PREDICTING THE EFFECTS OF THE DIDACTIC PROCESS USING FORGETTING CURVES

Authors

DOI:

https://doi.org/10.28925/2414-0325.2019s25

Keywords:

didactic process simulation, didactic process modelling, educational analogy, learning and forgetting curves, educational environment, education as microsystem

Abstract

The paper presents the method of predicting the effects of the didactic process using the forgetting curves. In the didactic process, learning and forgetting processes play an important role. The learning time, the number of repetitions and their distribution over time are important. These issues can be analyzed using a deterministic description. The flow of information and the learning process can be described thanks to the educational environment developed by the author, enabling the creation of a model of the didactic process described by differential equations. The differential equations can be represented in the form of a network of connected elements in a similar way to the electrical circuits and represented in the form of an intuitive schematic. The network can be simulated using a microsystem simulator. The use of the microsystems simulator enables simulation of the didactic process in time and prediction of effects also after its completion in the long-term. It also enables prediction of the repetitions also during the didactic process. The presented approach enables the easy creation of the macro models and enables the use of many advanced simulation algorithms. The examples of simulations of the didactic process based on the real data are included. Short and long-term simulations for individual students and groups of students are presented. An example of the prediction of the optimal repetitions is shown. Based on the results, appropriate conclusions were drawn. The issues discussed in the work may be of interest to those involved in the analysis and mathematical description of the didactic process. They can also be interesting for developers of the e-learning systems especially e-learning platforms.

Downloads

Download data is not yet available.

Author Biography

Paweł Plaskura, Jan Kochanowski University, Branch in Piotrków Trybunalski

Dr Eng., Faculty of Social Sciences

 

References

Anki. (2017).

https://apps.ankiweb.net

Anzanello, M. J. & Fogliatto, F. S. (2011). Learning curve models and applications: Literature review and research directions. International Journal of Industrial Ergonomics, 41(5), 573–583. doi:10.1016/j.ergon.2011.05.001

Badiru, A. (1992). Computational survey of univariate and bivariate learning curve models.

IEEE Trans. Eng. Manage. 39, 176–188.

El-Bakry, H. (2015). Handling big data in e-learning. International Journal of Advanced Re- search in Computer Science & Technology, 3, 47–51.

Baškarada, S. & Koronios, A. (2013). Data, Information, Knowledge, Wisdom (DIKW): A semi- otic Theoretical and Empirical Exploration of the Hierarchy and its Quality Dimension. Australasian Journal of Information Systems, 18(1).

http:// journal.acs. org.au/index.php/ajis/article/view/748

Birjali, M., Beni-Hssane, A., & Erritali, M. (2018). A novel adaptive e-learning model based on big data by using competence-based knowledge and social learner activities. Applied Soft Computing, 69, 14–32. doi:10.1016/j.asoc.2018.04.030

Bloom’s taxonomy. (2017).

http://www.aritzhaupt.com/eBook_ADDIE/design. html%5C#Introduction_to_Design

Ebbinghaus, H. (1913). Memory: A contribution to experimental psychology. Original work published 1885.

https://web.archive.org/web/20051218083239/

http ://psy.ed.asu.edu:80/%20classics/Ebbinghaus/index.htm

Essa, A. (2016). A possible future for next generation adaptive learning systems. Smart Learning Environments, 3(1), 16. doi:10.1186/s40561-016-0038-y

Gerstner, W. & Kistler, W. (2002). Spiking Neuron Models: Single Neurons, Populations, Plas- ticity. Cambridge University Press.

Heller, O., Mack, W., & Seitz, J. (1991). Replikation der Ebbinghaus’schen Vergessenskurve mit der Ersparnis-methode: Das Behalten und Vergessen als Function der Zeit. Zeitschrift für Psychologie, (199), 3–18.

Ho, C., Ruehli, A., & Brennan, P. (1975). The modified nodal approach to network analysis.

IEEE Trans. Circuits Syst. CAS-22(6), 504–509.

