Ranking of problems and solutions in the teaching and learning of object-oriented programming.

This study describes the most relevant problems and solutions found in the literature on teaching and learning of object-oriented programming (OOP). The identification of the problem was based on tertiary studies from the IEEE Xplore, Scopus, ACM Digital Library and Science Direct repositories. The problems and solutions identified were ranked through the multi-criteria decision methods DEMATEL and TOPSIS in order to determine the best solutions to the problems found and to apply these results in the academic context. The main contribution of this study was the categorization of OOP problems and solutions, as well as the proposal of strategies to improve the problem. Among the most relevant problems it was found: 1) difficulty in understanding, teaching and implementing object-orientation, 2) difficulties related to understanding classes and 3) difficulty in understanding object-oriented relationships. After doing the multicriteria analysis, it was found that the most important solutions to face the problems found in the teaching of OOP were: 1) use of active learning techniques and intrinsic rewards and 2) emphasize on basic programming concepts and introduce the object-oriented paradigm at an early point in the curriculum. As a conclusion, it was evidenced that there is coherence between the literary guarantee that gives support to the problems and solutions in the teaching of OOP presented in this study and the approaches that experts in the area of development highlight as relevant when they identify weaknesses in the process.

Dataset for solving a hybrid flexibility strategy on personnel scheduling problem in the retail industry.

This data article describes datasets from a home improvement retail store located in Santiago, Chile. The datasets have been developed to simultaneously solve a staffing and tour scheduling problem that incorporates flexible contracts and multiskilled staff. This Data in Brief article is related to the published article "Hybrid flexibility strategy on personnel scheduling: Retail case study" [1]. The datasets contain real, processed, and simulated data. Regarding the real and processed datasets, they are presented for three different store sizes (4, 5 or 6 departments). Real datasets include information about the employment-contract characteristics, cost parameters, and a forecast of the number of employees required in each department for each day of the week and each time period into which the operating day is divided. As regards the data processed for the case study, they include the set of skill sets considering that the employees can be trained in a maximum of two store departments. Regarding the simulated datasets, they include information about the random parameter of staff demand in each store department. The simulated data are presented in 90 text files classified by: (i) Store size (4, 5 or 6 departments). (ii) Coefficient of variation (10, 20, 30%). (iii) Instance identification number (10 instances per scenario that resulted from combining the store sizes and coefficients of variation). Researchers can use the datasets for benchmarking the performance of different approaches with the one presented by Porto et al. [1], and in consequence, they can find solutions to the same (or similar) type of personnel scheduling problem. The dataset includes an Excel workbook that can be used to randomly generate staff demand instances according to a chosen coefficient of variation.