In recent times, Educational Data Mining and Learning Analytics have been abundantly used to model decision-making to improve teaching/learning ecosystems. However, the adaptation of student models in different domains/courses needs a balance between the generalization and context specificity to reduce the redundancy in creating domain-specific models. This paper explores the predictive power and generalization of a feature--"context-bound cognitive skill score"--in estimating the likelihood of success or failure of a student in a traditional higher education course so that the appropriate intervention is provided to help the students. To identify the students at risk in different courses, we applied classification algorithms on "context-bound cognitive skill scores" of a student to estimate the chances of success or failure, especially failure. The context-bound cognitive skill scores were aggregated based on the learning objective of a course to generate meaningful visual feedback to teachers and students so that they can understand why some students are predicted to be at risk. Evaluation of the generated model shows that this feature is applicable in a range of courses, and it mitigates the effort in engineering features/models for each domain. We submit that overall, "context-bound cognitive skill scores" prove to be effective in flagging the student performance when the accurate metrics related to learning activities and social behaviors of the students are unavailable.