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This research explores how individual factors drive early-stage strategic entrepreneurship (SE) in Vietnam and Taiwan. The authors extend SE and integrate knowledge spillover theory to gain insights into the relationship between individual factors and SE. The research highlights the importance of a dual process, which involves advantage-creating by innovation, as value creation and capture, and advantage-leveraging by growth and international expansion, as value retention and capture.

Innovation-oriented SE (ISE), growth-oriented SE (GSE) and internationalization-oriented SE (ITSE) are identified as new measures of SE. There are six hypotheses containing the effect of six personal characteristics have on SE. The authors employed logit regression to estimate the effect of independent variables on SE based on a pooled cross-sectional dataset drawn from Global Entrepreneurship Monitoring (GEM) in Vietnam and Taiwan during 2013–2018.

Opportunity sensing, education, self-funding ability, startup knowledge and skills and startup experience are crucial to the engagement of at least one type of SE in Vietnam. In contrast, education, self-funding ability and start-up knowledge and skills are key factors in Taiwan.

This study contributes to the extension of SE at the individual level in the early phase of new venturing and the integration of knowledge spillover theory. In order to drive early-stage SE further, the authors recommend to prioritize learning from spillovers within and among organizations, industries and communities, as well as through quality institutions, in addition to the individual drivers.

This study aims to propose a blackboard approach using multistrategy machine learning student modeling techniques to learn the properties of students' inconsistent behaviors during their learning process.

These multistrategy machine learning student modeling techniques include inductive reasoning (similarity‐based learning), deductive reasoning (explanation‐based learning), and analogical reasoning (case‐based reasoning).

According to the properties of students' inconsistent behaviors, the ITS (intelligent tutoring system) may then adopt appropriate methods, such as intensifying teaching and practicing, to prevent their inconsistent behaviors from reoccurring.

This research sets the learning object on a single student. After the inferences are accumulated from a group of students, what kinds of students tend to have inconsistent behaviors or under what conditions the behaviors happened for most students can be learned.