Prediction of Learning Success in an Integrated Educational Environment Using Online Analytics Tool

  • Elena E. Kotova Saint Petersburg Electrotechnical University, 5, building 2, st. Professora Popova, 197376, Saint Petersburg, Russia
Keywords: educational process, data analysis methods, educational activities, individual differences student mode, predictive analytics.

Abstract

The need to formulate qualifications and prepare students for the digital future is changing the teaching strategies and approaches to university education in the direction of digital design of the learning process. The expandable space of accessible data allows the use of new educational data mining (EDM) methods in order to explore unique data types, understand student actions activity, predict academic results, improve process performance, make management decisions and adapt the learning environment.
The objective of this study is to create a personalized educational environment for individual accompaniment support of students on the basis of a model of cognitive potential.
The task of supporting the learning process is to obtain information on the dynamics of cognitive growth (“growth” of the knowledge level) of each student based on the data obtained during the learning process. The task of differentiating students, predicting the success of training to improve the adaptation and customization of the learning process is considered.
An approach to predicting the success of learning based on a cognitive model is important for understanding the productivity of learning materials by students in an informationrich environment. The task of differentiating students, predicting the success of learning to improve adaptation and tuning the learning process is considered. Organization of feedback in the structure of the learning process based on student differentiation allows you to manage and customize learning scenarios to improve the adaptation of the individual process. An integrated web environment combines traditional learning tools with innovative digital online tools.

Author Biography

Elena E. Kotova, Saint Petersburg Electrotechnical University, 5, building 2, st. Professora Popova, 197376, Saint Petersburg, Russia

Associate professor, Department of Automation and Control Processes, Saint-Petersburg Electrotechnical University "LETI Russian Federation, 197376, Saint Petersburg, Professora Popova str., 5 , eekotova@gmail.com

References

Was ist Hochschulbildung (im digitalen Zeitalter)? [Online]. Available: https://www.e-teaching.org/ community/communityevents/ringvorlesung/was-ist-hochschulbildung-im-digitalen-zeitalter (in German).

EDUCAUSE Horizon Report: 2019 Higher Education Edition. [Online]. Available: https://library.educause.edu/resources/2019/4/2019-horizon-report

2018 European Skills Index Technical report. [Online]. Available: https://skillspanorama.cedefop.europa.eu/en/useful_resources/2018-european-skills-index-technical-report

H. Dumont and D. Istance, “Analysing and designing learning environments for the 21st century,” in The nature of learning: Using research to inspire practice, H. Dumont, D. Istance, and F. Benavides, eds., 2010, pp. 19–34; doi: 10.1787/9789264086487-en

Organisation for Economic Cooperation and Development, “Skills for a digital world,” Background report 2016 Ministerial Meeting on the digital economy, no. 250, 2016; doi: 10.1787/5jlwz83z3wnw-en

G. Carri¸co, “The EU and artificial intelligence: A human-centred perspective,” European View, vol. 17, no. 1, pp. 29–36, 2018; doi: 10.1177/1781685818764821

A. C. Graesser et al, “Challenges of assessing collaborative problem solving,” in Assessment and teaching of 21st century skills, E. Care, P. Griffin, and M. Wilson eds., Springer, Cham, 2018, pp. 75–91; doi: 10.1007/978-3-319-65368-6_5

B. De Wever et al. “Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review,” Computers & education, vol. 46, no. 1, pp. 6–28, 2006; doi: 10.1016/j.compedu.2005.04.005

Y. Lou, P. C. Abrami, and S. d’Apollonia, “Small group and individual learning with technology: A meta-analysis,” Review of educational research, vol. 71, no. 3, pp. 449–521, 2001; doi: 10.3102/00346543071003449

Learning Analytics Dream, Nightmare or Fairydust? [Online]. Available: http://simon.buckinghamshum.net/2011/12/learning-analytics-ascilite2011-keynote-/

S. B. Shum and R. D. Crick, “Learning dispositions and transferable competencies: pedagogy, modelling and learning analytics,” in Proc. of the 2nd int. conf. on learning analytics and knowledge, 2012, pp. 92–101; doi: 10.1145/2330601.2330629

A. De Liddo et al., “Discourse-centric learning analytics,” in Proc. of the 1st int. conf. on learning analytics and knowledge, 2011, pp. 23–33; doi: 10.1145/2090116.2090120

G. Siemens and R. S. J. d Baker, “Learning analytics and educational data mining: towards communication and collaboration,” in Proc. of the 2nd int. conf. on learning analytics and knowledge, 2012, pp. 252–254; doi: 10.1145/2330601.2330661

N. Sclater, A. Peasgood, and J. Mullan, “Learning analytics in higher education,” A review of UK and international practice, Jisc, London, 2017. [Online]. Available: https://goo.gl/g0roCB

J. M. Vargas, Modern learning: Quizlet in the social studies classroom, Diss., Wichita State University, KS, 2011.

V. Aleven, “Help seeking and intelligent tutoring systems: Theoretical perspectives and a step towards theoretical integration,” in International handbook of metacognition and learning technologies, R. Azevedo and V. Aleven Eds, New York: Springer, 2013, pp. 311–335; doi: 10.1007/978-1-4419-5546-3_21

V. Aleven et al., “Instruction based on adaptive learning technologies,” Handbook of research on learning and instruction, pp. 522–560, 2016.

