Collaboration Analytics Need More Comprehensive Models and Methods: An Opinion Paper

Han, Areum; Krieger, Florian; Greiff, Samuel

doi:10.18608/jla.2021.7288

Cited by 10 publications

(8 citation statements)

References 92 publications

(102 reference statements)

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“…Because all loadings are positive, this component is most likely a combination of individual activity in the game‐based learning environment. The loadings reveal that PBL provided a useful framework for how in‐game actions can be meaningfully designed and interpreted to account for the contexts of learning and accounting for how individual students interact with different activities and tasks across time and with other students (Han et al., 2021; Zimmermann et al., 2007).…”

Section: Resultsmentioning

confidence: 99%

A learning analytics approach towards understanding collaborative inquiry in a problem‐based learning environment

Saleh

Phillips

Hmelo‐Silver

et al. 2022

Brit J Educational Tech

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This exploratory paper highlights how problem‐based learning (PBL) provided the pedagogical framework used to design and interpret learning analytics from Crystal Island: EcoJourneys, a collaborative game‐based learning environment centred on supporting science inquiry. In Crystal Island: EcoJourneys, students work in teams of four, investigate the problem individually and then utilize a brainstorming board, an in‐game PBL whiteboard that structured the collaborative inquiry process. The paper addresses a central question: how can PBL support the interpretation of the observed patterns in individual actions and collaborative interactions in the collaborative game‐based learning environment? Drawing on a mixed method approach, we first analyzed students' pre‐ and post‐test results to determine if there were learning gains. We then used principal component analysis (PCA) to describe the patterns in game interaction data and clustered students based on the PCA. Based on the pre‐ and post‐test results and PCA clusters, we used interaction analysis to understand how collaborative interactions unfolded across selected groups. Results showed that students learned the targeted content after engaging with the game‐based learning environment. Clusters based on the PCA revealed four main ways of engaging in the game‐based learning environment: students engaged in low to moderate self‐directed actions with (1) high and (2) moderate collaborative sense‐making actions, (3) low self‐directed with low collaborative sense‐making actions and (4) high self‐directed actions with low collaborative sense‐making actions. Qualitative interaction analysis revealed that a key difference among four groups in each cluster was the nature of verbal student discourse: students in the low to moderate self‐directed and high collaborative sense‐making cluster actively initiated discussions and integrated information they learned to the problem, whereas students in the other clusters required more support. These findings have implications for designing adaptive support that responds to students' interactions with in‐game activities. What is already known about this topic Learning analytic methods have been effective for understanding student learning interactions for the purposes of assessment, profiling student behaviour and the effectiveness of interventions. However, the interpretation of analytics from these diverse data sets are not always grounded in theory and challenges of interpreting student data are further compounded in collaborative inquiry settings, where students work in groups to solve a problem. What this paper adds Problem‐based learning as a pedagogical framework allowed for the design to focus on individual and collaborative actions in a game‐based learning environment and, in turn, informed the interpretation of game‐based analytics as it relates to student's self‐directed learning in their individual investigations and collaborative inquiry discussions. The combination of principal component analysis and qualitat...

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Section: Resultsmentioning

confidence: 99%

A learning analytics approach towards understanding collaborative inquiry in a problem‐based learning environment

Saleh

Phillips

Hmelo‐Silver

et al. 2022

Brit J Educational Tech

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“…This is where collaborative problem-solving (ColPS; Graesser et al 2018 ) skills are of great value. ColPS has been defined in different ways (see Han et al 2021 ), but one common definition describes ColPS as “the capacity of an individual to effectively engage in a process whereby two or more agents attempt to solve a problem by sharing the understanding and effort required to come to a solution and pooling their knowledge, skills, and efforts to reach that solution” (Organization for Economic Co-operation and Development; OECD 2017 ). In a sense, ColPS reflects the application of individual problem-solving skills in a group context ( Greiff 2012 ).…”

Section: Consequential World Problems and How They Relate To Problem-...mentioning

confidence: 99%

The Fight against Infectious Diseases: The Essential Role of Higher-Order Thinking and Problem-Solving

2022

Self Cite

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The development of a vaccine marks a breakthrough in the fight against infectious diseases. However, to eradicate highly infectious diseases globally, the immunization of large parts of the population is needed. Otherwise, diseases, such as polio, measles, or more recently COVID-19, will repeatedly flare-up, with devastating effects on individuals and, in the worst case, on significant shares of the world population. For example, polio has been almost eradicated over the past three decades through an unprecedented global effort, but complete immunization has not yet been achieved. In this article, we use polio as an example to show how the global effort of developing and administering a vaccine can be understood as solving a complex problem since it involves cultural, political, and geographical barriers that demand solutions in dynamically changing and highly versatile environments. Referring to the literature on problem-solving, higher-order thinking, and complex reasoning, we demonstrate how the ability to deal with real-world environments that are complex and dynamically changing, adapting initial solutions to new circumstances and collaborate efficiently with others, has been essential for this endeavor. We argue that problem-solving abilities form one basis for solving consequential world problems.

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“…In the first article, Han, Krieger, and Greiff (2021) discuss the importance of definitions and theory for collaboration analytics.…”

Section: Summary Of Papersmentioning

confidence: 99%

Collaboration Analytics — Current State and Potential Futures

Schneider

Dowell

Thompson

2021

JLA

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This special issue brings together a rich collection of papers in collaboration analytics. With topics including theory building, data collection, modelling, designing frameworks, and building machine learning models, this issue represents some of the most active areas of research in the field. In this editorial, we summarize the papers; discuss the nature of collaboration analytics based on this body of work; describe the associated opportunities, challenges, and risks; and depict potential futures for the field. We conclude by discussing the implications of this special issue for collaboration analytics.

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Collaboration Analytics Need More Comprehensive Models and Methods: An Opinion Paper

Cited by 10 publications

References 92 publications

A learning analytics approach towards understanding collaborative inquiry in a problem‐based learning environment

A learning analytics approach towards understanding collaborative inquiry in a problem‐based learning environment

The Fight against Infectious Diseases: The Essential Role of Higher-Order Thinking and Problem-Solving

Collaboration Analytics — Current State and Potential Futures

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