Learning Analytics to Explore Dropout in Online Entrepreneurship Education

Abstract

The technological disruption of E-Learning offers Entrepreneurship Education (EE) an unprecedented opportunity to leverage the affordances of web 2.0 and to widen the scope of entrepreneurship educational programs. However, in practice, high dropout rates in online courses (near 90%) pose major challenges to EE researchers. In this paper, we use learning analytics to explore the case of dropout during a female-oriented online entrepreneurship educational program. We observed that the evolution of dropouts and learning behavior of the participants in this program is not linear in time. Persistence decays in a two-step process: The first dropout phase reaches approximately 30% before the middle of the program and then stabilizes around that number. A second dropout phase is triggered in the last quarter of the program and it continues declining until the end of the program. This dropout pattern corresponds to the interaction indicators at aggregated level: Before the middle of the program the level of interaction drops together with the number of active users. However, the reduction in the interaction frequency is disproportionate in relation to the fall of number of the active users. After dividing by the percentage of active users the number of interactions still drops before the middle of the program. Using social network analysis (SNA) we show that initial dropouts have a considerable effect on the connectivity of the communication network, this is consistent with the observed decrease in social interactions. We also found significant correlations between entrepreneurial competencies and indicators of learning behavior and persistence, at the individual level. In line with the relevant literature, the most significant feature associated with persistent behavior was found to be the risk-taking orientation. Our findings suppose the first step towards an empirical model of persistence in Entrepreneurship Education online programs providing valuable insights for future research and for developing retention methods in online courses.

Online and blended entrepreneurship education: a systematic review of applied educational technologies

“…A variety of digital technologies in the online and blended environment have been adopted into EE such as cloud computing (Holinska et al, 2019 ), learning analytics (Toledo et al, 2020 ), there digital (3D) virtual reality, SGs (Lameras et al, 2015 ), social media, digital platforms, big data (Secundo et al, 2020 ; Sousa, 2019 ) and so on. The emphasis of these works has been on the implementation, and lack of appropriate or relevant research on the pedagogical and usability aspects.…”

Artificial intelligence in entrepreneurship education: a scoping review

“…within the domain of entrepreneurship education is conspicuously constrained concerning teaching, learning and administrative applications. Specifically, in contrast to the contextualization of students' requirements and preferences within the ambit of big data, coupled with the provision of personalized learning guidance facilitated by algorithmic models, the learning analytics method within entrepreneurship education identified, through the employment of both surveys and log data, eight potential factors influencing the heightened attrition rates observed among female entrepreneurs in the online entrepreneurship education environment (Toledo et al, 2020); (2) big data analytics operates entrepreneurship analytics to predict the success of an entrepreneurial project for learners. Using statistics analytics, entrepreneurial analytics applies data insights to social sustainable development, extracting entrepreneurial opportunities/ideas and enhancing learners' entrepreneurial competencies (Sedkaoui, 2018); (3) data storage, multiple modal data and the amount of data are considered.…”

“…Additionally, certain data is intentionally recorded, while some is captured through automated processes on social media platforms. A survey with 23 participants and platform log data were captured (Toledo et al, 2020). The research, on the other hand, gathered a limited amount of data that did not possess the attributes typically associated with big data, such as volume, variety and velocity.…”

The Need for Modification: The Impact of COVID-19 on Pitch Competitions

“…Further, they conducted a thorough systemic review was conducted to explore the different learning technologies used in online entrepreneurial education. Technologies such as cloud computing (Holinska et al, 2019), 3-D virtual reality (Lameras et al, 2015), learning analytics (Toledo et al, 2020), social media, and big data (Secundo et al, 2020; Sousa, 2019) all aided in effective learning. Chen et al (2021) has discussed the role of each of these vehicles in online entrepreneurial education and the plethora of references listed as part of the systemic review.…”