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13-03-2020 | Methodologies and Application

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

Authors: Ioannis Kazanidis, Stavros Valsamidis, Elias Gounopoulos, Sotirios Kontogiannis

Published in: Soft Computing | Issue 19/2020

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

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

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

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

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

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

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

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

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

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

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):2385–2397MathSciNet

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):658MATH

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Title: Proposed S-Algo+ data mining algorithm for web platforms course content and usage evaluation
Authors: Ioannis Kazanidis
Stavros Valsamidis
Elias Gounopoulos
Sotirios Kontogiannis
Publication date: 13-03-2020
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 19/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-04841-8

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