Due to the arrival of positive psychology (PP) in the development of teaching, the construct of engagement has been thrived and got a notable function in the educational arena. Alternatively, numerous individual differences, containing ambiguity of tolerance, have been taken into consideration as a result of the key role they can play in the process of learning, and thus, on different facets of the learners’ engagement. Furthermore, resilience is recommended to be an alternate and effective way of engaging English as a foreign language (EFL) learners. Also, it is a significant feature of the human adaptation system in which students can efficaciously manage and tackle stressful involvements despite their troubles and disasters. Given the eminence of both ambiguity tolerance and resilience in educational settings and the fact that little attention has been given to these constructs in foreign language learning, the present review makes an effort to scrutinize the impact of ambiguity of tolerance and resilience on EFL learners’ engagement. Succinctly, the fundamental roles of ambiguity tolerance and resilience in learners’ engagement were confirmed, and consistent with the conclusions drawn from the present review, some suggestions are set forth concerning the implications of this paper.

However, the number of college students enrolling in our country has only begun to increase on a large scale in the 21st century. As educational big data gets more and more attention, it has its own distinct characteristics in the field of education and at the same time consolidates the overall trend of big data development. The countermeasure analysis of innovation and entrepreneurship education under the reform of the education supply side has become a subject to be explored in an all-round way. This paper is mainly aimed at the importance of a big data analysis model to college students, explores the current development status of innovation and entrepreneurship education for college students, finds some problems in the application of big data, and improves the existing problems based on the purpose.