Feedback is a vital element for improving student
learning in a simulation-based training as it guides and refines
learning through scaffolding. A number of studies in literature have
shown that students’ learning is enhanced when feedback is provided
with personalized tutoring that offers specific guidance and adapts
feedback to the learner in a one-to-one environment. Thus, emulating
these adaptive aspects of human tutoring in simulation provides an
effective methodology to train individuals. This paper presents the results of a study that investigated the
effectiveness of automating different types of feedback techniques
such as Knowledge-of-Correct-Response (KCR) and Answer-Until-
Correct (AUC) in software simulation for learning basic information
technology concepts. For the purpose of comparison, techniques like
simulation with zero or no-feedback (NFB) and traditional hands-on
(HON) learning environments are also examined. The paper presents the summary of findings based on quantitative
analyses which reveal that the simulation based instructional
strategies are at least as effective as hands-on teaching methodologies
for the purpose of learning of IT concepts. The paper also compares
the results of the study with the earlier studies and recommends
strategies for using feedback mechanism to improve students’
learning in designing and simulation-based IT training.
[1] McCrea, F., Gay, R., & Bacon, R. (2000). Riding the big waves: A white
paper on B2B e-learning industry. San Francisco, CA: Thomas Weisel.
[2] Bell, B., Kanar, A. M., & Kozlowski, S. J. (2008). Current issues and
future directions in simulation-based training. Ithaca, NY: Center for
Advanced Human Resources. Cornell University.
[3] Chen, D. (2003). Uncovering the provisos behind flexible learning.
Educational Technology and Society, 6(2), 25-30.
[4] Sancristobal, E., Castro, M., Martin, S., & Tawkif, M. (2011, April).
Remote labs as learning services in the educational arena. Paper
presented at the Global Engineering Education Conference, Amman,
Jordan.
[5] Gillet, D., Ngoc, A. V., & Rekik, Y. (2005). Collaborative web-based
experimentation in flexible engineering education. IEEE Transactions
on Education, 48(4), 696-704.
[6] Schiflett, S. G., Elliott, L. R., Salas, E., & Coovert, M. D., (Eds.).
(2004). Scaled worlds: Development validation, and application. Surrey,
England, Ashgate Publishing Limited, 75-99.
[7] Steele-Johnson, D., & Hyde, B. G. (1997). Advanced technologies in
training: Intelligent tutoring systems and virtual reality. In M. A.
Quiñones & A. Ehrenstein (Eds.), Training for a rapidly changing
workplace: Applications of psychological research, (pp. 225-248).
Washington, DC: American Psychological Association.
[8] Clariana, R. B., Ross, S. M., & Morrison, G. R. (1991). The effects of
different feedback strategies using computer-administered multiplechoice
questions as instruction. Educational Technology Research and
Development, 39(2), 5-17.
[9] Institute for Creative Technologies. (2009). Intelligent guided
experiential learning: Tutoring for practice. Retrieved from
http://ict.usc.edu/projects
[10] Cuevas, H. M., Fiore, S. M., Bowers, C. A., & Salas, E. (2004).
Fostering constructive cognitive and metacognitive activity in computerbased
complex task training environments. Computers in Human
Behavior, 20(2), 225-241.
[11] Azevedo, R., & Bernard, R. M. (1995). A meta analysis of the effects of
feedback in computer-based instruction. Journal of Educational
Computer Research, 13(2), 111-127.
[12] Morrison, G. R., Ross, S. M., Gopalakrishnan, M., & Casey, J. (1995).
The effects of feedback and incentives on achievement in computerbased
instruction. Contemporary Educational Psychology, 20(1), 32-50.
[13] Clariana, R. B. (1993). A Review of Multiple-Try Feedback in
Traditional and Computer-Based Instruction. Journal of Computer-
Based Instruction, 20, 67-74.
[14] Clariana, R. B. (1990). A comparison of answer-until-correct feedback
and knowledge-of- correct-response feedback under two conditions of
contextualization. Journal of Computer-Based Instruction, 17(4), 125-
129.
[15] Moreno, R. (2004). Decreasing cognitive load for novice students:
Effects of explanatory versus corrective feedback in discovery-based
multimedia. Instructional Science, 32(1-2), 99-113.
[16] Agina, A. M., Komers, P., & Steehouder, M. (2011). The effect of the
nonhuman external regulator’s AUC versus KCR task feedback on
children’s behavioral regulation during learning tasks. Computers in
Human Behavior, 27(5), 1710‐1723.
