The digital ecologies allow us to lead and can lead to compelling learning outcomes and progress. Digital technology creates infinite amounts of capacity for interaction and immediate feedback, and that's something that we need to consider how to harness to reach our learners' goals. Learning analytics is one of the strategies to enhance learners' earning experience.

Learning analytics refers to the collection and analysis of data about learners and their environments to understand and improve learning outcomes in digital ecologies. Learning analytics is one of the digital strategies and technologies expected to enter mainstream use soon (The Horizon Report, 2019). Analytics capabilities comprise dynamic, connected, predictive, and personalized systems and data. Institutions and institutional leaders will need to develop these advanced analytics capabilities through innovative leadership, new computational technologies and systems, and a highly-skilled workforce that is equipped to understand and effectively share and use large and complex data resources. Universities and massive open online course providers collect data about learners and how they learn. Learning analytics and educational data mining are the tools to transform this data into knowledge and lead, in the end, to improved education. Learning analytics could measure key indicators of student performance, support student development, understand and improve the effectiveness of teaching practices and inform institutional decisions and strategy. If executed and maintained successfully, it can transform institutions and deeply enrich student and faculty educational experiences and success. Analytics technologies are a crucial element of student success initiatives across institutions and a driving force behind higher education leaders' collaborative, targeted strategic planning and decision-making. Analytics provides data input to facilitate descriptive analyses of student learning, grades, and behaviours that could provide better educational outcomes.

As the education process is shifted to the internet and digital ecologies, the way we give assessment feedback also requires a shift in thinking. The internet and the digital ecologies that are available to us can capture continuous and real-time data on our journey in reproducing or representing knowledge. Learning analytics could be used to give recursive and just-in-time feedback. Traditionally feedback was given by teachers after (summative assessments) or along the way of learning (formative assessments). Digital ecologies have immense potential for building up the strength of formative assessment. Machine feedback is getting better, more precisely, machine-mediated human feedback. E-learning environments can be used to create better feedback loops to ensure incremental learning.

For example, in a conventional classroom setup, the teacher gives feedback to individual students at the end of a test, which is only a summative assessment and cannot provide an opportunity to improve learning. Though formative assessment is much spoken, the practical implementation of formative assessment feedback is still a Utopian view. However, as have better real-time, comprehensive data on students' works from learning analytics, teachers can see progress over time, provide responsive feedback, and make formative assessments possible and feasible.

References:

What is Learning Analytics & How Can it Be Used? (northeastern.edu), Accessed on January 28, 2022.
Horizon Report (2019)| EDUCAUSE, Accessed on January 28, 2022.
Recursive Feedback, Part 4A: Why Feedback Matters | Coursera, Accessed on January 28, 2022.