Capturing who participates and how: the stability of classroom observations using EQUIP

Reinholz, Daniel L.; Pelaez, Kevin; Shah, Niral

doi:10.1007/s43545-021-00190-x

Cited by 3 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further explorations are required to assess the usability of the visualizations with young learners. We also suggest follow‐up interview studies to investigate how students make sense of various visualizations and to further refine the current visualization designs (Reinholz et al, 2023). These efforts will contribute to enhancing the effectiveness of the iTalk–iSee intervention for promoting participation equity among young students.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Improving participation equity in dialogic collaborative problem solving: A participatory visual learning analytical approach

Hu,

Chen,

2024

Computer Assisted Learning

View full text Add to dashboard Cite

BackgroundThe existing research on dialogue‐based learning and teaching predominantly highlights its capacity to yield productive educational outcomes, yet it often overlooks the pivotal factor of participation equity, which is fundamental to ensuring the efficacy of dialogic teaching and learning.ObjectivesIn this study, participation equity refers to a condition in which participation itself and opportunities to participate are fairly distributed among participants and all participants are equally listened to and respected. We designed a technology‐enhanced participatory visual learning analytical approach to promote equitable participation in dialogic collaborative problem solving from four dimensions: participation (i.e., ensuring equal contributions from all participants), opportunity (i.e., promoting equal engagement with others), responsiveness (i.e., encouraging equal attentiveness and responsiveness to others' input), and respect (i.e., cultivating a respectful communication style).MethodsThe intervention class of fourth‐grade students (n = 59) interacted with a participatory visual learning analytical tool to reflect on their participation equity and learn productive peer talk moves to address equity issues concerning the four dimensions, while a comparison class (n = 59) only received simple feedback on participation and respect.ResultsThe results indicated that equal participation rates among group members were insufficient to secure participation equity. The intervention was effective in helping students realize and address equity‐related issues. Intervention students were more equitable regarding responsiveness and participation opportunity than the comparison students.ConclusionsThe proposed multidimensional participation equity framework has the potential to deepen the understanding of equity and promote equitable learning interactions.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Further explorations are required to assess the usability of the visualizations with young learners. We also suggest follow-up interview studies to investigate how students make sense of various visualizations and to further refine the current visualization designs (Reinholz et al, 2023).…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Improving participation equity in dialogic collaborative problem solving: A participatory visual learning analytical approach

Hu,

Chen,

2024

Computer Assisted Learning

View full text Add to dashboard Cite

show abstract

“…The included studies by Reinholz et al ( 2020a , b ), as well as their associated publications (Sukumar et al, 2020 ; Reinholz et al, 2021 ), are a good starting point toward the above call to action. Not only were these studies explicitly designed to mitigate teachers' implicit biases, but they measured the impact of the intervention.…”

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.

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

ClassID: Enabling Student Behavior Attribution from Ambient Classroom Sensing Systems

Patidar,

Ngoon,

Zimmerman

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

Ambient classroom sensing systems offer a scalable and non-intrusive way to find connections between instructor actions and student behaviors, creating data that can improve teaching and learning. While these systems effectively provide aggregate data, getting reliable individual student-level information is difficult due to occlusion or movements. Individual data can help in understanding equitable student participation, but it requires identifiable data or individual instrumentation. We propose ClassID, a data attribution method for within a class session and across multiple sessions of a course without these constraints. For within-session, our approach assigns unique identifiers to 98% of students with 95% accuracy. It significantly reduces multiple ID assignments compared to the baseline approach (3 vs. 167) based on our testing on data from 15 classroom sessions. For across-session attributions, our approach, combined with student attendance, shows higher precision than the state-of-the-art approach (85% vs. 44%) on three courses. Finally, we present a set of four use cases to demonstrate how individual behavior attribution can enable a rich set of learning analytics, which is not possible with aggregate data alone.

show abstract

Capturing who participates and how: the stability of classroom observations using EQUIP

Cited by 3 publications

References 18 publications

Improving participation equity in dialogic collaborative problem solving: A participatory visual learning analytical approach

Improving participation equity in dialogic collaborative problem solving: A participatory visual learning analytical approach

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

ClassID: Enabling Student Behavior Attribution from Ambient Classroom Sensing Systems

Contact Info

Product

Resources

About