Learning Analytics. [Online]. Available: https://www.e-teaching.org/didaktik/qualitaet/learning_analytics

S. B. Shum, Learning analytics policy brief, UNESCO Institute for Information Technology in Education, 2012.

R. Ferguson, “Learning analytics: drivers, developments and challenges,” International Journal of Technology Enhanced Learning, vol. 4, no. 5/6, pp. 304–317, 2012; doi: 10.1504/IJTEL.2012.051816

A. C. Graesser et al. “ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics,” International journal of STEM education, vol. 5, no. 1, pp. 15, 2018; doi: 10.1186/s40594-018-0110-y

D. H. Imaev and E. E. Kotova, Modelirovanie i imitatsiya protsessov obucheniya s razdeleniem didakticheskikh resursov. Dinamicheskii podkhod [Modeling and simulation of learning processes with separation didactic resources. Dynamic approach], St. Petersburg, Russia: ETU LETI, 2014 (in Russian).

Spetsproekt AIF.RU. Uchit’sya v Internete. Chto nuzhno znat’ onlain-obrazovanii. [Online]. Available: http://education.aif.ru/

E. B. Luchenkova and V. A. Shershneva, “Vozmozhnosti organizatsii smeshannogo obucheniya matematike studentov inzhenernykh napravlenii podgotovki” [Blended Learning Opportunities math students engineering training], Perspektivy nauki i obrazovaniya, no. 4, pp. 66–71, 2018.

M. Lapchik et al., “Ot korporativnoi komp’yuternoi seti k integrirovannoi informatsionno-obrazovatel’noi srede” [From a corporate computer network to an integrated educational environment], Vysshee obrazovanie v Rossii, no. 6, pp. 93–99, 2008.

N. A. Dmitrievskaya, “Integrirovannaya intellektual’naya sreda nepreryvnogo razvitiya kompetentsii” [Integrated Intelligent Continuous Development Environment competencies], Otkrytoe obrazovanie, no. 3, pp. 4–8, 2011.

Informatsionnye i kommunikatsionnye tekhnologii v obrazovanii [Information and communication technologies in education], B. Dendeva ed., Moscow: IITO YuNESKO, 2013.

Stil’ cheloveka. Psikhologicheskii analiz [Man style. Psychological analysis], A. V. Libina ed., Moscow: Smysl, 1998.

M. A. Kholodnaya, Kognitivnye stili: O prirode individual’nogo uma. Uchebnoe posobie [Cognitive styles: On the nature of the individual mind. Tutorial], Moscow: PER SE, 2002.

L. L. Thurstone, A factorial study of perception, Chicago, IL: University of Chicago Press, 1944.

J. Kagan, “Reflection-impulsivity: The generality and dynamics of conceptual tempo,” Journal of abnormal psychology, vol. 71, no. 1, pp. 17–24, 1966; doi: 10.1037/h0022886

J. R. Stroop, “Studies of interference in serial verbal reactions,” J. of Exper. Psychology, vol. 18, pp. 643–662, 1935; doi: 10.1037/h0054651

Kognitivnye stili. Tezisy nauchno-prakticheskogo seminara [Cognitive styles. Theses of the scientific and practical seminar], V. Kolgi ed., Tallinn, Estonia: Tallinskii ped. Institut im. E. Vil’de, 1986.

V. A. Averin, N. N. Kireeva, and E. E. Kotova, Intellektual’no-stilevaya organizatsiya cheloveka [Intellectual-style organization of man], Saint Petersburg, Russia: Publishing center SPbSPMU, 2014 (in Russian).

E. E. Kotova and P. I. Paderno, “Ekspress-diagnostika kognitivno-stilevogo potentsiala obuchayushchikhsya v integrirovannoi obrazovatel’noi srede,” Educational Technologies and Society, vol. 18, no. 1, pp. 561–576, 2015 (in Russian).

E. E. Kotova, Modeli i metody intellektual’noi podderzhki adaptivnogo upravleniya protsessom obucheniya, Saint Petersburg, Russia: Pechatnyi tsekh, 2019 (in Russian).

