Scoping Review on the Role of Learning Analytics in Assessing and Fostering Creativity in Educational Contexts

Authors

DOI:

https://doi.org/10.18608/jla.2025.8833

Keywords:

learning analytics, automatic analysis, educational data mining, creativity assessment, computational creativity, process visualization, educational contexts, research paper

Abstract

Learning Analytics (LA) is increasingly applied to assess and foster creativity in educational settings. Whereas existing applications have shown promise in STEM contexts, less is known about the diversity of approaches across educational domains. Therefore, we conducted a scoping review that systematically mapped LA applications for creativity in educational contexts. Searches returned 278 articles, with 41 studies meeting eligibility criteria. Analysis revealed five fundamental mechanisms through which LA fosters creativity: process visualization, adaptive feedback, automated pattern recognition, behavioural analytics, and real-time intervention. Computational creativity (10 studies) was the most prevalent conceptualization, with log data as the primary source (12 studies) and automated assessment via platform-based metrics as the leading approach (10 studies). Programming platforms represented the main technological applications (11 studies), while collaborative learning was the most common pedagogical strategy (7 studies). Problem-solving emerged as the most frequently linked complementary skill (17 studies). However, research showed extensive STEM focus; methodological fragmentation, with 38 studies lacking specified study duration; and theoretical gaps, with nine studies missing explicit theoretical frameworks. These findings highlight LA’s transformative potential for creativity assessment and fostering while revealing opportunities for interdisciplinary expansion and methodological standardization.

References

Avital, B., Hershkovitz, A., & Israel-Fishelson, R. (2023). Associations between computational thinking and figural, verbal creativity. Thinking Skills and Creativity, 50, Article 101417. https://doi.org/10.1016/j.tsc.2023.101417

Berland, M., & Kumar, V. (2023). Joint choice time: A metric for better understanding collaboration in interactive museum exhibits. In I. Hilliger, H. Khosravi, B. Rienties, & S. Dawson (Eds.), LAK23: 13th international learning analytics and knowledge conference (pp. 626–629). ACM Press. https://doi.org/10.1145/3576050.3576088

Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1–2), 205–220. https://doi.org/10.1007/s10758-014-9223-7

Blikstein, P. (2011). Using learning analytics to assess students’ behavior in open-ended programming tasks. In P. Long, G. Siemens, G. Conole, & D. Gašević (Eds.), LAK ’11: Proceedings of the 1st international conference on learning analytics and knowledge (pp. 110–116). ACM Press. https://doi.org/10.1145/2090116.2090132

Britain, G., Jain, A., Lupfer, N., Kerne, A., Perrine, A., Seo, J., & Sungkajun, A. (2020). Design is (a)live: An environment integrating ideation and assessment. In R. Bernhaupt, F. Mueller, D. Verweij, J. Andres, J. McGrenere, A. Cockburn, I. Avellino, A. Goguey, P. Bjørn, S. Zhao, B. P. Samson, & R. Kocielnik (Eds.), CHI EA ’20: Extended abstracts of the 2020 CHI conference on human factors in computing systems (pp. 1–8). ACM Press. https://doi.org/10.1145/3334480.3382947

Bubenkova, L., & Pietrikova, E. (2024). Game development: Enhancing creativity and independent creation in university course. In A. L. Santos & M. Pinto-Albuquerque (Eds.), 5th international computer programming education conference (ICPEC 2024) (Vol. 122, pp. 12:1–12:13). Schloss Dagstuhl–Leibniz-Zentrum für Informatik. https://doi.org/10.4230/OASIcs.ICPEC.2024.12

Cheng, S.-C., Hwang, G.-J., & Lai, C.-L. (2020). Effects of the group leadership promotion approach on students’ higher order thinking awareness and online interactive behavioral patterns in a blended learning environment. Interactive Learning Environments, 28(2), 246–263. https://doi.org/10.1080/10494820.2019.1636075

Chien, Y.-C., Liu, M.-C., & Wu, T.-T. (2020). Discussion-record–based prediction model for creativity education using clustering methods. Thinking Skills and Creativity, 36, Article 100650. https://doi.org/10.1016/j.tsc.2020.100650

Chiu, M.-S., & Hsiao, Y.-P. (2023). Process indicators for grading group essays: Learning analytics of assessment data and online behaviour. Athens Journal of Education, 10(4), 575–592. https://doi.org/10.30958/aje.10-4-2

