Student progress assessment with the help of an intelligent pupil analysis system

Engineering Applications of Artificial Intelligence

Volumn 26, Issue 1, 2013, Pages 35-50

  Kaklauskas, Arturas ^a   Vlasenko, Andrej ^a   Raudonis, Vidas ^b   Zavadskas, Edmundas Kazimieras ^a   Gudauskas, Renaldas ^c   Seniut, Mark ^a   Juozapaitis, Algirdas ^a   Jackute, Ieva ^a   Kanapeckiene, Loreta ^a   Rimkuviene, Silva ^a   Kaklauskas, Gabrielius ^d  

^a VILNIUS GEDIMINAS TECHNICAL UNIVERSITY   (Lithuania)

^b KAUNAS UNIVERSITY OF TECHNOLOGY   (Lithuania)

^c Martynas Mazvydas National Library of Lithuania   (Lithuania)

^d VILNIUS UNIVERSITY   (Lithuania)

Author keywords

e Examination; e Learning; Historical information; Intelligent systems; Knowledge; Pupil analysis

Indexed keywords

ANALYSIS SYSTEM; COGNITIVE LOADS; DECISION SUPPORTS; E-EXAMINATION; HISTORICAL INFORMATION; INNOVATIVE MODELS; INTELLIGENT TUTORING SYSTEM; KNOWLEDGE; PUPIL ANALYSIS; PUPIL SIZE; RESEARCH RESULTS; STUDY MATERIALS;

COMPUTER AIDED INSTRUCTION; DECISION SUPPORT SYSTEMS; E-LEARNING; INTELLIGENT SYSTEMS; RESEARCH;

STUDENTS;

EID: 84870066191     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2012.01.006     Document Type: Article

References (49)

