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Semelhante a Lak18 - Driving Data Storytelling from Learning Design
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Lak18 - Driving Data Storytelling from Learning Design
- 1. Driving Data Storytelling from Learning Design Vanessa Echeverria, Roberto Martinez-Maldonado, Roger Granda, Katherine Chiluiza, Cristina Conati, Simon Buckingham Shum
- 2. “A learning dashboard is a single display that aggregates different indicators about learner(s), learning processes, or learning contexts into one or multiple visualisations” Learning Dashboards Schwendimann, et al., 2017
- 3. Growing concern about rolling out dashboards (Bodily & Verbert, 2017, Reimers & Neovesky, 2015) poor InfoViz design (Reimers & Neovesky, 2015) lack of users’ participation (Schwendimann, et al., 2017, Jivet el al., 2018) (Greller & Drachsler, 2012) (Bakharia et al., 2016) (Aguilar, 2016; Corrin & de Barba, 2015; Teasley, 2017) disconnection with the learning context and audience misalignment with the learning design data literacy requirements possible negative effects on learning!
- 5. Data storytelling principles Goal oriented
- 6. Data storytelling principles Focus of attention Knaflic - Storytelling with Data (2015)
- 7. Data storytelling principles Appropriate visual Knaflic - Storytelling with Data (2015)
- 8. Knaflic - Storytelling with Data (2015)
- 9. Knaflic - Storytelling with Data (2015)
- 10. Knaflic - Storytelling with Data (2015) Prescriptive title Narrative sColour
- 11. Knaflic - Storytelling with Data (2015)
- 12. Knaflic - Storytelling with Data (2015)
- 14. Data storytelling elements Description Heading Shows a straightforward message from the graph Lines to Emphasise relevant information Key data points Focus attention on key data Decluttering Remove elements that do not add value to the graph Text labels Add narratives to the graph Shaded areas Add context to a group of data points
- 15. Adapted from Verbert et al. Learning design driven conceptual model
- 16. Adapted from Verbert et al. Learning Design driven conceptual model Learning design driven data storytelling approach Data DS processing DS Visual analytics
- 17. Adapted from Verbert et al. Learning Design driven conceptual model Learning design driven data storytelling approach Data Learning design DS processing DS Visual analytics Data storytelling (DS) elements
- 18. Adapted from Verbert et al. Learning Design driven conceptual model Learning design driven data storytelling approach Data Learning design DS processing DS Visual analytics Learning intentions (rules) Data storytelling (DS) elements
- 19. Adapted from Verbert et al. Learning Design driven conceptual model Learning design driven data storytelling approach Data Learning design DS processing DS Visual analytics Responses Actions Analytics Learning intentions (rules) Data storytelling (DS) elements Questions
- 20. Equal participation Highlight where there are not equal participation Text label, colours, data points Learning design Learning intentions Data storytelling elements
- 21. Potential role that data storytelling elements play to support interpretations of visual learning analytics
- 22. Previous Study 15 students - 5 groups Collaborative Design activity Participation Performance
- 23. Collaborative Database Design Activity 15 undergrad. students Student – facing dashboards Learning Situation
- 24. Previous Study 15 students - 5 groups Collaborative Design activity Participation Performance 2 groups Data Storytelling Participation Performance
- 25. Participation
- 27. Performance
- 29. Previous Study Evaluation with teachers 15 students - 5 groups Collaborative Design activity Participation Performance 6 teachers M1: Focus of attention (Eye tracker) M2: Helpfulness of data storytelling elements (ranking tool) 2 groups Data Storytelling Participation Performance M3: Orchestration support and interpretation of stories (interview/think aloud)
- 30. M1: Focus of attention http://bit.ly/teachersHeatmaps
- 31. M1: Focus of attention http://bit.ly/teachersHeatmaps
- 32. M2: Helpfulness of data storytelling elements
- 33. M2: Helpfulness of data storytelling elements Add context to the visualisation
- 34. text labels "add noise and graph complexity" M2: Helpfulness of data storytelling elements prescriptive title carefully crafted, due to possible misunderstandings
- 35. "… these visualisations (with data storytelling elements) show me exactly what I need. I don’t have to guess what happened in the activity" M3: Orchestration support and interpretation of stories Interpretation
- 36. " … the visualization (with data storytelling elements) shows me what elements students have created very clearly, compared to the other (original )visualization " M3: Orchestration support and interpretation of stories Interpretation
- 37. " With the original visualisation, I would have to explore the whole visualisation in order to understand team’s performance" M3: Orchestration support and interpretation of stories Interpretation
