Predictive Learning Analytics and its Use in Higher Education

By Education Technology Insights | Wednesday, June 17, 2020

By collecting learning data from a wide array of resources, predictive learning analytics (PLA) are utilized to identify learners who might not complete a course, typically described as being at risk. 

Fremont, CA: Mixed-effects are witnessed on how teachers perceive, use, and interpret PLA data, necessitating further research in this direction. An often-cited model of visualization of learning analytics tools distinguishes four different stages of use, such as data-gathering of students’ activities in VLE, data collection, and data mining using learning analytics techniques, visualization of student activities in a widget, application, or VLE, and reflection by the teacher. The teachers are expected to interpret visualizations, understand their impact on teaching and learning, and judge their effectiveness in real-time. Although PLA visualization must lead teachers to action in terms of teaching interventions, there is still no guarantee that teachers can make informed teaching interventions and act accordingly.

Top 10 Elearning Technology Companies in APAC - 2020Technology acceptance

Many studies have found that users’ technology acceptance, which is conceptualized in the technology acceptance model, can have a substantial impact on the adoption of information systems. TAM is a widely used model and has proved to be highly informative in explaining teachers’ uptake of educational technology. The developed models have been successfully applied to educational settings. The TAM model is founded on the well-established theory, which states that human behavior is directly preceded by the intention to showcase this behavior. In return, three factors are identified to influence plans, such as personal beliefs about their behavior, norms, and the intensity of behavioral control an individual has.

Academic resistance model

Secondly, some teachers may be more willing to adopt PLA, which can be resistance or ambivalence towards change. In a review, it was argued that the ambivalence of academics towards change has to be understood against three dimensions of attitudes, cognitive, emotional, and intentional. Academic resistance models (ARM) explain resistance about PLA in terms of cognitive, emotional, and intentional approaches. The ARM can allow the teachers’ to illuminate their perceptions about the use of learning analytics and whether these relate to specific beliefs, feelings, or plans. 

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