Learning Analytics is the process of collecting, measuring, analyzing, and reporting data about learners and their contexts to improve learning outcomes. It involves using data to understand how learners are engaging with learning materials, identifying areas where learners may be struggling, and identifying opportunities for improvement in teaching methods and materials.

Learning Analytics draws on a variety of data sources, including student performance data, learning management system (LMS) data, and other forms of digital data generated by learners. This data is then analyzed using various techniques, such as predictive modeling, machine learning, and data visualization, to uncover patterns and trends in learner behavior and performance.

The goal of Learning Analytics and AI is to help educators and institutions make data-driven decisions about how to optimize the learning process and improve student outcomes. By leveraging the insights provided by learning analytics, educators can better understand how students learn and tailor their instruction to meet individual needs, ultimately leading to improved learning outcomes for all learners. Learning Analytics allows educators to gain insights into student behavior, performance, and engagement, which can then be used to make informed decisions about curriculum design, teaching methods, and individualized learning support.

By analyzing data from various sources, such as learning management systems, student records, and online learning activities, Learning Analytics can identify patterns and trends that help educators understand how students learn and how to optimize their learning experiences.

Artificial intelligence (AI) plays a crucial role in Learning Analytics, as it enables the processing of vast amounts of data in real-time. AI algorithms can analyze and predict student behavior, identify areas of weakness and strengths, and even generate personalized learning paths for students. By integrating AI with Learning Analytics, educators can gain actionable insights that support data-driven decision-making in education.

One significant benefit of AI and Learning Analytics in education is the ability to identify and support struggling students. Early identification of at-risk students allows educators to intervene and provide targeted support, which can improve student outcomes and prevent dropout rates. For example, AI-powered Learning Analytics can identify students who are falling behind in a particular subject or struggling with specific concepts and recommend additional resources or personalized learning plans to help them catch up.

Another benefit of AI and Learning Analytics is their ability to support personalized learning experiences. With data analytics, educators can tailor learning activities to individual student needs, preferences, and learning styles. This approach helps to ensure that students receive the right level of support and challenge, which can boost engagement, motivation, and achievement.

Learning Analytics and AI have significant potential to transform education by enabling data-driven decision-making and personalized learning experiences. By harnessing the power of data and machine learning, educators can identify patterns, predict outcomes, and improve student success. The integration of AI with Learning Analytics has the potential to revolutionize education and create more equitable and effective learning experiences for all students.

EDLIGO is one of the prominent players in the field of Learning Analytics, offering advanced analytics tools and solutions to educational institutions worldwide. With EDLIGO’s Learning Analytics platform, educators can gain actionable insights into student learning patterns, engagement, and performance. 

The platform utilizes machine learning algorithms to generate personalized learning paths, identify at-risk students, and measure the effectiveness of teaching methods. By leveraging EDLIGO’s Learning Analytics solutions, educators can make data-driven decisions to optimize the learning process and improve student outcomes.

Overall, EDLIGO’s Learning Analytics solutions are an invaluable tool for any educational institution looking to improve student outcomes and stay ahead in the ever-evolving landscape of education technology.

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:

Dr. Juan Alejo Arenas Ruiz

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.

Kirsi Elina Kallio

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.

Due to the fast-changing environment and the development of new technologies, the education sector is facing lots of challenges. A recent study by McKinsey discovered that youth unemployment is increasing as young people are being held back because of the lack of skills relevant to the workplace.

According to Gallup Poll, only 11% of business leaders agreed that graduates have the necessary skills and competencies to succeed in the workplace.

Employers need to work with education providers so that students learn the skills they need to succeed at work, and governments also have a crucial role to play.

Why don’t we know what works in moving young people from school to employment? Because there is little hard data on the issue. This information gap makes it difficult to begin to understand what practices are most promising – and what it will take to train young people so that they can take their place as productive participants in the global economy.

The search for better teaching strategies to address the problem will never end. As a school leader, you probably spend too much of your time thinking about how to improve the learning experience and learning outcomes of the students that pass through your school throughout the years and how to prepare them for future work.

One-to-many, subject-based teaching is still the norm in most educational institutions, however, recently a competency-based approach has been advocated as a more effective option, encouraging students to develop important skills and skills of their interest while reducing inefficiencies in teaching by moving away from traditional subject silos and implementing cross-curriculum learning. The basic purpose of competency-based education is to give every student an equal chance to master required skills and grow into successful individuals.

Competency-based education, in its most basic form, means that rather than focusing on grades, subjects, and yearly curriculum schedules, the primary focus is spent on whether a student learned the necessary competencies. It measures skills and learning rather than time spent in a classroom.

