Cognitive State Monitoring and the Design of Adaptive Instruction in Digital Environments: Lessons Learned from Cognitive Workload Assessment using a Passive Brain-Computer Interface Approach
According to Cognitive Load Theory, one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing c...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2014-12-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00385/full |
| _version_ | 1818505138959024128 |
|---|---|
| author | Peter eGerjets Carina eWalter Wolfgang eRosenstiel Martin eBogdan Martin eBogdan Thorsten O Zander |
| author_facet | Peter eGerjets Carina eWalter Wolfgang eRosenstiel Martin eBogdan Martin eBogdan Thorsten O Zander |
| author_sort | Peter eGerjets |
| collection | DOAJ |
| description | According to Cognitive Load Theory, one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners’ current working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners’ WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI) approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing EEG data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work. |
| first_indexed | 2024-12-10T21:46:43Z |
| format | Article |
| id | doaj.art-92c5bf4c1e94456fa6640413f41cb94a |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-10T21:46:43Z |
| publishDate | 2014-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-92c5bf4c1e94456fa6640413f41cb94a2022-12-22T01:32:21ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2014-12-01810.3389/fnins.2014.0038586703Cognitive State Monitoring and the Design of Adaptive Instruction in Digital Environments: Lessons Learned from Cognitive Workload Assessment using a Passive Brain-Computer Interface ApproachPeter eGerjets0Carina eWalter1Wolfgang eRosenstiel2Martin eBogdan3Martin eBogdan4Thorsten O Zander5Knowledge Media Research CenterEberhard-Karls University of TübingenEberhard-Karls University of TübingenUniversity of LeipzigEberhard-Karls University of TübingenTechnische Universität BerlinAccording to Cognitive Load Theory, one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners’ current working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners’ WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI) approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing EEG data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work.http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00385/fullEEGWorking-memory loadPassive Brain-Computer InterfaceAdaptive InstructionCross-task ClassificationCognitive Load Theory |
| spellingShingle | Peter eGerjets Carina eWalter Wolfgang eRosenstiel Martin eBogdan Martin eBogdan Thorsten O Zander Cognitive State Monitoring and the Design of Adaptive Instruction in Digital Environments: Lessons Learned from Cognitive Workload Assessment using a Passive Brain-Computer Interface Approach Frontiers in Neuroscience EEG Working-memory load Passive Brain-Computer Interface Adaptive Instruction Cross-task Classification Cognitive Load Theory |
| title | Cognitive State Monitoring and the Design of Adaptive Instruction in Digital Environments: Lessons Learned from Cognitive Workload Assessment using a Passive Brain-Computer Interface Approach |
| title_full | Cognitive State Monitoring and the Design of Adaptive Instruction in Digital Environments: Lessons Learned from Cognitive Workload Assessment using a Passive Brain-Computer Interface Approach |
| title_fullStr | Cognitive State Monitoring and the Design of Adaptive Instruction in Digital Environments: Lessons Learned from Cognitive Workload Assessment using a Passive Brain-Computer Interface Approach |
| title_full_unstemmed | Cognitive State Monitoring and the Design of Adaptive Instruction in Digital Environments: Lessons Learned from Cognitive Workload Assessment using a Passive Brain-Computer Interface Approach |
| title_short | Cognitive State Monitoring and the Design of Adaptive Instruction in Digital Environments: Lessons Learned from Cognitive Workload Assessment using a Passive Brain-Computer Interface Approach |
| title_sort | cognitive state monitoring and the design of adaptive instruction in digital environments lessons learned from cognitive workload assessment using a passive brain computer interface approach |
| topic | EEG Working-memory load Passive Brain-Computer Interface Adaptive Instruction Cross-task Classification Cognitive Load Theory |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00385/full |
| work_keys_str_mv | AT peteregerjets cognitivestatemonitoringandthedesignofadaptiveinstructionindigitalenvironmentslessonslearnedfromcognitiveworkloadassessmentusingapassivebraincomputerinterfaceapproach AT carinaewalter cognitivestatemonitoringandthedesignofadaptiveinstructionindigitalenvironmentslessonslearnedfromcognitiveworkloadassessmentusingapassivebraincomputerinterfaceapproach AT wolfgangerosenstiel cognitivestatemonitoringandthedesignofadaptiveinstructionindigitalenvironmentslessonslearnedfromcognitiveworkloadassessmentusingapassivebraincomputerinterfaceapproach AT martinebogdan cognitivestatemonitoringandthedesignofadaptiveinstructionindigitalenvironmentslessonslearnedfromcognitiveworkloadassessmentusingapassivebraincomputerinterfaceapproach AT martinebogdan cognitivestatemonitoringandthedesignofadaptiveinstructionindigitalenvironmentslessonslearnedfromcognitiveworkloadassessmentusingapassivebraincomputerinterfaceapproach AT thorstenozander cognitivestatemonitoringandthedesignofadaptiveinstructionindigitalenvironmentslessonslearnedfromcognitiveworkloadassessmentusingapassivebraincomputerinterfaceapproach |