- 38. " … the shaded area in the graph made me aware that groups took very different amounts of time to reflect and so, with this information, I would encourage students to reflect for a longer time before submitting a final version of the task" M3: Orchestration support and interpretation of stories Orchestration
- 39. " … the title in the graph made me think that this group needed to collaborate more equally" M3: Orchestration support and interpretation of stories Orchestration
- 40. Summary Learning design driven data storytelling approach Data Learning design DS processing DS Visual analytics Responses Actions Analytics Learning intentions (rules) Data storytelling (DS) elements Questions
- 41. Summary Learning design driven data storytelling approach Data Learning design DS processing DS Visual analytics Responses Actions Analytics Learning intentions (rules) Data storytelling (DS) elements Questions
- 42. Equal participation Highlight where there are not equal participation Text label, colours, data points Learning design Rules Data storytelling elements participant = dominates(P1,P2,P3, time) msg = ‘participant %d did not have an equal participation during %d time’, (participant,time) Learning intentions
- 43. Implementation of learning design rules Generate dashboard close to real time Look impact on debriefing to teachers/learners Further directions
Notas do Editor
- What it is important? What story is the data telling them? Dashboard that can be used quickly and efficiently, information and a glance
- From recent literature, we have found that learning dashboards have some criticisms such as: In short, these can be considered as analytics dashboards rather than for supporting learning
- learning dashboards - generic charts - how can we enhance these dashboards tru the use of DS Plots, narratives, call to action Exploratory vs. explanatory dashboards
- communicate a key message clearly and effectively emphasizing the context and meaning
- These principles are taken from an Info Vis perspective (data and visual) and data storytelling techniques (narratives, plot, call to action) Exploratory vs. explanatory dashboards explain data storytelling - info vis approach 1. enhance sm through DS use this in one slide
- These principles are taken from an Info Vis perspective (data and visual) and data storytelling techniques (narratives, plot, call to action) Exploratory vs. explanatory dashboards explain data storytelling - info vis approach 1. enhance sm through DS use this in one slide
- This is a pre-post questionnaire applied to children who participated in a pilot science program A project manager wants to convince the board committee to continue with a science program from children at a school
- budget
- IT manager wants to show to the board committee that they need another technician in their team
- adapted from previous work, here we can see the data-driven visual analytics approach. From the data captured by logs, clickstreams, etc, one can generate visualisations and then, the user will reflect upon this, based on the exploration of the vis If the tutor will be sitted with the learner, what would s/he said? what would recommend?
- our approach takes the learning design as part of the generation of analytics. The learning design, which describes the educational process of the whole teaching/learning experience, has valuable information about the pedagogical scenario. From this, now we can know what are the right things that tutors and learners need to be informed. First DS How to use them using the learning design
- With DS elements, our aim is to reduce cognitive load of interpretation of vis, give a very concise take away message to the audience
- From the learning design, we can take some learning intentions, and convert them into rules, to then automatically add DS elements into the processind and visualisation of learning data. It is important to note that, this takes and explanatory approach - not exploratory that is up to the analyst - this is the idea, we are here, we are aiming to this add table with one rule
- The purpose of this research is to all white
- change fig remove icons left - previous study right - evaluation with teachers
- visualisations were used for debrief - for both users - teacher and learner
- change fig remove icons left - previous study right - evaluation with teachers explain about both facing
- split the figures put in english version (figs)
- split the figures put in english version (figs) before and after vis removing the buubles
- change fig remove icons left - previous study right - evaluation with teachers we randomly selected the visualisations assiting for debriefing, make debriefing more effective
- thumbnails of other vis big hotspots on the right
- thumbnails of other vis big hotspots on the right show only 1 example we started to experimenting with eye tracking to assess the impact of visual attention how we can experimenting with eye trackers
- - one message -explanatory diagram - venn diagram
- - one message -explanatory diagram - venn diagram
- generate dashboard close to real time and look impact on debriefing to teachers/learners
- Rules Data literacy