Evolutionary computation approaches to the Curriculum Sequencing problem

Sarab AlMuhaideb1, Mohamed El Bachir Menaï2
1Department of Computer and Information Sciences, College for Women, Prince Sultan University, Riyadh, Saudi Arabia
2Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

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Acampora G, Gaeta M, Loia V (2009) Hierarchical optimization of personalized experiences for e-Learning systems through evolutionary models. Neural Comput Appl. doi: 10.1007/s00521-009-0273-z

Bautista J, Pereira J (2004) Ant algorithms for urban waste collection routing. In: Proceedings of the ANTS workshop, Brussels, Belgium, September 5–8, 2004, pp 302–309

Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Sci Am 284(5):35–43

Bloom BS (1956) Taxonomy of educational objectives: the classification of educational goals, handbook I, cognitive domain. Longman, New York

Brusilovsky P (1992) A framework for intelligent knowledge sequencing and task sequencing. In: Frasson C, Gauthier G, McCella G (eds) Intelligent tutoring systems, Lecture Notes in Computer Science (LNCS), vol 608. Springer-Verlag, Berlin, pp 499–506

Brusilovsky P (1995) Intelligent tutoring systems for World-Wide Web. In: Proceedings of third international WWW conference, Darmstadt, pp 42–45

Brusilovsky P (1996) Methods and techniques of adaptive hypermedia. In: User modeling and user-adapted interaction, vol 6. Kluwer Academic Publishers, pp 87–129

Brusilovsky P, Schwarz E, Weber G (1996) ELM-ART: an intelligent tutoring system on World Wide Web. In: Frasson C, Gauthier G, Lesgold A (eds) Third international conference on intelligent tutoring systems, ITS-96, vol 1086. Springer Verlag, Berlin, pp 261–269

Champaign J, Cohen R (2010) A model for content sequencing in intelligent tutoring systems based on the ecological approach and its validation through simulated students. In: Proceedings of the twenty-third international Florida artificial intelligence research society conference (FLAIRS 2010), pp 486–491

Chen C (2008) Intelligent web-based learning system with personalized learning path guidance. Comput Educ 51:787–814

Chen C, Liu C, Chang M (2006) Personalized curriculum sequencing utilizing modified item response theory for web-based instruction. Expert Syst Appl 30(2):378–396

Chen C, Peng C, Shiue J (2008) Ontology-based concept map for planning personalized learning path. In: Proceedings of the IEEE conference on cybernetics and intelligent systems, pp 1337–1342

Chu C, Chang Y, Tsai C (2009) PC2PSO: Personalized e-Course Composition based on Particle Swarm Optimization. Applied Intelligence, SCI. doi: 10.1007/s10489-009-0186-7

Dawkins R (1976) The selfish gene. Oxford University Press, New York

De Bra P, Calvi L (1998) AHA! an open adaptive hypermedia architecture. New Rev Hypermedia Multimedia 4:115–139

de-Marcos L, Pages C, Martinez J, Gutiérrez J (2007) Competency-based learning object sequencing using particle swarms. In: Proceedings of the 19th IEEE international conference on tools with artificial intelligence, vol 2, pp 111–116

de-Marcos L, Martinez JJ, Gutiérrez JA (2008a) Swarm intelligence in e-learning: a learning object sequencing agent based on competencies. In: Proceedings of the 10th annual conference on genetic and evolutionary computation. ACM Special Interest Group on Genetic and Evolutionary Computation, pp 17–24

de-Marcos L, Barchino R, Martínez J, Gutiérrez J, Hilera J (2008b) Competency-based intelligent curriculum sequencing: comparing two evolutionary approaches. In: Proceedings of the IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, vol 3, pp 339–342

de-Marcos L, Barchino R, Martínez J, Gutiérrez J (2009a) A new method for domain independent curriculum sequencing: a case study in a web engineering master program. Int J Eng Edu 25(4):632–645

de-Marcos L, Martinez J, Gutiérrez J A, Barchino R, Gutiérrez JM (2009b) A new sequencing method in web-based education. In: Proceedings of the IEEE congress on evolutionary education (CEC), pp 3219–3225

Dolog P, Henze N, Nejdl W, Sintek M (2004) Personalization in distributed e-Learning environments. In: Proceedings of the 13th international World Wide Web conference on alternate track papers & posters, New York, NY, USA, pp 170–179

Dorigo M (1992) Optimization, learning and natural algorithms. PhD thesis, Politecnico di Milano, Italie

Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan, pp 39–43

Eberhart R, Shi Y (2007) Computational intelligence. Morgan Kaufmann Publishers, Madison

Eiben G, Smith J (2007) Introduction to evolutionary computing. Natural computing series. Springer, Berlin

