This study aims to identify whether the use of concept mapping (CM) strategy assists a student to extend and revise their expertise in oncology and analyze the abilities developed in a student in order to go through theoretical to practical knowledge. This study is descriptive and qualitative, with 20 undergraduate students of the Undergraduate Nursing Course of Paulista School of Nursing of Federal University of São Paulo, Brazil. The critical incident technique and content analysis were used. There were 12 categories represented by facilities, difficulties, and learning applicability in oncology provided by CM strategy during the surgical and clinical nursing discipline. The graphics resource, CMapTools®, and the clinical case data arranged in mapping for resolution generated an active search and exercise of self-learning in oncology. Despite the challenges of the use of CM as a teaching strategy-pedagogical, the results suggested an increase of autonomy and clinical reasoning in nursing practice.

A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or difficult to be obtained. Considering this scenario semi-supervised learning (SSL), the branch of machine learning concerned with using labeled and unlabeled data has expanded in volume and scope. Since no recent survey exists to overview how SSL has been used in text classification, we aim to fill this gap and present an up-to-date review of SSL for text classification. We retrieve 1794 works from the last 5 years from IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Then, 157 articles were selected to be included in this review. We present the application domain, datasets, and languages employed in the works. The text representations and machine learning algorithms. We also summarize and organize the works following a recent taxonomy of SSL. We analyze the percentage of labeled data used, the evaluation metrics, and obtained results. Lastly, we present some limitations and future trends in the area. We aim to provide researchers and practitioners with an outline of the area as well as useful information for their current research.

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