Artificial Intelligence in Education
Artificial intelligence, robotics, and “deep learning” are game-changing technologies transforming how people think, learn, live, and work. Now is the time for educators to think about how emerging technologies and AI-based Learning Analytics will affect teaching, learning, and the environment that students will inherit in the coming years.
AI has changed every industry, and education is no exception. Artificial intelligence is now being used in schools and colleges to improve teaching methods and increase student engagement. According to Prescient and Strategic Intelligence, artificial intelligence in the education market is expected to reach 25.7 billion USD by 2030. Therefore, to keep up to date, it’s important to gain a better understanding of this subject.
AI-based Learning Analytics
Learning analytics is an emerging field in which sophisticated and advanced analytics tools are used to improve learning and education. Many applications of learning analytics and AI would be impossible to achieve without the use of rich, continuous real-time data.
While there are many definitions of learning analytics, UW-Madison’s Learning Analytics Roadmap Committee (LARC) has contextually defined it as the undertaking of activities designed to improve student outcomes by informing structure, content, delivery, or support of the learning environment. Learning Analytics refers to the collection and analysis of data about learners and their environments for understanding and improving learning outcomes. Governments, universities, and massive open online course providers are collecting data about learners and how they learn for offering personalized recommendations and improving learning outcomes.
Learning analytics has the potential to change the way we assess impact and results in learning settings, allowing providers to create new strategies to achieve excellence in teaching and learning while also presenting students with new information to help them make the best educational decisions.
During one of our interviews, Iouri Kotorov, a senior Lecturer in International Business, educator, and a strategic business consultant, stated that “learning analytics, helps HEIs to utilize data effectively in decision making. Learning analytics helps HEIs facilitate the evaluation of the effectiveness of teaching and helps to monitor students’ learning. It can also provide instructors and students with data about their teaching and learning performance, which can make their teaching and learning experiences more personal and engaging.”
Analytics solutions offer a convenient way to leverage data. Institutions use analytics to explore and examine their data and then transform their findings into insights that ultimately help them make better and more informed decisions.
Descriptive analytics uses static data from a variety of sources, including course evaluations, student departure surveys, student information systems, LMS activities, and e-Portfolio interactions. This strategy evaluates the student’s past and attempts to uncover patterns in their learning development by analyzing the data. Descriptive analytics outlines what has occurred and the present situation, allowing you to make strategic judgments about the optimal teaching method for each individual student.
For example, you can use descriptive analytics to find out how much your class has learned about the lesson. After analyzing the data, you might find that implementing scaffolding strategies or differentiated learning processes into your lessons may be an effective way to reach more students. Some universities and districts have descriptive analytics tools built right into or integrated with the instructor’s management system.
Predictive analytics may extend data from the same sources but focus on trying to measure actual learning. The data may come from intelligent agents, task-specific games, log files, simulations designed to capture the learning process, and direct observation. This approach not only provides instructors with data you can then use to make actionable decisions, but it provides alternative suggestions to make teaching more effective. Based on the student data collected, the analytics tool generates suggestions on different educational resources and tools to utilize to make a greater impact on students. Prescriptive analytics gives schools and instructors insights into student knowledge as well as adaptable educational strategies based on student performance.
During one of our interviews, Gabriele Riedmann de Trinidad, founder and managing director of Platform 3L GmbH, stated that “learning analytics allows hyper-individualized support for each person, it is the most powerful and economic way for an organization to reach expected learning levels while taking care of each individual. Taking into consideration the huge individual gaps students have due to COVID-related school closures, learning analytics is most probably the only way to give individual support to close the gaps. With a continuous shortage of teachers, learning analytics could be the “digital partner” for high quality individualized learning.”
The use of big data is beneficial for education and includes various aspects from learning analytics that closely examine the educational process to improve learning. Through careful analysis of big data, you can determine useful information that can benefit educational institutions, students, and instructors. These stakeholder benefits include targeted course offerings, curriculum development, student learning outcomes and behavior, personalized learning, improved instructor performance, and post-educational employment opportunities.
Key Benefits of AI-based Learning Analytics
1. Benefits to Students
Learning analytics have the potential to provide students with more detailed information about their performance. For instance, learning analytics can help students see and reflect on their behavior in constructive ways to help them manage their progress toward their learning goals.
Boosting Student Retention
Student data analytics can be used to predict which students will not be able to continue to the following academic year.
According to the National Student Clearinghouse Research Center, in average 30% of students who entered college in the fall do not return in the second year.
The impact of analytics on retention has been beneficial. Once an at-risk student has been identified, specific interventions such as guidance or tutoring can be utilized to encourage them to stay in school and complete their education.
