EXPLODE – a new model of exploratory learning environment for neural networks to improve learning outcomes

References

• Apache Software Foundation. (2020). Apache NetBeans (Version 12.0) [Computer software]. http://netbeans.apache.org/
   Google Scholar
• Baptista, D., & Morgado-Dias, F. (2013). A survey of artificial neural network training tools. Neural Computing and Applications, 23(3-4), 609–615. https://doi.org/10.1007/s00521-013-1408-9
   Web of Science ®Google Scholar
• Barak, M., Ashkar, T., & Dori, Y. J. (2011). Learning science via animated movies: Its effect on students’ thinking and motivation. Computers & Education, 56(3), 839–846. https://doi.org/10.1016/j.compedu.2010.10.025
   Web of Science ®Google Scholar
• Ben-Naim, D., Marcus, N., & Bain, M. (2008). Visualization and analysis of student interactions in an adaptive exploratory learning environment. 1st International workshop on intelligent support for exploratory environments, Maastricht, The Netherlands, Sep 17.
   Google Scholar
• Chapman, D. L., & Wang, S. (2015). Multimedia instructional tools and student learning in a computer applications course. International Journal of Information and Communication Technology Education, 11(2), 57–67. https://doi.org/10.4018/ijicte.2015040105
   Google Scholar
• Corbett, F., & Card, H. (1998). Java tools for research and education in artificial neural networks. Conference proceedings. IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.98TH8341), Waterloo, Canada, May 25-28, https://doi.org/10.1109/ccece.1998.682773.
   Google Scholar
• Deperlioglu, O., & Kose, U. (2011). An educational tool for artificial neural networks. Computers & Electrical Engineering, 37(3), 392–402. https://doi.org/10.1016/j.compeleceng.2011.03.010
   Web of Science ®Google Scholar
• Fresnedo, Ó, Laport, F., Castro, P. M., & Dapena, A. (2021). Educational graphic tool for teaching fundamentals of digital image representation. Computer Applications in Engineering Education, 29(6), 1489–1504. https://doi.org/10.1002/cae.22402
   Web of Science ®Google Scholar
• Gibson, A. (2013). deeplearning4j (Version 1.0.0) [Computer software]. https://deeplearning4j.org/
   Google Scholar
• Google. (2015). Tensorflow: An end-to-end open source machine learning platform (Version 2.3) [Computer software]. https://www.tensorflow.org/
   Google Scholar
• Hamilton, R., & Ghatala, E. (1994). Learning and instruction. McGraw-Hill.
   Google Scholar
• Heaton, J. (2008). Encog Machine Learning Framework (Version 3.4) [Computer software]. https://www.heatonresearch.com/encog
   Google Scholar
• Hestenes, D. (1987). Toward a modeling theory of physics instruction. American Journal of Physics, 55(5), 440–454. https://doi.org/10.1119/1.15129
   Web of Science ®Google Scholar
• Hsu, J. J., Chapelle, C. A., & Thompson, A. D. (1993). Exploratory learning environments: What are they and do students explore? Journal of Educational Computing Research, 9(1), 1–15. https://doi.org/10.2190/VLPQ-EC65-GBT5-32D4
   Web of Science ®Google Scholar
• Jong, T. D., & Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179–201. https://doi.org/10.3102/00346543068002179
   Web of Science ®Google Scholar
• Lieberman, D. A., & Linn, M. C. (1991). Learning to learn revisited: Computers and the development of self-directed learning skills. Journal of Research on Computing in Education, 23(3), 373–395. https://doi.org/10.1080/08886504.1991.10781968
   Google Scholar
• Mandinach, E. B., & Linn, M. C. (1986). The cognitive effects of computer learning environments. Journal of Educational Computing Research, 2(4), 411–427. https://doi.org/10.2190/TVFD-B7T8-GUBF-FW86
   Google Scholar
• Marrone, P. (2007). Joone - Java Object Oriented Neural Engine (Version 2.0.0) [Computer software]. https://www.jooneworld.com
   Google Scholar
• MathWorks. (2020). Matlab (Version 2020) [Computer software]. https://www.mathworks.com/products/matlab.html
   Google Scholar
• Murali, M. D. (2021). Exploratory Learning. https://www.slideshare.net/suryalekshmi2018/exploratory-learning
   Google Scholar
• Murphy, E. (1997). Constructivism from philosophy to practice. Educational Resources Information Center (ERIC), U.S. Department of Education.
   Google Scholar
• Nasr, G., Joun, C., & Zaatar, W. (2004). A GUI-based artificial neural network simulator. 7th seminar on Neural Network Applications in Electrical Engineering, NEUREL 2004, Belgrade, Serbia, Sep 23-25, https://doi.org/10.1109/neurel.2004.1416556.
   Google Scholar
• Neurosolutions. (2015). Neurosolutions (Version 7) [Computer software]. http://www.neurosolutions.com/neurosolutions/
   Google Scholar
• Njoo, M., & De Jong, T. (1993). Exploratory learning with a computer simulation for control theory: Learning processes and instructional support. Journal of Research in Science Teaching, 30(8), 821–844.https://doi.org/10.1002/tea.3660300803
   Web of Science ®Google Scholar
• Ormrod, J. E. (1995). Educational psychology: Principles and applications (pp. 442). Merrill.
   Google Scholar
• Papert, S. (2020). Mindstorms: Children, computers, and powerful ideas. Basic Books.
