Adaptive Education Platforms for Development of Algorithmic Thinking in Corporate Environment: Criteria for Selection of Experimental Environment
DOI:
https://doi.org/10.70862/CSIR.2026.0301-21Keywords:
adaptive learning, algorithmic thinking, critical thinking, corporate learning, multi-criteria evaluation, transfer of learningAbstract
This article develops a methodological framework for screening and subsequently verifying adaptive learning platforms intended to support algorithmic and critical thinking among adults in corporate settings. It combines a conceptual analysis of selected literature, document analysis, and multi-criteria modelling. Six criteria are operationalized through five-level descriptors, provisional normative weights, and non-compensatory thresholds. Documentation for five platforms is examined to distinguish vendor claims from demonstrated functionality and secured access rights. A separate numerical example using five hypothetical profiles illustrates weighted aggregation and sensitivity to weights and input assumptions; it is not an empirical ranking of the products. A schematic corporate case provides separate indicators for algorithmic thinking, critical thinking, and transfer. The main contribution is a two-stage procedure linking documentary screening to technical and expert verification. The framework requires validation. No intervention study is reported, and no platform-specific educational effect is established.
References
Baldwin, T. T., & Ford, J. K. (1988). Transfer of training: A review and directions for future research. Personnel Psychology, 41(1), 63–105. https://doi.org/10.1111/j.1744-6570.1988.tb00632.x
Wang, X., Huang, R. T., Sommer, M., Pei, B., et al. (2024). The efficacy of artificial intelligence-enabled adaptive learning systems from 2010 to 2022 on learner outcomes: A meta-analysis. Journal of Educational Computing Research, 62(6), 1348–1383. https://doi.org/10.1177/07356331241240459
Martin, F., Chen, Y., Moore, R. L., & Westine, C. D. (2020). Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development, 68(4), 1903–1929. https://doi.org/10.1007/s11423-020-09793-2
Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, Article 100017. https://doi.org/10.1016/j.caeai.2021.100017
Valeva, P., Stoitsov, G., & Dimitrov, I. (2024). Artificial intelligence in advanced learning: Opportunities and challenges. Mathematics and Informatics, 67(5), 561–579. https://doi.org/10.53656/math2024-5-7-art
Futschek, G. (2006). Algorithmic thinking: The key for understanding computer science. In R. T. Mittermeir (Ed.), Informatics education – The bridge between using and understanding computers (pp. 159–168). Springer. https://doi.org/10.1007/11915355_15
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
Facione, P. A. (1990). Critical thinking: A statement of expert consensus for purposes of educational assessment and instruction (The Delphi report). California Academic Press.
Halpern, D. F. (1998). Teaching critical thinking for transfer across domains: Dispositions, skills, structure training, and metacognitive monitoring. American Psychologist, 53(4), 449–455. https://doi.org/10.1037/0003-066X.53.4.449
Brusilovsky, P. (2012). Adaptive hypermedia for education and training. In P. J. Durlach & A. M. Lesgold (Eds.), Adaptive technologies for training and education (pp. 46–68). Cambridge University Press. https://doi.org/10.1017/CBO9781139049580.006
Knowles, M. S. (1980). The modern practice of adult education: From pedagogy to andragogy. Cambridge Adult Education.
Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
Branch, R. M. (2009). Instructional design: The ADDIE approach. Springer. https://doi.org/10.1007/978-0-387-09506-6
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, et al. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850
Area9 Lyceum. (n.d.-c). Why does Area9 Rhapsode measure learner confidence? Retrieved October 14, 2026, from https://help.area9lyceum.com/biological-adaptive-model/why-does-area9-rhapsode-measure-learner-confidence/
Area9 Lyceum. (n.d.-a). Does Area9 Rhapsode support xAPI? Retrieved October 14, 2026, from https://help.area9lyceum.com/integration/does-area9-rhapsode-support-xapi/
Area9 Lyceum. (n.d.-b). How is Area9 Rhapsode licensed? Retrieved October 14, 2026, from https://help.area9lyceum.com/faqs/how-is-area9-rhapsode-licensed/
Honke, N., & Becker-Genschow, S. (2025). Adaptive learning in bionics: Transforming science education. Frontiers in Education, 10, Article 1427083. https://doi.org/10.3389/feduc.2025.1427083
Cornerstone. (n.d.). Learning management system. Retrieved October 14, 2026, from https://www.cornerstoneondemand.com/platform/learning-management-lms/
Coursera. (n.d.). Online business learning platform. Retrieved October 14, 2026, from https://www.coursera.org/business
Docebo. (n.d.). AI-first LMS for enterprise learning and skills. Retrieved October 14, 2026, from https://www.docebo.com/
Litmos. (n.d.). Litmos AI. Retrieved October 14, 2026, from https://www.litmos.com/platform/litmos-ai/
Downloads
Published
License
Copyright (c) 2026 Tsvetelina Rashkova, Gencho Stoitsov (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
