The overarching goal of learner assessments is to identify areas of skill and deficit and to use this infor-mation to guide future instruction or error correction through feedback. To date, a variety of methods have been used to better understand individual learners. However, the tools used to assess learners’ internal states have only allowed researchers to infer these states and, as a result, the information provided lacks the prescriptive specificity needed to most appropriately address learners’ needs. The technological advances of current neuro-physiological measures may provide such specificity in real-time learning environments. These data show preliminary support for the use of electroencephalography measurements of workload and engagement to predict learning and knowledge acquisition. Additionally, the data suggest that the relation-ship between these two internal states may differ based on the level of information being assessed.
Get full access to this article
View all access options for this article.
References
1.
AbbottR.G.ForsytheC. (2009). Characterizing cognitive adaptability via robust automated knowledge capture. Presented at the 13th annual confer-ence on human computer interaction international, San Diego, CA, July 19-24.
2.
AndersonA.R. (2004). Student engagement & dropout: an investigation with students who have mild disabilities. Dissertation, University of Minneso-ta.
3.
Bangert-DrownsR.L. (1991). The instructional effect of feedback in test-like events. Review of educational research, 61(2), 213-238.
4.
BerkaC.. (2007) . Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. IJHCI, 17(2), 151-170.
5.
BerkaC.. (2004). EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat Space Environ Med, 78(5), 231-244.
6.
BoltonA. (2006). Immediate versus delayed feedback in simulation based training: Matching feedback delivery timing to the cognitive demands of the training exercise. Dissertation, University of Central Florida.
7.
ChinD.N. (2007). Information filtering, expertise and cognitive load. In SchmorrowD.D.ReevesL.M. (Eds.): Augmented cognition (pp. 75-83). Berlin Heidelberg: Springer-Verlag.
8.
De GreefT.LafeberH.Van OostendorpH.LindenbergJ. (2009). Eye movement as indicators of mental workload to trigger adaptive automa-tion. Presented at the 13th annual conference on HCII, San Diego, CA, July 19-24.
9.
DieterleE.MurrayJ. (2009). Realizing adaptive instruction (Ad-In): The convergence of learning, instruction, and assessment. Presented at the 13th annual conference on HCII, San Diego, CA, July 19-24.
10.
FredricksJ.A.BlumenfeldP.C.ParisA.H. (2004). School engagement: Potential of the concept, state of the evidence. Review of educational research, 74(1), 59-109.
11.
HarrisonA. (1984). Creativity, Class, and Boredom: Cognitive Models for Intelligent Activities. Journal of Education, 166(2), 150-169.
12.
JarvisS.SeifertT. (2002). Work avoidance as a manifestation of hostility, helplessness, and boredom. Alberta Journal of Educational Research, 48(2), 174-187.
13.
KatzI.AssorA.Kanat-MaymonY.Bereby-MeyerY. (2006). Interest as a motivational resource: Feedback and gender matter, but interest makes the difference. Social Psychology of Education, 9, 27-42.
14.
KemberD.. (1997). Evaluating the effectiveness of educational innovations: Using the study process questionnaire to show that meaningful learning occurs. Studies in educational evaluation, 23(2), 141-157.
15.
KingL. (2009). Visual navigation patterns and cognitive load. Present-ed at the 13th annual conference on HCII, San Diego, CA, July 19-24.
16.
KirschnerP.A.SwellerJ.ClarkR.E. (2006). Why minimal guid-ance during instruction does not work: An analysis of the failure of construc-tivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational psychologist, 41(2), 75-86.
17.
KirschnerP.A. (2002). Cognitive load theory: Implications of cognitive load theory on the design of learning. Learning and instruction, 12, 1-10.
18.
KlugerA. N.DeNisiA. (1996). Effects of feedback intervention on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254-284.
19.
LuuP.PoulsonC.TuckerD. (2009). Neurophysiological measures of brain activity: Going from the scalp to the brain. Presented at the 13th annual conference on HCII, San Diego, CA, July 19-24.
20.
McCarthyJ.E. (2008). Military applications of adaptive training technology. Technology enhanced learning: Best practices (pp. 304-341). Hershey, PA: IGI Global Publishing.
21.
NASA Task Load Index (TLX): Computerized Version. (1986). Moffett Field, CA: NASA-Ames Research Center, Aerospace Human Factors Re-search Division.
22.
MoryE.H. (2004). Feedback research revisited. In JonassenD.H. (Ed.) Handbook of Research for Educational Communications and Technology. New York: Simon & Schuster Macmillan.
23.
PaasF.TuovinenJ.E.TabbersH.Van GervenP.W.M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational psychologist, 38(1), 63-71.
24.
PalmerE.D.KobusD.A. (2007). The future of augmented cognition systems in education and training. In SchmorrowD.D.ReevesL.M. (Eds.): Augmented cognition (pp. 373-379). Berlin Heidelberg: Springer-Verlag.
25.
PekrunR.GoetzT.TitzW.PerryR.P. (2002). Academic emo-tions in students’ self- regulated learning & achievement: A program of quali-tative and quantitative research. Educational Psychologist, 37(2), 91-105
26.
PekrunR.ElliotA.J.MaierA.J. (2006). Achievement goals & dis-crete achievement emotions: A theoretical model & prospective test. J. of Educational Psychology, 98 (3), 583-597.
27.
SansoneC.ThomanD.B. (2005). Maintaining activity engagement: Individual differences in the process of self-regulation motivation. Journal of Personality, 74(6), 1697-1720.
28.
ShuteV.J. (2008). Focus on formative feedback. Review of educational research, 78(1), 153-189.
29.
StevensR.GallowayT.BerkaC.SprangM. (2009). Can neuro-physiologic synchronies provide a platform for adapting team performance?Presented at the 13th annual conference on HCII, San Diego, CA, July 19-24.
30.
SwellerJ. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12, 257-285.
31.
van GogT.PaasF. (2008). Instructional efficiency: Revisiting the original construct in educational research. Educational psychologist, 43(1), 16-26.
32.
van GogT.PaasF.van MerrienboerJ. J. G. (2008). Effects of stud-ying sequences of process-oriented and product-oriented worked examples on troubleshooting transfer efficiency. Learning and instruction, 18(3), 211-222.
33.
van MerrienboerJ. J. G.SwellerJ. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational psychology review, 17(2), 147-177.
34.
Vogel-WalcuttJ.J.SchatzS.BowersC.GebrimJ.B.NicholsonD. (2008). Varying Workload: A validation of the DVTE system. Paper pre-sented at the 52nd Annual Conference of the Human Factors and Ergonomics Society, New York, NY, September 22-26.
35.
Vogel-WalcuttJ.J.NicholsonD. (2009). Applied Learning Science Team Update. Paper presented at the ONR HPT&E: AITE program review, January 22, San Diego, CA.
36.
Vogel-WalcuttJ.J.NicholsonD.BowersC. (2009). Translating learning theories into physiological hypotheses. Presented at the 13th annual conference on HCII, San Diego, CA, July 19-24.
37.
WickensC. D. (1999). Cognitive factors in aviation. In DursoF.T.NickersonR.S.SchvaneveldtR.W.DumaisS.T.LindsayD.S.ChiM.T.H. (Eds.), Handbook of Applied Cognition. John Wiley & Sons Ltd.
38.
WulfeckW. (2009). Adapting instruction. Presented at the 13th annual conference on human computer interaction international, San Diego, CA, July 19-24.
