The Role Of e-learning Analytics In Online Learning
In the 21st century, technology has been evolving at an overwhelming rate. It has given us many useful outcomes that have made our lives convenient and expeditious like never before. The Internet, one of the most influential inventions ever in the history of the modern world, has spread like wildfire since its inception. In India alone, there are more than 718.74 million (roughly 72 crores) active internet users. Initially, it was available only to the educational and research community. Even today, the education sector expands on this massive network. People from all over the world can connect with each other and learn from each other.
One of the most important things that the internet provides us is the platform for open online learning, often shortened as E-learning. An E-learning facility is a boon for the people who are in need of quality education. Today, you can connect to the best teachers across the world through this platform, thanks to E-learning.
Especially, in unprecedented situations like the covid-19 pandemic, E-learning platforms have proven to be effective in ensuring that the flow of education is not getting disturbed. Schools and colleges around the world are functioning just because of the grace of the internet.
E-learning analytics refers to the set of techniques used to enhance the learning outcome of the persons involved, by using the data available online. It allows us to carve out a model to see our performance in online learning. It also measures the success, usefulness, and popularity of specific eLearning courses. Moreover, it helps us design our teaching methods in order to make the learning process fertile.
According to the Society of Learning Analytics, these are the methodologies used to formulate a model:
Descriptive Analysis: learning from the past
Uses the aggregated data to understand trends and evaluative metrics over time. The majority of statistics uses past data and includes
- Feedback gathered from students through surveys.
- Analysis of data at all stages of the student lifecycle starting from admissions to student orientation, enrolments, exams, and graduations.
Diagnostic Analysis: cause of the problem
This form of advanced analytics is used to examine data or content to find the cause of something. It includes:
- Analysis of data to inform and uplift key performance indicators
- Analysis of patterns to design appropriate measure
- Learning management system metrics to improve student engagement
Prescriptive Analysis: advise on possible outcomes
It uses machine learning, algorithms, business rules, and computational modeling to recommend changes such as:
- Focusing on the courses where minor changes make a major impact
- Data visualization to understand the collected data in a better way
Importance of E-learning Analytics
There are various ways in which learning analytics helps us, a few of them are enlisted below.
- Predicts the learning outcomes: The instructors can keep a record of a student’s progress, which can help them predict their success based on their performance. Educators are able to analyze learners compared to historic data as well as compared to other learners and understand if it is necessary to provide them supplementary attention.
- Improved eLearning Courses: Based on the data, the organizers can ascertain the number of students taking a course. They can find the number of dropouts from a certain course and the reasons given by the droppers help the educators to improve their content. Learning analytics enables educators to refine their content at every stage in order to increase its effectiveness. If they’re not effective, then they can be modified. If they are effective, then make sure the content is exposed to as many learners as possible.
- Helps decide which content is relevant for a given user: It is extremely important to filter out the courses available online as per the requirement of the audience. Learning analytics helps us to provide the relevant content to the relevant users so that the process becomes hassle-free.
- Reducing tutoring costs: The information made accessible to us by learning analytics helps us understand the best possible way to impart knowledge with the best educators. It helps us know what kind of teaching methods are popular among the learners.
Challenges in Learning Analytics
Despite the many benefits, there are some challenges too in the path of data analytics. Like,
- Inability to capture accurate data: Sometimes it becomes difficult to collect accurate data as there is too much diversity between the learners. Some may like the pre-recorded videos and others may want the live lectures.
- Lack of implementation: Even after having the required analytics some learning platforms cannot make the appropriate alterations because of the lack of funds or the lack of resources.
- Privacy issues: A lot of users refrain from providing the correct information citing their concern for their privacy which makes the data collection process even more challenging.