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Published in: Knowledge and Information Systems 9/2020

04-04-2020 | Regular Paper

A novel possibilistic artificial immune-based classifier for course learning outcome enhancement

Authors: Ilyes Jenhani, Ammar Elhassan, Ghassen Ben Brahim

Published in: Knowledge and Information Systems | Issue 9/2020

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Abstract

In this paper, we propose PAIRS3: a possibilistic classification approach based on artificial immune recognition system (AIRS) and the possibility theory. PAIRS3 is applied to address shortcomings in student attainment rates of course learning outcomes by predicting effective remedial actions through learning from assessment rubrics instances. For most of assessment rubric instances, it is difficult to determine the unique most effective remedial action to take. Consequently, each rubric instance will be labeled with uncertain remedial actions which are elicited from quality assurance experts. Elements from possibility theory are used to (1) model the uncertainty about the most effective remedial action labeling each rubric instance and (2) adapt several parts of the standard AIRS algorithm in order to address the uncertainty in class labels. The performance of the proposed method is evaluated against an academic, university level, assessment dataset that has been built progressively over multiple academic semesters. Despite the uncertainty related to the class labels in the dataset, PAIRS3 showed a good performance in terms of accuracy level (close to 75%). Also, when compared to existing state-of-the-art possibilistic classifiers such as PAIRS2, non-specificity possibilistic decision trees (NSPDT), and cluster-based possibilistic decision trees (Clust-PDT), PAIRS3 achieved better accuracy improvement ranging from 10% (in case of Clust-PDT) to 17% (in case of PAIRS2).

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Footnotes
1
ARB stands for artificial recognition ball which is an abstract concept that represents a number of identical B-cells and which is used to control duplications of B-cells. In AIRS, ARBs and B cells have the same representations.
 
3
Blackboard Learn is a learning management system developed by Blackboard Inc. which features course management and scalable design that allows integration with student information systems.
 