Student learning outcomes, behavior, and process
Another key benefit of big data and text mining focuses on the ability of schools and instructors to determine student learning outcomes in the educational process and determine how to improve student performance.
Learner interactions with technological tools such as e-learning and mobile learning can help educators understand the student learning experience through data analysis.
By assessing the implications on learner outcomes, the use of data offers approaches to improve student learning and performance in academic education. As a result, learning analytics allows educators to evaluate various forms of knowledge and alter instructional content as needed.
Enabling students to take control of their learning
Another key application of learning analytics is to provide students with more information about how they are progressing and what they need to do to achieve their educational goals. Learning analytics can enable students to take charge of their education by giving them a better understanding of their current performance in real-time and assisting them in making decisions about what to study.
Personalized learning
Faculty can utilize learning analytics to look at the frequency of student logins using data collected by the learning management system. Instructors can see how students interact in class and their overall engagement, pace, and grades. These elements can help predict whether a student will succeed. Learning analytics enables students to receive relevant data in real-time, examine and incorporate it, and receive real-time feedback.
2. Benefits to Instructors
According to McKinsey, technology can help teachers reallocate 30% of their time toward activities that support student learning.
By gathering more information about the students’ experiences, an institution may be able to identify and fix issues that students are concerned about. Lecturers and tutors can utilize analytics to track their students’ progress during a module and compare the results across several modules, allowing them to adjust their instructions if, for example, they notice that some students are failing.
Improved instructors’ performance
Learning analytics can be used to assess the performance of instructors. The use of data allows instructors to improve their training so that instructors are better prepared to work with students in a technological learning environment.
Analysts can evaluate online activities by acquiring data generated from instructor usage of technology and research tools in online libraries. As a result, using this data can assist instructors in identifying areas where they can improve to allow improved instructor-student interactions in the classroom.
An instructor can use Learning Analytics data to measure, monitor, and respond to a student’s comprehension of the content in real-time. Before the final mark is given, educators can use analytics and data for changing their teaching techniques and addressing student needs. This is a big advancement for instructors since it can assist them in overcoming any implicit biases they may have about their students’ engagement or performance.
Improved quality of teaching
Learning analytics provides teaching professionals with more information about the quality of the educational content, assignments, and tasks given to students and various activities they deliver during teaching processes, as well as their teaching and assessment methods, allowing them to improve over time.
Curriculum improvement
Big data allows instructors to make changes and modifications to improve curriculum development in the educational system, such as through data curricular mapping. Educators can use massive data analysis to identify gaps in student learning and comprehension and assess whether curriculum changes are required. Instructors can participate in educational strategic planning to guarantee that the learning curriculum is adapted to the needs of students to optimize their learning potential.
3. Benefits to Institutions
Learning analytics allows program directors, or other administrators, to more easily see how well their program is performing. In addition to the potential need to drill down into specific faculty, students, and course-level data, learning analytics can allow for meaningful comparisons across courses.
Identifying target courses
An initial benefit that evolves from using big data analysis in education is the ability of educational institutions to identify targeted courses that more closely align with student needs and preferences for their program of study. By examining trends in student enrollment and interests in various disciplines, institutions can focus educational and teaching resources in programs that maximize student enrollment in the most needed areas of study. Schools can better predict graduate numbers for long-term planning of enrollment.
Post-educational employment
Using big data and AI allows educational institutions to identify post-education employment opportunities for graduates and help target education that more closely aligns with employment market needs. It can also predict graduate employment, unemployment, or undetermined situations about job opportunities.
Big data can aid education institutions in better understanding students’ career possibilities and assessing student learning programs for professional compatibility. In a global learning environment, this type of information not only can facilitate better educational and post-education vocational planning, but also may prove useful to organizations as they make hiring and budgeting decisions for college graduates in different disciplines.
Learning analytics research community
The research community also benefits from the use of analytics in education. Researchers can share information and collaborate more easily. They can identify gaps between industry and academia so that research can determine how to overcome problems. Also, useful data analysis represents an important component of the ability of scholars to generate knowledge as well as continue to progress in research disciplines.
To discover more about AI-based Learning Analytics, EDLIGO team has interviewed thought leaders in the field about their experiences and opinions.
Here are some great insights:
Vice President for Digital Channels and Business at Universidad Tecmilenio
1. To what extent is Learning Analytics improving the performance of learners and learning institutions?
In the last 10 years, we have seen an increase in applications and solutions that have been emerging and it is a clear representation of the success that learning analytics is having. There is an increase in the development of this type of application as well as the investment of resources for its development, from the public perspective as well as from the private initiative.