[17] Kalyuga, S. (2006). Assessment of learners’ organized knowledge
structures in adaptive learning environments. Applied Cognitive
Psychology, 20(3), 333-342.
[18] Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in
problem solving. Journal of Child Psychology and Psychiatry, 17(2),
89-100.
[19] Sherin, B., Reiser, B. J., & Edelson, D. (2004). Scaffolding analysis:
Extending the scaffolding metaphor to learning artifacts. Journal of
Learning Sciences, 13(3), 387-421.
[20] Jackson, S. L., Krajcik, J., & Soloway, E. (1998). The design of guided
learner-adaptable scaffolding in interactive learning environments.
Proceeding of the SIGCHI Conference on Human Factors in Computer
Systems (pp. 187-194). New York, NY: Addison-Wesley Publishing.
[21] Hannafin, M. J. & Hooper, S. R. (1993). Learning principles. In M. L.
Fleming & W. H. Levie (Eds.), Instructional message design: Principles
from the behavioral and cognitive sciences (pp. 191-231). Englewood
Cliffs, NJ: Educational Technology Publications.
[22] Linton, F. (2000). The Intranet: An Open Learning Environment.
Retrieved from http:// virtcampus.cl-ki.uni-osnabrueck.de/its-
2000/paper/poster4/ws2-poster-4.htm
[23] Jaehnig, W., & Miller, M. L. (2007). Feedback types in programmed
instructions: A systematic review, Psychological Record, 57(2), 219-
232.
[24] Bruning, R., & Mason, B. J. (2001). Providing feedback in computerbased
instruction: What the research tells us. Retrieved from
http://dwb.unl.edu/Edit/MB/ MasonBruning
[25] Narciss, S. (2008). Feedback strategies for interactive learning tasks. In
J. M. Spector, M. D. Merrill, J. Van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and
technology (pp. 125-144). New York, NY: Lawrence Erlbaum.
[26] Corter, J., Nickerson, J., Esche, S., Chassapis, C., Im, S., & Ma, J.
(2007). Constructing reality: A study of remote, hands-on, and simulated
laboratories. ACM Transactions on Computer-Human Interaction, 14(2),
17-37.
[27] Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha.
International Journal of Medical Education, 2, 53-55.
[1] McCrea, F., Gay, R., & Bacon, R. (2000). Riding the big waves: A white
paper on B2B e-learning industry. San Francisco, CA: Thomas Weisel.
[2] Bell, B., Kanar, A. M., & Kozlowski, S. J. (2008). Current issues and
future directions in simulation-based training. Ithaca, NY: Center for
Advanced Human Resources. Cornell University.
[3] Chen, D. (2003). Uncovering the provisos behind flexible learning.
Educational Technology and Society, 6(2), 25-30.
[4] Sancristobal, E., Castro, M., Martin, S., & Tawkif, M. (2011, April).
Remote labs as learning services in the educational arena. Paper
presented at the Global Engineering Education Conference, Amman,
Jordan.
[5] Gillet, D., Ngoc, A. V., & Rekik, Y. (2005). Collaborative web-based
experimentation in flexible engineering education. IEEE Transactions
on Education, 48(4), 696-704.
[6] Schiflett, S. G., Elliott, L. R., Salas, E., & Coovert, M. D., (Eds.).
(2004). Scaled worlds: Development validation, and application. Surrey,
England, Ashgate Publishing Limited, 75-99.
[7] Steele-Johnson, D., & Hyde, B. G. (1997). Advanced technologies in
training: Intelligent tutoring systems and virtual reality. In M. A.
Quiñones & A. Ehrenstein (Eds.), Training for a rapidly changing
workplace: Applications of psychological research, (pp. 225-248).
Washington, DC: American Psychological Association.
[8] Clariana, R. B., Ross, S. M., & Morrison, G. R. (1991). The effects of
different feedback strategies using computer-administered multiplechoice
questions as instruction. Educational Technology Research and
Development, 39(2), 5-17.
[9] Institute for Creative Technologies. (2009). Intelligent guided
experiential learning: Tutoring for practice. Retrieved from
http://ict.usc.edu/projects
[10] Cuevas, H. M., Fiore, S. M., Bowers, C. A., & Salas, E. (2004).
Fostering constructive cognitive and metacognitive activity in computerbased
complex task training environments. Computers in Human
Behavior, 20(2), 225-241.
[11] Azevedo, R., & Bernard, R. M. (1995). A meta analysis of the effects of
feedback in computer-based instruction. Journal of Educational
Computer Research, 13(2), 111-127.
[12] Morrison, G. R., Ross, S. M., Gopalakrishnan, M., & Casey, J. (1995).
