn’t know anything; however, eventually it learns as it adapts and adjusts to the world around it. For Machine Learning, the world is the data.
In education, Machine Learning can help teachers save time, and it can help enhance student success. Here are a few of the ways:
- Customized learning: using the algorithms, Machine Learning can assess a student’s understanding of a certain topic, and only move forward once the content is fully understood. This makes sure that no student falls behind.
If you’re interested in learning more about personalized learning, check out this video:
- Analyze content: Educators can teach students using machines. The machines will then analyze the information and decide whether it is suitable for each student based on their own unique needs.
- Grading: For teachers, grading is extremely time consuming. Machine Learning can save teachers from this monotonous task. They can mark assignments in a more efficient and effective way. Not to mention, they can detect plagiarism or cheating very easily.
- Taking attendance: Another tedious task is taking attendance. It’s so simple to do, yet it wastes valuable time. A Machine Learning system can do these types of tasks so that the teachers spend more time on what is truly important, such as teaching material.
- Student’ progress: Using Machine Learning, a teacher can track and evaluate a students progress in the course. They can then find the best way of teaching concept for that specific student.
There is a lot in store for the Future of Education, and AI will have a huge role in it.
RIANNA AND KAYCEE
Biometrics and Security
What is Biometrics?
Biometric identification requires a certain technology that does either one of two things.
It either identifies you with an image that is run through a database of pictures or authenticates your identity by accessing an image from a device to establish a match.
A few examples of these Biometrics indicators includes fingerprints, facial patterns, voice, your iris etc.
It’s normally used for unlocking computers, phones, and applications.
Whats the purpose?
- Tighten security
- Protecting personal documents
- Actions (withdrawing money or entering private premises to make it safer/easier)
How does it work?
Step 1 : Enrollment
After using the biometrics system, it will record basic information about you (ID number, capture an image or record a specific trait.)
Step 2 : Storage
The trait recorded is analyzed and translated into a line of code or a graph.
Step 3 :Comparison
The next time you encounter the system, it compares the trait you present to the information it has stored on file. The system will then either accept or reject who you claim to be.
How Biometrics ties into Security in Technology
In 2013, Apple came out with a new fingerprint sensor which is built into the Iphone. After that, a lot of other companies started to get on board and start incorporating Biometrics into their devices.
Currently, MasterCard wants to use your heartbeat so they can confirm your purchases.
Google’s new Abicus Project intends to monitor your speech patterns, how you walk and type, to verify that it’s really you holding your smartphone.
Is it safe?
According to a survey by Ping Identity survey, 92 percent of enterprises rank biometric authentication as “effective” or “very effective” to secure stored identity data.
Even though it may be effective, this could interfere with people’s privacy and improperly expose personal information.
Any compilation of data has the potential of getting hacked.
As the increase of popularity of biometrics, your information will most likely be available in more places which will not allow the same level of security.
The Dangers
Several pieces of your physical identity can be copied.
For example, a criminal can take a high quality photo of your ear from afar or duplicate your fingerprints from a glass you leave at a cafe.
This information could be used to hack into your devices or accounts.
Laws governing biometrics are still a work in progress and different in different places in the world.
Media Lab AI 