Learning Analytics in Saudi Arabia: Beyond Completion

Completion reports identify who finished an activity according to a system’s definition. They do not independently establish who can perform a job task. Useful learning analytics in Saudi organizations starts with a decision: selecting people for a project, addressing a shared capability gap or improving a pathway that is not producing the expected readiness.

Define the decision before the metric

Choose a role and a specific task, then describe the required performance and reviewable evidence. A customer-request analysis task may require evaluation of the decision and its rationale, rather than time spent viewing a module. Identify the information that would justify action, further support or a delayed decision.

Separate program completion, assessment results and verified workplace application. Someone may finish the content without passing a practical task. Another employee may possess the skill but lack an opportunity to demonstrate it. Combining these situations into one percentage can create misleading conclusions.

Build a short evidence chain

• Access: could the intended audience start the pathway?

• Participation: did learners attempt the activity, and how well?

• Proficiency: did performance meet a defined assessment standard?

• Application: was the capability used in a real task?

• Outcome: did a related work indicator change, and what else contributed?

There is no need to collect every possible event from every employee indefinitely. A focused sample and a well-designed practical task may answer a more important question than a large clickstream. Match collection effort to the decision and its consequences.

Standardize the definitions

Create a data dictionary covering completion, attempts, passing criteria, dates, content versions and employee identifiers. Specify how reassessment, updated materials and records from multiple systems are handled. Results from an easy knowledge quiz and a demanding simulation should not be treated as equivalent measures.

xAPI supports structured learning activity records exchanged with a learning record store, or LRS. It does not define proficiency for your organization or automatically prove business impact. Assessment design, evidence quality and the connection to the job remain essential.

Give each audience an actionable view

Leaders need priority capability gaps and decisions requiring attention. Learning teams need diagnostic detail about barriers and assessment quality. Managers need application tasks that require follow-up. Avoid distributing the same comprehensive dataset to everyone or exposing information they do not need.

Assign an owner, review date and potential action to each measure. When proficiency is low, examine explanation quality, question difficulty and audience fit before adding more content. When proficiency improves but application remains limited, investigate practice opportunities, authority and manager support.

Distinguish contribution from causation

A reduction in errors may coincide with training, a system update and a change of supervisor. Record these factors and use a baseline and an appropriate comparison where feasible. Do not attribute the entire change to training without a measurement design that supports that conclusion.

Avoid using an experimental skills metric as the sole basis for a consequential employment decision. Start with one capability, one proficiency measure, one application task and a regular review.

Contact SkillUp MENA to discuss learning analytics that connects evidence with practical skills decisions.