IEEE Standard VHDL Language Reference Manual. (2009). IEEE Std 1076-2008 (Revision of IEEE Std 1076-2002), c1–626. doi:10.1109/IEEESTD.2009.4772740

Jaap, M., Murre, J., & Dros, J. (2015). Replication and Analysis of Ebbinghaus’ Forgetting Curve. Plus One, 2, 396–408. doi:10.1371/journal.pone.0120644

Jaber, M. (Ed.). (2011). Learning curves: Theory, models, and applications. eBook - PDF 2016.

Boca Raton: CRC Press.

Jaber, M. & Saadany, A. (2011). An economic production and manufacturing model with learn- ing effects. International Journal of Production Economics, 131 (1), 115–127.

Lolli, F., Balugani, E., Gamberini, R., Rimini, B., & Rossi, V. (2018). A human-machine learn- ing curve for stochastic assembly line balancing problems. IFAC-PapersOnLine, 51(11), 1186–1191. 16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018. doi:10.1016/j.ifacol.2018.08.429

Mazur, J. & Hastie, R. (1978). Learning as accumulation: A re-examination of the learning curve. Psychol. Bull. 85, 1256–1274.

McIntyre, E. (1977). Cost-volume-profit analysis adjusted for learning. Manage. Sci. 24, 149– 160.

Kamhawi, E. (2017). The three tiers architecture of knowledge flow and management activities. October 12, 2010.

http ://www. sciencedirect . com / science / article / pii / S1471772710000321

Murre, J., Meeter, M., & Chessa, A. (2007) In M. Wenger & C. Schuster (Eds.), Statistical and Process Models for Neuroscience and Aging (Chap. Modeling amnesia: Connectionist and mathematical approaches, pp. 119–162). Mahwah, NJ: Lawrence Erlbaum.

Ogrodzki, J. (1986). One-dimensional orthogonal search–a method for a segment approximation to acceptability regions. International Journal of Circuit Theory and Applications. doi:10. 1002/cta.4490140302

Ogrodzki, J. (1994). Circuit simulation methods and algorithms. Boca Raton, USA: CRC Press. Panadero, M. F., Pardo, A., Panadero, J. F., & Andreas, M. (2002). A mathematical model for reusing student learning skills across didactical units. 32nd ASEE/IEEE Frontiers in Education Conference.

Plaskura, P. (2001). Kierowana zdarzeniami symulacja systemów analogowych o opisie behaw- ioralnym (Event-driven simulation of analog systems with behavioral description) (praca doktorska (PhD dissertation), Politechnika Warszawska (Warsaw University of Technol- ogy), Warszawa).

Plaskura, P. (2013a). Symulator mikrosystemów Dero v4. Metody i algorytmy obliczeniowe, modelowanie behawioralne, przykłady. (Microsystems simulator Dero v4. Computational methods and algorithms, behavioral modelling, examples.) AIVA. http://epub.aiva.pl/?isbn=978-83-937245-1-2

Plaskura, P. (2013b). Zaawansowane metody symulacji układów elektronicznych. Metody i algo- rytmy obliczeniowe. (Advanced methods of electronic circuit simulation. Computational methods and algorithms.) AIVA.

http ://epub.aiva.pl/?isbn = 978 - 83 - 937245-0-5

Plaskura, P. (2016). Quela - a platform for managing the didactical process. Матерiали Мiжнародної науково-практичної конференцiї: Методика навчання природничих ди- сциплiн у середнiй та вищiй школi (ХХIII Каришинськi читання), Poltava,Ukraine

Poltava V.G. Korolenko National Pedagogical University, 337–340. Materials of the In- ternational Scientific and Practical Conference: Methodology of teaching natural sciences in secondary and high school (XXIII Karischinskiy reading).

