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Towards Personalized Instruction: Co-designing a Teacher-Centered Dashboard for Learning Engagement Analysis in Blended Learning Environments

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Learning and Collaboration Technologies (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14040))

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Abstract

Learning engagement is an important factor in academic success and instructional quality. In authentic settings, analyzing learner engagement status can be challenging due to the complexity of its multidimensional construct. It requires constant monitoring of multimodal engagement indicators (cognitive-level indicators, e.g., effort, emotional-level indicators, e.g., positive/negative emotions) and individualized pacing of learners, identifying when and who needs assistance, as well as making pedagogical strategy that matches the needs of the learners (or group of learners). This study combines user requirements with computational techniques to construct a multimodal learning engagement analysis framework in the blended setting. In addition, a teacher-faced dashboard prototype is developed to statistically summarize and visualize multimodal indicators in a way that enables teachers to deploy personalized instruction schemes. The teachers’ perspectives discussed in this study portray the great potential of introducing Artificial Intelligent (AI)-augmented models and visual analytics techniques aimed at deploying personalized instructions in the blended learning environment.

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References

  1. Carrillo, R., Renaud, C., Prié, Y., Lavoué, É.: Dashboard for monitoring student engagement in mind mapping activities. In: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), pp. 433–437 (2017). https://doi.org/10.1109/ICALT.2017.100

  2. Dourado, R.A., Rodrigues, R.L., Ferreira, N., Mello, R. F., Gomes, A. S., Verbert, K.: A teacher-facing learning analytics dashboard for process-oriented feedback in online learning. In: LAK21: 11th International Learning Analytics and Knowledge Conference, pp. 482–489 (2021)

    Google Scholar 

  3. Ez-Zaouia, M.: Teacher-centered dashboards design process. In: 2nd International Workshop on EXplainable Learning Analytics, Companion Proceedings of the 10th International Conference on Learning Analytics & Knowledge LAK20 (2020, March)

    Google Scholar 

  4. Ez-zaouia, M., Lavoué, E.: EMODA: A tutor oriented multimodal and contextual emotional dashboard. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 429–438 (2017). https://doi.org/10.1145/3027385.3027434

  5. Ez-zaouia, M., Tabard, A., Lavoué, E.: PROGDASH: lessons learned from a learning dashboard In-the-wild: In: Proceedings of the 12th International Conference on Computer Supported Education, pp. 105–117 (2020). https://doi.org/10.5220/0009424801050117

  6. Fernandez Nieto, G.M., Kitto, K., Buckingham Shum, S., Martinez-Maldonado, R.: Beyond the learning analytics dashboard: alternative ways to communicate student data insights combining visualisation, narrative and storytelling. In: LAK22: 12th International Learning Analytics and Knowledge Conference, pp. 219–229 (2022). https://doi.org/10.1145/3506860.3506895

  7. Goldberg, P., et al.: Attentive or Not? Toward a machine learning approach to assessing students’ visible engagement in classroom instruction. Educ. Psychol. Rev. 33(1), 27–49 (2019). https://doi.org/10.1007/s10648-019-09514-z

    Article  MathSciNet  Google Scholar 

  8. Halverson, L.R., Graham, C.R.: Learner engagement in blended learning environments: a conceptual framework. Online Learn. 23(2) (2019). https://doi.org/10.24059/olj.v23i2.148

  9. Hillaire, G.E., Schlichtmann, G., Ducharme, K.: Prototyping visual learning analytics guided by an educational theory informed goal. J. Learn. Anal. 3(3), 115–142 (2016). https://doi.org/10.18608/jla.2016.33.7

  10. Holstein, K., Hong, G., Tegene, M., McLaren, B. M., Aleven, V.: The classroom as a dashboard: Co-designing wearable cognitive augmentation for K-12 teachers. In: Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pp. 79–88 (2018). https://doi.org/10.1145/3170358.3170377

  11. Johnson, J., Conderman, G., Van Laarhoven, T., Liberty, L.: Wearable technologies: a new way to address student anxiety. Kappa Delta Pi Record 58(3), 124–129 (2022). https://doi.org/10.1080/00228958.2022.2076531

    Article  Google Scholar 

  12. Krithika, L.B., Priya, G.L.: Student emotion recognition system (SERS) for E-learning improvement based on learner concentration metric. Procedia Comput. Sci. 85, 767–776 (2016)

    Article  Google Scholar 

  13. Lechuga, C.G., Doroudi, S.: Three algorithms for grouping students: a bridge between personalized tutoring system data and classroom pedagogy. Int. J. Artif. Intell. Educ (2022). https://doi.org/10.1007/s40593-022-00309-y