   Google Scholar
• Patwardhan, M. (2016). Determining interactivity enriching features for effective interactive learning environments [Unpublished doctoral dissertation]. Indian Institute of Technology
   Google Scholar
• Rieman, J. (1996). A field study of exploratory learning strategies. ACM Transactions on Computer-Human Interaction, 3(3), 189–218. https://doi.org/10.1145/234526.234527
   Google Scholar
• Ringwood, J. V., & Galvin, G. (2002). Computer-aided learning in artificial neural networks. IEEE Transactions on Education, 45(4), 380–387. https://doi.org/10.1109/TE.2002.804401
   Web of Science ®Google Scholar
• Roselló, E. G., Pérez-Schofield, J. B., Dacosta, J. G., & Pérez-Cota, M. (2003). Neuro-Lab: a highly reusable software-based environment to teach artificial neural networks. Computer Applications in Engineering Education, 11(2), 93–102. https://doi.org/10.1002/cae.10042
   Web of Science ®Google Scholar
• Rosenblatt, F. (1962). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Spartan Books.
   Google Scholar
• Rumelhart, D. E., Hinton, G. E., & Williams, R. J.. (1985). Learning Internal Representations by Error Propagation, Technical rept. Mar-Sep 1985, https://apps.dtic.mil/sti/citations/ADA164453
   Google Scholar
• Rutten, N., Joolingen, W. R., & Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers & Education, 58(1), 136–153. https://doi.org/10.1016/j.compedu.2011.07.017
   Web of Science ®Google Scholar
• Scardamalia, M., Bereiter, C., McLean, R. S., Jonathan, S., & Woodruff, E. (1989). Computer-supported intentional learning environment. Journal of Educational Computing Research, 5(1), 51–68. https://doi.org/10.2190/CYXD-6XG4-UFN5-YFB0
   Web of Science ®Google Scholar
• Sevarac, Z. (2006). Neuro fuzzy reasoner for student modeling. Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06), Kerkrade, Netherlands, Jul 5-7, https://doi.org/10.1109/ICALT.2006.1652548
   Google Scholar
• Sevarac, Z. (2008). Java Neural Network Framework Neuroph (Version 2.98) [Computer software]. http://neuroph.sourceforge.net
   Google Scholar
• Sevarac, Z. (2012). Neuroph - an open source software framework for neural network development. Info M, 11(43), 40–44. https://scindeks.ceon.rs/article.aspx?artid=1451-43971243040S
   Google Scholar
• Sevarac, Z., Devedzic, V., & Jovanovic, J. (2012). Adaptive neuro-fuzzy pedagogical recommender. Expert Systems with Applications, 39(10), 9797–9806. https://doi.org/10.1016/j.eswa.2012.02.174
   Web of Science ®Google Scholar
• Sevarac, Z., & Wielenga, G. (2012). Building Smart Java Applications with Neural Networks Using the Neuroph Framework, Java One, San Francisco, California, USA.
   Google Scholar
• Shade, D. D., & Watson, J. A. (1990). Computers in early education: Issues put to rest, theoretical links to sound practice, and the potential contribution of microworlds. Journal of Educational Computing Research, 6(4), 375–392. https://doi.org/10.2190/3RW1-W5QF-DH25-6EGP
   Web of Science ®Google Scholar
• Teodoro, V. (1993). A model to design computer exploratory software for science and mathematics. In D. Y. Towne, T. de Jong, & H. Spada (Eds.), The use of computer models for explication, analysis, and experiential learning (NATO-ASI series. F: Computer systems and sciences) (pp. 177–189). Springer-Verlag.
   Google Scholar
• Thompson, A. D., & Wang, H. C. (1988). Effects of a logo microworld on student ability to transfer a concept. Journal of Educational Computing Research, 4(3), 335–347. https://doi.org/10.2190/1U7L-33HQ-R2R1-6DCF
   Web of Science ®Google Scholar
• Ursachi, G., Horodnic, I. A., & Zait, A. (2015). How reliable are measurement scales? External factors with indirect influence on reliability estimators. Procedia Economics and Finance, 20(1), 679–686. https://doi.org/10.1016/S2212-5671(15)00123-9
   Google Scholar
• Vujicic, T., Matijević, T., Ljucovic, J., Balota, A., & Sevarac, Z. (2016). Comparative analysis of methods for determining number of hidden neurons in artificial neural network. [Paper presentation]. Central European Conference on Information and Intelligent Systems (CECIIS), Varazdin, Croatia, Sep 21-23.
   Google Scholar
• Widrow, B. (1962). Generalization and information storage in networks of adaline neurons. In M. D. Yovits, G. T. Jacobi, & G. D. Goldstein (Eds.), Self-organizing systems (pp. 435–461). Spartan Books.
   Google Scholar
• Yigit, T., Isik, A. H., & Bilen, M.. (2014). Web based educational software for artificial neural networks. The Eurasia Proceedings of Educational & Social Sciences (EPESS), (1), 276–279. http://www.epess.net/en/pub/issue/30314/333414
   Google Scholar
• Zhang, J., Gao, M., Holmes, W., Mavrikis, M., & Ma, N. (2019). Interaction patterns in exploratory learning environments for mathematics: A sequential analysis of feedback and external representations in Chinese schools. Interactive Learning Environments, 1–18. https://doi.org/10.1080/10494820.2019.1674880
   Web of Science ®Google Scholar