Subscribe

Issue No.02 - April-June (2013 vol.6)

pp: 103-116

I. E. Achumba , Electron. & Comput. Eng. Dept., Univ. of Portsmouth, Portsmouth, UK

D. Azzi , Electron. & Comput. Eng. Dept., Univ. of Portsmouth, Portsmouth, UK

V. L. Dunn , Electron. & Comput. Eng. Dept., Univ. of Portsmouth, Portsmouth, UK

G. A. Chukwudebe , Electr. & Electron. Eng. Dept., Fed. Univ. of Technol., Oweri, Oweri, Nigeria

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TLT.2013.1

ABSTRACT

Laboratory work is critical in undergraduate engineering courses. It is used to integrate theory and practice. This demands that laboratory activities are synchronized with lectures to maximize their derivable learning outcomes, which are measurable through assessment. The typical high costs of the traditional engineering laboratory, which often militate against the synchronization of laboratory activities and lectures, have catalyzed the increased adoption of virtual laboratories in engineering laboratory education. The principles of assessment in the virtual learning environment are essentially the same as in the traditional learning environment, with the same requirements for fairness, reliability, and validity. This motivated the incorporation, in a Virtual Electronic Laboratory (VEL) environment, of a Bayesian network-based tool for the performance assessment of students' laboratory work in the environment. This paper details a description of the assessment tool, its verification, evaluation (as an assessment tool within the VEL environment), and application processes.

INDEX TERMS

student experiments, belief networks, computer aided instruction, engineering education, further education, performance evaluation,intelligent performance assessment, performance assessment tool, Bayesian network-based tool, VEL environment, virtual learning environment, engineering laboratory education, laboratory activity lecture synchronization, undergraduate engineering courses, virtual electronic laboratory environment, student laboratory work,Laboratories, Educational institutions, Synchronization, Computer network reliability, Reliability, Bayesian methods,validation and reliability, Laboratories, Educational institutions, Synchronization, Computer network reliability, Reliability, Bayesian methods, virtual laboratory, Bayesian networks, laboratory work, performance assessment, sensitivity analysis

CITATION

I. E. Achumba, D. Azzi, V. L. Dunn, G. A. Chukwudebe, "Intelligent Performance Assessment of Students' Laboratory Work in a Virtual Electronic Laboratory Environment",

*IEEE Transactions on Learning Technologies*, vol.6, no. 2, pp. 103-116, April-June 2013, doi:10.1109/TLT.2013.1REFERENCES

