Learning Analytics: A Better Way to Measure Student Satisfaction

As the time is passing and as means of checking out the abilities and talents of students is becoming more difficult, teachers and supervisors are trying to come up with better and more accurate means to measure student satisfaction.  Learning analytics can provide much better and quick answers to the standard questions as it is all about ongoing learning, evolving understanding and applying all that incremental learning and understanding in a variety of ways to enrich and support the student experience.

 

Many academic institutes use data to combine personalized and automated feedback to undergraduate science and technology students as a part of learning process. During this process, every student is associated with a set of indicators (or markers) that outline their learning progression. For each level of these indicators, students are assigned personalized feedback and this is just like completing a level on a computer game where the feedback various depending on their starting point and their overall score. PhD Dissertation Writing Services

 

It is necessary to understand that this is not the way to label students and put them in certain categories. It is all about identifying student as multifaceted subjects characterized with a sequence of indicators. As they move forward with their learning, it is necessary to know that some of the indicators will change along with the topics or the type of task that they are working on which result in different feedbacks at various times. 

 

It is an important question if the learning analytics provide more accurate data and when students complete various surveys, the use of learning analytics provide us evidenced data on the actual experience that include different facets of engagement and learning gains from one assignment to the next.  The data mostly comes from several sources that include digital and real-life interactions – learning management systems, student information systems, discussion forums, library use, assessments and observation – to build a model of each student so that their abilities and their performance can be analyzed the right way.

 

in many countries like UK and the Netherlands, the targeted support for higher achieving students is generally poor as they are focusing more on the lowest achieving quartile of students as compared to other countries like Australia where the mid to low range students receive specific support. The most important thing here is to support the students who are working hard to achieve a first as well as to those who are underperforming. There are a lot of challenges that student satisfaction techniques face and they are being worked out slowly and steadily but there is a long way for them to go before they can be rated equally by all the concerned parties.

 

The best way to check out if the learning analytics are providing the right data is based on the data collected on basis of students’ actual experience, including different facets of engagement and learning gains from one assignment to the next. 

 

The University of Sydney last month started a pilot project using feedback analytics to measure student satisfaction and has already seen satisfaction with feedback growth that has progressed from 3.35 to 3.85 on a five point Likert scale. These programs are enough to give us ideas about how learning analytics can be used to measure student satisfaction and what more can be done in this regard to help students progress in their academics.

 

The first and the most important thing to understand in this regard is what satisfaction means in context of learning and how it can be used the right way to analyze the students’ progress. Happiness is not necessarily an indication of good learning and while students face lots of difficulties in working on their course, these can also be overcome with practice or self-testing. It is important to check out the effective approaches to learning that can be promoted by analysis and hard work in the right direction.

Learning analytics offers some great ways to understand the complex interplay between learning gains, effective study and teaching practices, and student satisfaction. 

 

While academics and students choose to use the data that is available to them very easily, they must not forget that content is the key. They cannot separate the data from the organizational culture, national and regional differences, pedagogical differences between courses, and the political context.It is not important to apply the same data across all providers and courses to reach a neat set of answers, but consider the context in which they are operating.