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Proposed S-Algo+ data mining algorithm for web platforms course content and usage evaluation

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Abstract

This paper suggests a novel data mining algorithm for the evaluation of e-learning courses from a Learning Management System. This new algorithm, which is called S-Algo+ (Superposition Algorithm), takes as input the course rankings and the suggestion results from any kind of ranking/hierarchical algorithms and evaluates the validity of a course ranking position. The ranking algorithms estimate the quantity and quality of the course content according to users’ actions and interest. S-Algo+ generates an improved final ranking suggestion output, combining the best results of the source ranking algorithms using statistical and mathematic techniques. In this way, the researchers and course instructors can use more accurate results. The efficiency and applicability of the S-Algo+ algorithm was evaluated successfully with a cross-comparison quantitative and qualitative process in a case study at a Greek university. Our new proposed S-Algo+ algorithm may lead to both theoretical and practical advantages. It may also apply not only for course evaluation but for any kind of web application such as e-commerce.

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References

  • Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19

    Google Scholar 

  • Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Google Scholar 

  • Arnold A, Scheines R, Back JE, Jerome B (2005) Time and attention: students, sessions, and tasks. In: Proceedings of the AAAI 2005 workshop educational data mining, pp 62–66

  • Ashraf M, Zaman M, Ahmed M, Sidiq SJ (2017) Knowledge discovery in academia: a survey on related literature. Int J Adv Res Comput Sci 8(1):303–310

    Google Scholar 

  • Baker RS, Inventado PS (2014) Educational data mining and learning analytics. In: Elias T (ed) Learning analytics. Springer, New York, pp 61–75

    Google Scholar 

  • Bannert M, Reimann P, Sonnenberg C (2014) Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacogn Learn 9(2):161–185

    Google Scholar 

  • Cairns AH, Gueni B, Fhima M, Cairns A, David S, Khelifa N (2014) Towards custom-designed professional training contents and curriculums through educational process mining. In: The fourth international conference on advances in information mining and management, pp 53–58

  • Carmona CJ, González P, del Jesus MJ, Romero C, Ventura S (2010) Evolutionary algorithms for subgroup discovery applied to e-learning data. In: Education engineering (EDUCON) 2010 IEEE, pp 983–990

  • Castro F, Vellido A, Nebot À, Mugica F (2007) Applying data mining techniques to e-learning problems. Evolution of teaching and learning paradigms in intelligent environment. Springer, Berlin, pp 183–221

    Google Scholar 

  • Claroline (2018) Claroline Connect: your e-learning platform. https://www.claroline.net/EN/index.html. Accessed 10 June 2018

  • DeBoer J, Ho AD, Stump GS, Breslow L (2014) Changing “course” reconceptualizing educational variables for massive open online courses. Educ Res 43(2):74–84

    Google Scholar 

  • Elghibari F, Elouahbi R, El Khoukhi F (2018) Data mining for detecting e-learning courses anomalies: an application of decision tree algorithm. Int J Adv Sci Eng Inf Technol 8(3):980–987

    Google Scholar 

  • Etinger D, Orehovački T, Babić S (2018) Applying process mining techniques to learning management systems for educational process model discovery and analysis. Int Conf Intell Hum Syst Integr. Springer, Cham, pp 420–425

    Google Scholar 

  • Fahmi A, Abdullah S, Amin F, Ali A, Rahman K (2018a) Expected values of aggregation operators on cubic triangular fuzzy number and its application to multi-criteria decision making problems. Eng Math 2(1):1–11

    Google Scholar 

  • Fahmi A, Amin F, Abdullah S, Ali A (2018b) Cubic fuzzy Einstein aggregation operators and its application to decision-making. Int J Syst Sci 49(11):2385–2397

    MathSciNet  Google Scholar 

  • Fahmi A, Amin F, Smarandache F, Khan M, Hassan N (2018c) Triangular cubic hesitant fuzzy Einstein hybrid weighted averaging operator and its application to decision making. Symmetry 10(11):658

    MATH  Google Scholar 

  • Gobert JD, Sao Pedro M, Raziuddin J, Baker RS (2013) From log files to assessment metrics: measuring students’ science inquiry skills using educational data mining. J Learn Sci 22(4):521–563

    Google Scholar 

  • Golding P, Donaldson O (2006) Predicting academic performance. In: Frontiers in education conference, 36th Annual, IEEE, pp 21–26

  • Gounopoulos E, Valsamidis S, Kazanidis I, Kontogiannis S (2017) Mapping and identifying features of e-learning technology through indexes and metrics. In: Proceedings of the 9th international conference on computer supported education, special session of analytics in educational environments—A2E, pp 649–655

  • Grigorova K, Malysheva E, Bobrovskiy S (2017) Application of data mining and process mining approaches for improving e-learning processes. In: Инфopмaциoнныe тexнoлoгии и нaнoтexнoлoгии, pp 1960–1966