During our many years of experience with large Learning Analytics based transformation projects, our EDLIGO team found out that a competency-based approach to education can be of real benefit.

Competency-based education versus traditional education

Let’s analyze the traditional learning approach and compare it to the newly advocated competency-based learning:

• Instruction and teaching methods

In traditional education, each classroom has a teacher who plans and delivers the curriculum with little differentiation.

In competency-based learning, teachers collaborate with community partners and students to create a personalized learning plan for each student based on their interests, learning requirements, and real-time data.

• Assessment system

In traditional education, assessments are performed at set times to evaluate and classify students based on the information learned during each course or subject. Quite often the assessment cannot evaluate the practical knowledge of the students or whether the students can apply the knowledge.

A comprehensive evaluation system that tries to measure competencies can assist students in determining whether they are learning the necessary abilities. Daily instruction is guided by formative assessments. Summative assessments are used to demonstrate skill improvement through flexible pathways and diversified approaches.

• Grading policies

Grades are norm-referenced, reflect course requirements, and are often based on weighted quarters and a final exam in traditional education.

Grades in personalized learning represent the level of competency mastery. If students do not receive course credit, their records show that competencies must be re-learned rather than the full course.

• Learning continuum

Students are expected to master grade-level college and career readiness benchmarks in traditional education.

Students are required to master competencies connected to college and career preparation requirements with clear, transferrable learning objectives in competency-based learning.

• Learning pace 

In traditional education, students progress at the pace of the teacher, regardless of proficiency or the need for more time.

Students in competency-based learning receive individualized support both in and out of school to ensure they receive what they need, when they need it, to graduate college and career ready.

The important elements of competency-based education

Competency-based learning provides opportunities for a student-driven, practical learning environment, and it successfully engages young people in their learning. The important elements of competency-based learning include:

• Student as a prime mover

Competency-based learning progressions empower students by giving them more control over their learning pace and direction. So, it is important to create a learning process equipped with tools that are taking into consideration learning needs and giving recommendations on how to advance. Teachers must give personalized instruction, feedback, and support to each student. Students should be taught in a way that builds on their prior knowledge and engages with the material in different ways such as through practice, dialogue, and project-based learning.

• Proficiency assessment

Competency-based learning goals are organized into progressions based on explicit standards. Standards create a roadmap while assessments and demonstrations give feedback about progress and pace toward mastery over expectations required for graduation. This design allows empowered students to advance ONLY based on a demonstration of competence. Finally, assessments must demonstrate mastery of the subject, allowing all students to advance when they’re fully competent.

• Personal pathways

The instructional system in competency-based learning can support both common and unique learning experiences (in school and out of school). Competency-based learning institutions can also allow for multiple ways for a learner to demonstrate competency.

The school systems (whether they be state, district, or other educational networks), have the responsibility to shape and sustain competency-based education systems in the schools they work with.

When school systems get involved in determining the framework of their schools’ competency-based education systems, this provides uniformity and coherence across the network. This helps teachers to have a clear definition of what mastery looks like, which competencies are important, and the assessments they should be using, while still giving them flexibility at a local level.

The benefits of competency-based education

Over the past decade, the movement to adopt competency-based education has gained popularity in many countries including, the United States, Brazil, and many European Countries, as more educators, parents, and educational leaders recognize that strong educational preparation is essential to success in today’s world. Institutions use competency-based learning to raise academic standards, ensure that more students meet those expectations, and more students will be better prepared for adult life.

To help schools establish the foundations of their processes, here we discuss the basic benefits of competency-based education

• Schools offer an equitable range of learning experiences

Equity does not imply that each student receives the same treatment as the others. Instead, it entails providing each student with the resources they require to achieve the same result.

Because competency-based education aims to analyze and eliminate bias in school leadership, this is a key principle. Students are taught and encouraged based on their unique talents and weaknesses, ensuring that everyone has an equal chance of succeeding. As a result, accomplishment can no longer be predicted based on culture, social class, household income, or language. Competency-based teaching also contributes to the development of a welcoming culture in which all students feel safe and respected.

• Transparency helps students take ownership

Both students and parents should be aware of the learning objectives specified for the class (and for the school as a whole). When students first enter a competency-based education class, they should be aware of three things:

–       What they need to learn

–       How mastery is defined

–       How they will be assessed

Students will take more responsibility for their education if they know what they want to achieve in the end. For example, a student recognizes that he must put his math knowledge to use by completing a project involving the design of a tiny garden. He’ll need to use his math skills to calculate the size of the area and the number of plants that will fit.