Plaskura, P. (2018a). Assessing the quality of the didactic process on the base of its monitor- ing with the use of ICT. Педагогiчнi науки: теорiя, iсторiя, iнновацiйнi техноло- гiї (Pedagogical Sciences: Theory, History, Innovative Technologies), 76(2), 185–196. doi:10.24139/2312-5993/2018.02/185-196

Plaskura, P. (2018b). Dero 4 simulator as a didactical tool. ABID, 23(1), 44–51. Retrieved from http://abid.cobrabid.pl

Plaskura, P. (2018c). The use of ICT in improving the effectiveness of the didactical process. Педагогiчнi науки: теорiя, iсторiя, iнновацiйнi технологiї, (17), 152–159. http://dspace.pnpu.edu.ua/handle/123456789/9739

Plaskura, P. (2018d). Wykorzystanie technologii informacyjnych do modelowania i monitorowa- nia jakości procesu dydaktycznego (The use of information technology for modelling and monitoring the quality of the didactical process). Piotrków Trybunalski: Wydawnictwo Uniwersytetu Jana Kochanowskiego.

Plaskura, P. (2019a). Educational analogy dedicated for didactical process simulation. Proceed- ings of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. 1, 286–301. http://ceur-ws.org/Vol-2387/20190286.pdf

Plaskura, P. (2019b). Modelling of Forgetting Curves in Educational E-environment. Informa- tion Technologies and Learning Tools, 71(3), 1–11.

Plaskura, P. (2019c). Monitorowanie jakości procesu dydaktycznego z wykorzystaniem ICT (Monitoring the quality of the didactical process with the use of ICT). In M. Leshchenko, O. Zamecka-Zalas, & I. Kiełtyk-Zaborowska (Eds.), Globalne i regionalne konteksty w edukacji wczesnoszkolnej. Wydawnictwo Uniwersytetu Jana Kochanowskiego w Kielcach Filia w Piotrkowie Trybunalskim.

Rao, K., Edelen-Smith, P., & Wailehua, C.-U. (2015). Universal design for online courses: Ap- plying principles to pedagogy. Open Learning: The Journal of Open, Distance and e- Learning, 30(1), 35–52. doi:10.1080/02680513.2014.991300

Rowley, J. (2007). The wisdom hierarchy: Representations of the DIKW hierarchy. Journal of Information Science, 33(2), 163–180. doi:10.1177/0165551506070706

Rubin, D., Hinton, S., & Wenzel, A. (1999). The precise time course of retention

n. Journal of Experimental Psychology: Learning, Memory, and Cognition, 1161–1176.

Senturia, S. (1998). Cad challenges for microsensors, microactuators and microsystems. Proc. of the IEEE, 86, 1611–1626.

SuperMemo. (2017). Retrieved from https://www.supermemo.com

Towill, D. (1990). Forecasting learning curves. International Journal of Forecasting, 6 (1), 25–38.

Whitaker, A. (2012). An Introduction to the Tin Can API. The Training Business.

Wickelgren, W. (1974). Single-trace fragility theory of memory dynamics. Memory and Cog- nition, 2, 775–780.

Womer, N. (1979). Learning curves, production rate and program costs. Management Science, 25 (4), 312–319.

Woźniak, P., Gorzelańczyk, E., & Murakowski, J. (1995). Two components of long-term mem- ory. Acta Neurobiologiae Experimentalis, 55, 301–305.

Woźniak, P. & Gorzelańczyk, E. (1998). Hypothetical molecular correlates of the two-component model of long-term memory. The 7-th International Symposium of the Polish Network of Molecular and Cellular Biology UNESCO/PAS.

xAPI. (2018).

http://www.xapi.com

Downloads


Abstract views: 301

Published

2019-09-24

How to Cite

Plaskura, P. (2019). PREDICTING THE EFFECTS OF THE DIDACTIC PROCESS USING FORGETTING CURVES. Electronic Scientific Professional Journal “OPEN EDUCATIONAL E-ENVIRONMENT OF MODERN UNIVERSITY”, 261–272. https://doi.org/10.28925/2414-0325.2019s25

Issue

Section

Special Edition “New pedagogical approaches in STEAM education”