What information should CSCL teacher dashboards provide to help teachers interpret CSCL situations?

Leeuwen, Anouschka van; Rummel, Nikol; Gog, Tamara van

doi:10.1007/s11412-019-09299-x

Cited by 58 publications

(39 citation statements)

References 59 publications

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“…Nowadays, learning analytics research focused on supporting collaboration and group learning has diversified, and perhaps even fragmented, across various research communities, such as CSCL (e.g., Spikol, Ruffaldi, & Cukurova, 2017a), technologyenhanced learning (e.g., Praharaj, Scheffel, Drachsler, & Specht, 2018), team science (e.g., Kim, Sottilare, Brawner, & Flowers, 2018), and human-computer interaction (e.g., Chandrasegaran, Bryan, Shidara, Chuang, & Ma, 2019). Novel analytic approaches have emerged and are applied to study collaboration with a wide variety of purposes, such as assessment and measurement of collaborative learning (Khan, 2017), theory building (Malmberg et al, 2018), orchestration support (Olsen, 2017), dashboards design (van Leeuwen, Rummel, & van Gog, 2019), and user modelling (Worsley, 2019). The emergence of accurate and inexpensive sensors is also enabling the exploration of multimodal aspects of group interaction, such as gaze tracking for measuring joint attention of students (Schneider & Pea, 2017), physiological sensing for identifying group synchrony Schneider, Dich, & Radu, 2020), and gesture/motion tracking for inferring collaboration strategies in maker-spaces (Worsley & Blikstein, 2018).…”

Section: Introductionmentioning

confidence: 99%

What Do You Mean by Collaboration Analytics? A Conceptual Model

Martínez-Maldonado

Gašević

Echeverria

et al. 2021

JLA

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Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to computational methods for identifying salient aspects of collaboration from multiple group data sources for learners, educators, or other stakeholders to gain and act upon insights. Yet, it remains unclear how collaboration analytics go beyond previous work focused on modelling group interactions for the purpose of adapting instruction. This paper provides a conceptual model of collaboration analytics to help researchers and designers identify the opportunities enabled by such innovations to advance knowledge in, and provide enhanced support for, collaborative learning and teamwork. We argue that mapping from low-level data to higher-order constructs that are educationally meaningful, and that can be understood by educators and learners, is essential to assessing the validity of collaboration analytics. Through four cases, the paper illustrates the critical role of theory, task design, and human factors in the design of interfaces that inform actionable insights for improving collaboration and group learning.

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Section: Introductionmentioning

confidence: 99%

What Do You Mean by Collaboration Analytics? A Conceptual Model

Martínez-Maldonado

Gašević

Echeverria

et al. 2021

JLA

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“…Previous research has implicitly considered that roles can be shared among multiple entities (Sharples 2013;Prieto 2012;Prieto et al 2015) but only recently have researchers investigated what it means to have an effective shared control for different types of support (Holstein et al 2020). Much of the current research focuses on the design of co-orchestration systems to support teachers in supporting student learning during a single activity (Holstein et al 2019;van Leeuwen et al 2019). Although the needs and values of the teacher may be the same whether they are providing cognitive, social, or social transition support, the role that they want to play in each of these processes may differ.…”

Section: Introductionmentioning

confidence: 99%

Designing for the co-Orchestration of Social Transitions between Individual, Small-Group and Whole-Class Learning in the Classroom

Olsen

Rummel

Aleven

2020

Int J Artif Intell Educ

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Educational technologies are often developed such that students work on specific social levels (e.g., individual, small group, whole class) at specific times. However, in the reality of the classroom, learning activities are not so cleanly divided, with transitions occurring between social levels for students at different times. To support these social transitions in a way that can promote student learning, we need to lower the teacher’s orchestration load around managing fluid social transitions. Co-orchestration, in which the orchestration decisions are shared between different parties, can help to lower the orchestration load when it is designed according to the teacher’s values and classroom culture. In this paper, we present a taxonomy of social transitions and investigate how the responsibilities of orchestration can be divided between primary school teachers and a co-orchestration system in order to support the extension from rigid social transitions to fluid transitions in technology-enhanced classrooms. Across two studies, we used a design process involving co-design and prototyping with teachers. We uncovered and refined co-orchestration design desires that balance teachers’ orchestration loads while providing them with a sense of control. We present six design desires for maintaining a balance between teacher and system responsibilities regarding the orchestration of social transitions that can be implemented, such as in our mid-fidelity prototype, to support the range of social transitions. The list of desires contributes to co-orchestration research and more broadly technology design for classrooms by highlighting the changing balance of teacher control depending on what is the focus of the orchestration support.

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“…Teacher awareness relates to operational definitions such as detection, attention, and interaction by teachers [36], [42], [45], [71], [85]. Schwendimann et al also mention "teacher awareness (of students)" as a construct for the evaluation of learning analytics dashboard [10].…”

Section: Analysis and Refinement Of The Classification Schemementioning

confidence: 99%

“…Schwendimann et al also mention "teacher awareness (of students)" as a construct for the evaluation of learning analytics dashboard [10]. Teacher productivity relates both to efficiency and effectiveness: operational definitions in this subcategory include, e.g., the number of messages a teacher sends [36], [42], the time it takes a teacher to respond or assess time [40], [85] and the quality of assessment by a teacher [85], [87]. We also recognize this subcategory in a previous review, that identifies "productivity and effectiveness in teaching" as an outcome of learning analytics interventions [22].…”

Section: Analysis and Refinement Of The Classification Schemementioning

confidence: 99%

Where is the Learning in Learning Analytics? A Systematic Literature Review on the Operationalization of Learning-Related Constructs in the Evaluation of Learning Analytics Interventions

Knobbout

Stappen

2020

IEEE Trans. Learning Technol.

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Learning technologies enable interventions in the learning process aiming to improve learning. Learning analytics provides such interventions based on analysis of learner data, which are believed to have beneficial effects on both learning and the learning environment. Literature reporting on the effects of learning analytics interventions on learning allows us to assess in what way learning analytics improves learning. No standard set of operational definitions for learning affected by learning analytics interventions is available. We performed a systematic literature review of 1932 search hits, which yielded 62 key studies. We analyzed how affected learning was operationalized in these key studies and classified operational definitions into three categories: 1) learning environment; 2) learning process; and 3) learning outcome. A deepening analysis yielded a refined classification scheme with 11 subcategories. Most of the analyzed studies relate to either learning outcome or learning process. Only nine of the key studies relate to more than one category. Given the complex nature of applying learning analytics interventions in practice, measuring the effects on a wider spectrum of aspects can give more insight into the workings of learning analytics interventions on the different actors, processes, and outcomes involved. Based on the results of our review, we recommend making deliberate decisions on the (multiple) aspects of learning one tries to improve by applying learning analytics. Our refined classification with examples of operational definitions may help both academics and practitioners doing so, as it allows for a more structured, grounded, and comparable positioning of learning analytics benefits.

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What information should CSCL teacher dashboards provide to help teachers interpret CSCL situations?

Cited by 58 publications

References 59 publications

What Do You Mean by Collaboration Analytics? A Conceptual Model

What Do You Mean by Collaboration Analytics? A Conceptual Model

Designing for the co-Orchestration of Social Transitions between Individual, Small-Group and Whole-Class Learning in the Classroom

Where is the Learning in Learning Analytics? A Systematic Literature Review on the Operationalization of Learning-Related Constructs in the Evaluation of Learning Analytics Interventions

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