TY - GEN
T1 - Consideration of a Bayesian Hierarchical Model for Assessment and Adaptive Instructions
AU - Kim, Jong W.
AU - Ritter, Frank E.
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - People appear to practice what they do know rather than what they do not know [1], suggesting a necessity of an improved assessment of multi-level complex skill components. An understanding of the changing knowledge states is also important in that such an assessment can support instructions. The changing knowledge states can be generally visualized through learning curves. These curves would be useful to identify and predict the learner’s changing knowledge states in multi-domains, and to understand the features of task/subtask learning. Here, we provide a framework based on a Bayesian hierarchical model that can be used to investigate learning and performance in the learner and domain model context—particularly a framework to estimate learning functions separately in a psychomotor task. We also take an approach of a production rule system (e.g., ACT-R) to analyze the learner’s knowledge and skill in tasks and subtasks. We extend the current understanding of cognitive modeling to better support adaptive instructions, which helps to model the learner in multi-domains (i.e., beyond the desktop) and provide a summary of estimating a probability that the learner has learned each of a production rule. We find the framework being useful to model the learner’s changing knowledge and skill states by supporting an estimate of probability that the learner has learned from a knowledge component, and by comparing learning curves with varying slopes and intercepts.
AB - People appear to practice what they do know rather than what they do not know [1], suggesting a necessity of an improved assessment of multi-level complex skill components. An understanding of the changing knowledge states is also important in that such an assessment can support instructions. The changing knowledge states can be generally visualized through learning curves. These curves would be useful to identify and predict the learner’s changing knowledge states in multi-domains, and to understand the features of task/subtask learning. Here, we provide a framework based on a Bayesian hierarchical model that can be used to investigate learning and performance in the learner and domain model context—particularly a framework to estimate learning functions separately in a psychomotor task. We also take an approach of a production rule system (e.g., ACT-R) to analyze the learner’s knowledge and skill in tasks and subtasks. We extend the current understanding of cognitive modeling to better support adaptive instructions, which helps to model the learner in multi-domains (i.e., beyond the desktop) and provide a summary of estimating a probability that the learner has learned each of a production rule. We find the framework being useful to model the learner’s changing knowledge and skill states by supporting an estimate of probability that the learner has learned from a knowledge component, and by comparing learning curves with varying slopes and intercepts.
UR - http://www.scopus.com/inward/record.url?scp=85069732639&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-22341-0_41
DO - 10.1007/978-3-030-22341-0_41
M3 - Conference contribution
AN - SCOPUS:85069732639
SN - 9783030223403
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 521
EP - 531
BT - Adaptive Instructional Systems - 1st International Conference, AIS 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings
A2 - Sottilare, Robert A.
A2 - Schwarz, Jessica
PB - Springer Verlag
T2 - 1st International Conference on Adaptive Instructional Systems, AIS 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019
Y2 - 26 July 2019 through 31 July 2019
ER -