Chniter, M., Kallel, I., Garcia, A. B., & Medina, J. S. (2024). Creative writing and social action: A data analysis based on learning analytics about how social, cultural and linguistic barriers can be overcome through educational innovation and international cooperation. In 2024 21st international conference on information technology based higher education and training (ITHET) (pp. 1–10). IEEE. https://doi.org/10.1109/ITHET61869.2024.10837631

Chou, E., Fossati, D., & Hershkovitz, A. (2024). A code distance approach to measure originality in computer programming. In O. Poquet, A. Orteg-Arranz, O. Viberg, I.-A. Chounta, B. McLaren, & J. Jovanovic (Eds.), Proceedings of the 16th international conference on computer supported education (CSEDU 2024) (Vol. 2, pp. 541–548). SciTePress. https://doi.org/10.5220/0012632100003693

Collard, P., & Looney, J. (2014). Nurturing creativity in education. European Journal of Education, 49(3), 348–364. https://doi.org/10.1111/ejed.12090

De Paula, D., Hahn, D., Matthies, C., & Uebernickel, F. (2022). InnoPulse: A tool to support collaborative reflection in creativity-driven engineering projects. In T. X. Bui (Ed.), Proceedings of the 55th Hawaii international conference on system sciences (pp. 34–43). HICSS. http://hdl.handle.net/10125/79335

Domalis, G., Karacapilidis, N., Karachristos, C., Komis, V., Manta, K., Misirli, A., Tsakalidis, D., & Filippidi, A. (2022). Augmented intelligence for pedagogically sustained training and education. In V. L. Uskov, R. J. Howlett, & L. C. Jain (Eds.), Smart education and e-learning: Smart pedagogy (pp. 87–98). Springer Singapore. https://doi.org/10.1007/978-981-19-3112-3_8

Dumas, D., Schmidt, L. C., & Alexander, P. A. (2016). Predicting creative problem solving in engineering design. Thinking Skills and Creativity, 21, 50–66. https://doi.org/10.1016/j.tsc.2016.05.002

Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x

Gajda, A., Karwowski, M., & Beghetto, R. A. (2017). Creativity and academic achievement: A meta-analysis. Journal of Educational Psychology, 109(2), 269–299. https://doi.org/10.1037/edu0000133

Gal, L., Hershkovitz, A., Morán, A. E., Guenaga, M., & Garaizar, P. (2017). Suggesting a log-based creativity measurement for online programming learning environment. In C. Urrea, J. Reich, & C. Thille (Eds.), Proceedings of the fourth (2017) ACM conference on learning @ scale (pp. 273–277). ACM Press. https://doi.org/10.1145/3051457.3054003

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x

Giannakos, M. N., Jaccheri, L., & Leftheriotis, I. (2012). Learning and creativity through tabletops: A learning analytics approach. Bulletin of the IEEE Technical Committee on Learning Technology, 14(4), 11–13.

Gibson, D. C. (2018). Unobtrusive observation of team learning attributes in digital learning. Frontiers in Psychology, 9, Article 834. https://doi.org/10.3389/fpsyg.2018.00834

Hadas, E., & Hershkovitz, A. (2024). Using large language models to evaluate alternative uses task flexibility score. Thinking Skills and Creativity, 52, Article 101549. https://doi.org/10.1016/j.tsc.2024.101549

Hernández-Leo, D. (2023). ChatGPT and generative AI in higher education: User-centered perspectives and implications for learning analytics. In A. Balderas, A. Martínez-Monés, J. M. Dodero, & S. Ros (Eds.), Proceedings of the learning analytics summer institute Spain 2023 (LASI Spain 2023) (Article 2). CEUR Workshop Proceedings. https://ceur-ws.org/Vol-3542/paper2.pdf

Hershkovitz, A., Sitman, R., Israel-Fishelson, R., Eguíluz, A., Garaizar, P., & Guenaga, M. (2019). Creativity in the acquisition of computational thinking. Interactive Learning Environments, 27(5–6), 628–644. https://doi.org/10.1080/10494820.2019.1610451

Hicks, D., Liu, Z., Eagle, M., & Barnes, T. (2016). Measuring gameplay affordances of user-generated content in an educational game. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the 9th international conference on educational data mining (pp. 78–85). International Educational Data Mining Society. https://www.educationaldatamining.org/EDM2016/proceedings/paper_100.pdf

