Context-aware multimodal learning analytics taxonomy

Analysis of learning interactions can happen for different purposes. As educational practices increasingly take place in hybrid settings, data from both spaces are needed. At the same time, to analyse and make sense of machine aggregated data afforded by Technology-Enhanced Learning (TEL) environments, contextual information is needed. We posit that human labelled (classroom observations) and automated observations (multimodal learning data) can enrich each other. Researchers have suggested learning design (LD) for contextualisation, the availability of which is often limited in authentic settings. This paper proposes a Context-aware MMLA Taxonomy, where we categorize systematic documentation and data collection within different research designs and scenarios, paying special attention to authentic classroom contexts. Finally, we discuss further research directions and challenges.Analysis of learning interactions can happen for different purposes. As educational practices increasingly take place in hybrid settings, data from both spaces are needed. At the same time, to analyse and make sense of machine aggregated data afforded by Technology-Enhanced Learning (TEL) environments, contextual information is needed. We posit that human labelled (classroom observations) and automated observations (multimodal learning data) can enrich each other. Researchers have suggested learning design (LD) for contextualisation, the availability of which is often limited in authentic settings. This paper proposes a Context-aware MMLA Taxonomy, where we categorize systematic documentation and data collection within different research designs and scenarios, paying special attention to authentic classroom contexts. Finally, we discuss further research directions and challenges

Contextualising Learning Analytics with Classroom Observations: a Case Study

Educational processes take place in physical and digital places. To analyse educational processes, Learning Analytics (LA) enable data collection from the digital learning context. At the same time, to gain more insights, the LA data can be complemented with the data coming from physical spaces enabling Multimodal Learning Analytics (MMLA). To interpret this data, theoretical grounding or contextual information is needed. Learning designs (LDs) can be used for contextualisation, however, in authentic scenarios the availability of machine-readable LD is scarce. We argue that Classroom Observations (COs), traditionally used to understand educational processes taking place in physical space, can provide the missing context and complement the data from the colocated classrooms. This paper reports on a co-design case study from an authentic scenario that used CO to make sense of the digital traces. In this paper we posit that the development of MMLA approaches can benefit from codesign methodologies; through the involvement of the end-users (project managers) in the loop, we illustrate how these data sources can be systematically integrated and analysed to better understand the use of digital resources. Results indicate that CO can drive sense-making of LA data where predefined LD is not available. Furthermore, CO can support layered contextualisation depending on research design, rigour and systematic documentation/data collection efforts. Also, co-designing the MMLA solution with the end-users proved to be a useful approach

CIMLA: A Modular and Modifiable Data Preparation, Organization, and Fusion Infrastructure to Partially Support the Development of Context-aware MMLA Solutions

Multimodal Learning Analytics (MMLA) solutions aim to provide a more holistic picture of a learning situation by processing multimodal educational data. Considering contextual information of a learning situation is known to help in providing more relevant outputs to educational stakeholders. However, most of the MMLA solutions are still in prototyping phase and dealing with different dimensions of an authentic MMLA situation that involve multiple cross-disciplinary stakeholders like teachers, researchers, and developers. One of the reasons behind still being in prototyping phase of the development lifecycle is related to the challenges that software developers face at different levels in developing context-aware MMLA solutions. In this paper, we identify the requirements and propose a data infrastructure called CIMLA. It includes different data processing components following a standard data processing pipeline and considers contextual information following a data structure. It has been evaluated in three authentic MMLA scenarios involving different cross-disciplinary stakeholders following the Software Architecture Analysis Method. Its fitness was analyzed in each of the three scenarios and developers were interviewed to assess whether it meets functional and non-functional requirements. Results showed that CIMLA supports modularity in developing context-aware MMLA solutions and each of its modules can be reused with required modifications in the development of other solutions. In the future, the current involvement of a developer in customizing the configuration file to consider contextual information can be investigated

Context-aware multimodal learning analytics taxonomy

Analysis of learning interactions can happen for different purposes. As educational practices increasingly take place in hybrid settings, data from both spaces are needed. At the same time, to analyse and make sense of machine aggregated data afforded by Technology-Enhanced Learning (TEL) environments, contextual information is needed. We posit that human labelled (classroom observations) and automated observations (multimodal learning data) can enrich each other. Researchers have suggested learning design (LD) for contextualisation, the availability of which is often limited in authentic settings. This paper proposes a Context-aware MMLA Taxonomy, where we categorize systematic documentation and data collection within different research designs and scenarios, paying special attention to authentic classroom contexts. Finally, we discuss further research directions and challenges