The development of a knowledge-based economy implies a change in the paradigms of behavior of our society that affects, among other things, two areas of life, i) the need of improving professional skills, which becomes a must for individuals who wish to be valid on the labor market, and ii) the use of explicit knowledge existing in learning contexts, which becomes a key factor to socioeconomic development. In this way, our research is aimed to analyse some of the network effects that occur in online education platforms for lifelong learning, which are based on increasing the tacit knowlege (internalization) through the use of explicit knowledge (externalization). We propose an approach based on study network effects related to e-learning by analysing the HarvardX and MITx courses of the edX platform public dataset in six European countries. We estimate and compare the value of the network of this dataset by using three laws related to netwok effects (Metcalfe, Odlyzko, and Sarnoff) and several metrics related to this network (nodes, edges, degree, diameter, density, path lenght, and topology). Thus, we analyse two areas of knowledge-based content activities in e-learning courses, i) processes of transforming explicit knowledge (content interactions, number of events, number of days, number of play videos, and number of chapters explored) into tacit knowledge (certificate); and ii) measurement of value of network externalities based on explicit knowledge existing in e-learning courses. This article shows how observable knowledge related to content interactions in online education is related to the effects of direct and indirect network externalities. Also, it puts in evidende its dependency by the intrinsic value of different types of explicit knowledge (events, days, videos, and chapters), the marginal value (the contribution of a new content-student interaction), and the relative size of the network (power-laws). The outcomes of the application are useful for estimating a power-law valid in student-content networks in online education. Moreover, the analysis of relationships between explicit and tacit knowledge in online education can help to design learning strategies that take into account how processes related to explicit knowledge management work in online education, which are very useful for detecting potential dropout.