Systems now may collect almost unlimited amounts of data on performance during learning. However, it is critical to ensure appropriate data are presented properly to the human to aid learning of a system. At the present time, extensive literature exists on training, but there is no agreed upon method of choosing exactly the data to present as feedback during training. This uncertainty is due to the lack of underlying principles for making feedback effective. This paper presents a formulation of feedback principles based on an understanding of the processing demands on the human operator. We explain how variables such as amount of structure and support in feedback interact with the human operator and the task to be learned. With this knowledge, designers may choose suitable feedback for training.
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References
1.
AckermanP. L. (1988). Determinants of individual differences during skill acquisition: Cognitive abilities and information processing. Journal of Experimental Psychology: General, 117, 288–318.
2.
AdamsJ. A. (1971). A closed-loop theory of motor learning. Journal of Motor Behavior, 3(2), 111–150.
3.
AndersonJ. R.CorbettA. T.KoedingerK. R.PelletierR. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4, 167–207.
4.
BirenbaumM.TatsuokaK. K. (1987). Effects of “online” test feedback on the seriousness of subsequent errors. Journal of Educational Measurement, 24(2), 145–155.
5.
BlackwellJ. R.NewellK. M. (1996). The informational role of knowledge of results in motor learning. Acta Psychologica, 92, 119–129.
ClarianaR. 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.
8.
CopeP.SimmonsM. (1994). Some effects of limited feedback on performance and problem-solving strategy in a logo microworld. Journal of Educational Psychology, 86(3), 368–379.
9.
CronbachL.SnowR. (1977). Aptitudes and instructional methods: A handbook for research on interactions. New York: Irvington.
10.
EngleR. W.KaneM. J.TuholskiS. W. (1999). Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. In MiyakeA.ShahP.Models of working memory (pp. 102–134. Cambridge: Cambridge University Press.
11.
FarquharJ. D.RegianJ. W. (1994). Effects of knowledge representation during computer-based training of console operation skill.
12.
GoodmanJ. S.WoodR. E.HendrickxM. (2004). Feedback specificity, exploration, and learning. Journal of Applied Psychology, 89(2), 248–262.
13.
HancockP. A.WarmJ. S. (1989). A dynamic model of stress and sustained attention. Human Factors, 31(5), 519–537.
14.
HoL.SheaJ. B. (1978). Effects of relative frequency of knowledge of results on retention of a motor skill. Perceptual and Motor Skills, 46, 859–866.
15.
HollnagelE. (2006). Task analysis: Why, what, and how. In SalvendyG.The handbook of human factors and ergonomics (pp. 373–383. Hoboken, NJ: Wiley & Sons.
16.
KramerA. F.LarishJ. J.StrayerD. L. (1995). Training for attentional control in dual-task settings: A comparison of young and old adults. Journal of Experimental Psychology: Applied, 1, 50–76.
17.
MaddoxW. T.AshbyF. G.BohilC. J. (2003). Delayed feedback effects on rule-based and information integration category learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 29(4), 650–662.
18.
MagillR. A.HallK. G. (1990). A review of the contextual interference effect in motor skill acquisition. Human Movement Science, 9, 241–289.
McLaughlinA. C.RogersW. A.FiskA. D. (2005). Importance and interaction of feedback variables: A model for effective, dynamic feedback. In HFA-TR-0602. Atlanta, GA: Human Factors and Aging Laboratory, Georgia Institute of Technology.
21.
NewellK. M. (1991). Motor skill acquisition. Annual Review of Psychology, 42, 213–237.
22.
NicholsonD. E.SchmidtR. A. (1991). Scheduling information feedback to enhance training effectiveness. Paper presented at the 35th Annual Meeting of the Human Factors and Ergonomics Society, Santa Monica, CA.
23.
RoperW. J. (1977). Feedback in computer-assisted instruction. Programmed Learning and Educational Technology, 14, 43–49.
24.
RumelhartD. E. (1989). The architecture of mind: A connectionist approach. In PosnerM. I.Foundations of cognitive science (pp. 33–160. Cambridge, MA: MIT Press.
25.
SalmoniA. W.SchmidtR. A.WalterC. B. (1984). Knowledge of results and motor learning: A review and critical reappraisal. Psychological Bulletin, 95(3), 355–386.
26.
SchmidtR. A.BjorkR. A. (1992). New conceptualizations of practice: Common principles in three paradigms suggest new concepts for training. Psychological Science, 3(4), 207–217.
27.
SchmidtR. A.LangC. A.YoungD. E. (1990). Optimizing summary knowledge of results for skill learning. Human Movement Science, 9, 325–348.
28.
SchmidtR. A.WulfG. (1997). Continuous concurrent feedback degrades skill learning: Implications for training and simulation. Human Factors, 39(4), 509–525.
29.
SchmidtR. A.YoungD. F.SwinnenS.ShapiroD. C. (1989). Summary knowledge of results for skill acquisition: Support for the guidance hypothesis. Journal of Experimental Psychology: Learning, Memory and Cognition, 15, 352–359.
30.
SchoolerL. J.AndersonJ. R. (1990). The disruptive potential of immediate feedback. Paper presented at the Proceedings of the Twelfth Annual Conference of the Cognitive Science Society.
31.
SchrothM. L. (1997). Effects of frequency of feedback on transfer in concept identification. American Journal of Psychology, 110(1), 71–79.
32.
SherwoodD. E. (1988). Effect of bandwidth knowledge of results on movement consistency. Perceptual and Motor Skills, 66, 535–542.
33.
SnowR. E.SwansonJ. (1992). Instructional psychology: Aptitude, adaptation, and assessment. Annual Review of Psychology, 43, 583–626.
34.
SwellerJ. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.
35.
SzalmaJ. L.HancockP. A.DemberW. N.WarmJ. S. (2006). Training for vigilance: The effect of knowledge of results format and dispositional optimism and pessimism on performance and stress. British Journal of Psychology, 97, 115–135.
36.
ThorndikeE. L. (1931). The evolution of learning in general. In ElliottR. M.Human learning (pp. 162–182. New York: The Century Co.
37.
WhyteM. M.KarolickD. M.NielsenM. C.ElderG. D. (1995). Cognitive styles and feedback in computer assisted instruction. Journal of Educational Computing Research, 12(2), 195–203.
38.
WinsteinC. J.SchmidtR. A. (1990). Reduced frequency of knowledge of results enhances motor skill learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 16(4), 677–691.
39.
WishartL. R.LeeT. D.CunninghamS. J.MurdochJ. E. (2002). Age-related differences and the role of augmented visual feedback in learning a bimanual coordination pattern. Acta Psychologica, 110, 247–263.
40.
WulfG.SchmidtR. A.DeubelH. (1993). Reduced feedback frequency enhances generalized motor program learning but not parameterization learning. Journal of Experimental Psychology, 19(5), 1134–1150.
41.
WulfG.SheaC. H. (2002). Principles derived from the study of simple skills do not generalize to complex skill learning. Psychonomic Bulletin & Review, 9(2), 185–211.
42.
WulfG.SheaC. H.MatschinerS. (1998). Frequent feedback enhances complex motor skill learning. Journal of Motor Behavior, 30(2), 180–192.
