Ronaldo Mota – Chancellor of Estácio Group, educational executive, lecturer, member of the board of the Brazilian Association of Owners of Private Institutions of Higher Education, he writes about new technologies in education and innovative educational methodologies.
Official data on Brazilian higher education shows that enrollments in the face-to-face modality between the years 2012 to 2016 grew 10%, while in distance education the growth was 34%. As for the number of graduates, in the face-to-face teaching the positive variation in this period was 7% and in the distance modality it was 32%. The most impressive data refers to the number of enrollments. Compared to the substantial 44% growth in distance education (approximately 542 thousand in 2012 versus 781 thousand in 2016), there was a reduction of more than 18% (2,204 thousand in 2012 to 1,858 thousand in 2016) in face-to-face teaching.
At the beginning of this decade, any warning about the incredible growth potential of the distance mode would be the object of some suspicion. Likewise, for the majority, the strong tendency for e-learning dominance (internet-based), as opposed to the so-called semi-presential, was not yet clear. The same skepticism would be predicted that the dominant device of online learning would be the cell phone, as it is today, and not computers, notebooks or tablets.
At the same time, one of the major challenges in higher education is to scale the role of learning analytics. This tool and its evolutions will prove to be increasingly essential and indispensable, contributing to the designs of the most effective learning processes.
Learning analytics refers to the technique that is characterized by the systematic collection and rigorous analysis of data of learners and their educational contexts, aiming at understanding the learning processes and the environments in which they occur. Thus, it is possible to develop and improve learning designs, in which multiple educational paths can be constructed and made available to students. From this perspective, it is possible to make personalized processes possible, attending to the characteristics peculiar to each student, or specific to the specific educational environment.
In the early stages of learning analytics, scholars limited themselves to simple predictive models based on data drawn from the available student information. The increasing use of digital platforms by students and of learning management systems by institutions has progressively generated an unprecedented amount of qualified data. From them, we see significant advances in the applications of learning analytics, in the proposed educational designs and in the pedagogical interventions derived from them.
More recently, the dispositional learning analytics strategy has been introduced, which combines general learning data with dispositional elements of learners, including their behaviors, attitudes and values. The collection of these dispositional data can be accomplished through responses provided directly by the students themselves, as well as via the monitoring of their reactions, from induced situations with specific purposes. The dispositional aspects that we are interested in should represent characteristic differentials of learners and their circumstances, including behavioral, cognitive, metacognitive (involving the learner’s perception about learning) and affective aspects.
In Brazil, we have the opportunity to adopt this strategy as soon as possible, in addition to the associated innovative methodologies and new available technologies. The application of analytical dispositional learning certainly contributes to the construction of educational approaches that enable everyone to learn, learn all the time and anywhere, and especially that each one learns in a unique and personalized way.