E. E. Kotova and A. S. Pisarev, “The problem of classification of use of students intellectual data analysis methods,” Proceedings of Saint Petersburg Electrotechnical University, no. 4, pp. 32–42, 2019 (in Russian).

S. Benomrane, Z. Sellami, and M. B. Ayed, “An ontologist feedback driven ontology evolution with an adaptive multi-agent system,” Advanced Engineering Informatics, vol. 30, no. 3, pp. 337–353, 2016; doi: 10.1016/j.aei.2016.05.002

E. E. Kotova and A. S. Pisarev, “Automated prediction of student learning outcomes,” in Proceedings of Saint Petersburg Electrotechnical University, no. 5, 2019, pp. 31–39 (in Russian).

C. Snijders, U. Matzat, and U. D. Reips, “‘Big Data’: big gaps of knowledge in the field of internet science,” International Journal of Internet Science, vol. 7, no. 1, pp. 1–5, 2012.

A. K. C. Wong and Y. Wang, “Pattern discovery: a data driven approach to deci-sion support,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 33, no. 1, pp. 114–124, 2003; doi: 10.1109/TSMCC.2003.809869

Baza dannykh kognitivno-stilevogo potentsiala studentov AnalyticSYSTEMS-hDS (StudCSP-DataSet), Certificate of state registration of the database №2019621974, Date of state registration in the Database Register 30.10.2019.

V. Donche et al., “Differential use of learning strategies in first-year higher education: The impact of personality, academic motivation, and teaching strategies,” British Journal of Educational Psychology, vol. 83, pp. 238–251, 2013; doi: 10.1111/bjep.12016

A. A. Vavilov, “Strukturnyi i parametricheskii sintez slozhnykh sistem upravleniya” [Structural and parametric synthesis of complex control systems], Saint Petersburg, Russia: LETI, 1979 (in Russian).

A. A. Vavilov et al., “Modellierung Analyse und evolutionaere Synthese komplizierter Steuerungsysteme,” Modellierung und Simulation von Produktionsprozessen, Berlin: VEB Verlag Technik, pp. 14–87, 1983.

E. E. Kotova and A. S. Pisarev, “Analiz dannykh v obrazovatel’noi srede s primeneniem intellektual’nykh agentov” [Data analysis in an educational environment using intelligent agents], in Proc. 7th All-Russia Scientific Conference ’Fuzzy Systems, Soft Computing and Intelligent Technologies’ (FSSCIT-2017), vol. 2, 2017, pp. 108–117 (in Russian).

P. Strecht, L. Cruz, C. Soares, and J. Mendes-Moreira, “A Comparative Study of Classification and Regression Algorithms for Modelling Students’ Academic Performance,” in Proceedings 8th International Conference on Educational Data Mining, Madrid, Spain, 2015, pp. 392–395.

K. R. Koedinger, E. A. McLaughlin, and J. C. Stamper, “Automated Student Model Improvement,” in Proc. International Educational Data Mining Society, Chania, Greece, 2012, pp. 17–24.

J. D. Gobert, M. Auer, A. Azad, A. Edwards, and T. L. de Jong, “Real-Time Scaffolding of Students’ Online Data Interpretation During Inquiry with Inq-ITS Using Educational Data Mining,” Cyber-physical laboratories in engineering and science education, Springer, Cham, pp. 191–217, 2018; doi: 10.1007/978-3-319-76935-6_8

J. W. Pellegrino, N. Chudowsky, and R. Glaser, Knowing what students know: The science and design of educational assessment, Washington: National Academy Press, 2001.

M. Vahdat, L. Oneto, D. Anguita, M. Funk, and G. W. M. Rauterberg, “Educational process mining (EPM): a learning analytics data set,” UCI Machine Learning Repository. [Online]. Available: https://research.tue.nl/en/datasets/educational-process-mining-epm-a-learning-analytics-data-set

Learning Analytics: Avoiding Failure. [Online]. Available: https://er.educause.edu/articles/2017/7/learning-analytics-avoiding-failure

A. Bogar´ın, R. Cerezo, and C. Romero, “A survey on educational process mining,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 1, pp. 1–17, 2018; doi: 10.1002/widm.1230

Programmnyi kompleks analiza informatsionnykh resursov OntoMASTER-Resurs, Certificate of state registration of the database №2018611107, Date of state registration in the Database Register 24.01.2018.

Published
2019-12-28
How to Cite
Kotova, E. E. (2019). Prediction of Learning Success in an Integrated Educational Environment Using Online Analytics Tool. Computer Tools in Education, (4), 55-80. https://doi.org/10.32603/2071-2340-2019-4-55-80
Section
Informational systems