- [1] G. Carter, "Assessment of Undergraduate Electrical Engineering Laboratory Studies,"
Proc. IEEE, vol. 127, no. 7, pp. 460-472, Sept. 1980.- [2] N.S. Edward, "The Role of Laboratory Work in Engineering Education: Student and Staff Perceptions,"
Int'l J. Electrical Eng. Education, vol. 39, no. 1, pp. 11-19, Jan. 2002.- [3] D.L. Feisel and D. Rosa, "The Role of the Laboratory in Undergraduate Engineering Education,"
J. Eng. Education, vol. 94, no. 1, pp. 121-130, 2005.- [4] R. Byrnes and A. Ellis, "The Prevalence and Characteristic of Online Assessment in Australian Universities,"
Australian J. Education Technology, vol. 22, no. 1, pp. 104-125, 2006.- [5] B.R. Wilkins, "The Design of a First-Year Laboratory Course,"
Proc. Conf. Teaching of Electronic Eng. in Degree Courses, pp. 10/1-10/11, 1973.- [6] M.K. McPhun, "A Programmed Laboratory Course in Electronics for First Year Students,"
Proc. Conf. Teaching of Electronic Eng. in Degree Courses, 1973.- [7] "Undergraduate Laboratory Handbook: The Laboratory Logbook," School of Electronic Eng., Dublin City Univ., http://www.eeng.dcu.ie/EE253-EE.html, 2013.
- [8] A. Pramanik and D. Ring, "An Evolving Teaching Laboratory for First Year Students of Electrical and Electronic Engineering,"
Int'l J. Electrical Eng. Education, vol. 14, pp. 17-25, 1977.- [9] G.J. Dearden, "Laboratory Assessment: Some Generalised Findings,"
Proc. Conf. Teaching of Electronic Eng. in Degree Courses, pp. 12/9, 1976.- [10] J. Jordana and F.J. Sànchez, "Cooperative Work and Continuous Assessment in an Electronic Systems Laboratory Course in a Telecommunication Engineering Degree,"
Proc. IEEE Eng. Education Conf., pp. 395-400, 2010.- [11] L.L. Watai, S.A. Francis, and A.J. Brodersen, "A Qualitative and Systematic Assessment Methodology for Course Outcomes from Formal Laboratory Work Products in Electrical Engineering,"
Proc. ASEE/IEEE 37th Frontiers in Education Conf., 2007.- [12] C. Davies, "Learning and Teaching in Laboratories: An Engineering Subject Centre Guide," LTSN Eng., http://www.engsc.ac.uk/downloads/scholarart laboratories.pdf, 2008.
- [13] Quality Assurance Agency, "Subject Benchmark Statements: Engineering," http://www.qaa.ac.uk/academicinfrasctructure/ benchmark/statementsEngineering06.pdf , 2008.
- [14] Accreditation Board for Eng. and Technology, ABET Criteria 2000, http:/www.abet.ba.md.gov, 2013.
- [15] A. Chamot and M. O'Malley,
The CALLA Handbook. Addison-Wesley, 1994.- [16] H. Chen et al., "The Use of Performance-Based Assessment in an Integrated Chemistry Laboratory Program," http://www.ntnu. edu.tw/acad/docmeet/95/ a3a302.doc, 1995.
- [17] U. Ganiel and A. Hofstein, "Objective and Continuous Assessment of Student Performance in the Physics Laboratory,"
Proc. Ann. Meeting of the Nat'l Assoc. for Research in Science Teaching (54th, Grossinger's in the Catskills), 1981.- [18] N. Alinier and G. Alinier,
Design of an Objective Assessment Tool to Evaluate Students' Basic Electrical Eng. Skills, Higher Education Academy, Oct. 2005.- [19] R.Z. Bahri and J.P. Trevelyan, "An Effective Way to Measure Practical Intelligence from a Laboratory Experience,"
Proc. Research in Eng. Education Symp., http://rees2009.pbworks.com/frees2009_submission_74.pdf , 2009.- [20] S.H. Tesfazgi, "Survey on Behavioural Observation Methods in Virtual Environments," research assignment, Delft Univ. of Tech., Apr. 2003.
- [21] R. Arrington, "Study Finds Virtual Observations Better than Naked Eye in Examining Moon Phases,"
UVAToday, 2010.- [22] Y.K. Cheung and M.V. Elkind, "Stochastic Approximation with Virtual Observations for Dose-Finding on Discrete Levels,"