If a student understands exactly what he needs to accomplish to be successful and progress in the class, he will feel more in control of his education. Then, when he encounters a problem in the project or lacks the knowledge to do it properly, he will recognize that he requires assistance on his own.

Students who have clear goals and outcomes are more likely to take charge of their learning. As a result of their ownership, they can learn more effectively now and in the future.

• Students get the support they need individually

Students should have a framework in a competency-based education setting to understand how long they should work on a task before asking for help and when they can approach the teacher during class time.

As previously stated, competency-based education works through bias to promote equity. As teachers engage with students to address their various deficiencies and assist them to build on their strengths, each student progresses toward mastery in his or her own unique (but equally successful) way. This individualized learning experience ensures that each student has an equal chance of succeeding.

Teachers must be available to assist kids for this process to run effectively. Furthermore, they cannot rely just on students seeking assistance; teachers must be completely informed of each student’s progress.

• Teachers assess for growth and mastery

There are many different types of assessments. Three types of examinations are particularly beneficial for competency-based learning:

– Formative assessments: These assessments assist teachers in determining where each student is in the learning process and making appropriate adjustments to their instruction.

Formative assessments allow teachers to make real-time adjustments by highlighting critical areas where pupils need to improve.

– Authentic assessments: Another fantastic technique to demonstrate mastery is to have students apply what they’ve learned in class to real-life circumstances. Furthermore, students gain abilities that will be useful in the future.

– Digital content assessment: Assessment becomes much easier in the classroom when technology is used. Many classroom software packages contain assessment and progress reporting features, allowing teachers to monitor where each student is in the learning process.

• Students move forward when they demonstrate mastery

Teachers can identify where each student is in the learning process by adding regular assessments and data-based progress reporting.

It’s time for students to move forward when they display a clear understanding of the material, demonstrate their ability to apply that understanding, and demonstrate how they’ve developed crucial skills.

How to make a move towards competency-based education

To make a smooth move towards competency-based education, based on the important elements that a skills-based approach should involve, we recommend the following shifts to be performed:

1.           From Content-Driven to Skills-Driven: Since skills and competencies are the core components of competency-based education, developing competencies frameworks and graduate profiles is essential and will help organize learning at your school around durable, transferable skills.

2.           From Time-Based to Performance-Based: Reconsidering how time disproportionately dictates how assessment systems work at school is important, as learning in competency-based education is personalized and driven by each student’s performance. Schools should use reassessment practices to build more flexibility into that system.

3.           From Grading to Feedback: Educational institutions should drive into the robust research on effective grading to establish more transparent, equitable, and meaningful feedback practices.

4.           From Lessons to Experiences: Summative assessments should be redesigned to consist of more relevant tasks for performance evaluation.

5.           From Educator Designed to Co-Designed: Schools should create opportunities for students to set, pursue, and reflect on their own goals, not focusing only on the goals of teachers and schools.

Change often succeeds in a series of small steps, and this is particularly true in competency-based learning, where it is important to spend time helping educators, students, and families understand what a competency-based environment means, and how to benefit from it. Changing how school should work must be combined with unlearning how the process was done before and educating the stakeholders about competencies-based education, and we can’t underestimate the value of time, patience, and support in that effort.

Competency-based learning assists students in gaining and demonstrating mastery of a topic, promotes fairness and inclusion, and prepares students for life beyond the classroom.

While this system poses adoption challenges for school leaders, the benefits of successfully overcoming those challenges greatly outweigh the time spent doing so.

Using the EDLIGO learning analytics students can check their results and see how their work is progressing. The dashboards are simple and intuitive to use for various age groups and display a wide and comprehensive set of data.

Student data is linked to educators’ dashboards, allowing them to track progress and identify problems and students at risk. Educators can see how far their students have progressed in each area, providing them a complete picture of what is going on in their classes. Moreover, educators also get recommendations on how the content can be improved and which tasks were the best to assess the students’ progress.

AI is used to recommend tasks and approaches for students to reinforce what they’ve learned or to improve on areas that require improvement based on personal needs and abilities.

Moreover, EDLIGO provides targeted academic support and drives timely actions for focus groups (e.g., students at risk, gifted students), evaluate whether/how students are learning and offer individualized learning experiences, identifies areas for improvement related to international benchmarks & accreditation plans sharpen identification of professional development needs for faculty/staff, and optimize resource allocation and make better-informed decisions boosting institutional performance.

Contact our EDLIGO team to learn more.