Teaching Image-Processing Programming in Java

Image processing (IP) can be taught very effectively by complementing the basic lectures with computer laboratories where the participants can actively manipulate and process images. This offering can be made even more attractive by allowing the students to develop their own IP code within a reasonable time frame. After a brief review of existing software packages that can be used for teaching IP, we present a system that we have designed to be as “student-friendly” as possible. The software is built around ImageJ, a freely available, full-featured, and user-friendly program for image analysis. The computer sessions are alternated with lectures, typically, a three-hour session at the end of every chapter. The sessions are in the form of assignments that guide the students towards the solution of simple imaging problems. The starting point is typically the understanding and testing of some standard IP algorithm in Java. Next, students are asked to extend the algorithms progressively. This constructive approach is made possible thanks to a programmer-friendly environment and an additional software interface layer that greatly facilitates the developments of plug-ins for ImageJ. Taking into account the fact that our students are not experienced programmers (they typically do not even know Java), we use a “learn by example” teaching strategy, with good success

Appling visual learning in the teaching of software measurement concepts

Applying new learning methodologies in education, such as visual learning based on virtual reality and three-dimensional (3D) environments, is an important aspect in education, since it offers possibilities that can remarkably improve the current education system. Technological advances, along with the chance to create and represent the varying contents offered by information technologies, make the new learning methodologies the focus of attention in the future. Currently, 3D methodologies are only used in Computer Science to improve physical characteristics (virtual laboratories, virtual worlds, etc.), but they are not used to improve the internal mental processes by which human beings understand and retain abstract concepts. In these cases, the use of visual learning helps to clarify them. In Computer Science, particularly in Software measurement courses, the complexity of the concepts is possibly greater than in other courses because there is a lot of learning material that is based on abstract concepts that students find hard to recognize in the real world. In this paper, we present a visual environment that can be used to learn software measurement concepts like the IFPUG functional size measurement method. To validate the new learning model, an experiment was carried out. © 2011 World Scientific Publishing Company.We would like to thank the Alcala University for supporting this research study through the research funds assigned to professor Cuadrado-Gallego under the program I3. We would also like to thank MEC under the project PROS-REQ TIN2010-19130-C02-02 and GVA ORCA PROMETEO/2009/015 for supporting this study.Cuadrado-Gallego, JJ.; Martin Herrera, B.; Pastor López, O.; Marín, B. (2011). Appling visual learning in the teaching of software measurement concepts. International Journal of Software Engineering and Knowledge Engineering. 21(3):431-446. https://doi.org/10.1142/S021819401100530XS431446213Skinner, B. F. (1950). Are theories of learning necessary? Psychological Review, 57(4), 193-216. doi:10.1037/h0054367Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological Review, 20(2), 158-177. doi:10.1037/h0074428Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological Review, 55(4), 189-208. doi:10.1037/h0061626Tolman, E. C. (1938). The determiners of behavior at a choice point. Psychological Review, 45(1), 1-41. doi:10.1037/h0062733Stasko, J. T. (1990). Tango: a framework and system for algorithm animation. Computer, 23(9), 27-39. doi:10.1109/2.58216Bowyer, K., Stockman, G., & Stark, L. (2000). Themes for improved teaching of image computation. IEEE Transactions on Education, 43(2), 221-223. doi:10.1109/13.848076Felder, R. M., Felder, G. N., & Dietz, E. J. (1998). A Longitudinal Study of Engineering Student Performance and Retention. V. Comparisons with Traditionally-Taught Students. Journal of Engineering Education, 87(4), 469-480. doi:10.1002/j.2168-9830.1998.tb00381.xB. Higgs and M. McCarthy, Emerging Issues in the Practice of University Learning and Teaching (Dublin, Ireland, 2005) pp. 37–44.Prince, M. (2004). Does Active Learning Work? A Review of the Research. Journal of Engineering Education, 93(3), 223-231. doi:10.1002/j.2168-9830.2004.tb00809.xTzafestas, C. S., Palaiologou, N., & Alifragis, M. (2006). Virtual and Remote Robotic Laboratory: Comparative Experimental Evaluation. IEEE Transactions on Education, 49(3), 360-369. doi:10.1109/te.2006.879255Rodrguez, D., Sicilia, M. ngel, Cuadrado-Gallego, J. J., & Pfahl, D. (2006). e-Learning in Project Management Using Simulation Models: A Case Study Based on the Replication of an Experiment. IEEE Transactions on Education, 49(4), 451-463. doi:10.1109/te.2006.882367Basili, V. R., & Rombach, H. D. (1988). The TAME project: towards improvement-oriented software environments. IEEE Transactions on Software Engineering, 14(6), 758-773. doi:10.1109/32.6156Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., & Wesslén, A. (2000). Experimentation in Software Engineering. The Kluwer International Series in Software Engineering. doi:10.1007/978-1-4615-4625-2Pfahl, D., Laitenberger, O., Ruhe, G., Dorsch, J., & Krivobokova, T. (2004). Evaluating the learning effectiveness of using simulations in software project management education: results from a twice replicated experiment. Information and Software Technology, 46(2), 127-147. doi:10.1016/s0950-5849(03)00115-