Skip to main content

Representation and Visualization of Students’ Progress Data Through Learning Dashboard

  • Conference paper
  • First Online:
Advances in Computing and Data Sciences (ICACDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1441))

Included in the following conference series:

Abstract

Learning process of students is very important, it motivates the students for acquiring skills of critical thinking and gives a learning experience. Hence, a systematic feedback is required by the students for measuring their learning processes. This measurement is performed through different educational techniques namely learning analytics (LA), Education Data Mining (EDM) and Learning Dashboard (LD). Students’ learning measurement can be performed by measuring their log activity, content they read, by tracking assignment solving method etc., however in a traditional learning system, students’ learning is measured through the marks they score in the different evaluations. Therefore, these evaluations must be well structured and carefully planned in advanced. Hence the systematic management tool is required which assists the teacher as well as students for their learning progress at any time. In this paper we propose and demonstrate the Learning Dashboard in traditional learning environment which measures the different skills of students through a systematic assessment plan and the results of these assessments are displayed through an analytical report which will be helpful for all stakeholders.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dolin, J., Black, P., Harlen, W., Tiberghien, A.: Exploring relations between formative and summative assessment. In: Dolin, J., Evans, R. (eds.) Transforming Assessment. Contributions from Science Education Research, vol. 4, pp. 53–80. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-63248-3_3

  2. Paiva, R., Bittencourt, I.I., Lemos, W., Vinicius, A., Dermeval, D.: Visualizing learning analytics and educational data mining outputs. In: Penstein Rosé, C., et al. (eds.) AIED 2018. LNCS, vol. 10948, pp. 251–256. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93846-2_46

  3. Scheffel, M., Drachsler, H., Stoyanov, S., Specht, M.: Quality indicators for learning analytics. J. Educ. Technol. Soc. 17(4), 117–132 (2014)

    Google Scholar 

  4. Yoo, Y., Lee, H., Jo, I.H., Park, Y.: Educational dashboards for smart learning: review of case studies. In: Chen, G., Kumar, V., Kinshuk, Huang, R., Kong, S. (eds.) Emerging Issues in Smart Learning. LNET, pp. 145–155. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-44188-6_21

  5. Ray, S., Saeed, M.: Applications of educational data mining and learning analytics tools in handling big data in higher education. In: Alani, M., Tawfik, H., Saeed, M., Anya, O. (eds.) Applications of Big Data Analytics, pp. 135–160. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76472-6_7

  6. Brouns, F., et al.: ECO D2.5 learning analytics requirements and metrics report (2015)

    Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. Park, Y., Jo, I.H.: Development of the learning analytics dashboard to support students’ learning performance. J. Univ. Comput. Sci. 21(1), 110–133 (2015)

    Google Scholar 

  9. Jivet, I., Scheffel, M., Specht, M., Drachsler, H.: License to evaluate: preparing learning analytics dashboards for educational practice. In: Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pp. 31–40. ACM (2018)

    Google Scholar 

  10. Wong, J., et al.: Educational theories and learning analytics: from data to knowledge. In: Ifenthaler, D., Mah, D.-K., Yau, J.-K. (eds.) Utilizing Learning Analytics to Support Study Success, pp. 3–25. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-64792-0_1

    Chapter  Google Scholar 

  11. Mutlu, B., Simic, I., Cicchinelli, A., Sabol, V., Veas, E.: Towards a learning dashboard for community visualization. In: Proceedings of the 1th Workshop on Analytics for Everyday Learning co-located with the 13th European Conference on Technology Enhanced Learning (EC-TEL 2018), Leeds, UK, pp. 1–10. CEUR Workshop Proceedings (2018)

    Google Scholar 

  12. Bodily, R., Verbert, K.: Trends and issues in student-facing learning analytics reporting systems research. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 309–318. ACM (2017)

    Google Scholar 

  13. Viberg, O., Hatakka, M., Bälter, O., Mavroudi, A.: The current landscape of learning analytics in higher education. Comput. Hum. Behav. 89, 98–110 (2018)

    Article  Google Scholar 

  14. Bennett, L., Folley, S.: Four design principles for learner dashboards that support student agency and empowerment. J. Appl. Res. High. Educ. 1, 15–26 (2019)

    Article  Google Scholar 

  15. Howell, J.A., Roberts, L.D., Mancini, V.O.: Learning analytics messages: impact of grade, sender, comparative information and message style on student affect and academic resilience. Comput. Hum. Behav. 89, 8–15 (2018)

    Article  Google Scholar 

  16. Haendchen Filho, A., Tomazoni, E.K., Paza, R., Perego, R., Raabe, A.: Bloom’s taxonomy-based approach for assisting formulation and automatic short answer grading. In: Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), 29, pp. 238–247 (2018)

    Google Scholar 

  17. Wilson, L.O.: Anderson and Krathwohl–Bloom’s taxonomy revised (2018). https://thesecondprinciple.com/teaching-essentials/beyond-bloom-cognitive-taxonomy-revised. Accessed

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vaidya, A., Sharma, S. (2021). Representation and Visualization of Students’ Progress Data Through Learning Dashboard. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-030-88244-0_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-88244-0_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88243-3

  • Online ISBN: 978-3-030-88244-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics