Abstract
The article presents a new method of modelling the didactical process using a developed educational network and the microsystems simulator. The didactical process can be represented in the intuitive form of a network of connected elements in a similar way to the electrical circuits. The network represents the differential equations describing a dynamic system which models the information flows as well as learning and forgetting phenomena. The solutions of the equations are more adequate than the direct formulas used in modelling i.e. the learning and forgetting curves known from the literature. The network variables and their meaning are relative to generalized variables defined in the generalized environment. This enables using any of the microsystems simulators and gives access to many advanced simulation and optimization algorithms. The use of the microsystems simulator enables simulation of the didactical process in time and prediction of effects also after its completion in the long-term. Based on the simulation, you can design a teaching process, as well as draw conclusions about the process itself and the composition of groups. Selected examples with a brief description have been included. The issues discussed in the work may be of interest to those involved in the analysis and mathematical description of the didactic process. The paper can be interesting for those who deal with modelling of the systems which incorporates the learning and forgetting process, in particular, in production processes or learning platforms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
IEEE Standard VHDL Language Reference Manual. IEEE Std 1076-2008 (Revision of IEEE Std 1076-2002), p. c1–626, January 2009. https://doi.org/10.1109/IEEESTD.2009.4772740
Anki (2017). https://apps.ankiweb.net
Bloom’s taxonomy (2017). http://www.aritzhaupt.com/eBook_ADDIE/design.html#Introduction_to_Design
SuperMemo (2017). https://www.supermemo.com
xAPI (2018). http://www.xapi.com
Mechanical-electrical analogies (2019). https://en.wikipedia.org/wiki/Mechanicals-electrical_analogies
Anzanello, M., Fogliatto, F.: Learning curve models and applications: literature review and research directions. Int. J. Ind. Ergon. 41(5), 573–583 (2011). https://doi.org/10.1016/j.ergon.2011.05.001. http://www.sciencedirect.com/science/article/pii/S016981411100062X
Badiru, A.: Computational survey of univariate and bivariate learning curve models. IEEE Trans. Eng. Manage. 39, 176–188 (1992)
Baškarada, S., Koronios, A.: Data, Information, Knowledge, Wisdom (DIKW): a semiotic theoretical and empirical exploration of the hierarchy and its quality dimension. Australas. J. Inf. Syst. 18(1) (2013). http://journal.acs.org.au/index.php/ajis/article/view/748
Birjali, M., Beni-Hssane, A., Erritali, M.: A novel adaptive e-learning model based on big data by using competence-based knowledge and social learner activities. Appl. Soft Comput. 69, 14–32 (2018). https://doi.org/10.1016/j.asoc.2018.04.030
Bloom, B.: Taxonomy of Educational Objectives: The Classification of Educational Goals: Handbook I: Cognitive Domain. Longmans, Green, New York (1956)
Busch-Vishniac, I.J.: Electromechanical Sensors and Actuators. Mechanical Engineering Series. Springer, New York (1999). https://doi.org/10.1007/978-1-4612-1434-2. https://books.google.pl/books?id=au4H_cUgWVEC
Care, C.: Technology for Modelling: Electrical Analogies, Engineering Practice, and the Development of Analogue Computing. History of Computing. Springer, London (2010). https://doi.org/10.1007/978-1-84882-948-0. https://books.google.pl/books?id=bkbBZcR7DG4C
Choi, B.C., Pak, A.W.: Multidisciplinarity, interdisciplinarity, and transdisciplinarity in health research, services, education and policy: 3. Discipline, inter-discipline distance, and selection of discipline. Clin. Invest. Med. 31(1) (2008). https://doi.org/10.25011/cim.v31i1.3140
