Data Storytelling for Feedback Analytics

Bhagya Maheshi, Mikaela Elizabeth Milesi, Hiruni Palihena, Aaron Zheng, Roberto Martinez-Maldonado, Yi Shan Tsai

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Feedback is an essential process of learning in higher education. Yet, capturing students’ interactions with feedback is challenging, which makes it difficult to evaluate its impact. Learning Analytics (LA) is a potential solution to address this issue as it is capable of capturing and analysing learners’ activities in a technology-enabled learning environment. LA often use dashboards to deliver insights derived from educational data, yet questions remain on how to most effectively communicate key insights to students. Data Storytelling (DS) is a promising technique to address this challenge by combining data, visuals and narrative to convey key insights. Co-design can facilitate the crafting of visualisations and data stories that best aligns with goals of the students. This study presents the preliminary findings from a design sprint conducted with students to co-design a prototype for a dashboard of an LA solution – PolyFeed – that captures and analyses students’ interactions with feedback. In developing the dashboards, students used DS principles – Explanatory titles, Annotations, Highlighting important data points, and Decluttering – to improve the selected visualisations. The results show that the student groups perceived visualising strengths and weaknesses identified in feedback, action plans based on feedback, and trends in their performance as key aspects to include in FA dashboard. However, they primarily used two DS principles: explanatory titles and highlighting key data points to improve visualisations because the dataset was pre-dominantly qualitative. Therefore, the effective use of DS to support qualitative data should be further explored.

Original languageEnglish
Title of host publication2024 Joint of International Conference on Learning Analytics and Knowledge Workshops
EditorsMartin Hlosta, Ivan Moser, Brendan Flanagan, Gloria Milena Fernandez-Nieto, Lixiang Yan, Angela Stewart, Amir Winer, Nitza Geri, Umesh Ramnarain, Christo van der Westhuizen, Atsushi Shimada, Fumiya Okubo, Hsiao-Ting Tseng, Albert C.M. Yang, Owen H.T. Lu, Hiroaki Ogata, Vanessa Echeverria, Roberto Martinez-Maldonado, Yi-shan Tsai, Lu Lawrence, Shaveen Singh, Stanislav Pozdniakov, Lujie Karen Chen, Jiaqi Gong, Louise Yarnall, Andy Nguyen, Lele Sha, Jionghao Lin, Mutlu Cukurova, Kshitij Sharma, Linxuan Zhao, Yuheng Li, Yueqiao Jin, Dragan Gašević, Caitlin Mills, Stephen Hutt
Place of PublicationAachen Germany
Publisherceur-ws.org
Pages118-123
Number of pages6
Publication statusPublished - 2024
EventData Storytelling and Learning Analytics Workshop 2024 - Kyoto, Japan
Duration: 19 Mar 202419 Mar 2024
https://datastorytelling-education.github.io/index (Website)
https://ceur-ws.org/Vol-3667/ (Proceedings)

Publication series

NameCEUR Workshop Proceedings
Publisher CEUR-WS
Volume3667
ISSN (Print)1613-0073

Conference

ConferenceData Storytelling and Learning Analytics Workshop 2024
Abbreviated titleDS-LAK24
Country/TerritoryJapan
CityKyoto
Period19/03/2419/03/24
Internet address

Keywords

  • Data Storytelling
  • Feedback Analytics
  • Feedback Traceability
  • Information Visualisation
  • Learning Analytics

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