Development of transformation of lecturer performance through Pro Growth Constructive Interaction with a multidimensional approach and machine learning based mathematical models

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.322723

Keywords:

lecturer performance optimization, mathematical model, deep neural network, accuracy, education

Abstract

The object of this research is to focus on the Pro Growth Constructive Interaction (PGCI) approach as a strategy to improve lecturer performance. PGCI integrates multidimensional interactions involving academic, social, technological, individual, external and temporal dimensions to achieve optimal productivity and efficiency. In this research, there is a main problem to be addressed, namely identifying and optimizing the factors that influence lecturer performance by developing a comprehensive model that is able to predict and improve performance through multidimensional interactions. The research results obtained were the dimension contribution showing the highest contribution to lecturer performance (0.177062), followed by the technological (0.174122), social (0.167044), external (0.165670), and individual (0.163610) dimensions). In the results of the mathematical model with the Lagrangian method optimized with a machine learning algorithm distributing weights with a focus on external dimensions (0.2650) and technology (0.2179), resulting in a performance increase of 7.35 %. This model is able to achieve an accuracy of 92.4 % in predicting lecturer performance using a deep neural network algorithm. In this research, there is a brief interpretation of the research findings showing that the temporal and technological dimensions have an important role in determining lecturer performance. By prioritizing these two dimensions, the optimized model yields significant improvements. Characteristics obtained from research, multidimensional analysis covering various aspects of performance and high accuracy and measurable performance improvements prove the reliability of the model. The results of this research have significant practical applications in higher education institutions