  • GUNet (2018) OPEN eClass. http://www.openeclass.org/. Accessed 15 June 2018

  • Hämäläinen W, Vinni M (2006) Comparison of machine learning methods for intelligent tutoring systems. Int Conf Intell Tutoring Syst. Springer, Berlin, pp 525–534

    Google Scholar 

  • Hung IC, Chen NS (2018) Embodied interactive video lectures for improving learning comprehension and retention. Comput Educ 117:116–131

    Google Scholar 

  • Hwang GJ, Tsai PS, Tsai CC, Tseng JC (2008) A novel approach for assisting teachers in analyzing student web-searching behaviors. Comput Educ 51(2):926–938

    Google Scholar 

  • Johnson L, Smith R, Willis H, Levine A, Haywood K (2011) The 2011 Horizon Report. 2011. http://www.mmkh.de/upload/dokumente/2011-Horizon-Report_German.pdf. Accessed 30 Sept 2018

  • Kazanidis I, Valsamidis S, Kontogiannis S, Karakos A (2012) An applicable method for evaluation of web-learning platforms content quality and usage. In: 2012 16th Panhellenic conference on informatics (PCI), pp 186–191. IEEE

  • Kazanidis I, Valsamidis S, Kontogiannis S, Gounopoulos E (2018) Assesing the usage of ubiquitous learning. In: Proceedings of the international conference on technology and innovation in learning, teaching and education, Thesalloniki, Aristotle University of Greece

  • Khan A, Ghosh SK (2018) Data mining based analysis to explore the effect of teaching on student performance. Educ Inf Technol 23:1677–1697

    Google Scholar 

  • Khribi MK, Jemni M, Nasraoui O (2015) Recommendation systems for personalized technology-enhanced learning. Ubiquitous learning environments and technologies. Springer, Berlin, pp 159–180

    Google Scholar 

  • Koedinger K, Cunningham K, Skogsholm A, Leber B (2008) An open repository and analysis tools for fine-grained, longitudinal learner data. In: Educational data mining 2008

  • Krüger A, Merceron A, Wolf B (2010) A data model to ease analysis and mining of educational data. In: Educational data mining 2010

  • Krumke SO, Quilliot A, Wagler AK, Wegener JT (2013) Models and algorithms for carsharing systems and related problems. Electron Notes Discrete Math 44:201–206

    Google Scholar 

  • Merceron A, Yacef K (2008) Interestingness measures for association rules in educational data. In: Educational data mining 2008

  • Monk D (2005) Using data mining for e-learning decision making. Electron J e-Learn 3(1):41–54

    Google Scholar 

  • Myller N, Suhonen J, Sutinen E (2002) Using data mining for improving web-based course design. In: Proceedings in international conference on computers in education 2002. IEEE, pp 959–963

  • Najafabadi MK, Mohamed AH, Mahrin MNR (2017) A survey on data mining techniques in recommender systems. Soft Comput 23:627–654

    Google Scholar 

  • Nasiri M, Minaei B (2012) Predicting GPA and academic dismissal in LMS using educational data mining: a case mining. In: 2012 Third international conference on E-learning and E-teaching (ICELET), IEEE, pp 53–58

  • Nguyen A, Piech C, Huang J, Guibas L (2014) Codewebs: scalable homework search for massive open online programming courses. In: Proceedings of the 23rd international conference on World Wide Web, ACM, pp 491–502

  • Papamitsiou Z, Economides AA (2014) Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. J Educ Technol Soc 17(4):49–64

    Google Scholar 

  • Patil V, Suryawanshi S, Saner M, Patil V (2017) Student performance prediction using classification data mining techniques. Int J Res Emerg Sci Technol 4(4):15–18

    Google Scholar 

  • Peña-Ayala A (2014) Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst Appl 41(4):1432–1462

    Google Scholar 

  • Psaromiligkos Y, Orfanidou M, Kytagias C, Zafiri E (2011) Mining log data for the analysis of learners’ behaviour in web-based learning management systems. Oper Res Int J 11(2):187–200

    Google Scholar 

  • Raghavan NS (2005) Data mining in e-commerce: a survey. Sadhana 30(2–3):275–289

    Google Scholar 

  • Ramesh V, Parkavi P, Ramar K (2013) Predicting student performance: a statistical and data mining approach. Int J Comput Appl 63(8):35–39

    Google Scholar 

  • Ranjan J, Khalil S (2008) Conceptual framework of data mining process in management education in India: an institutional perspective. Inf Technol J 7(1):16–23

    Google Scholar 

  • Retalis S, Papasalouros A, Psaromiligkos Y, Siscos S, Kargidis T (2006) Towards networked learning analytics—a concept and a tool. In: Proceedings of the fifth international conference on networked learning, pp 1–8

  • Romero C, Ventura S (2010) Educational data mining: a review of the state of the art. IEEE Transac Syst Man Cybern Part C Appl Rev 40(6):601–618