Israel-Fishelson, R., & Hershkovitz, A. (2020). Shooting for the stars: Micro-persistence of students in game-based learning environments. In D. Glick, A. Cohen, & C. Chang (Eds.), Early warning systems and targeted interventions for student success in online courses (pp. 239–258). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-7998-5074-8.ch012

Israel-Fishelson, R., & Hershkovitz, A. (2022). Log-based multidimensional measurement of CT acquisition. In X. Zhang, C. Glahn, N. Fanchamps, & M. Specht (Eds.), Proceedings of the sixth APSCE international conference on computational thinking and STEM education 2022 (pp. 128–133). TU Delft Open Publishing. https://doi.org/10.34641/ctestem.2022.477

Israel-Fishelson, R., Hershkovitz, A., Eguíluz, A., Garaizar, P., & Guenaga, M. (2020). Computational thinking and creativity: A test for interdependency. In S.-C. Kong, H. U. Hoppe, T.-C. Hsu, R.-H. Huang, B.-C. Kuo, K.-Y. Li, C.-K. Looi, M. Milrad, J.-L. Shih, K.-F. Sin, K.-S. Song, M. Specht, F. Sullivan, & J. Vahrenhold (Eds.), Proceedings of international conference on computational thinking education 2020 (pp. 15–20). The Education University of Hong Kong.

Israel-Fishelson, R., Hershkovitz, A., Eguíluz, A., Garaizar, P., & Guenaga, M. (2021a). A log-based analysis of the associations between creativity and computational thinking. Journal of Educational Computing Research, 59(5), 926–959. https://doi.org/10.1177/0735633120973429

Israel-Fishelson, R., Hershkovitz, A., Eguíluz, A., Garaizar, P., & Guenaga, M. (2021b). The associations between computational thinking and creativity: The role of personal characteristics. Journal of Educational Computing Research, 58(8), 1415–1447. https://doi.org/10.1177/0735633120940954

Joksimović, S., Dawson, S., Barthakur, A., Poquet, O., Wang, Y. E., Marmolejo-Ramos, F., & Siemens, G. (2022). Mapping the landscape of social and emotional learning analytics. In Y. Wang, S. Joksimović, M. O. Z. San Pedro, J. D. Way, & J. Whitmer (Eds.), Social and emotional learning and complex skills assessment: An inclusive learning analytics perspective (pp. 27–47). Springer International Publishing. https://doi.org/10.1007/978-3-031-06333-6_3

Kim, Y. S., Shin, J. H., & Shin, Y. K. (2011). Conceptual design and cognitive elements of creativity: Toward personalized learning supports for design creativity. In T. Taura & Y. Nagai (Eds.), Design creativity 2010 (pp. 105–111). Springer London. https://doi.org/10.1007/978-0-85729-224-7_15

Krumm, A., Means, B., & Bienkowski, M. (2018). Learning analytics goes to school: A collaborative approach to improving education. Routledge. https://doi.org/10.4324/9781315650722

Lee, U., Han, A., Lee, J., Lee, E., Kim, J., Kim, H., & Lim, C. (2024). Prompt aloud!: Incorporating image-generative AI into STEAM class with learning analytics using prompt data. Education and Information Technologies, 29(8), 9575–9605. https://doi.org/10.1007/s10639-023-12150-4

Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J. H., Ogata, H., Baltes, J., Guerra, R., Li, P., & Tsai, C.-C. (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology, 11, Article 580820. https://doi.org/10.3389/fpsyg.2020.580820

Manske, S., & Hoppe, H. U. (2014). Automated indicators to assess the creativity of solutions to programming exercises. In D. G. Sampson, J. M. Spector, N.-S. Chen, R. Huang, & Kinshuk (Eds.), 2014 IEEE 14th international conference on advanced learning technologies (pp. 497–501). IEEE. https://doi.org/10.1109/ICALT.2014.147

Marrone, R. L., & Cropley, D. H. (2022). The role of learning analytics in developing creativity. In Y. Wang, S. Joksimović, M. O. Z. San Pedro, J. D. Way, & J. Whitmer (Eds.), Social and emotional learning and complex skills assessment: An inclusive learning analytics perspective (pp. 75–91). Springer Cham. https://doi.org/10.1007/978-3-031-06333-6_5

