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Deep models need a large number of labeled samples to be trained. Furthermore, in practical application settings where objects’ features are added or changed over time, it is difficult and expensive to get enough labeled samples in the beginning. Cognitive learning mechanism can actively raise the deep models’ proficiency online with a few training labels gradually. In this paper, inspired by human being’s cognition procedure to acquire new knowledge stage by stage, we develop a novel deep active recognition framework based on the analysis of models’ cognitive error knowledge to fine-tune the deep models online. The transformation of the cognitive errors is defined, and the corresponding knowledge is obtained to identify the models’ cognitive information. Based on the cognitive knowledge, the sensitive samples are selected to finely tune the models online. To avoid forgetting the previous learned knowledge, the selected prior training samples are used as the refreshening samples at the same time. The experiments demonstrate that the sensitive samples can benefit the target recognition and the cognitive learning mechanism can boost the deep models’ performance efficiently. The characterization of cognitive information can restrain the other samples’ disturbance to the models’ cognition effectively and the online training method can save mass of the time evidently. In conclusion, we introduce this work to provide a trial of thought about the cognitive lifelong learning used in deep learning scenarios.

In this technological era, programming skill has become highly valuable across IT and non-IT sectors. Teaching programming can be challenging, requiring a deep understanding of the subject and effective communication skills. Traditional pedagogy, focused on lectures, textbooks, and written assessments, tends to emphasize content over student-centered learning, resulting in limited practical application and knowledge retention. This chapter explores the potential of “Game-Based Learning” (GBL) and gamification as an alternative pedagogy to improve learner’s engagement, problem-solving skills, and learning skills. This chapter provides insights on GBL and its application in programming education, which enables instructors to make better decisions in their teaching practices for programming courses and improve students’ learning experiences.