Current Projects

Temporal motivation and learning performance Using Eye-tracking and Wireless Electroencephalogram (EEG)   D. Nembhard & J.-E. Kim

The purpose of the study is to investigate potential factors influencing the learning ability and find ways to improve learning ability. The study will focus on instructional method evaluation and the exploration of relationship between emotion level and learning performance. We hypothesize that the performance and emotion level will be different with different teaching methods. We also hypothesize a relationship between emotion and learning performance.

Kim, J-E., Nembhard, D. A., and J-H Kim, “The Effects of Task Complexity and Group Size on Deadline Reactivity,” Int. J. Industrial Ergonomics, 56, 106-114, 2016. dx.doi.org/10.1016/j.ergon.2016.09.011

Cellular team formation with heterogeneous learning and knowledge transfer. D. Nembhard & Y. Mendez- Vazquez

This work addresses grouping and assignment of heterogeneous workers within a cellular manufacturing configuration when the objective is to maximize system throughput. Research related to the worker assignment in cellular manufacturing often does not consider worker heterogeneity nor knowledge transfer. Prior research demonstrated the impact of knowledge transfer in system performance and highlighted the importance of grouping workers for worker-task assignment. We present a meta-heuristic optimization applied to grouping and assignment workers to tasks considering the knowledge transfer. Results show the value of knowledge transfer and importance of informed team formation.

Online Learning Knowledge Representation, Emotion and Performance. D. Nembhard. Y. Sun, and R. Elatlassi.

This research aims at investigating the relationship between instructional media form( static or dynamic) and learning performance; the relationship between instructional media, emotion, and learning performances; and whether the last relationship differs according to students’ personalities. The project is to investigate factors that may influence computer-based distance learning process and to better understand the relationship between instructional methods, emotion levels, and learning performance


Past Projects

Human Resource Allocation Based Productivity Improvement 

Empirically measured human learning and forgetting is used to model worker productivity under under various experimental conditions, such as levels of prior experience, turnover, and education levels based on plant location. Workplace decisions have an affect on individual worker learning and forgetting and hence, overall productivity. This material is based upon work supported by the The National Science Foundation under Grants  SES9986385 and SES0217666. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Shafer, S. M., Nembhard, D. A. and Uzumeri, M. V., “An Empirical Investigation of Learning, Forgetting, and Worker Heterogeneity on Assembly Line Productivity,” Management Science, 47(12), 1639-1653, 2001.

Nembhard, D.A. and N.  Osothsilp.  “An Empirical Comparison of Forgetting Models,” IEEE Transactions on Engineering Management, 48(3), 283-291, 2001

Nembhard, D. A., “A Heuristic Approach for Assigning Workers to Tasks Based on Individual Learning Rate,” International Journal of Production Research, 39(9), 1955-1968, 2001.

Nembhard, D. A and Norman, B.A., “Workforce Scheduling Considering Learning and Forgetting Effects” Invited Session on Scheduling Theory and Applications, INFORMS National Meeting, Miami, November 2001.

Leopairote and D. A. Nembhard, “Worker Assignment with Individual Learning and Forgetting Effects in a Cellular Manufacturing System,” Contributed Session on Operations Management, INFORMS National Meeting, Miami, November 2001.

Osothsilp and D. A. Nembhard, “Worker-Task Assignment with Learning, Forgetting and Task Heterogeneity,” Contributed Session on Operations Management, INFORMS National Meeting, Miami, November 2001.

Analog On-Line Analytical Processing in Manufacturing

This research project will develop a data mining model for characterizing the analog information contained in a traditional “digital” database and develop an On-Line Analytical Processing (OLAP) software tool for prototyping and this approach such that it would provide real-time feedback to system operators and managers. More specifically, the model will measure frontline worker performance obtained through extant data acquisition systems. To be useful such a system should ostensibly provide continuous feedback on the system state, (i.e., patterns of worker productivity). The project will additionally identify and classify the various types of problems appropriate for this approach. This Research was sponsored in part by a grant from Sun Microsystems.

Uzumeri M. V. and Nembhard, D.A., “A Population of Learners: A New Way to Measure Organizational Learning,” Journal of Operations Management, 16(5), 515-528, 1998.

Identifying System Variation in Worker Productivity Data.

Workers in manufacturing each improve their productivity over time in a unique albeit similar manner to one another. Confounding these improvement patters are patterns of plant-wide system variation, in which many workers experience correlated shifts, up or down, in productivity over time. The purpose of this research is to identify such patterns and to develop a framework for parsing system variance with the goal of providing knowledge with respect to informed decision making to improve quality and productivity.

Nembhard, D. A. and Uzumeri, M.V., “Experiential Learning and Forgetting for Manual and Cognitive Tasks,” International Journal of Industrial Ergonomics, 25(4), 315-326, 2000.

Yeh, Y-J, and Nembhard, D. A., “Human Factors in Productivity and System Variation,” INFORMS Fall National Meeting, Philadelphia, November 1999.

Uzumeri, M. V. and Nembhard, D. A., “A Population of Learners: A New Way to Measure Organizational Learning,” Journal of Operations Management, 16(5), 515-528, 1998.

Measuring Experiential Learning Retention

A performance comparison of models from the literature for measuring the retention rate for skills obtained trough experiential learning in industry. Empirical study indicates that breaks in experience acquisition occur intermittently. While most models were designed and tested for use on single breaks in production, we examine their performance under intermittent breaks.

Nembhard, D.A. and N.  Osothsilp.  “An Empirical Comparison of Forgetting Models,” IEEE Transactions on Engineering Management, 48(3), 283-291, 2001

Nembhard, D. A. and Uzumeri, M.V., “An Individual-Based Description Of Learning Within an Organization,” IEEE Transactions on Engineering Management, 47(3), 370-378, 2000.

Nembhard, D. A., Mehrotra, V., Osothsilp, P., and Sharma, V. “Worker Learning and Forgetting in Manufacturing Industries,” Center for Human Performance in Complex Systems 1999 Workshop, Poster Session, University of Wisconsin-Madison, 1999.

Task Complexity and its relation to Human Learning and Forgetting

A statistical investigation of how manual task complexity with respect to the method, machine, and material used in production affects patterns of human learning and forgetting. Data includes over 2800 workers on over 100 different but related tasks. Results benefit decision makers by providing knowledge of which worker allocations will provide higher overall productivities.

Nembhard, D. A. and Osothsilp, N. “Task Complexity Effects on Between-Individual Learning/Forgetting Variability,” International Journal of Industrial Ergonomics, in review, 2001.

Nembhard, D.A., “The Effects of Task Complexity and Experience on Learning and Forgetting: A Field Study,” Human Factors, 42(2), 272-286,  2000.