Menchaca Sierra, I., & Doran, P. (2019). Learning about STEAM through the resolution of real problems and the involvement of local stakeholders. In L. Gómez Chova, A. López Martínez, & I. Candel Torres (Eds.), EDULEARN19 proceedings (pp. 6609–6613). IATED Academy. https://doi.org/10.21125/edulearn.2019.1587

Nacu, D., Martin, C. K., Schutzenhofer, M., & Pinkard, N. (2016). Beyond traditional metrics: Using automated log coding to understand 21st century learning online. In J. Haywood, V. Aleven, J. Kay, & I. Roll (Eds.), Proceedings of the third (2016) ACM conference on learning@ scale (pp. 197–200). ACM Press. https://doi.org/10.1145/2876034.2893413

OECD. (2023). PISA 2022 assessment and analytical framework. OECD Publishing. https://doi.org/10.1787/471ae22e-en

Olivares-Rodríguez, C., Guenaga, M., & Garaizar, P. (2017). Automatic assessment of creativity in heuristic problem solving based on query diversity. Dyna, 92(4), 449–455. http://dx.doi.org/10.6036/8243

Olivares-Rodríguez, C., Guenaga, M., & Garaizar, P. (2018). Using children’s search patterns to predict the quality of their creative problem solving. Aslib Journal of Information Management, 70(5), 538–550. https://doi.org/10.1108/AJIM-05-2018-0103

Organisciak, P., Acar, S., Dumas, D., & Berthiaume, K. (2023). Beyond semantic distance: Automated scoring of divergent thinking greatly improves with large language models. Thinking Skills and Creativity, 49, Article 101356. https://doi.org/10.1016/j.tsc.2023.101356

Organisciak, P., Newman, M., Eby, D., Acar, S., & Dumas, D. (2023). How do the kids speak? Improving educational use of text mining with child-directed language models. Information and Learning Sciences, 124(1/2), 25–47. https://doi.org/10.1108/ILS-06-2022-0082

Peters, M. D. J., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis, 18(10), 2119–2126. https://doi.org/10.11124/JBIES-20-00167

Plucker, J. A., & Makel, M. C. (2010). Assessment of creativity. In J. C. Kaufman & R. J. Sternberg (Eds.), The Cambridge handbook of creativity (pp. 48–73). Cambridge University Press. https://doi.org/10.1017/CBO9780511763205.005

Pollock, D., Peters, M. D. J., Khalil, H., McInerney, P., Alexander, L., Tricco, A. C., Evans, C., de Moraes, É. B., Godfrey, C. M., Pieper, D., Saran, A., Stern, C., & Munn, Z. (2023). Recommendations for the extraction, analysis, and presentation of results in scoping reviews. JBI Evidence Synthesis, 21(3), 520–532. https://doi.org/10.11124/JBIES-22-00123

Reiter-Palmon, R., Forthmann, B., & Barbot, B. (2019). Scoring divergent thinking tests: A review and systematic framework. Psychology of Aesthetics, Creativity, and the Arts, 13(2), 144–152. https://doi.org/10.1037/aca0000227

Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3), Article e1355. https://doi.org/10.1002/widm.1355

Romero, M., Lepage, A., & Lille, B. (2017). Computational thinking development through creative programming in higher education. International Journal of Educational Technology in Higher Education, 14, Article 42. https://doi.org/10.1186/s41239-017-0080-z

Saleeb, N. (2021). Closing the chasm between virtual and physical delivery for innovative learning spaces using learning analytics. International Journal of Information and Learning Technology, 38(2), 209–229. https://doi.org/10.1108/IJILT-05-2020-0086

Shabani, N., Beheshti, A., Farhood, H., Bower, M., Garrett, M., & Rokny, H. A. (2022). iCreate: Mining creative thinking patterns from contextualized educational data. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners’ and doctoral consortium: 23rd international conference, AIED 2022, Durham, UK, July 27–31, 2022, proceedings, part II (pp. 352–356). Springer Cham. https://doi.org/10.1007/978-3-031-11647-6_68

Shabani, N., Beheshti, A., Farhood, H., Bower, M., Garrett, M., & Alinejad-Rokny, H. (2023). A rule-based approach for mining creative thinking patterns from big educational data. AppliedMath, 3(1), 243–267. https://doi.org/10.3390/appliedmath3010014

Shettar, A., Vijaylakshmi, M., & Tewari, P. (2020). Categorizing student as a convergent and divergent thinker in problem-solving using learning analytics framework. Procedia Computer Science, 172, 3–8. https://doi.org/10.1016/j.procs.2020.05.001

Siemens, G., & Gašević, D. (2012). Guest editorial: Learning and knowledge analytics. Educational Technology & Society, 15(3), 1–2.