van Duyn, D.: Modeling and simulation of solid-state transducers: the thermal and electrical energy domain. Sens. Actuators A41, 268–274 (1994)
Ebbinghaus, H.: Memory: a contribution to experimental psychology (1913). Original Work Published in 1885. https://web.archive.org/web/20051218083239/http://psy.ed.asu.edu:80/classics/Ebbinghaus/index.htm
El-Bakry, H.: Handling big data in e-learning. Int. J. Adv. Res. Comput. Sci. Technol. 3, 47–51 (2015)
Essa, A.: A possible future for next generation adaptive learning systems. Smart Learn. Environ. 3(1), 16 (2016). https://doi.org/10.1186/s40561-016-0038-y
Gear, C.W.: Simultaneous numerical solution of differential-algebraic equations. IEEE Trans. Circ. Theory 18(1), 89–95 (1971)
Gerstner, W., Kistler, W.: Spiking Neuron Models. Cambridge University Press, Cambridge (2002)
Heller, O., Mack, W., Seitz, J.: Replikation der Ebbinghaus’schen Vergessenskurve mit der Ersparnis-methode: Das Behalten und Vergessen als Function der Zeit. Zeitschrift für Psychologie 199(1), 3–18 (1991)
Ho, C., Ruehli, A., Brennan, P.: The modified nodal approach to network analysis. IEEE Trans. Circuits Syst. CAS–22(6), 504–509 (1975)
Jaber, M. (ed.): Learning Curves. CRC Press, Boca Raton (2011). eBook - PDF 2016
Jaber, M., Saadany, A.: An economic production and manufacturing model with learning effects. Int. J. Prod. Econ. 131(1), 115–127 (2011)
Kamhawi, E.: The three tiers architecture of knowledge flow and management activities. Inf. Organ. 20, 169–186 (2017). http://www.sciencedirect.com/science/article/pii/S1471772710000321
Lolli, F., Balugani, E., Gamberini, R., Rimini, B., Rossi, V.: A human-machine learning curve for stochastic assembly line balancing problems. IFAC-PapersOnLine 51(11), 1186–1191 (2018). https://doi.org/10.1016/j.ifacol.2018.08.429. http://www.sciencedirect.com/science/article/pii/S2405896318315568. 16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018
Mazur, J., Hastie, R.: Learning as accumulation: a re-examination of the learning curve. Psychol. Bull. 85, 1256–1274 (1978)
McIntyre, E.: Cost-volume-profit analysis adjusted for learning. Manage. Sci. 24, 149–160 (1977)
Murre, J., Dros, J.: Replication and analysis of Ebbinghaus’ forgetting curve. PLoS ONE 10(7), 1–23 (2015). https://doi.org/10.1371/journal.pone.0120644
Murre, J., Meeter, M., Chessa, A.: Modeling amnesia: connectionist and mathematical approaches. In: Wenger, M., Schuster, C. (eds.) Statistical and Process Models for Neuroscience and Aging, pp. 119–162. Lawrence Erlbaum, Mahwah (2007)
Ogrodzki, J.: One-dimensional orthogonal search—a method for a segment approximation to acceptability regions. Int. J. Circ. Theory Appl. (1986). https://doi.org/10.1002/cta.4490140302
Ogrodzki, J.: Circuit Simulation Methods and Algorithms. CRC Press, Boca Raton (1994)
Panadero, M., Pardo, A., Panadero, J., Andreas, M.: A mathematical model for reusing student learning skills across didactical units. In: 32nd ASEE/IEEE Frontiers in Education Conference, November 2002
Plaskura, P.: Kierowana zdarzeniami symulacja systemów analogowych o opisie behawioralnym (Event-driven simulation of analog systems with behavioral description). Praca doktorska (Ph.D. dissertation), Politechnika Warszawska (Warsaw University of Technology), Warszawa (2001)
Plaskura, P.: Symulator mikrosystemów Dero v4. Metody i algorytmy obliczeniowe, modelowanie behawioralne, przykłady. (Microsystems simulator Dero v4. Computational methods and algorithms, behavioral modelling, examples.). AIVA (2013). http://epub.aiva.pl/?isbn=978-83-937245-1-2
Plaskura, P.: Zaawansowane metody symulacji układów elektronicznych. Metody i algorytmy obliczeniowe. (Advanced methods of electronic circuit simulation. Computational methods and algorithms.). AIVA (2013). http://epub.aiva.pl/?isbn=978-83-937245-0-5
Plaskura, P.: Quela - a platform for managing the didactical process. In: Stiepanienko, M., Grinowoji, M. (eds.) (Materials of the International Scientific and Practical Conference: Methodology of Teaching Natural Sciences in Secondary and High School (XXIII Karischinskiy Reading)), pp. 337–340, Poltava V.G. Korolenko National Pedagogical University, Poltava, May 2016