Elo A (1978) The rating of chess players past and present. Arco Publishing, New York

Felder RM (1988) Learning and teaching styles in engineering education. J Eng Educ 78(7):674–681

Fogel L (1994) Evolutionary programming in perspective: the top-down view. In: Zurada J, Marks R II, Robinson C (eds) Computational intelligence: imitating life. IEEE Press, New York, pp 135–146

Fogel L, Owens A, Walsh M (1965) Artificial intelligence through a simulation of evolution. In: Callahan A, Maxfield M, Fogel LJ (eds) Biophysics and cybernetic systems. Spartan, Washington DC, pp 131–155

Forsberg M, Höök K, Svensson M (1998) Design principles for social navigation tools. In: Paper presented in the 4th ERCIM workshop user interfaces for all (UI4All), Stockholm, Sweden, 19–21 October 1998

Gil A, Garcia-Penalvo FJ (2008) Learner course recommendation in e-Learning based on swarm intelligence. J Univers Comput Sci 14(16):2737–2755

Glover F (1989) Tabu search—part I. ORSA J Comput 1(3):190–206

Glover F (1990) Tabu search—part II. ORSA J Comput 2(1):4–32

Groen GJ, Atkinson R (1966) Models for optimizing the learning process. Psychol Bull 66(4):309–320

Guo Q (2008) Implement individual web-based learning environment. In: Proceedings of the 19th international conference on database and expert systems application, pp 639–643. IEEE. doi: 10.1109/DEXA.2008.17

Guo Q, Zhang M (2009) Implement web learning environment based on data mining. Knowl Based Syst 22:439–442

Gutiérrez S, Pardo A (2007) Sequencing in web-based education: approaches, standards and future trends. Studies in Computational Intelligence (SCI), vol 62. Springer-Verlag, Berlin, pp 83–117

Gutiérrez S, Valigiani G, Collet P, Kloos CD (2007) Adaptation of the ACO heuristic for sequencing learning activities. In: Proceedings of the EC-TEL 2007 poster session, Crete, Greece, September 17–20. doi: 10.1.1.142.8362

Hauger D, Kock M (2007) State of the art of adaptivity in e-Learning platforms. Workshop at adaptivity and user modeling in interactive systems ABIS 2007, Halle/Salle, Germany. doi: 10.1.1.91.6409

Holland JH (1975) Adaptation in natural and artificial systems. MIT Press, Cambridge, 1992. 1st edition: 1975, The University of Michigan Press, Ann Arbor

Holland JH (1976) Adaptation. In: Rosen R, Snell FM (eds) Progress in theoretical biology, vol 4. Plenum, New York

Hovakimyan A, Sargsyan S, Barkhoudaryan S (2004) Genetic algorithm and the problem of getting knowledge in e-Learning systems. In: Proceedings of the IEEE international conference on advanced learning technologies (ICALT), pp 336–339

Huang M, Huang H, Chen M (2007) Constructing a personalized e-Learning system based on genetic algorithm and case-based reasoning approach. Expert Syst Appl 33:551–564

Hwang G, Yin P, Wang T, Tsengand J, Hwang G (2008) An enhanced genetic approach to optimizing auto-reply accuracy of an e-Learning system. Comput Educ 51(1):337–353

Janssen J, Tattersall C, Waterink W, den Berg B, van Es R, Bolman C, Koper R (2005) Self-organising navigational support in lifelong learning: how predecessors can lead the way. Comput Educ 49:781–793

Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, Perth, WA, Australia, pp 1942–1948

Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

Koza JR (1992) Genetic programming. MIT Press, Cambridge

Lopes RS, Fernandes MA (2009) Adaptative instructional planning using workflow and genetic algorithms. In: Proceedings of the eighth IEEE/ACIS international conference on computer and information science, pp 87–92

Lord FM (1980) Applications of item response theory to practical testing problems. Lawrence Erlbaum Associates, Inc, Mahwah

Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms—caltech concurrent computation program. Technical report C3P report 826

Moscato P, Cotta C (2007) Memetic algorithms. In: Gonzalez TF (ed) Handbook of approximation algorithms and metaheuristics. Taylor & Francis, Boca Raton

Rechenberg I (1965) Cybernetic solution path of an experimental problem. Royal Aircraft Establishment, UK

Rey-lópez M, Brusilovsky P, Meccawy M, Daz-Redondo R, Fernandez-Vilas A, Ashman H (2008) Resolving the problem of intelligent learning content in learning management systems. Int J e-Learning 7(3):363–381

Romero C, Bra PD, Ventura S, de Castro C (2002) Using knowledge levels with AHA! for discovering interesting relationships. In: Proceedings of world conference on e-Learning in corporate, government, healthcare, and higher education, pp 2721–2722