Biometrika, vol. 97, no. 1, pp. 109-121, 2010.- [23] R. Gallimore and J. Stigler, "LESSONLAB: Evolving Teaching into a Profession,"
TechKnowLogia, pp. 32-34, Jan.-Mar. 2003.- [24] M. Xenos, "Prediction and Assessment of Student Behaviour in Open and Distance Education in Computers Using Bayesian Networks,"
Computers and Education, vol. 43, no. 4, pp. 345-359, Dec. 2004.- [25] A. Corbett et al., "A Formative Evaluation of the PACT Algebra II Tutor: Support for Simple Hierarchical Reasoning,"
Proc. Fourth Int'l Conf Intelligent Tutoring Systems (ITS), B. Goettle, et al., eds., pp. 374-383, 1998.- [26] R.J. Mislevy, R.G. Almond, D. Yan, and L.S. Steinberg, "Bayes Nets in Educational Assessment: Where the Numbers Come from,"
Proc. 15th Conf. Uncertainty in Artificial Intelligence (AI), pp. 437-446, 2000.- [27] V. Collins, J.E. Greer, and S.X. Huang, "Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets,"
Proc. Third Int'l Conf. Intelligent Tutoring Systems, pp. 569-577, 1996.- [28] L. Zhang, Y. Zhuang, Z. Yuan, and G. Zhan, "Auto Diagnosing: An Intelligent Assessment System Based on Bayesian Networks,"
Proc. ASEE/IEEE 37th Frontiers in Education Conf., pp. T1G-7-T1G-10, 2007.- [29] C.C. Liu et al., "Student Performance Assessment Using Bayesian Network and Web Portfolios,"
J. Educational Computing Research, vol. 27, no. 4, pp. 437-469, 2002.- [30] L. Sbattela and R. Tedesco, "Profiling and Tutoring Users in Virtual Campus,"
Proc. Fifth Int'l Conf. Information Technology-Based Higher Education and Training (ITHET '04), 2004.- [31] E. Horvitz, J. Breeze, and D. Heckerman, "The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users,"
Proc. 14th Conf. Uncertainty in Artificial Intelligence, pp. 256-265, July 1998.- [32] R. Tedesco et al., "Distributed Bayesian Networks for User Modeling," http://www.cs.aau.dk/~dolog/pubelearn2006.pdf , 2006.
- [33] M. Feng, N. Heffernan, C. Heffernan, and M. Mani, "Using Mixed-Effects Modeling to Analyze Different Grain-Sized Skill Models in an Intelligent Tutoring System,"
IEEE Trans. Learning Technologies, vol. 2, no. 2, pp. 79-92, Apr.-June 2009.- [34] E. Millałn and J. Pełrez-De-La-Cruz, "A Bayesian Diagnostic Algorithm for Student Modeling and Its Evaluation,"
User Modeling and User-Adapted Interaction, vol. 12, pp. 281-330, 2002.- [35] E. Millan, T. Loboda, and J. Perez-de-la-Cruz, "Bayesian Networks for Student Model Engineering,"
Computers & Education, vol. 55, no. 4, pp. 1663-1683, 2010.- [36] K. VanLehn, "Olae: A Bayesian Performance Assessment for Complex Problem Solving,"
Proc. Nat'l Conf. Measurement in Education, http://www.pitt.edu/~vanlehn/distribNCMEOlae3. html , Apr. 2001.- [37] K. Vanlehn and J. Martin, "Evaluation of an Assessment System Based on Bayesian Student Modeling,"
Int'l J. AI and Education, vol. 8, no. 2, pp. 179-221, May 1998.- [38] J. Noguez, E. Sucar, and F. Ramos, "A Probabilistic Relational Student Model for Virtual Laboratories," http://www.cs.usyd. edu.au/~aied/vol3vol3_noguez.pdf , 2013.
- [39] M. Duarte, B.P. Butz, S.M. Miller, and A. Mahalingam, "An Intelligent Universal Virtual Laboratory (UVL),"
IEEE Trans. Education, vol. 51, no. 1, pp. 2-9, Feb. 2008.- [40] I.E. Chika, D. Azzi, J. Stocker, and B.P. Haynes, "Genuine Lab Experiences for Students in Resource Constrained Environments: The RealLab with Integrated Intelligent Assessment,"
IEEE Multidisciplinary Eng. Education Magazine, vol. 3, no. 4, Dec. 2008.- [41] R.M. Gagne and E.A. Fleishman,
Psychology and Human Performance: An Introduction to Psychology. Holt-Dryden and Company, 1959.- [42] W.N. Dember and J.J. Jenkins,
General Psychology: Modelling Behaviour and Experience. Prentice-Hall, 1970.- [43] B. Schwartz and S.J. Robbins,