Author Biographies

Julfansyah Margolang, Universitas Sumatera Utara

Master of Management

Department of Economics and Business

Yeni Absah, Universitas Sumatera Utara

Doctor in Management

Department of Economics and Business

Sirojuzilam Hasyim, Universitas Sumatera Utara

Professor of Economy

Department of Economics and Business

Parapat Gultom, Universitas Sumatera Utara

Doctor of Industrial Systems Engineering

Department of Economics and Business

References

  1. Weng, A. K.-W., Chang, H.-Y., Lai, K.-K., Lin, Y.-B. (2024). Topic Modeling on Peer Interaction in Online and Mobile Learning of Higher Education: 1993–2022. Education Sciences, 14 (8), 867. https://doi.org/10.3390/educsci14080867
  2. Chu, W., Liu, H., Fang, F. (2021). A Tale of Three Excellent Chinese EFL Teachers: Unpacking Teacher Professional Qualities for Their Sustainable Career Trajectories from an Ecological Perspective. Sustainability, 13 (12), 6721. https://doi.org/10.3390/su13126721
  3. Nam, P. S., Tuong, H. A., Weinhandl, R., Lavicza, Z. (2022). Mathematics Teachers’ Professional Competence Component Model and Practices in Teaching the Linear Functional Concept – An Experimental Study. Mathematics, 10 (21), 4007. https://doi.org/10.3390/math10214007
  4. Suh, J., Matson, K., Seshaiyer, P., Jamieson, S., Tate, H. (2021). Mathematical Modeling as a Catalyst for Equitable Mathematics Instruction: Preparing Teachers and Young Learners with 21st Century Skills. Mathematics, 9 (2), 162. https://doi.org/10.3390/math9020162
  5. Ponce-Jara, M. A., Ruiz, E., Gil, R., Sancristóbal, E., Pérez-Molina, C., Castro, M. (2017). Smart Grid: Assessment of the past and present in developed and developing countries. Energy Strategy Reviews, 18, 38–52. https://doi.org/10.1016/j.esr.2017.09.011
  6. Manzhos, S., Ihara, M. (2022). Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies. Physchem, 2 (2), 72–95. https://doi.org/10.20944/preprints202203.0007.v1
  7. Mystakidis, S., Berki, E., Valtanen, J.-P. (2021). Deep and Meaningful E-Learning with Social Virtual Reality Environments in Higher Education: A Systematic Literature Review. Applied Sciences, 11 (5), 2412. https://doi.org/10.3390/app11052412
  8. Tang, J., Zhou, X., Wan, X., Daley, M., Bai, Z. (2022). ML4STEM Professional Development Program: Enriching K-12 STEM Teaching with Machine Learning. International Journal of Artificial Intelligence in Education, 33 (1), 185–224. https://doi.org/10.1007/s40593-022-00292-4
  9. Kyriakides, L., Creemers, B. P. M., Antoniou, P. (2009). Teacher behaviour and student outcomes: Suggestions for research on teacher training and professional development. Teaching and Teacher Education, 25 (1), 12–23. https://doi.org/10.1016/j.tate.2008.06.001
  10. Antoni, A., Arfah, M., Fachrizal, F., Nugroho, O. (2024). Developing a model of association rules with machine learning in determining user habits on social media. Eastern-European Journal of Enterprise Technologies, 3 (2 (129)), 55–61. https://doi.org/10.15587/1729-4061.2024.305116
  11. Rahmatika, A., Al-khowarizmi, A., Akrim, A., Nugroho, O., Anu, T. A. (2024). Using relational learning in exploring the effectiveness of using hashtags in future topics and user relations in X. Eastern-European Journal of Enterprise Technologies, 3 (2 (129)), 62–68. https://doi.org/10.15587/1729-4061.2024.306726
  12. Liu, H., Ding, J., Yang, L. T., Guo, Y., Wang, X., Deng, A. (2020). Multi-Dimensional Correlative Recommendation and Adaptive Clustering via Incremental Tensor Decomposition for Sustainable Smart Education. IEEE Transactions on Sustainable Computing, 5 (3), 389–402. https://doi.org/10.1109/tsusc.2019.2954456
  13. Yanes, N., Mostafa, A. M., Ezz, M., Almuayqil, S. N. (2020). A Machine Learning-Based Recommender System for Improving Students Learning Experiences. IEEE Access, 8, 201218–201235. https://doi.org/10.1109/access.2020.3036336
  14. Mahmud, M., Kaiser, M. S., Hussain, A., Vassanelli, S. (2018). Applications of Deep Learning and Reinforcement Learning to Biological Data. IEEE Transactions on Neural Networks and Learning Systems, 29 (6), 2063–2079. https://doi.org/10.1109/tnnls.2018.2790388
  15. Jdid, B., Hassan, K., Dayoub, I., Lim, W. H., Mokayef, M. (2021). Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey. IEEE Access, 9, 57851–57873. https://doi.org/10.1109/access.2021.3071801
  16. Kim, J. (2023). Leading teachers’ perspective on teacher-AI collaboration in education. Education and Information Technologies, 29 (7), 8693–8724. https://doi.org/10.1007/s10639-023-12109-5
  17. Wang, Z. A. (2024). Physical Education Teaching Quality Assessment Model Based on Gaussian Process Machine Learning Algorithm. International Journal of Maritime Engineering, 1 (1). https://doi.org/10.5750/ijme.v1i1.1399
  18. Phillips, P. A., Wright, C. (2009). E-business’s impact on organizational flexibility. Journal of Business Research, 62 (11), 1071–1080. https://doi.org/10.1016/j.jbusres.2008.09.014
  19. Arnold, V., Benford, T., Canada, J., Sutton, S. G. (2011). The role of strategic enterprise risk management and organizational flexibility in easing new regulatory compliance. International Journal of Accounting Information Systems, 12 (3), 171–188. https://doi.org/10.1016/j.accinf.2011.02.002
  20. Okorie, O., Subramoniam, R., Charnley, F., Patsavellas, J., Widdifield, D., Salonitis, K. (2020). Manufacturing in the Time of COVID-19: An Assessment of Barriers and Enablers. IEEE Engineering Management Review, 48 (3), 167–175. https://doi.org/10.1109/emr.2020.3012112
  21. Shukla, S. K., Sushil, Sharma, M. K. (2019). Managerial Paradox Toward Flexibility: Emergent Views Using Thematic Analysis of Literature. Global Journal of Flexible Systems Management, 20 (4), 349–370. https://doi.org/10.1007/s40171-019-00220-x
  22. Leelaluk, S., Minematsu, T., Taniguchi, Y., Okubo, F., Shimada, A. (2022). Predicting student performance based on Lecture Materials data using Neural Network Models. Proceedings of the 4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior - DC in LAK22 co-located with 12th International Learning Analytics and Knowledge Conference (LAK22), 11–20. Available at: https://ceur-ws.org/Vol-3120/paper2.pdf
  23. Kang, W. (2021). Explaining Effects of Transformational Leadership on Teachers’ Cooperative Professional Development through Structural Equation Model and Phantom Model Approach. Sustainability, 13 (19), 10888. https://doi.org/10.3390/su131910888
  24. Goga, M., Kuyoro, S., Goga, N. (2015). A Recommender for Improving the Student Academic Performance. Procedia - Social and Behavioral Sciences, 180, 1481–1488. https://doi.org/10.1016/j.sbspro.2015.02.296
  25. Suyatmo, S., Ekohariadi, E., Wardhono, A. (2024). Identify Factors That Influence Hard Skill Competency and Soft Skill Competency Through the Quality of Teaching in Aviation Vocational Education. IJORER : International Journal of Recent Educational Research, 5 (3), 599–611. https://doi.org/10.46245/ijorer.v5i3.584
  26. Cao, B., Hassan, N. C., Omar, M. K. (2024). The Impact of Social Support on Burnout among Lecturers: A Systematic Literature Review. Behavioral Sciences, 14 (8), 727. https://doi.org/10.3390/bs14080727
Development of transformation of lecturer performance through Pro Growth Constructive Interaction with a multidimensional approach and machine learning based mathematical models

Downloads

Published

2025-02-27

How to Cite

Margolang, J., Absah, Y., Hasyim, S., & Gultom, P. (2025). Development of transformation of lecturer performance through Pro Growth Constructive Interaction with a multidimensional approach and machine learning based mathematical models. Eastern-European Journal of Enterprise Technologies, 1(2 (133), 43–52. https://doi.org/10.15587/1729-4061.2025.322723