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Volumn 60, Issue 2, 2009, Pages 372-380

Early and dynamic student achievement prediction in E-learning courses using neural networks

Author keywords

[No Author keywords available]

Indexed keywords

E-LEARNING; FEEDFORWARD NEURAL NETWORKS; INTERNET; MULTIMEDIA SYSTEMS; STATISTICAL TESTS; STUDENTS;

EID: 60549093397     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.20970     Document Type: Article
Times cited : (101)

References (28)
  • 1
    • 0029484103 scopus 로고
    • Survey and critique of techniques for extracting rules from trained artificial neural networks
    • Andrews, R., Diederich, J., & Tickle, A.B. (1995). Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems, 8, 373-389.
    • (1995) Knowledge-Based Systems , vol.8 , pp. 373-389
    • Andrews, R.1    Diederich, J.2    Tickle, A.B.3
  • 2
    • 84944317270 scopus 로고    scopus 로고
    • Beck, J.E., & Woolf, B.P. (2000). High-level student modeling with machine learning. In G. Goos, J. Hartmanis, & J. van Leeuwen (Eds.), Lecture Notes in Computer Science, 1839: Intelligent Tutoring Systems (pp. 584-593). Berlin: Springer.
    • Beck, J.E., & Woolf, B.P. (2000). High-level student modeling with machine learning. In G. Goos, J. Hartmanis, & J. van Leeuwen (Eds.), Lecture Notes in Computer Science, Vol. 1839: Intelligent Tutoring Systems (pp. 584-593). Berlin: Springer.
  • 3
    • 85002129924 scopus 로고    scopus 로고
    • Using artificial neural nets to predict academic performance
    • February, Paper presented at the, Philadelphia, PA
    • Cripps, A. (1996, February). Using artificial neural nets to predict academic performance. Paper presented at the 1996 ACM Symposium on Applied Computing, Philadelphia, PA.
    • (1996) 1996 ACM Symposium on Applied Computing
    • Cripps, A.1
  • 5
    • 60549090709 scopus 로고    scopus 로고
    • Grouping gifted students: Issues and concerns
    • Linda E. Brody Ed, Thousand Oaks, CA: Corwin Press
    • Feldhusen, J., & Moon, S. (2004). Grouping gifted students: Issues and concerns. In Linda E. Brody (Ed.), Grouping and acceleration practices in gifted education, (pp. 81-90). Thousand Oaks, CA: Corwin Press.
    • (2004) Grouping and acceleration practices in gifted education , pp. 81-90
    • Feldhusen, J.1    Moon, S.2
  • 6
    • 33646061306 scopus 로고    scopus 로고
    • Looking for sources of error in predicting student's knowledge
    • July, Paper presented at the, Pittsburgh, PA
    • Feng, M., Heffernan, N., & Koedinger, K. (2005, July). Looking for sources of error in predicting student's knowledge. Paper presented at the AAAI Workshop on Educational Data Mining, Pittsburgh, PA.
    • (2005) AAAI Workshop on Educational Data Mining
    • Feng, M.1    Heffernan, N.2    Koedinger, K.3
  • 7
    • 0024866495 scopus 로고
    • On the approximate realization of continuous mappings by neural networks
    • Funahashi, K.-I. (1989). On the approximate realization of continuous mappings by neural networks. Neural Networks, 2, 183-192.
    • (1989) Neural Networks , vol.2 , pp. 183-192
    • Funahashi, K.-I.1
  • 8
    • 0028543366 scopus 로고
    • Training feed-forward networks with the Marquardt algorithm
    • Hagan, M.T., & Menhaj, M.B. (1994). Training feed-forward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5, 989-993.
    • (1994) IEEE Transactions on Neural Networks , vol.5 , pp. 989-993
    • Hagan, M.T.1    Menhaj, M.B.2
  • 12
    • 0027812765 scopus 로고
    • Some new results on neural network approximation
    • Hornik, K. (1993). Some new results on neural network approximation. Neural Networks, 6, 1069-1072.
    • (1993) Neural Networks , vol.6 , pp. 1069-1072
    • Hornik, K.1
  • 13
    • 0024880831 scopus 로고
    • Multilayer feed-forward networks are universal approximators
    • Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feed-forward networks are universal approximators. Neural Networks, 2, 359-366.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 14
    • 0033100606 scopus 로고    scopus 로고
    • A method to determine the required number of neural-network training repetitions
    • Iyer, M.S., & Rhinehart, R.R. (1999). A method to determine the required number of neural-network training repetitions. IEEE Transactions on Neural Networks, 10, 427-432.
    • (1999) IEEE Transactions on Neural Networks , vol.10 , pp. 427-432
    • Iyer, M.S.1    Rhinehart, R.R.2
  • 15
    • 38149108331 scopus 로고    scopus 로고
    • Junemann, M.A.P., Lagos, P.A.S., & Amagada, R.C. (2007). Neural networks to predict schooling failure/success. In J. Mira & J.R. Alvarez (Eds.), Lecture Notes in Computer Science, 4528: Nature Inspired Problem-Solving Methods in Knowledge Engineering (pp. 571-579). Berlin: Springer.