  14. Li, H., Ding, W., Liu, Z.: Identifying At-Risk K-12 students in multimodal online environments: a machine learning approach (arXiv:2003.09670). arXiv. http://arxiv.org/abs/2003.09670 (2020)

  15. Martinez-Maldonado, R., Pardo, A., Mirriahi, N., Yacef, K., Kay, J., Clayphan, A.: The LATUX workflow: designing and deploying awareness tools in technology-enabled learning settings. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, pp. 1–10. https://doi.org/10.1145/2723576.2723583

  16. Nazaretsky, T., Hershkovitz, S., Alexandron, G. Kappa learning: a new item-similarity method for clustering educational items from response data, 10p (2019)

    Google Scholar 

  17. Pozdniakov, S., et al.: The question-driven dashboard: how can we design analytics interfaces aligned to teachers’ inquiry? In: LAK22: 12th International Learning Analytics and Knowledge Conference, pp. 175–185 (2022). https://doi.org/10.1145/3506860.3506885

  18. Schwendimann, B.A., et al.: Perceiving learning at a glance: a systematic literature review of learning dashboard research. IEEE Trans. Learn. Technol. 10(1), 30–41 (2017). https://doi.org/10.1109/TLT.2016.2599522

    Article  Google Scholar 

  19. Sellier, N., & An, P.: How peripheral interactive systems can support teachers with differentiated instruction: using fireflies as a probe. In: Proceedings of the 2020 ACM Designing Interactive Systems Conference, 1117–1129 (2020). https://doi.org/10.1145/3357236.3395497

  20. Shankar, S.K., Prieto, L.P., Rodríguez-Triana, M. J., Ruiz-Calleja, A.: A review of multimodal learning analytics architectures. In: 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT), pp. 212–214 (2018). https://doi.org/10.1109/ICALT.2018.00057

  21. Sharma, K., Giannakos, M.: Multimodal data capabilities for learning: what can multimodal data tell us about learning? Br. J. Edu. Technol. 51(5), 1450–1484 (2020). https://doi.org/10.1111/bjet.12993

    Article  Google Scholar 

  22. Vieira, C., Parsons, P., Byrd, V.: Visual learning analytics of educational data: a systematic literature review and research agenda. Comput. Educ. 122, 119–135 (2018). https://doi.org/10.1016/j.compedu.2018.03.018

    Article  Google Scholar 

  23. Zamecnik, A., et al.: Team interactions with learning analytics dashboards. Comput. Educ. 185, 104514 (2022). https://doi.org/10.1016/j.compedu.2022.104514

    Article  Google Scholar 

  24. Zhao, L., et al.: Modelling co-located team communication from voice detection and positioning data in healthcare simulation. In: LAK22: 12th International Learning Analytics and Knowledge Conference, pp. 370–380 (2022). https://doi.org/10.1145/3506860.3506935

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Acknowledgement

This work was supported by the first Batch of 2021 MOE of PRC Industry-University Collaborative Education Program (Program No. 202101042006, Kingfar-CES “Human Factors and Ergonomics” Program).

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Authors and Affiliations

  1. University of Nottingham Ningbo, China, 199 Taikang East Road, Ningbo, 315100, China

    Han Zhang, Xu Sun, Yanhui Zhang & Qingfeng Wang

  2. Ningbo Research Institute, Zhejiang University, 1 Qianhu South Road, Ningbo, 315100, China

    Han Zhang & Cheng Yao

  3. Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, 211 Xingguang Road, Ningbo, 315100, China

    Xu Sun

  4. Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China

    Cheng Yao

Authors
  1. Han Zhang
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  2. Xu Sun
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  3. Yanhui Zhang
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  4. Qingfeng Wang
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  5. Cheng Yao
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Corresponding author

Correspondence to Xu Sun .

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Editors and Affiliations

  1. Cyprus University of Technology, Limassol, Cyprus

    Panayiotis Zaphiris

  2. Cyprus University of Technology, Limassol, Cyprus

    Andri Ioannou

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Zhang, H., Sun, X., Zhang, Y., Wang, Q., Yao, C. (2023). Towards Personalized Instruction: Co-designing a Teacher-Centered Dashboard for Learning Engagement Analysis in Blended Learning Environments. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies. HCII 2023. Lecture Notes in Computer Science, vol 14040. Springer, Cham. https://doi.org/10.1007/978-3-031-34411-4_38

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  • DOI: https://doi.org/10.1007/978-3-031-34411-4_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34410-7

  • Online ISBN: 978-3-031-34411-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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