Samia A, Mostafa B (2007) Re-use of resources for adapted formation to the learner. In: Proceedings of the international symposium on computational intelligence and intelligent informatics (ISCIII), pp 213–217

Seki K, Matsui T, Okamoto T (2005) An adaptive sequencing method of the learning objects for the e-learning environment. Electron Commun Jpn 88(3):54–71

Semet Y, Jamont Y, Biojout R, Lutton E, Collet P (2003a) Ant Colony Optimization for e-Learning: observing the emergence of pedagogic suggestions. In: Proceedings of the IEEE swarm intelligence symposium, Indianapolis, USA, April 24–26, pp 46–52

Semet Y, Jamont Y, Biojout R, Lutton E, Collet P (2003b) Artificial ant colonies and e-Learning: an optimization of pedagogical paths. In: Paper presented at the 10th international conference on human-computer interaction HCII’03, Crete, Greece, June 22–27, 2003

Shute V, Towle B (2003) Adaptive e-Learning. Educ Psychol 38(2):105–114

Shwefel H (1965) Kybernetische Evolution als Strategie der Experimentellen Forschung in der Stromungstechnik. Diploma thesis, Technical University of Berlin, Germany

Sim K, SunW (2002) Multiple ant-colony optimization for network routing. In: Proceedings of the first international symposium on cyber worlds (CW’02), pp 277–281

Sleeman DH, Brown JS (1982) Intelligent tutoring systems. Academic Press, New York

Solnon C (2000) Solving permutation constraint satisfaction problems with artificial ants. In: Proceedings of ECAI’2000. IOS Press, Amsterdam, pp 118–122

Solnon C (2002) Ants can solve constraint satisfaction problems. IEEE Trans Evol Comput 6(4):347–356

Tang T, McCalla G (2004) Utilizing artificial learners to help overcome the cold-start problem in a pedagogically-oriented paper recommendation system. In: Nejdl W, De Bra P (eds) Adaptive hypermedia and adaptive web based systems, Lecture Notes in Computer Science (LNCS), vol 3137. Springer, Berlin, pp 395–423

Tattersall C, Manderveld J, den Berg B, van Es R, Janssen J, Koper R (2005) Self organising wayfinding support for lifelong learners. Educ Inform Technol 10(1–2):109–121

Terry RE, Harb JN (1993) Using learning style theory to improve learning & teaching in the engineering classroom. Frontiers in education conference, 1993. In: Proceedings of the twenty-third annual conference. Engineering education: renewing america’s technology, pp 22–23

Tsang E (1993) Foundations of constraint satisfaction. Academic Press, London

Valigiani G, Jamont Y, Bourgeois Republique C, Biojout R, Lutton E, Collet P (2005) Experimenting with a real-size man-hill to optimize pedagogical paths. In: Proceedings of the 2005 ACM symposium on applied computing, pp 4–8

Valigiani G, Lutton E, Jamont Y, Biojout R, Collet P (2006) Automatic rating process to audit a man-hill. WSEAS Trans Adv Eng Educ 3(1):1–7

Wang TJ, Tsai KH (2009) Interactive and dynamic review course composition system utilizing contextual semantic expansion and discrete particle swarm optimization. Expert Syst Appl 36(6):9663–9673

Wang T, Wang K, Huang Y (2008) Using a style-based ant colony system for adaptive learning. Expert Syst Appl 34(4):2449–2464

Wescourt KT, Beam M, Gould L, Barr A (1976) Knowledge-based CAI: CINS for individualized curriculum sequencing. Final technical report no. 290. Institute for Mathematical Studies in Social Science, Stanford University, CA

Wiley D (2000) Connecting learning objects to instructional design theory: a definition, a metaphor and a taxonomy. In: Wiley DA (ed) The instructional use of learning objects. http://reusability.org/read/chapters/wiley.doc . Accessed 24 April 2010

Wilson SW (1994) ZCS: a zeroth-level learning classifier system. Evol Comput 2(1):1–18

Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149–175

Wong L, Looi C (2009) Adaptable learning pathway generation with ant colony optimization. Educ Technol Soc 12(3):309–326

Yang YJ, Wu C (2009) An attribute-based ant colony system for adaptive learning object recommendation. Expert Syst Appl 36(2):3034–3047

Yang JT, Yu PT, Chen NS, Tsai CY, Lee CC, Shih TK (2005) Using ontology as scaffolding for authoring teaching materials. Int J Distance Educ Technol 3(1):81–96

Zaiane OR (2002) Building a recommender agent for e-Learning systems. In: Proceedings of the international conference on computers in education, vol 1. IEEE Computer Society, pp 55–59