Literature
1.
go back to reference Romero C, Ventura S (2010) Educational data mining: a review of the state-of-the-art. IEEE Trans SMC C Appl Rev 40(6):601–618 Romero C, Ventura S (2010) Educational data mining: a review of the state-of-the-art. IEEE Trans SMC C Appl Rev 40(6):601–618
2.
go back to reference Jenhani I, Brahim GB, Elhassan A (2016) Course learning outcome performance improvement: a remedial action classification based approach. In: Proceedings of the 15th international conference on machine learning and applications (ICMLA), Anaheim, CA, pp 408–413 Jenhani I, Brahim GB, Elhassan A (2016) Course learning outcome performance improvement: a remedial action classification based approach. In: Proceedings of the 15th international conference on machine learning and applications (ICMLA), Anaheim, CA, pp 408–413
3.
go back to reference Elhassan A, Jenhani I, Brahim GB (2018) Remedial actions recommendation via multi-label classification: a course learning improvement method. Int J Mach Learn Comput 8(6):583–588 Elhassan A, Jenhani I, Brahim GB (2018) Remedial actions recommendation via multi-label classification: a course learning improvement method. Int J Mach Learn Comput 8(6):583–588
4.
5.
go back to reference Dubois D, Prade H (1998) Possibility theory: qualitative and quantitative aspects. In: Gabbay DM, Smets Ph (eds) Handbook of defeasible reasoning and uncertainty management systems, vol 1. Springer, New YorkMATH Dubois D, Prade H (1998) Possibility theory: qualitative and quantitative aspects. In: Gabbay DM, Smets Ph (eds) Handbook of defeasible reasoning and uncertainty management systems, vol 1. Springer, New YorkMATH
6.
go back to reference Hentech R, Jenhani I, Elouedi Z (2016) Possibilistic AIRS induction from uncertain data. Soft Comput 20(1):3–17CrossRef Hentech R, Jenhani I, Elouedi Z (2016) Possibilistic AIRS induction from uncertain data. Soft Comput 20(1):3–17CrossRef
7.
go back to reference Watkins A, Timmis J, Boggess L (2004) Artificial immune recognition system (AIRS): an immune-inspired supervised learning algorithm. Genet Program Evol Mach 5:291–317CrossRef Watkins A, Timmis J, Boggess L (2004) Artificial immune recognition system (AIRS): an immune-inspired supervised learning algorithm. Genet Program Evol Mach 5:291–317CrossRef
8.
go back to reference Sabri FNM, Norwawi NM, Seman K (2011) Hybrid of rough set theory and artificial immune recognition system as a solution to decrease false alarm rate in IDS. In: Proceedings of the 7th international conference on information assurance and security (IAS), pp 134–138 Sabri FNM, Norwawi NM, Seman K (2011) Hybrid of rough set theory and artificial immune recognition system as a solution to decrease false alarm rate in IDS. In: Proceedings of the 7th international conference on information assurance and security (IAS), pp 134–138
9.
go back to reference Xu L, Chow M-Y, Timmis J, Taylor LS (2007) Power distribution outage cause identification with imbalanced data using artificial immune recognition system (AIRS) algorithm. IEEE Trans Power Syst 22(1):198–204CrossRef Xu L, Chow M-Y, Timmis J, Taylor LS (2007) Power distribution outage cause identification with imbalanced data using artificial immune recognition system (AIRS) algorithm. IEEE Trans Power Syst 22(1):198–204CrossRef
10.
go back to reference Polat K, Günes S (2007) Prediction of hepatitis disease based on principal component analysis and artificial immune recognition system. Appl Math Comput 189(2):1282–1291MathSciNetMATH Polat K, Günes S (2007) Prediction of hepatitis disease based on principal component analysis and artificial immune recognition system. Appl Math Comput 189(2):1282–1291MathSciNetMATH
11.
go back to reference Catal C, Diri B (2007) Software defect prediction using artificial immune recognition system. In: Proceedings of the 25th conference on IASTED international multi-conference: software engineering, pp 285–290 Catal C, Diri B (2007) Software defect prediction using artificial immune recognition system. In: Proceedings of the 25th conference on IASTED international multi-conference: software engineering, pp 285–290
12.
go back to reference Luan J (2002) Data mining and its applications in higher education. J New Dir Inst Res 113:17–36 Luan J (2002) Data mining and its applications in higher education. J New Dir Inst Res 113:17–36
13.
go back to reference Jindal R, Dutta M (2013) A survey on educational data mining and research trends. Int J Datab Manag Syst IJDMS 5(3):53–73 Jindal R, Dutta M (2013) A survey on educational data mining and research trends. Int J Datab Manag Syst IJDMS 5(3):53–73
14.
go back to reference Mohd MA (2013) Role of data mining in education sector. Int J Comput Sci Mob Comput 2(4):374–383 Mohd MA (2013) Role of data mining in education sector. Int J Comput Sci Mob Comput 2(4):374–383
