Integrating machine learning and the Internet of Things into education (and daily life) is inevitable...and becoming necessary. Artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are already intertwined in many systems, from education and healthcare to industry.
Interconnected devices, such as robots, sensors, cameras, microphones, smartphones, and other smart devices, will have the capacity to learn and self-improve in certain ways for them to be more efficient and dependable tools.
This can be done both at the level of the individual devices and at the collective level of a network, and has a wide range of uses as we’ll explore later on.
What are the IoT and machine learning?
The IoT is a range of internet-connected objects, such as sensors, machines, and smart devices. They can either be within a small area, like a school, or this can be on a global scale. For example, there are CCTV security systems that can be accessed via smartphones from anywhere in the world as long as there is an Internet connection. You can even remotely operate a wheeled version of an IP camera much like you would operate a remote-controlled toy car.
Machine learning is based on the analysis of massive amounts of data, which include texts, numbers, photos, audios, and videos, in order to form generalizations of patterns. In turn, this can be used for specific applications to produce unique outputs, such as generating artistic images.
ML uses sophisticated algorithms as part of AI and is the foundation of artificial intelligence. It can be applied to the IoT for various purposes, such as machine automation, data storage and analysis, and education.
The difference between the IoT & machine learning
Both the IoT and ML use data, but in different ways, and this is their main difference.
Let’s take an example. The IoT offers multiple entry points and storage points for data. For example, during a national election, voting booths might be networked with each other and with national servers. Votes can be automatically tallied at various levels: in voting precincts or centers, in cities and regions, and also nationally.
Meanwhile, the analysis of the data that’s gathered by the IoT is done with the help of machine learning algorithms. They might look for possible glitches or fraud, for example.
Machine learning helps to predict future trends and detect anomalies. It also improves artificial intelligence. ML needs IoT systems to collect large amounts of data. Meanwhile, IoT systems need ML to effectively collect and process data.
IoT & machine learning project ideas for high school
Smart parking system for schools One example of a feasible school-based project for students is a smart parking system for your school. Machine learning will first need to analyze the car traffic volume in the school for at least a week or one month. Students can be tasked with writing the algorithms that analyze this data. Sensors and cameras - ideally operating 24/7 - can be connected to an IoT system to collect data. Once operational, RFID visitor cards can be temporarily issued to all car drivers upon entering the campus. The chips in the cards can send data to the smart device of the drivers directing them to the most convenient spot to park.
Healthy diet monitoring system for students Individual and exclusive prepaid debit cards or QR code cards can be issued to students. The cards can only be exclusively used in the canteen for food purchases. The card serves as an automatic key to access the database associated with the student purchases based on the canteen’s menu. Everytime a student makes a purchase, their data is updated on the computer servers. This will then be automatically fed to an algorithm that analyzes and recommends healthy food options for each student.
How can machine learning change education?
To some extent, machine learning is already being utilized in the education sector. For instance, some higher education institutions are using ML to attract students with excellent potential based on the analysis of their previous academic performance, entrance exam results, essays, and interviews. In this way, ML can also be used to forecast enrollment and allocate quotas for specific courses.
However, ML is not yet widely adopted as an integral component of school operations. The technology is still developing and it is difficult (financially and logistically) for all schools to be retrofitted with all the necessary devices and software updates in order for the system to fully function. Plus, machine learning ideally requires fully integrated IoT to work efficiently.
However, ML has the power to change education in three main ways: administration, instruction, and assessment.
- Administration - many of the aspects of school administration, such as screening student applications, monitoring enrolment, and allocating course quotas, can be simplified and made more efficient with the help of machine learning. ML can also help with hiring teachers and in other HR matters, such as evaluating employee performance.
- Instruction - Together with the IoT system, ML can be utilized to make learning more immersive, hands-on, and personalized for all students.
- Assessment - assessing students’ competencies and learning improvements can be made more objective and scientifically valid with the help of ML. Grading students should be based on crystallized knowledge and skills rather than ephemeral ability to memorize facts for the sole purpose of passing an exam, and ML can provide more comprehensive assessment capabilities.
Why is machine learning important for students?
ML uses complex algorithms to analyze large volumes of data points. It can compare data points from various sources, such as scientific journals, textbooks, and the internet.
ML can make accurate predictions and suggestions on how to make the learning process more engaging and effective, both individually and collectively. It can guide both teachers and students to focus on specific topics and issues.
What are the basics of machine learning?
In a nutshell, machine learning is a device’s ability to process new information and perform new tasks or desired outcomes without being specifically programmed for such tasks, and make predictions or forecasts based on the data.
For example, a robotic arm with a tracking system can learn how to catch a ball through trial and error. It does not need to be specifically programmed to catch a ball, but it does need to be able to calculate or predict the trajectory of a ball based on its initial conditions.
Machine learning can be categorized based on the absence or presence of human supervision. The degree of reinforcement and calibration is also very important.
These are the three types of machine learning.
1. Supervised & non-supervised learning
Computers that are designed to learn can learn based on examples of certain categories. In supervised learning, the machine is fed with examples together with labels or targets for each example. The algorithm correlates the characteristics of the samples.
In unsupervised learning, there are no labels or targets. The machine learns by recognising patterns through clustering. For example, facial recognition technology relies on clustering by identifying the generic and specific features of a face and then comparing it to other data sets of faces of other individuals.
2. Reinforcement learning
Reinforcement machine learning involves goal-oriented algorithms that optimize certain tasks or steps to achieve an ultimate goal.
For example, a group of wheeled robots might optimize traffic routes in a maze of simulated roads and exhibit spontaneous coordination. This can be done using a digital/electronic equivalent of reward-and-punishment reinforcement method.
3. Semi-supervised learning
In semi-supervised learning, a small amount of labeled data is combined with a large amount of unlabeled data during the training process. The algorithm can apply the correct labels to the large amount of unlabeled data based on the labeled data it has.
Summary
IoT and machine learning will have significant positive impacts on education, making it more engaging and responsive to students’ needs.
Machine learning itself models its capabilities of learning new tasks based on the data supplied in the education sector, such as the results of aptitude tests. Computer algorithms can help predict student performance and also provide recommendations for the best pedagogical response.
Are you an educator looking for IoT resources for middle school, high school or university? Take a look at the Arduino Education Explore IoT Kit Rev2 and how it can support your hands-on IoT lessons.