2. How can the education sector benefit from Learning Analytics in the short term?
According to experts on this subject, the advantage of learning analytics is that it can develop personalized education. In recent decades, the traditional model has been difficult to change. The traditional model is the classroom that is not inverted and that tries to teach many students in one classroom, especially now with the pandemic, in a virtual classroom. What learning analytics does is precisely focus on each student, looking for personalized learning.
Personalized education is one of the results of the use of artificial intelligence (AI). From there follows the learning mining, which is data mining applied to this problem. Data mining is an area of computer science that works with statistical algorithms and AI: machine learning. Most of the algorithms that are applied to this type of solution analyze the numbers of the students, their browsing patterns, how much time they spend on homework, on social networks; With all that information, the data mining algorithms are able to make inferences and they can know what the level of knowledge of the student is and that’s when personalized education can be achieved.
3. How do you see AI-based Learning Analytics transforming the education sector?
AI algorithms continue to improve and will continue to advance their development and also their own self-learning. There are controversial issues about the benefits of AI due to the different positions that this issue generates. The bias that often shows up in AI algorithms is because algorithms are trained and learn by what they are fed. If you are given information that is biased, then the result will be biased. It doesn’t infer like a human.
Experts on the subject note that AI continues to evolve because it now does hybrid combinations. Until now the approach that AI has is known as a collector, there are already hybrid models where it combines the collector approach with the symbolic one and that is going to translate into even better algorithms that are closer to reality.
Learning and Development Specialist, CEO and Founder of Kasvun Katalyytti Oy, Finland, Future-oriented University Instructor at HELBUS Helsinki School of Business
1. To what extent is Learning Analytics improving the performance of learners and learning institutions?
Assessing learning outcomes is somehow “the Holy Grail” for all the professionals and institutions operating in the educational sector. Learning is a complex process, which cannot be simplified as numbers or statistics. However, learning analytics can be used to understand some elements of this complex phenomenon. Instant statistics help you improve learning performance while it’s going on and then improve the learning results.
2. How can the education sector benefit from Learning Analytics in the short term?
More evidence-based decisions could be made concerning the design of learning activities.
3. How do you see AI-based Learning Analytics transforming the education sector?
Not analytics as such. More critical are which are the issues you are assessing. For this, educators need a more systemic understanding of all the elements affecting educational activities and learning experiences. These kinds of elements could be, for instance, the rules and division of labor in educational institutions directing learning activities.
Ángela Erazo Múñoz, Professor at the Universidade Federal da Paraíba en João Pessoa, Latin America
1. To what extent is Learning Analytics improving the performance of learners and learning institutions?
It is a necessary and complex issue. I am not a specialist in the subject, so I could not give a quantifiable or justified answer in this regard. However, as a spectator, I believe that the different evaluation and measurement tools that learning analytics has developed and is developing would effectively allow us to evaluate and improve as teachers, as well as provide solutions to learners with the aim of improving their acquisition processes and knowledge production. On the other hand, in the institution where I work, it is up to each professor to make an individual and group evaluation of each one of the subjects, focusing on the field of each one. Regarding the field of language teaching in general, much progress has been made in terms of learning materials and the type of format, material and proposals have been diversified.
2. How can the education sector benefit from Learning Analytics in the short term?
The education sector will benefit from short-term learning analytics through:
– Doing research
– Developing measurement and monitoring systems that are easy to apply, understand and use for both students and teachers
– Training teachers and students for the best analysis and search for solutions that improve learning and teaching.
– Making experiences from different levels of training
– Disseminating and sharing information and research on the matter
3. How do you see AI-based Learning Analytics transforming the education sector?
From various perspectives, I believe that there will be a great transformation and I think that the pandemic has already accelerated the process. For example, in the field of translation and language acquisition, much progress has been made thanks to experiences with artificial intelligence. Nowadays we find many super-sophisticated translation programs that help in the processes as well as different ways of learning languages from applications, manuals, books, learning platforms, etc.
AI-based analytics enables your organization to measure the impact of a range of metrics on learning performance and make decisions based on data.
If you manage learning at a school, university, or organization and don’t use data to make choices, you should look into EDLIGO Learning Analytics to improve learning outcomes.
EDLIGO proved to scale from one team to corporate level, from one classroom to state level.
EDLIGO can help you:
– Track the results of your studies and compare them with other students.
– Track the results of your studies and compare them with other students.
– Visualize the realities of what is happening at any level
– Monitor and drive learning progress
– Target academic support and design personalized learning experiences
– Establish a quality assurance framework with integrated data analytics and AI
Please contact the EDLIGO team or request a demo if you’d like to learn more about how Learning Analytics might help your organization to achieve higher outcomes using data and AI.