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In circumstances when policy procedures for using student data for learning analytics are in place, inefficient communications frequently result in information asymmetry and misaligned expectations about how learning analytics should be used and how duties should be established.
FREMONT, CA: Learning analytics has a lot of potential in assisting decision-making in learning, teaching, and educational management. However, real-world applications remain small in scale due to several constraints such as stakeholder participation and buy-in, a lack of pedagogical basis, ethics and privacy concerns, and resource demand. As a result, the value and impact of learning analytics on learning enhancement have yet to be established in the field of learning analytics.
There are a few areas in particular that deserve attention:
Educational Systems Are Complex
While some in a company regard learning analytics as a way to innovate and adapt to a changing environment, others see it as a way to maintain organizational efficiency and responsibility. Tensions that emerge in a complex organizational network where individual actors have their own agenda have been noticed as barriers to learning analytics adoption (as also demonstrated above). If these conflicts are not handled, they might act as a drag on the adoption of learning analytics, resulting in stagnation or discontinuity.
In actuality, learning analytics may necessitate new infrastructure, system processes, and knowledge, all of which could destabilize institutional stability and processes. As a result, essential leadership is required to embrace the conflicts, mobilize resources, build a governance system. Most importantly it is needed to link learning analytics solutions with current problems that need to be solved, that is, a problem-driven rather than a solution data-driven strategy.
Communication Is Essential for Achieving A Common Goal
Power is unequally allocated among stakeholders in the educational system, which is a complicated network. Thus, teachers frequently find themselves ‘caught in the middle,’ unable to meet the demands of both management and students while mourning the loss of academic autonomy. On the other hand, students find themselves isolated from decision-making processes that are based on information about them.
In circumstances when policy procedures for using student data for learning analytics are in place, inefficient communications frequently result in information asymmetry and misaligned expectations about how learning analytics should be used and how duties should be established. As a result, cultivating a shared vision and a sense of ownership over learning analytics necessitates creating communication channels with various stakeholders. Researchers, for example, have argued for a co-design method.