With the growing importance for quality education across sectors, data mining and learning analytics applications in educational research is gaining traction. Especially, the role of big data in understanding learner’s behavior & performance, improving educational systems, modeling and educational data warehouse, and the integration of big data into the curriculum, has garnered much attention.

Big Data in the context of learning and learning analytics can deliver specific outcomes. These include:

1. Delivering learning as a product
Big Data can provide better understanding of learners’ behavior and performance through analysis and identification of patterns. The data can then be used to improve a given learning experience by modifying the content, delivery, and/or assessment methods. Additionally, data can be used to identify at-risk learners and provide targeted interventions.
For example, big data and learning analytics in higher education can be used to develop customized learning experiences for individual learners. By identifying students’ needs and preferences, and by recognizing students who are more engaged and motivated by certain types of activities and content, student learning experiences can be tailored to individual needs.

2. Modeling and educational data warehouse
Big Data can identify patterns and trends in data that can be used to improve models and create educational data warehouses. The information helps improve the accuracy of predictions made by models and educational data warehouses by providing more data for the model to learn from. A larger dataset allows systems to consume more knowledge, and assess more outcomes rapidly.[1]
Ways in which Big Data helps create modeling and educational data warehouses:
● Spotting trends over time
● Helping to develop and refine models
● Identifying relationships between different data sets
● Assisting with the creation of reports and dashboards
● Analyzing patterns in data to identify areas that need improvement

3. Improving the learning system and driving behavior change through education
Data can be used to track students’ progress and identify areas where they are struggling. This information can then be used to tailor the teaching methods and materials to better meet the needs of the students.
Big data in educational data mining and learning analytics can be used to assess the effectiveness of different educational interventions and programs. Educators can use this information to make decisions about programs that are worth investing in, and those that need improvement.
Consequently, Big Data can also be used to evaluate the impact of education on society. This information can be used to inform policy decisions and help ensure that education is providing value to the community.

Measuring The Effectiveness of Learning Interventions

While Big Data helps in understanding performance differences and bridging gaps through data, the application of learning analytics is where the primary outcome is. Here are some ways to measure the effectiveness of learning interventions, using the acquired data.

1. Using analytics to measure progress
Analytics can help identify which students are struggling and which students are excelling. This information can then be used to target interventions and support students who need it. Additionally, analytics can be used to measure how effective instructional methods are and whether students are engaging with the material.[2]
There is no single silver bullet when it comes to measuring progress with learning analytics. The best way to use learning analytics to measure progress will vary depending on the specific goals and objectives of the organization or individual. However, here are some common metrics that help gauge the progress:
● Engagement rate
● Completion rates
● Time spent on tasks
● Performance on assessments
● Patterns in student behavior
● Tracking changes in learning outcomes over time, and the
● Impact of specific interventions as a measure of the above-mentioned metrics

2. Measuring the effectiveness of tactical learning approaches
The application of learning analytics can help in measuring the effectiveness of tactical learning approaches, by providing data on how well students are engaging with the material, how well they are retaining information, how well they are applying the information, what they have learned, and how often they return to the learning environment to review or practice new skills. The use of a big data architecture for learning analytics in higher education can also help educators adapt their teaching methods to meet the needs of their students better.

3. Studying the short-term vs. long-term retention of knowledge
The effects of different learning interventions on short-term vs. long-term retention of knowledge could be studied by administering a test to participants immediately after they receive the intervention, and again after a delay (e.g., one week, one month, six months). The scores on the two tests could then be compared to see if there are any differences in performance. Additionally, participants could be asked to rate their confidence in their answers on the second test, to gauge how well they remember the material.
Such information can be read with learning analytics to understand the effectiveness of different regimens, their importance and value for students, and how they impact the education regime.

4. Studying the implementation of knowledge gained from learning interventions
Learning analytics can inform the level at which learners are able to implement the knowledge gained from interventions by providing data on how learners interact with learning materials, and how they perform on assessments.

Tracking how students use and apply the knowledge they have learned can help identify areas where learners are struggling and where additional support may be needed. Such applications of deep learning in big data analytics can point to areas for improvement in future interventions and identify patterns in student learning and application of knowledge, which can be leveraged to inform future instructional design.

Learning analytics as the way forward

As we drift from traditional and standardized education towards more impactful practices, we need to deploy measures to assess their success. Fortunately, the digital age has enabled us to leverage metrics to comprehend complex issues in quantifiable terms. Learning analytics allows us to gauge learning outcomes, and how well they fit into the education landscape for all students in a single cohort, while accommodating diversity, difficulty, and differentiation in every group.

With companies such as Google Inc. making investments and acquiring companies in the Ed-Tech analytics space[3], we must acknowledge how critical it is to the education industry, and invest in leveraging the technology as we move forward.

References
1. Comparison of learning analytics and educational data mining: A topic modeling approach, Elsevier (ScienceDirect) https://www.sciencedirect.com/science/article/pii/S2666920X21000102
2. Learning analytics don’t just measure students’ progress – they can shape it by Rebecca Ferguson, The Guardian https://www.theguardian.com/education/2014/mar/26/learning-analytics-student-progress
3. Google Discreetly Acquires Edtech Analytics Company BrightBytes by Daniel Mollenkamp, EdSurge https://www.edsurge.com/news/2022-10-11-google-discreetly-acquires-edtech-analytics-company-brightbytes