How can you make learning analytics data understandable for all stakeholders?

My PhD was focused on Effective Score Reporting. For me, one of the key elements was ensuring that the intended use of a score (in this instance, a learning analytics report) aligns with the actual use. What this means is that if we want our audience to engage with a dashboard, identify, say, different instructional groups within a class or the learnings of a student, then this is what our stakeholders are. Key to this is then understanding the intended interpretations and actions for a particular report. In the work I have done this has involved mapping out the reports needed, identifying interpretations and uses and then seeking feedback from end-users to refine. With this completed we were then able to begin the task of report design.

Embarking on the journey of learning analytics demands a thoughtful approach. Defining clear goals and identifying your audience are the foundational steps. Articulate the main questions the data should answer, specifying the intended users and beneficiaries. Understanding how the data will shape decisions and actions ensures its practical value. This strategic clarity guides the selection of data sources and methods, focusing efforts on the most relevant insights for informed, impactful decision-making in education.

Navigating the landscape of learning analytics involves selecting data sources aligned with your objectives and audience. Whether it's online platforms, assessments, surveys, or feedback, the key is to ensure data quality and validity. For instance, gauging student engagement may involve tracking online activities like clicks and participation. Yet, it's crucial to acknowledge potential limitations and biases, including technical glitches, privacy issues, and the reliability of self-reported data. Striking a balance between harnessing valuable insights and being mindful of these considerations is pivotal for meaningful and accurate analytics in education.

With intend use of a dashboard mapped out (as per my previous comment) it is then possible to understand what data is needed to support the interpretations stakeholders need to make. At this stage, it is also necessary to best think about drawing upon a variety of sources of data and also considering validity - is the data being draw upon appropriate for the types of decisions that need to be made.

?? I think the big data must be visualized for all the stakeholders at relevant time. However, one must keep in mind that there are certain data such as student's progress and results which must not be presented without factoring the humane terms.

I would ask yourself, "Who is my audience and what do they care about? What do they need to know?" Share only what they need to know and share only what they care about. Make it simple so everyone in the room can get it easily through charts and visual graphs such as pie charts or bar graphs. A mistake people can make is going way too into the weeds when their audience only needs to know a sliver of the information.

This is the most critical step, where the rubber hits the road. No matter how beautifully data is visualised, if the report is not interpreted as intended, then it fails. This means that report design needs to be iterative, involving prototyping, feedback from stakeholders and testing - not for useability (which is important) but for interpretability and actionability.

Effective communication of learning analytics data extends beyond presentation—it's about involving and empowering stakeholders. Engage them in the entire data lifecycle, seeking feedback and input. Empower stakeholders by translating data into actionable, personalized insights. For students, this might mean reports highlighting strengths and weaknesses, coupled with tailored resources and tips for improvement. By fostering this collaborative and supportive environment, stakeholders are not just recipients of data but active participants in leveraging it for informed decision-making and enhanced learning outcomes.

I have already commented. Stakeholders need to be involved through the ideation and development cycle to ensure that a series of reports are fit for purpose.

Upholding ethical and legal standards is paramount in navigating the landscape of learning analytics data. It necessitates a meticulous approach, respecting stakeholders' rights through consent, privacy, security, and ownership considerations. Ensuring data accuracy, transparency, and accountability becomes a commitment to avoid errors and manipulations. A tangible way to establish these principles is through a code of conduct or policy document, outlining the ethical and legal standards specific to the context of learning analytics data. This framework safeguards the integrity of the process, fostering trust and responsible use of information.

My throwaway line is that "it's not about the data; it's what you do with it that matters". This means, in the context of education, that data via a report or dashboard, is meaningfully interpreted and acted upon.