    Google Scholar 

  • Romero C, Ventura S (2013) Data mining in education. Wiley Interdiscip Rev Data Min Knowl Discov 3(1):12–27

    Google Scholar 

  • Romero C, Ventura S, Espejo PG, Hervás C (2008) Data mining algorithms to classify students. In: Educational data mining 2008

  • Romero C, Espejo PG, Zafra A, Romero JR, Ventura S (2013) Web usage mining for predicting final marks of students that use Moodle courses. Comput Appl Eng Educ 21(1):135–146

    Google Scholar 

  • Scheuer O, McLaren BM (2012) Educational data mining. In: Seel NM (ed) Encyclopedia of the sciences of learning. Springer, Boston, pp 1075–1079

    Google Scholar 

  • Siemens G, Baker RS (2012) Learning analytics and educational data mining: towards communication and collaboration. In: Proceedings of the 2nd international conference on learning analytics and knowledge, ACM, pp 252–254

  • Tang TY, Daniel BK, Romero C (2015) Recommender systems in social and online learning environments. Expert Syst 32(2):261–263

    Google Scholar 

  • Tarus JK, Niu Z, Kalui D (2018) A hybrid recommender system for e-learning based on context awareness and sequential pattern mining. Soft Comput 22(8):2449–2461

    Google Scholar 

  • Thomas EH, Galambos N (2004) What satisfies students? Mining student-opinion data with regression and decision tree analysis. Res High Educ 45(3):251–269

    Google Scholar 

  • Tomtsis D, Kontogiannis S, Kokkonis G, Kazanidis I, Valsamidis S (2014) Proposed architecture and algorithm for academic personnel evaluation and decision support making, In: Proceedings of the international C* conference on computer science and software engineering, C3S2E, Augut 2014, Montreal, pp 181–184. ACM, New York

  • Valsamidis S, Kazanidis I, Kontogiannis S, Karakos A (2010a) Course ranking and automated suggestions through web mining. In: Proceedings of the 10th IEEE international conference on advanced learning technologies (ICALT 2010), 5–7 July, Susse, pp 197–199. IEEE Press

  • Valsamidis S, Kazanidis I, Kontogiannis S, Karakos A (2010b) Homogeneity and enrichment: two metrics for web applications assessment. In: Proceedings of the 14th Panhellenic conference on informatics (PCI 2010), 10–12 September, Tripoli, Greece, pp 186–191. IEEE Press

  • Valsamidis S, Kontogiannis S, Karakos A (2011a) Two algorithms for web applications assessment. iJET 6(3):57–63

    Google Scholar 

  • Valsamidis S, Kontogiannis S, Kazanidis I, Karakos A (2011b) A combined algorithm for LMS usage assessment. In: 15th Panhellenic conference on informatics (PCI2011), 30 September–2 October, 2011, Kastoria

  • Ventura S, Romero C, Hervás C (2008) Analyzing rule evaluation measures with educational datasets: a framework to help the teacher. In: Educational data mining 2008

  • Wu AK, Leung CH (2002) Evaluating learning behavior of web-based training (WBT) using web log. In: Proceedings in international conference on computers in education 2002. IEEE, pp 736–737

  • Zafra A, Ventura S (2009) Predicting student grades in learning management systems with multiple instance genetic programming. In: International working group on educational data mining

  • Zorrilla ME, Álvarez E (2008) MATEP: monitoring and analysis tool for e-learning platforms. In: Eighth IEEE international conference on advanced learning technologies 2008. ICALT’08. IEEE, pp 611–613

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Acknowledgements

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Authors and Affiliations

  1. Advanced Educational Technologies and Mobile Applications Lab, International Hellenic University, Kavála, Greece

    Ioannis Kazanidis

  2. Department of Accounting and Finance, International Hellenic University, Kavála, Greece

    Stavros Valsamidis

  3. Department of Business Administration, TEI of Western Macedonia, Grevena, Greece

    Elias Gounopoulos

  4. Department of Mathematics, University of Ioannina, Ioannina, Greece

    Sotirios Kontogiannis

Authors
  1. Ioannis Kazanidis
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  2. Stavros Valsamidis
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  3. Elias Gounopoulos
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  4. Sotirios Kontogiannis
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Correspondence to Ioannis Kazanidis.

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Appendix

Appendix

See Tables 8, 9, 10, 11 and 12.

Table 8 SUGAL ranking and suggestions
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Table 9 CCA ranking and suggestions
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Table 10 Hierarchy ranking reconstruction and proposed ranking based on S-Algo first run
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Table 11 Hierarchy ranking reconstruction and proposed ranking based on S-Algo final run
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Table 12 Questionnaire course evaluation for students’ course grades per course
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Kazanidis, I., Valsamidis, S., Gounopoulos, E. et al. Proposed S-Algo+ data mining algorithm for web platforms course content and usage evaluation. Soft Comput 24, 14861–14883 (2020). https://doi.org/10.1007/s00500-020-04841-8

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