Plaskura, P.: Assessing the quality of the didactic process on the base of its monitoring with the use of ICT. (Pedag. Sci. Theory Hist. Innov. Technol.) 76(2), 185–196 (2018). https://doi.org/10.24139/2312-5993/2018.02/185-196
Plaskura, P.: Dero 4 simulator as a didactical tool. ABID 23(1), 44–51 (2018). http://abid.cobrabid.pl
Plaskura, P.: The use of ICT in improving the effectiveness of the didactical process. (Pedag. Sci. Theory Hist. Innov. Technol.) (17), 152–159 (2018). http://dspace.pnpu.edu.ua/handle/123456789/9739
Plaskura, P.: Wykorzystanie technologii informacyjnych do modelowania i monitorowania jakości procesu dydaktycznego (The use of information technology for modelling and monitoring the quality of the didactical process). Wydawnictwo Uniwersytetu Jana Kochanowskiego, Piotrków Trybunalski, December 2018
Plaskura, P.: Modelling of forgetting curves in educational e-environment. Inf. Technol. Learn. Tools 71(3), 1–11 (2019)
Plaskura, P.: Monitorowanie jakości procesu dydaktycznego z wykorzystaniem ICT (Monitoring the quality of the didactical process with the use of ICT). In: Leshchenko, M., Zamecka-Zalas, O., Kiełtyk-Zaborowska, I. (eds.) Globalne i regionalne konteksty w edukacji wczesnoszkolnej, pp. 151–164. Wydawnictwo Uniwersytetu Jana Kochanowskiego w Kielcach Filia w Piotrkowie Trybunalskim (2019)
Rao, K., Edelen-Smith, P., Wailehua, C.U.: Universal design for online courses: applying principles to pedagogy. Open Learn. J. Open Distance e-Learning 30(1), 35–52 (2015). https://doi.org/10.1080/02680513.2014.991300
Rowley, J.: The wisdom hierarchy: representations of the DIKW hierarchy. J. Inf. Sci. 33(2), 163–180 (2007). https://doi.org/10.1177/0165551506070706
Rubin, D., Hinton, S., Wenzel, A.: The precise time course of retention. J. Exp. Psychol. Learn. Mem. Cogn. 25, 1161–1176 (1999). https://doi.org/10.1037/0278-7393.25.5.1161
Saleh, R., Jou, S.-J., Newton, A.: Mixed-Mode Simulation and Analog Multilevel Simulation. The Springer International Series in Engineering and Computer Science, vol. 279. Springer, New York (1994). https://doi.org/10.1007/978-1-4757-5854-2
Senturia, S.: CAD challenges for microsensors, microactuators and microsystems. Proc. IEEE 86, 1611–1626 (1998)
Senturia, S.: Simulation and design of microsystems: a 10-year perspective. Sens. Actuators A67, 1–7 (1998)
Stoer, J., Bulirsch, R.: Introduction to Numerical Analysis. Texts in Applied Mathematics, vol. 12. Springer, New York (2002). https://doi.org/10.1007/978-0-387-21738-3
Towill, D.: Forecasting learning curves. Int. J. Forecast. 6(1), 25–38 (1990)
Whitaker, A.: An Introduction to the Tin Can API. The Training Business, June 2012
Wickelgren, W.: Single-trace fragility theory of memory dynamics. Mem. Cogn. 2, 775–780 (1974)
Womer, N.: Learning curves, production rate and program costs. Manage. Sci. 25(4), 312–319 (1979)
Woźniak, P., Gorzelańczyk, E.: Hypothetical molecular correlates of the two-component model of long-term memory. In: The 7th International Symposium of the Polish Network of Molecular and Cellular Biology UNESCO/PAS, June 1998
Woźniak, P., Gorzelańczyk, E., Murakowski, J.: Two components of long-term memory. Acta Neurobiol. Exp. 55, 301–305 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Plaskura, P. (2020). The Use of Analogy to Simplify the Mathematical Description of the Didactical Process. In: Ermolayev, V., Mallet, F., Yakovyna, V., Mayr, H., Spivakovsky, A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2019. Communications in Computer and Information Science, vol 1175. Springer, Cham. https://doi.org/10.1007/978-3-030-39459-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-39459-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39458-5
Online ISBN: 978-3-030-39459-2
eBook Packages: Computer ScienceComputer Science (R0)