Nina Pavlin-Bernardić

Academic Cheating in Mathematics Classes: A Motivational Perspective

We investigated the frequency of secondary school students’ self-reported cheating in mathematics and relationships between cheating and motivational beliefs, as well as neutralizing attitudes. Two types of cheating were examined: active cheating, which is aimed to increase a person’s own success, and second-party cheating, aimed to help other students achieve success. Students use second-party cheating very often and more than active cheating. Motivational beliefs are significantly related to active cheating but uncorrelated with second-party cheating. Thus, although active and second-party cheating are both classified as dishonest acts, they do not have the same motivational mechanisms in their background.

Primjena umjetnih neuronskih mreža u predviđanju darovitosti učenika

Artificial neural networks have a wide use in the prediction and classification of different variables, but their application in the area of educational psychology is still relatively rare. The aim of this study was to examine the accuracy of artificial neural networks in predicting students’ general giftedness. The participants were 221 fourth grade students from one Croatian elementary school. The input variables for artificial neural networks were teachers’ and peers’ nominations, school grades, earlier school readiness assessment and parents’ education. The output variable was result on the Standard progressive matrices (Raven, 1994), according to which students were classified as gifted or non-gifted. We tested two artificial neural networks’ algorithms: multilayer perceptron and radial basis function. Within each algorithm, a number of different types of activation functions were tested. 80% of the sample was used for training the network and the remaining 20% was used to test the network. For a criterion according to which students were classified as gifted if their result on Standard progressive matrices was in 95th centile or above, the best model was obtained by the hyperbolic tangent multilayer perceptron, which had a high accuracy of 100% of correctly classified non-gifted students and 75% correctly classified gifted students in the test sample. When the criterion was 90th centile or above, the best model was also obtained by the hyperbolic tangent multilayer perceptron, but the accuracy was lower: 94.7% in the classification non-gifted students and 66.7% in the classification of gifted students. The study has shown artificial neural networks’ potential in this area, which should be further explored.