15.
go back to reference Ayala AP (2014) Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst Appl 41(4):1432–1462CrossRef Ayala AP (2014) Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst Appl 41(4):1432–1462CrossRef
16.
go back to reference Baker R (2010) Data mining for education. In: McGaw B, Peterson P, Baker E (eds) International Encyclopedia of education, vol 7, 3rd edn. Elsevier, Amsterdam, pp 112–118CrossRef Baker R (2010) Data mining for education. In: McGaw B, Peterson P, Baker E (eds) International Encyclopedia of education, vol 7, 3rd edn. Elsevier, Amsterdam, pp 112–118CrossRef
17.
go back to reference Priya KS, SenthilKumar AV (2013) Improving the student’s performance using educational data mining. Int J Adv Netw Appl 4(4):1680–1685 Priya KS, SenthilKumar AV (2013) Improving the student’s performance using educational data mining. Int J Adv Netw Appl 4(4):1680–1685
18.
go back to reference Borkar S, Rajeswari S (2013) Predicting students academic performance using education data mining. Int J Comput Sci Mob Comput 2(7):273–279 Borkar S, Rajeswari S (2013) Predicting students academic performance using education data mining. Int J Comput Sci Mob Comput 2(7):273–279
19.
go back to reference Bresfelean VP, Bresfelean M, Ghisoiu N (2008) Determining students’ academic failure profile founded on data mining methods. In: Proceedings of the 30th international conference on information technology interfaces (ITI), Dubrovnik, Croatia, pp 317–322 Bresfelean VP, Bresfelean M, Ghisoiu N (2008) Determining students’ academic failure profile founded on data mining methods. In: Proceedings of the 30th international conference on information technology interfaces (ITI), Dubrovnik, Croatia, pp 317–322
20.
go back to reference Muehlenbrok M (2005) Automatic action analysis in an interactive learning environment. In: Workshop on usage analysis in learning systems, pp 73–80 Muehlenbrok M (2005) Automatic action analysis in an interactive learning environment. In: Workshop on usage analysis in learning systems, pp 73–80
21.
go back to reference Bravo J, Ortigosa A (2009) Detecting symptoms of low performance using production rules. In: Proceedings of the 2nd international conference on educational data mining, Cordoba, Spain, pp 31–40 Bravo J, Ortigosa A (2009) Detecting symptoms of low performance using production rules. In: Proceedings of the 2nd international conference on educational data mining, Cordoba, Spain, pp 31–40
22.
go back to reference Dekker GW, Pechenizkiy M, Vleeshouwers JM (2009) Predicting students drop out: a case study. In: Proceedings of the 2nd international conference on educational data mining, Cordoba, Spain, pp 41–50 Dekker GW, Pechenizkiy M, Vleeshouwers JM (2009) Predicting students drop out: a case study. In: Proceedings of the 2nd international conference on educational data mining, Cordoba, Spain, pp 41–50
23.
go back to reference Cocea M, Weibelzahl S (2007) Cross-system validation of engagement prediction from log files. In: Proceedings of the 2007 international conference on technology enhanced learning (EC-TEL), Crete, Greece, pp 14–25 Cocea M, Weibelzahl S (2007) Cross-system validation of engagement prediction from log files. In: Proceedings of the 2007 international conference on technology enhanced learning (EC-TEL), Crete, Greece, pp 14–25
24.
go back to reference Kotsiantis S, Pierrakeas C, Pintelas P (2003) Preventing student dropout in distance learning systems using machine learning techniques. In: Proceedings of the 7th international conference on knowledge-based intelligent information & engineering systems (KES), Oxford, UK, pp 267–274 Kotsiantis S, Pierrakeas C, Pintelas P (2003) Preventing student dropout in distance learning systems using machine learning techniques. In: Proceedings of the 7th international conference on knowledge-based intelligent information & engineering systems (KES), Oxford, UK, pp 267–274
25.
go back to reference Lykourentzou I, Giannoukos I, Nikolopoulos V, Mpardis G, Loumos V (2009) Dropout prediction in e-learning courses through the combination of machine learning techniques. Comput Educ J 53(3):950–965CrossRef Lykourentzou I, Giannoukos I, Nikolopoulos V, Mpardis G, Loumos V (2009) Dropout prediction in e-learning courses through the combination of machine learning techniques. Comput Educ J 53(3):950–965CrossRef
27.
go back to reference Jenhani I, Benferhat S, Elouedi Z (2010) Possibilistic similarity measures. In: Bouchon-Meunier B, Magdalena L, Ojeda-Aciego M, Verdegay J-L, Yager RR (eds) Foundations of Reasoning under uncertainty. Springer, New York, pp 99–123CrossRef Jenhani I, Benferhat S, Elouedi Z (2010) Possibilistic similarity measures. In: Bouchon-Meunier B, Magdalena L, Ojeda-Aciego M, Verdegay J-L, Yager RR (eds) Foundations of Reasoning under uncertainty. Springer, New York, pp 99–123CrossRef
