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Objective: This paper presents an empirical study of a formative mobile-based assessment approach that can be used to provide students with intelligent diagnostic feedback to test its educational effectiveness.Method: An audience response system called SIDRA was integrated with a neural network-based data analysis to generate diagnostic feedback for guided learning. A total of 200 medical students enrolled in a General and Descriptive Anatomy of the Locomotor System course were taught using two different methods. Ninety students in the experimental group used intelligent SIDRA (i-SIDRA), whereas 110 students in the control group received the same training but without employing i-SIDRA.Results: In the students' final exam grades, a statistically significant difference was found between those students that used i-SIDRA as opposed to a traditional teaching methodology (T(162)=2.597; p=0.010). The increase in the number of correct answers during the feedback guided learning process from the first submission to the last submission in four multiple choice question tests was also analyzed. There were average increases of 20.00% (Test1), 11.34% (Test2), 8.88% (Test3) and 13.43% (Test4) in the number of correct answers. In a questionnaire rated on a five-point Likert-type scale, the students expressed satisfaction with the content (M=4.2) and feedback (M=3.5) provided by i-SIDRA and the methodology (M=4.2) used to learn anatomy.Conclusions: The use of audience response systems enriched with feedback such as i-SIDRA improves medical degree students' performance as regards anatomy of the locomotor system. The knowledge state diagrams representing students' behavior allow instructors to study their progress so as to identify what they still need to learn. [ABSTRACT FROM AUTHOR]

Recently, the total net assets of mutual funds have increased considerably and turned them into one of the main investment instruments. Despite this increment, every year a considerable number of funds disappear. The main purpose of this paper is to determine if the neural networks can be a valid instrument to detect the survival capacity of a fund, using the traditional variables linked to the literature of disappearance funds: age, size, performance and volatility. This paper also incorporates annualized variation in return and the Sharpe ratio as variables. The data used is a sample of Spanish mutual funds during 2018 and 2019. The results show that the network correctly classifies funds into surviving and non-surviving with a total error of 13%. Moreover, it shows that not all variables are significant to determine the survival capacity of a fund. The results indicate that surviving and non-surviving funds differ in variables related to performance and its variation, volatility and the Sharpe ratio. However, age and size are not significant variables. As a conclusion, the neural network correctly predicts the 87% of survival capacity of mutual funds. Therefore, this methodology can be used to classify this financial instrument according to its survival or disappearance. [ABSTRACT FROM AUTHOR]

The paper aims to identify which variables related to capital structure theory predict business failure in the Spanish construction sector during the subprime crisis. An artificial neural network (ANN) approach based on Self-Organizing Maps (SOM) is proposed, which allows one to cluster between default and active firms' groups. The similarities and differences between the main features in each group determine the variables that explain the capacities of failure of the analyzed firms. The network tests whether the factors that explain leverage, such as profitability, growth opportunities, size of the company, risk, asset structure, and age of the firm, can be suitable to predict business failure. The sample is formed by 152 construction firms (76 default and 76 active) in the Spanish market. The results show that the SOM correctly predicts 97.4% of firms in the construction sector and classifies the firms in five groups with clear similarities inside the clusters. The study proves the suitability of the SOM for predicting business bankruptcy situations using variables related to capital structure theory and financial crises. [ABSTRACT FROM AUTHOR]