Quantitative methods to determine the student workload. I. Empirical study based on digital platforms

Abstract

We present a quantitative study of an online course developed during COVID19 sanitary emergency in Chile. We reconstruct the teaching-learning process considering the activity logs on digital platforms in order to answer the question of How do our students study? The results from the analysis evidence the complex adaptive character of the academic environment, which exhibits regularities similar to those found in financial markets (e.g., distributions of the daily time devoted to learning activities follow patterns like Pareto's or Zipf's law). Our empirical results illustrate (i) the relevance of economic notions in the understanding of the teaching-learning processes and (ii) the reliability of quantitative methods based on digital platforms to conduct experimental studies in this framework. We introduce in the present work a series of indicators to characterize the performance of professors, students' follow-up of the course, and their learning progress by crossing information with the results of assessments. In this context, the learning rate appears as a key statistical descriptor for the allocation of the student workload.