    • Junemann, M.A.P., Lagos, P.A.S., & Amagada, R.C. (2007). Neural networks to predict schooling failure/success. In J. Mira & J.R. Alvarez (Eds.), Lecture Notes in Computer Science, Vol. 4528: Nature Inspired Problem-Solving Methods in Knowledge Engineering (pp. 571-579). Berlin: Springer.
  • 16
    • 33745868949 scopus 로고    scopus 로고
    • Analyzing student performance in distance learning with genetic algorithms and decision trees
    • Kalles, D., & Pierrakeas, C. (2006). Analyzing student performance in distance learning with genetic algorithms and decision trees. Applied Artificial Intelligence, 20, 655-674.
    • (2006) Applied Artificial Intelligence , vol.20 , pp. 655-674
    • Kalles, D.1    Pierrakeas, C.2
  • 18
    • 2942552288 scopus 로고    scopus 로고
    • Predicting students' performance in distance learning using machine learning techniques
    • Kotsiantis, S., Pierrakeas, C., & Pintelas, P. (2004). Predicting students' performance in distance learning using machine learning techniques. Applied Artificial Intelligence, 18, 411-426.
    • (2004) Applied Artificial Intelligence , vol.18 , pp. 411-426
    • Kotsiantis, S.1    Pierrakeas, C.2    Pintelas, P.3
  • 20
    • 60549085480 scopus 로고    scopus 로고
    • Multimedia Technology Lab, E-Learning services
    • Retrieved January 23, 2007, from
    • Medialab. (2007). Multimedia Technology Lab, E-Learning services. National Technological University of Athens. Retrieved January 23, 2007, from http://elearn.medialab.ntua.gr/
    • (2007) National Technological University of Athens
  • 21
    • 57549094534 scopus 로고    scopus 로고
    • Retrieved October 16, 2008, from
    • Moodle. (2007). Moodle LMS. Retrieved October 16, 2008, from http://moodle.org/
    • (2007) Moodle LMS
  • 23
    • 60549088979 scopus 로고    scopus 로고
    • Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986a). Learning internal representations by error propagation. In D.E. Rumelhart & J.L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition, 1: Foundations (pp. 318-362). Cambridge, MA: MIT Press.
    • Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986a). Learning internal representations by error propagation. In D.E. Rumelhart & J.L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 1: Foundations (pp. 318-362). Cambridge, MA: MIT Press.
  • 24
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986b). Learning representations by back-propagating errors. Nature, 323, 533-536.
    • (1986) Nature , vol.323 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 25
    • 84957868463 scopus 로고    scopus 로고
    • Sheel, S.J., Vrooman, D., Renner, R.S., & Dawsey, S.K. (2001). A comparison of neural networks and classical discriminant analysis in predicting students' mathematics placement examination scores. In V.N. Alexandrov, J.J. Dongarra, B.A. Juliano, R.S. Renner, & C.J.K. Tan (Eds.), Lecture Notes in Computer Science, 2074: Computational Science-ICCS 2001 (pp. 952-957). Berlin: Springer.
    • Sheel, S.J., Vrooman, D., Renner, R.S., & Dawsey, S.K. (2001). A comparison of neural networks and classical discriminant analysis in predicting students' mathematics placement examination scores. In V.N. Alexandrov, J.J. Dongarra, B.A. Juliano, R.S. Renner, & C.J.K. Tan (Eds.), Lecture Notes in Computer Science, Vol. 2074: Computational Science-ICCS 2001 (pp. 952-957). Berlin: Springer.
  • 26
    • 0003696226 scopus 로고
    • Extracting provably correct rules from artificial neural networks
    • Bonn, Germany: Institut for Informatik III Universitat Bonn
    • Thrun, S.B. (1994). Extracting provably correct rules from artificial neural networks (No. IAI-TR-93-5). Bonn, Germany: Institut for Informatik III Universitat Bonn.
    • (1994) IAI-TR-93-5)
    • Thrun, S.B.1
  • 27
    • 84964462305 scopus 로고    scopus 로고
    • Using neural networks to predict students' performance
    • December, Paper presented at the, Auckland, New Zealand
    • Wang, T., & Mitrovic, A. (2002, December). Using neural networks to predict students' performance. Paper presented at the International Conference on Computers in Education, Auckland, New Zealand.
    • (2002) International Conference on Computers in Education
    • Wang, T.1    Mitrovic, A.2
  • 28
    • 0003123930 scopus 로고    scopus 로고
    • Forecasting with artificial neural networks: The state of the art
    • Zhang, G., Patuwo, B.E., & Hu, M.Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14, 35-62.
    • (1998) International Journal of Forecasting , vol.14 , pp. 35-62
    • Zhang, G.1    Patuwo, B.E.2    Hu, M.Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.