1. U. Ahlstrom, and F.J. Friedman-Berg Using eye movement activity as a correlate of cognitive workload Int. J. Ind. Ergon. 36 7 2006 623 636
2. I. AlFallay The role of some selected psychological and personality traits of the rater in the accuracy of self- and peer-assessment System 32 3 2004 407 425
3. J.L. Andreassi Psychophysiology - human behavior and physiological response 2007 Lawrence Erlbaum Associatex, Inc.; Publishers
4. J.L. Andreassi Pupillary response and behavior. In Psychophysiology: Human behavior and physiological response 5th ed. 2006 Routledge
5. R.W. Backs, and L.C. Walrath Eye movement and pupillary response indices of mental workload during visual search of symbolic displays Appl. Ergon. 23 4 1992 243 254
6. J.D. Barlow Pupillary size as a index ofpreference in political candidates Perceptual and MotorSkills 28 1969 927 932
7. H. Barros, A. Silva, E. Costa, I.I. Bittencourt, O. Holanda, and L. Sales Steps, techniques, and technologies for the development of intelligent applications based on Semantic Web Services: A case study in e-learning systems Eng. Appl. Artif. Intell. 24 8 2011 1355 1367
8. J. Beatty Task-evoked pupillary responses, processing load, and the structure of processing resources Psychol. Bull. 91 2 1982 276 292
9. J. Beatty, and B. Lucero-Wagoner The pupillary system J.T. Cacioppo, L.G. Tassinary, G.G. Berntson, Handbook of Psychophysiology 2nd ed. 2000 Cambridge University Press Cambridge, England 142 162
10. Y. Blanco-Fernández, M. López-Nores, J.J. Pazos-Arias, and J. García-Duque An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles Eng. Appl. Artif. Intell. 24 8 2011 1385 1397
11. J.P.V. Braak Domains and determinants of university students' self-perceived computer competence Comput. Educ. 43 3 2004 299 312
12. Burke, R.; 2007. Hybrid web recommender systems. In: The AdaptiveWeb, Springer-Verlag Berlin, Heidelberg 377-408.
13. J.T. Fitzgerald, C.B. White, and L.D. Gruppen A longtitudinal study of self-assessment accuracy Med. Educ. 37 2003 645 649
14. Golbeck, J.; 2006. Generating predictive movie recommendations from trust in social networks. In: Trust Management, 4th International Conference, iTrust 2006, Pisa, Italy, May 16-19, 2006, Proceedings, pp. 93-104.
15. B.C. Goldwater Psychological significance of pupillary movements Psychol. Bull. 77 5 1972 340 355
16. E. Granholm, and S.R. Steinhauer Introduction: Pupillometric measures of cognitive and emotional processing Int. J. Psychophysiol. 52 2004 1 6
17. E.H. Hess The tell-tale eye 1975 Van Nostrand Reinhold New York
18. E.H. Hess, and J.M. Polt Pupil size in relation to mental activity during simple problem-solving Science 143 3611 1964 1190 1192
19. J. Hyona, J. Tommola, and A.M. Alaja Pupil dilation as a measure of processing load in simultaneous interpretation and other language tasks Q. J. Exp. Psychol. 48A 1995 598 612
20. M.A. Just, and P.A. Carpenter The intensity dimension of thought: Pupillometric indices of sentence processing Can. J. Exp. Psychol./Revue canadienne de psychologie expirimentale 47 2 1993 310 339
21. D. Kahneman, and J. Beatty Pupil diameter and load on memory Science 154 3756 1966 1583 1585
22. D. Kahneman, and W.S. Peavler Incentive effects and pupillary changes in association learning J. Exp. Psychol. 79 1969 312 318
23. D. Kahneman, B. Tursky, D. Shapiro, and A. Crider Pupillary, heart rate, and skin resistance changes during a mental task J. Exp. Psychol. 79 1969 164 167
24. Kaklauskas, A.; Vlasenko, A.; Krutinis, M.; Kaklauskas, G.; 2007. Integration of intelligent, voice stress analysis, and iris recognition technologies in construction and real estate. 7th International conference on reliability and statistics in transportation and communication (RelStat 07), October 24-27, 2007, Riga, Latvia. Transport and Telecommunication Institute, pp. 238-245.
25. A. Kaklauskas, E.K. Zavadskas, V. Pruskus, A. Vlasenko, L. Bartkiene, R. Paliskiene, L. Zemeckyte, V. Gerstein, G. Dzemyda, and G. Tamulevicius Recommended biometric stress management system Expert. Syst. Appl. 38 11 2011 14011 14025
26. A. Kaklauskas, E.K. Zavadskas, M. Seniut, G. Dzemyda, V. Stankevic, C. Simkevicius, T. Stankevic, R. Paliskiene, A. Matuliauskaite, S. Kildiene, L. Bartkiene, S. Ivanikovas, and V. Gribniak Web-based biometric computer mouse advisory system to analyze a user's emotions and work productivity Eng. Appl. Artif. Intell. 24 6 2011 928 945
27. A. Kaklauskas, E.K. Zavadskas, V. Pruskus, A. Vlasenko, M. Seniut, G. Kaklauskas, A. Matuliauskaite, and V. Gribniak Biometric and intelligent self-assessment of student progress system Comput. Educ. 55 2 2010 821 833
28. Klingner, J.; 2010. Fixation-aligned Pupillary Response Averaging. In: Proceedings of the 2010 Symposium on Eye Tracking Research and Applications, ACM, New York, pp. 275-282.
29. Klingner, J.; Kumar, R.; Hanrahan, P.; 2008. Measuring the Task-Evoked Pupillary Response with a Remote Eye Tracker. In: Proceedings of the 2008 symposium on Eye tracking research and applications, ACM, New York, pp. 69-72.
30. T. Lin, A. Imamiya, W. Hu, and M. Omata Combined User Physical, Physiological and Subjective Measures for Assessing User Cost Universal Access in Ambient Intelligence Environments 2007 Springer-Verlag pp. 304-316
31. T. Lin, A. Imamiya, and X. Mao Using multiple data sources to get closer insights into user cost and task performance Interact. Comput. 20 3 2008 364 374
32. H.H. Lou, and Y.L. Huang Hierarchical decision making for proactive quality control: system development for defect reduction in automotive coating operations Eng. Appl. Artif. Intell. 16 3 2003 237 250
33. T. Mahmood, and F. Ricci Improving recommender systems with adaptive conversational strategies C. Cattuto, G. Ruffo, F. Menczer, Hypertext 2009 ACM 73 82
34. S. Matsuno Self-, peer-, and teacher-assessments in Japanese university EFL writing classrooms Language Testing 26 1 2009 75 100
35. S. Mynttinen, A. Sundström, J. Vissers, M. Koivukoski, K. Hakuli, and E. Keskinen Self-assessed driver competence among novice drivers - a comparison of driving test candidate assessments and examiner assessments in a Dutch and Finnish sample J. Safety Res. 40 4 2009 301 309
36. M. Papagelis, and D. Plexousakis Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents Eng. Appl. Artif. Intell. 18 7 2005 781 789
37. M.H. Papesh, S.D. Goldinger, and M.C. Hout Memory strength and specificity revealed by pupillometry Int. J. Psychophysiol 2011 Available online 20 October 2011
38. T. Partala, and V. Surakka Pupil size as an indication of affective processing Int. J. Human-Comp. Stud. 59 1-2 2003 185 198
39. W.S. Peavler Pupil size, information overload, and performance differences Psychophysiology 11 5 1974 559 566
40. G. Porter, T. Troscianko, and I.D. Gilchrist Effort during visual search and counting: Insights from pupillometry Q. J. Exp. Psychol. 60 2 2007 211 229
41. P. Resnick, and H.R. Varian Recommender systems Commun. ACM 40 3 1997 56 58
42. F. Ricci, L. Rokach, B. Shapira, and P.B. Kantor Recommender Systems Handbook 2011 Springer 840 p
43. Sinha, R.R.; Swearingen, K.; 2001. Comparing recommendations made by online systems and friends. In: DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries.
44. V. Uraikul, and C.W. Chan Paitoon Tontiwachwuthikul. Artificial intelligence for monitoring and supervisory control of process systems Eng. Appl. Artif. Intell. 20 2 2007 115 131
45. Valverde, L.; De Lera, E.; Fernandez, C.; 2010. Inferencing emotions through the triangulation of pupil size data, facial heuristics and self-assessment techniques. eL&mL International conference on Mobile, Hybrid and Online learning, Netherland Antilles, vol. 62, pp. 972-988, doi:10.1109/eLmL.2010.37
46. M.L. Võ, A.M. Jacobs, L. Kuchinke, M. Hofmann, M. Conrad, A. Schacht, and F. Hutzler The coupling of emotion and cognition in the eye: introducing the pupil old/new effect Psychophysiology 45 2008 30 40
47. J.T. Wang Pupil Dilation and Eye-Tracking Michael Schulte-Mecklenbeck, Anton Kuhberger, Rob Ranyard, A Handbook of Process Tracing Methods for Decision Research: A Critical Review and User's Guide 2010 Psychology Press
48. J. Whiting Development of intelligent tutoring systems for open and distance learning Eng. Appl. Artif. Intell. 1 2 1988 126 137
49. Zavadskas, E.K.; Kaklauskas, A.; 2007. Development and integration of intelligent, voice stress analysis and IRIS recognition technologies in construction. 24th International symposium on automation & robotics in construction (ISARC 2007), September 19-21, 2007, Kochi, Kerala, India, pp. 467-472.