Investigating Students’ Experiences with Collaboration Analytics for Remote Group Meetings

Zhou, Qi; Suraworachet, Wannapon; Pozdniakov, Stanislav; Martínez-Maldonado, Roberto; Bartindale, Tom; Chen, Peter; Richardson, Dan; Cukurova, Mutlu

doi:10.1007/978-3-030-78292-4_38

Cited by 17 publications

(6 citation statements)

References 46 publications

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“…As Bourdieu ( 2000 , p.4430) states, “Habitus is not destiny; but symbolic action cannot, on its own, without transformation of the conditions of the production and transformation of dispositions, extirpate bodily beliefs, which are passions and drives that remain totally indifferent to the injunctions or condemnations of humanistic universalism (itself, moreover, rooted in dispositions and beliefs)”, which emphasizes the difficulties in transforming people's engrained habits and implicit beliefs. Similar results of regressing back to “normal behaviors” were also observed in semester-long analytics visualization interventions for students (e.g., Zhou et al, 2021 ). As such, future research should seek to measure the effects of data-driven technologies on teaching behaviors over an extended period, to allow time for change to become integrated into teachers' habitus and sustained.…”

Section: Discussionsupporting

confidence: 70%

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.

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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.

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Section: Discussionsupporting

confidence: 70%

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

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“…As a result, "passengers" would be expected to report higher peer engagement. However, previous research reported that students may have misconceptions about others' engagement during collaboration without any help [52]. According to our result, some students who occupy too much speaking time in group discussions may not be aware of others' engagement in collaborative learning.…”

Section: Discussioncontrasting

confidence: 47%

“…Since driver students were actively discussing with each member of the group, they tended to think everyone was actively involved in collaboration. Therefore, as previous research suggested, providing information on individual student's engagement may help them to be aware of each other's contributions to collaborative learning [52]. In this study, neither the "drivers" nor the passengers were aware of others' engagement accurately, therefore needed external scaolding to regulate their own behaviours as well as others' behaviours.…”

Section: Discussionmentioning

confidence: 80%

“…Therefore, visualising individual engagement through the proposed approach might be a potential support for students to socially share regulated learning. However, it is worth noting that, a previous study reported students' dierent experiences of engagement compared with the machine-detected engagement [52]. This is not an attempt at retrospective correction of opinions.…”

Section: Discussionmentioning

confidence: 92%

“…Third, existing research illustrated the possibility of applying learning analytics to provide learning support and feedback for learners [41] and teachers [33] in collaborative learning. Specically, Zhou et al, [52] explored the potential of a learning analytics tool, ZoomSense, which can visualise students' speaking time and turn-talking behaviours, to support students in online synchronous collaborative learning. It is reported that the learning analytics tool can help students to be aware of each other's contributions to the group discussion and make changes accordingly to foster collaboration.…”

Section: Background 21 Analytics Of Collaborative Learningmentioning

confidence: 99%

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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

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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.

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Examining the Relationship Between Reflective Writing Behaviour and Self-regulated Learning Competence: A Time-Series Analysis

Suraworachet

Villa-Torrano

Zhou

et al. 2021

Lecture Notes in Computer Science

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Self-Regulated Learning (SRL) competence is imperative to academic achievement. For reflective academic writing tasks, which are common for university assessments, this is especially the case since students are often required to plan the task independently to be successful. The purpose of the current study was to examine different reflection behaviours of postgraduate students that were required to reflect on individual tasks over a fifteen-week-long higher education course. Forty students participated in a standardised questionnaire at the beginning of the course to assess their SRL competence and then participated in weekly individual reflection tasks on Google Docs. We examined students' reflective writing behaviours based on time-series and correlation analysis of fine-grained data retrieved from Google Docs. More specifically, reflection behaviours between students with high SRL and low SRL competence were investigated. The results show that students with high SRL competence tend to reflect more frequently and more systematically than students with low SRL competence. Even though no statistically significant difference in academic performance between the two groups was found, there were statistical correlations between academic performance and individual reflective writing behaviours. We conclude the paper with a discussion on the insights into the temporal reflection patterns of different SRL competence student clusters, the impact of these behaviours on students' academic performance, and potential suggestions for appropriate support for students with different levels of SRL.

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Investigating Students’ Experiences with Collaboration Analytics for Remote Group Meetings

Cited by 17 publications

References 46 publications

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

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

Examining the Relationship Between Reflective Writing Behaviour and Self-regulated Learning Competence: A Time-Series Analysis

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