The Question-driven Dashboard: How Can We Design Analytics Interfaces Aligned to Teachers’ Inquiry?

Pozdniakov, Stanislav; Martínez-Maldonado, Roberto; Tsai, Yi‐Shan; Cukurova, Mutlu; Bartindale, Tom; Chen, Peter; Marshall, Harrison; Richardson, Dan; Gašević, Dragan

doi:10.1145/3506860.3506885

Cited by 21 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Findings from the interviews clearly showed participants' expectation of the dashboard and, later, their interpretations of and reflections on student engagement in relation to the specific teaching context (e.g., remote teaching during COVID-19 lockdown). They showed that the qualitative interviews employed in this study served not only to collect data for research purposes but to promote users' reflective practice, a desired outcome of dashboard applications (e.g., Pozdniakov et al, 2022;Rodrı´guez-Triana et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Analytics-Informed Design: Exploring Visualization of Learning Management Systems Recorded Data for Learning Design

Liu,

Gladman,

Muir

et al. 2023

SAGE Open

View full text Add to dashboard Cite

One apparent challenge associated with learning analytics (LA) has been to promote adoption by university educators. Researchers suggest that a visualization dashboard could serve to help educators use LA to improve learning design (LD) practice. We therefore used an educational design approach to develop a pedagogically useful and easy-to-use LA visualization solution to support data-informed LD. We interviewed four staff in a medical degree program at a New Zealand university, designed and piloted a dashboard, and evaluated it through interviews. As a proof-of-concept project, our study showed that educational design research could be meaningfully used to develop a visualization dashboard that is easy-to-use and useful. In particular, the preliminary design principles identified provide implications for practitioners who are seeking to use LA to inform LD. Finally, we reflect on the purpose of visualization dashboards in relation to the literature and identify areas for future developments.

show abstract

Section: Discussionmentioning

confidence: 99%

Analytics-Informed Design: Exploring Visualization of Learning Management Systems Recorded Data for Learning Design

Liu,

Gladman,

Muir

et al. 2023

SAGE Open

View full text Add to dashboard Cite

show abstract

“…As discussed in the previous section, since data storytelling techniques can also facilitate sensemaking of data visualizations (Echeverria et al, 2017 ), it might also be considered a cognitive strategy. Moreover, through participatory and inquiry-driven design of data visualizations with teachers (e.g., Pozdniakov et al, 2022 ), teacher biases can be made explicit and potentially addressed in a safe and supportive social environment before they are fed into to the final data visualizations and narratives.…”

Section: Discussionmentioning

confidence: 99%

Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases

Gauthier

Rizvi

Cukurova

et al. 2022

Front. Artif. Intell.

Self Cite

View full text Add to dashboard Cite

Data-driven technologies for education, such as artificial intelligence in education (AIEd) systems, learning analytics dashboards, open learner models, and other applications, are often created with an aspiration to help teachers make better, evidence-informed decisions in the classroom. Addressing gender, racial, and other biases inherent to data and algorithms in such applications is seen as a way to increase the responsibility of these systems and has been the focus of much of the research in the field, including systematic reviews. However, implicit biases can also be held by teachers. To the best of our knowledge, this systematic literature review is the first of its kind to investigate what kinds of teacher biases have been impacted by data-driven technologies, how or if these technologies were designed to challenge these biases, and which strategies were most effective at promoting equitable teaching behaviors and decision making. Following PRISMA guidelines, a search of five databases returned n = 359 records of which only n = 2 studies by a single research team were identified as relevant. The findings show that there is minimal evidence that data-driven technologies have been evaluated in their capacity for supporting teachers to make less biased decisions or promote equitable teaching behaviors, even though this capacity is often used as one of the core arguments for the use of data-driven technologies in education. By examining these two studies in conjunction with related studies that did not meet the eligibility criteria during the full-text review, we reveal the approaches that could play an effective role in mitigating teachers' biases, as well as ones that may perpetuate biases. We conclude by summarizing directions for future research that should seek to directly confront teachers' biases through explicit design strategies within teacher tools, to ensure that the impact of biases of both technology (including data, algorithms, models etc.) and teachers are minimized. We propose an extended framework to support future research and design in this area, through motivational, cognitive, and technological debiasing strategies.

show abstract

“…It is found that although it can help teachers with their sensemaking process of students' learning process, teachers were concerned about the lack of transparency in terms of the visual interface and the algorithms behind it. The authors argued that this concern reected a wider debate on how to provide insights into the design decisions of the analytics intended to represent the end users [32].…”

Section: Background 21 Analytics Of Collaborative Learningmentioning

confidence: 99%

Harnessing Transparent Learning Analytics for Individualized Support through Auto-detection of Engagement in Face-to-Face Collaborative Learning

Zhou,

Suraworachet,

Cukurova

2024

Proceedings of the 14th Learning Analytics and Knowledge Conference

Self Cite

View full text Add to dashboard Cite

Using learning analytics to investigate and support collaborative learning has been explored for many years. Recently, automated approaches with various articial intelligence approaches have provided promising results for modelling and predicting student engagement and performance in collaborative learning tasks. However, due to the lack of transparency and interpretability caused by the use of "black box" approaches in learning analytics design and implementation, guidance for teaching and learning practice may become a challenge. On the one hand, the black box created by machine learning algorithms and models prevents users from obtaining educationally meaningful learning and teaching suggestions. On the other hand, focusing on group and cohort level analysis only can make it dicult to provide specic support for individual students working in collaborative groups. This paper proposes a transparent approach to automatically detect student's individual engagement in the process of collaboration. The results show that the proposed approach can reect student's individual engagement and can be used as an indicator to distinguish students with dierent collaborative learning challenges (cognitive, behavioural and emotional) and learning outcomes. The potential of the proposed collaboration analytics approach for scaolding collaborative learning practice in face-to-face contexts is discussed and future research suggestions are provided. CCS CONCEPTS• Applied computing ! Education; Collaborative learning; • Human-centered computing ! Collaborative and social computing; Empirical studies in collaborative and social computing.

show abstract

The Question-driven Dashboard: How Can We Design Analytics Interfaces Aligned to Teachers’ Inquiry?

Cited by 21 publications

References 23 publications

Analytics-Informed Design: Exploring Visualization of Learning Management Systems Recorded Data for Learning Design

Analytics-Informed Design: Exploring Visualization of Learning Management Systems Recorded Data for Learning Design

Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases

Harnessing Transparent Learning Analytics for Individualized Support through Auto-detection of Engagement in Face-to-Face Collaborative Learning

Contact Info

Product

Resources

About