Psychology of Learning and Behaviour, fourth ed. W.W. Norton and Company, 1995.- [44]
QAA Code of Practice Section 6: Assessment of Students, paragraph 13, 2006.- [45] R.J. Mislevy, R.G. Almond, D. Yan, and L.S. Steinberg, "Bayes Nets in Educational Assessment: Where the Numbers Come from,"
Proc. 15th Conf. Uncertainty in Artificial Intelligence (AI), pp. 437-446, 2000.- [46] J.L. Mohler, "Using Interactive Multimedia Technologies to Improve Student Understanding of Spatially-Dependent Engineering Concepts,"
Proc. Int'l Conf. Computer Graphics and Vision (GraphiCon), pp. 252-300, 2001.- [47] F.V. Jenson,
Bayesian Networks and Decision Graphs. Springer, 2001.- [48] D.L. Marrison and M.J. Frick, "The Effect of Agricultural Students' Learning Styles on Academic Achievement and Their Perceptions of Two Methods of Instruction,"
J. Agricultural Education, vol. 35, no. 1, pp. 26-30, http://202.198.141.77/upload/soft/00135-01-26.pdf , 1994.- [49] R.M. Felder and L.K. Silverman, "Learning and Teaching Styles in Engineering Education,"
Eng. Education, vol. 78, no. 7, pp. 674-681, Apr. 1988.- [50] R.G. Cowell, "Parameter Learning from Incomplete Data for Bayesian Networks,"
Proc. Seventh Int'l Workshop Artificial Intelligence and Statistics, 1999.- [51] C.A. Pollino, O. Woodberry, A. Nicholson, K. Korb, and B.T. Hart, "Parameterisation and Evaluation of a Bayesian Network for Use in an Ecological Risk Assessment,"
J. Environmental Modelling & Software, vol. 22, pp. 1140-1152, 2007.- [52] R. Rajabally, P. Sen, S. Whittle, and J. Dalton, "Aids to Bayesian Belief Network Construction,"
Proc. IEEE Second Int'l Conf. Intelligent Systems, vol. 2, pp. 457-461, 2004.- [53] R.G. Almond, "Modeling Diagnostic Assessments with Bayesian Networks,"
J. Educational Measurement, vol. 44, no. 4, pp. 341-359, 2007.- [54] A. Mahalingam, B.P. Butz, and M. Duarte, "An Intelligent Circuit Analysis Module to Analyze Student Queries in the Universal Virtual Laboratory,"
Proc. ASEE/IEEE 35th Frontiers in Education Conf., pp. 1-6, 2005.- [55] I. Vago,
Graph Theory: Application to the Calculation of Electrical Networks. Elsevier Science and Akademiai Kiado, 1985.- [56] L. Chua and P. Lin,
Computer-Aided Circuit Analysis of Electronic Circuits: Algorithms and Computational Techniques. Prentice-Hall, 1975.- [57] W. Pedrycz and F. Gomide,
An Introduction to Fuzzy Sets: Analysis and Design. MIT, 1998.- [58] J. Pearl,
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.- [59] B. Das, "Representing Uncertainties Using Bayesian Networks," Report No. DSTO-TR-0918, Dept. of Defence, Australia, 1999.
- [60] W. Harlen, "Criteria for Evaluating Systems for Student Assessment,"
Studies in Educational Evaluation, vol. 33, pp. 15-28, 2007.- [61] S. Russel and P. Norvig,
Artificial Intelligence: A Modern Approach. Pearson Education, 2003.- [62] R. Stathacopoulou, M. Grigoriadou, M. Samarakou, and D. Mitropoulos, "Monitoring Students' Actions and Using Teachers' Expertise in Implementing and Evaluating the Neural Network-Based Fuzzy Diagnostic Model,"
Expert Systems with Applications, vol. 32, no. 4, pp. 955-975, 2007.- [63] S.C. Abdulla and R.E. Cooley, "Using Simulated Students to Evaluate an Adaptive Testing System,"
Proc. Int'l Conf. Computers in Education (ICCE '02), p. 614, 2002.- [64] K. Vanlehn, S. Ohlsson, and R. Nason, "Applications of Simulated Students: An Exploration,"
J. Artificial Intelligence in Education, vol. 5, no. 2, pp. 135-175, 1994.- [65] R.P. Runyon, K.A. Coleman, and D.J. Pittenger,
Fundamentals of Behavioural Statistics. McGraw-Hill, 2000.- [66] S.M. Downing, "Validity: On the Meaningful Interpretation of Assessment Data,"
The Metric of Medical Education, vol. 37, pp. 830-837, 2003. |