Which Strategies are Used in the Design of Technical LA Infrastructure?: A Qualitative Interview Study

Ciordas-Hertel, George-Petru; Schneider, Ján; Drachsler, Hendrik

doi:10.1109/educon45650.2020.9125363

Cited by 7 publications

(9 citation statements)

References 22 publications

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“…To the best of our knowledge, nine new data infrastructures have been proposed in MMLA after our review , Munoz et al, 2018, Tamura et al, 2019, Ciordas-Hertel et al, 2019, Huertas Celdrán et al, 2020, Camacho et al, 2020, Domínguez et al, 2021, Serrano Iglesias et al, 2021, Slupczynski and Klamma, 2021. The main focus of eight of these infrastructures is to solve other aspects rather than following a standard data processing pipeline (for example, educational aspects -individual learner's engagement [Camacho et al, 2020], smart learning environments [Serrano Iglesias et al, 2021], inclusive learning [Tamura et al, 2019], oral presentations [Munoz et al, 2018], and customizable learning experience ; technical aspects -dynamic deployment of MMLA instances in smart classroom [Huertas Celdrán et al, 2020], using AI and blockchain [Slupczynski and Klamma, 2021], and wearables [Camacho et al, 2020]; or adoption aspects -at institutional level [Domínguez et al, 2021]).…”

Section: Existing Mmla Infrastructuresmentioning

confidence: 99%

“…The main focus of eight of these infrastructures is to solve other aspects rather than following a standard data processing pipeline (for example, educational aspects -individual learner's engagement [Camacho et al, 2020], smart learning environments [Serrano Iglesias et al, 2021], inclusive learning [Tamura et al, 2019], oral presentations [Munoz et al, 2018], and customizable learning experience ; technical aspects -dynamic deployment of MMLA instances in smart classroom [Huertas Celdrán et al, 2020], using AI and blockchain [Slupczynski and Klamma, 2021], and wearables [Camacho et al, 2020]; or adoption aspects -at institutional level [Domínguez et al, 2021]). Only one infrastructure is focused on following a standard data processing pipeline, but that is from the lens of the 3Vs of Big Data (Volume, Velocity, and Veracity) to optimize the scalability and performance issues of a technical solution [Ciordas-Hertel et al, 2019]. Hence, the limitation highlighted in our previous review remains the same even though nine new infrastructures have been proposed: none of the data infrastructures in MMLA follow a standard data processing pipeline while processing multimodal educational data.…”

Section: Existing Mmla Infrastructuresmentioning

confidence: 99%

“…We reviewed 18 data infrastructures in MMLA (nine from our last review [Di Mitri et al, 2017, Muñoz-Cristóbal et al, 2018, Fiaidhi, 2014, Domínguez and Chiluiza, 2016, Ruffaldi et al, 2016, Harrer, 2013, Berg et al, 2016, Wagner et al, 2011, Segal et al, 2017 and nine proposed after our review , Munoz et al, 2018, Tamura et al, 2019, Ciordas-Hertel et al, 2019, Huertas Celdrán et al, 2020, Camacho et al, 2020, Domínguez et al, 2021, Serrano Iglesias et al, 2021, Slupczynski and Klamma, 2021). We started our review by finding out how many learning tools were supported in the proposals.…”

Section: Existing Mmla Infrastructuresmentioning

confidence: 99%

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

Shankar¹,

Ruiz‐Calleja²,

Prieto³

et al. 2023

jucs

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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.

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Section: Existing Mmla Infrastructuresmentioning

confidence: 99%

Section: Existing Mmla Infrastructuresmentioning

confidence: 99%

Section: Existing Mmla Infrastructuresmentioning

confidence: 99%

See 1 more Smart Citation

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

Shankar¹,

Ruiz‐Calleja²,

Prieto³

et al. 2023

jucs

View full text Add to dashboard Cite

show abstract

“…For the software architecture design, we used the Big Data Value Chain (BDVC) [ 64 ] as a guideline. The BDVC can be used to model the high-level activities that make up any information system [ 1 ]. The BDVC identifies the following key high-level activities: data acquisition, data analysis, data curation, data storage, and data usage.…”

Section: The Software Infrastructurementioning

confidence: 99%

“…Traditionally, LA research focuses on learner behavior as it interfaces with the keyboard and mouse in the learning management system. This narrow perspective on learner behavior in digital environments can lead to incomplete or ambiguous data traces because many other factors are difficult to capture and thus cannot be taken into account [ 1 ]. To counteract such potential shortcomings, approaches such as multimodal learning analytics (MMLA) are used [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Mobile Sensing with Smart Wearables of the Physical Context of Distance Learning Students to Consider Its Effects on Learning

Ciordas-Hertel

Rödling

Schneider

et al. 2021

Sensors

Self Cite

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Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners’ physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners’ smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner.

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Digitalisierung und Diagnostik in Schulen – Herausforderungen für Bildungspraxis und Bildungsforschung

Jude,

Ziehm-Eicher,

Goldhammer

et al. 2023

Edition ZfE

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Which Strategies are Used in the Design of Technical LA Infrastructure?: A Qualitative Interview Study

Cited by 7 publications

References 22 publications

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

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

Mobile Sensing with Smart Wearables of the Physical Context of Distance Learning Students to Consider Its Effects on Learning

Digitalisierung und Diagnostik in Schulen – Herausforderungen für Bildungspraxis und Bildungsforschung

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