The effects of feedback and incentives on achievement in computerbased
instruction. Contemporary Educational Psychology, 20(1), 32-50.
[13] Clariana, R. B. (1993). A Review of Multiple-Try Feedback in
Traditional and Computer-Based Instruction. Journal of Computer-
Based Instruction, 20, 67-74.
[14] Clariana, R. B. (1990). A comparison of answer-until-correct feedback
and knowledge-of- correct-response feedback under two conditions of
contextualization. Journal of Computer-Based Instruction, 17(4), 125-
129.
[15] Moreno, R. (2004). Decreasing cognitive load for novice students:
Effects of explanatory versus corrective feedback in discovery-based
multimedia. Instructional Science, 32(1-2), 99-113.
[16] Agina, A. M., Komers, P., & Steehouder, M. (2011). The effect of the
nonhuman external regulator’s AUC versus KCR task feedback on
children’s behavioral regulation during learning tasks. Computers in
Human Behavior, 27(5), 1710‐1723.
[17] Kalyuga, S. (2006). Assessment of learners’ organized knowledge
structures in adaptive learning environments. Applied Cognitive
Psychology, 20(3), 333-342.
[18] Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in
problem solving. Journal of Child Psychology and Psychiatry, 17(2),
89-100.
[19] Sherin, B., Reiser, B. J., & Edelson, D. (2004). Scaffolding analysis:
Extending the scaffolding metaphor to learning artifacts. Journal of
Learning Sciences, 13(3), 387-421.
[20] Jackson, S. L., Krajcik, J., & Soloway, E. (1998). The design of guided
learner-adaptable scaffolding in interactive learning environments.
Proceeding of the SIGCHI Conference on Human Factors in Computer
Systems (pp. 187-194). New York, NY: Addison-Wesley Publishing.
[21] Hannafin, M. J. & Hooper, S. R. (1993). Learning principles. In M. L.
Fleming & W. H. Levie (Eds.), Instructional message design: Principles
from the behavioral and cognitive sciences (pp. 191-231). Englewood
Cliffs, NJ: Educational Technology Publications.
[22] Linton, F. (2000). The Intranet: An Open Learning Environment.
Retrieved from http:// virtcampus.cl-ki.uni-osnabrueck.de/its-
2000/paper/poster4/ws2-poster-4.htm
[23] Jaehnig, W., & Miller, M. L. (2007). Feedback types in programmed
instructions: A systematic review, Psychological Record, 57(2), 219-
232.
[24] Bruning, R., & Mason, B. J. (2001). Providing feedback in computerbased
instruction: What the research tells us. Retrieved from
http://dwb.unl.edu/Edit/MB/ MasonBruning
[25] Narciss, S. (2008). Feedback strategies for interactive learning tasks. In
J. M. Spector, M. D. Merrill, J. Van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and
technology (pp. 125-144). New York, NY: Lawrence Erlbaum.
[26] Corter, J., Nickerson, J., Esche, S., Chassapis, C., Im, S., & Ma, J.
(2007). Constructing reality: A study of remote, hands-on, and simulated
laboratories. ACM Transactions on Computer-Human Interaction, 14(2),
17-37.
[27] Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha.
International Journal of Medical Education, 2, 53-55.
@article{"International Journal of Business, Human and Social Sciences:72031", author = "Usman Ghani", title = "Role of Feedbacks in Simulation-Based Learning", abstract = "Feedback is a vital element for improving student
learning in a simulation-based training as it guides and refines
learning through scaffolding. A number of studies in literature have
shown that students’ learning is enhanced when feedback is provided
with personalized tutoring that offers specific guidance and adapts
feedback to the learner in a one-to-one environment. Thus, emulating
these adaptive aspects of human tutoring in simulation provides an
effective methodology to train individuals. This paper presents the results of a study that investigated the
effectiveness of automating different types of feedback techniques
such as Knowledge-of-Correct-Response (KCR) and Answer-Until-
Correct (AUC) in software simulation for learning basic information
technology concepts. For the purpose of comparison, techniques like
simulation with zero or no-feedback (NFB) and traditional hands-on
(HON) learning environments are also examined. The paper presents the summary of findings based on quantitative
analyses which reveal that the simulation based instructional
strategies are at least as effective as hands-on teaching methodologies
for the purpose of learning of IT concepts. The paper also compares
the results of the study with the earlier studies and recommends
strategies for using feedback mechanism to improve students’
learning in designing and simulation-based IT training.", keywords = "Simulation, feedback, training, hands-on, labs.", volume = "10", number = "1", pages = "283-8", }