Sopher, H. (2020). Analysing divergent–convergent activities in the architectural studio, with the aid of the ‘Knowledge Construction Activities’ model. In J.-F. Boujut, G. Cascini, S. Ahmed-Kristensen, G. V. Georgiev, & N. Iivari (Eds.), Proceedings of the sixth international conference on design creativity (ICDC 2020) (pp. 302–310). The Design Society. https://doi.org/10.35199/ICDC.2020.38

Sun, D., Ouyang, F., Li, Y. & Chen, H. (2020). Exploring creativity, emotion and collaborative behavior in programming for two contrasting groups. In S.-C. Kong, H. U. Hoppe, T.-C. Hsu, R.-H. Huang, B.-C. Kuo, K.-Y. Li, C.-K. Looi, M. Milrad, J.-L. Shih, K.-F. Sin, K.-S. Song, M. Specht, F. Sullivan, & J. Vahrenhold (Eds.), Proceedings of international conference on computational thinking education 2020 (pp. 36–37). The Education University of Hong Kong.

Sun, M., Wang, M., & Wegerif, R. (2019). Using computer‐based cognitive mapping to improve students’ divergent thinking for creativity development. British Journal of Educational Technology, 50(5), 2217–2233. https://doi.org/10.1111/bjet.12825

T., S., Chris Junni, A.V., Naga Harshith, Jessenth Ebenezer, S., Shabari Girish, S., & Priyaadharshini, M. (2020). Learning analytics: Virtual reality for programming course in higher education. Procedia Computer Science, 172, 433–437. https://doi.org/10.1016/j.procs.2020.05.095

Torrance, E. P. (1972). Predictive validity of the Torrance tests of creative thinking*. The Journal of Creative Behavior, 6(4), 236–252. https://doi.org/10.1002/j.2162-6057.1972.tb00936.x

Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K., Colquhoun, H., Kastner, M., Levac, D., Ng, C., Sharpe, J. P., Wilson, K., Kenny, M., Warren R., Wilson, C., Stelfox, H. T., & Straus, S. E. (2016). A scoping review on the conduct and reporting of scoping reviews. BMC Medical Research Methodology, 16, Article 15. https://doi.org/10.1186/s12874-016-0116-4

Tricco, A.C., Rios, P., Zarin, W., Cardoso, R., Diaz, S., Nincic, V., Mascarenhas, A., Jassemi, S., & Straus, S. E. (2018). Prevention and management of unprofessional behaviour among adults in the workplace: A scoping review. PLoS ONE, 13(7), Article e0201187. https://doi.org/10.1371/journal.pone.0201187

Wang, S.-M. (2014). A module-based learning analytics system for Facebook supported collaborative creativity learning. In D. G. Sampson, J. M. Spector, N.-S. Chen, R. Huang, & Kinshuk (Eds.), 2014 IEEE 14th international conference on advanced learning technologies (pp. 495–496). IEEE. https://doi.org/10.1109/ICALT.2014.146

Zaki, N. A. A., Zain, N. Z. M., Noor, N. A. Z. M., & Hashim, H. (2020). Developing a conceptual model of learning analytics in serious games for STEM education. Jurnal Pendidikan IPA Indonesia, 9(3), 330–339.

Zhu, G., Zeng, Y., Xing, W., Du, H., & Xie, C. (2021). Reciprocal relations between students’ evaluation, reformulation behaviors, and engineering design performance over time. Journal of Science Education and Technology, 30(5), 595–607. https://doi.org/10.1007/s10956-021-09906-3

Downloads

Published

2025-08-29

How to Cite

Manganello, F., & Fante, C. (2025). Scoping Review on the Role of Learning Analytics in Assessing and Fostering Creativity in Educational Contexts. Journal of Learning Analytics, 12(2), 5-18. https://doi.org/10.18608/jla.2025.8833

Issue

Section

Special Section on Human Creativity and Learning Analytics