28.
go back to reference Klir GJ, Wierman MJ (1998) Uncertainty-based information: elements of generalized information theory. Physica-Verlag, HeidelbergMATH Klir GJ, Wierman MJ (1998) Uncertainty-based information: elements of generalized information theory. Physica-Verlag, HeidelbergMATH
29.
go back to reference Meng L, van der Putten P, Wang H (2005) A comprehensive benchmark of the artificial immune recognition system (AIRS). In: Proceedings of the 1st advanced data mining and applications conference, Wuhan, China, pp 575–582 Meng L, van der Putten P, Wang H (2005) A comprehensive benchmark of the artificial immune recognition system (AIRS). In: Proceedings of the 1st advanced data mining and applications conference, Wuhan, China, pp 575–582
30.
go back to reference Watkins A, Timmis J (2002) Artificial immune recognition system (AIRS): revisions and refinements. In: Proceedings of the 1st international conference on artificial immune systems (ICARIS), pp 173–181 Watkins A, Timmis J (2002) Artificial immune recognition system (AIRS): revisions and refinements. In: Proceedings of the 1st international conference on artificial immune systems (ICARIS), pp 173–181
31.
go back to reference Jenhani I, Elouedi Z (2014) Re-visiting the artificial immune recognition system: a survey and an improved version. Artif Intell Rev 42(4):821–833CrossRef Jenhani I, Elouedi Z (2014) Re-visiting the artificial immune recognition system: a survey and an improved version. Artif Intell Rev 42(4):821–833CrossRef
32.
go back to reference Hüllermeier E (2010) Uncertainty in clustering and classification. In: Proceedings of the 4th international conference on scalable uncertainty management (SUM), pp 16–19 Hüllermeier E (2010) Uncertainty in clustering and classification. In: Proceedings of the 4th international conference on scalable uncertainty management (SUM), pp 16–19
33.
go back to reference Lowen R, Roubens M (1993) Fuzzy logic: state of the art. Springer, New YorkCrossRef Lowen R, Roubens M (1993) Fuzzy logic: state of the art. Springer, New YorkCrossRef
34.
go back to reference Jenhani I, Ben Amor N, Elouedi Z (2008) Decision trees as possibilistic classifiers. Int J Approx Reason 48(3):784–807CrossRef Jenhani I, Ben Amor N, Elouedi Z (2008) Decision trees as possibilistic classifiers. Int J Approx Reason 48(3):784–807CrossRef
35.
go back to reference Sulc Z, Rezankova H (2019) Comparison of similarity measures for categorical data in hierarchical clustering. J Classif 36(1):58–72MathSciNetCrossRef Sulc Z, Rezankova H (2019) Comparison of similarity measures for categorical data in hierarchical clustering. J Classif 36(1):58–72MathSciNetCrossRef
36.
go back to reference Eskin E, Arnold A, Prerau M, Portnoy L, Stolfo S (2002) A geometric framework for unsupervised anomaly detection. In: Barbara D, Jajodia S (eds) Applications of data mining in computer security. Springer, New York, pp 78–100 Eskin E, Arnold A, Prerau M, Portnoy L, Stolfo S (2002) A geometric framework for unsupervised anomaly detection. In: Barbara D, Jajodia S (eds) Applications of data mining in computer security. Springer, New York, pp 78–100
37.
go back to reference Jones KS (1972) A statistical interpretation of term specificity and its application in retrieval. In: Document retrieval systems, vol. 3 of Taylor Graham series in foundations of information science, pp 132–142 Jones KS (1972) A statistical interpretation of term specificity and its application in retrieval. In: Document retrieval systems, vol. 3 of Taylor Graham series in foundations of information science, pp 132–142
38.
go back to reference Lin D (1998) An information-theoretic definition of similarity. In: Proceedings of the 15th international conference on machine learning (ICML), pp 296–304 Lin D (1998) An information-theoretic definition of similarity. In: Proceedings of the 15th international conference on machine learning (ICML), pp 296–304
39.
go back to reference Jenhani I, Benferhat S, Elouedi Z (2009) On the use of clustering in possibilistic decision tree induction. In: Proceedings of the 15th European conference on symbolic and quantitative approaches to reasoning with uncertainty (ECSQARU), pp 505–517 Jenhani I, Benferhat S, Elouedi Z (2009) On the use of clustering in possibilistic decision tree induction. In: Proceedings of the 15th European conference on symbolic and quantitative approaches to reasoning with uncertainty (ECSQARU), pp 505–517
Metadata
Title
A novel possibilistic artificial immune-based classifier for course learning outcome enhancement
Authors
Ilyes Jenhani
Ammar Elhassan
Ghassen Ben Brahim
Publication date
04-04-2020
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 9/2020
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-020-01465-0

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