Any problems? a wearable sensor-based platform for representational learning-analytics.

Pirkl, Gerald; Hevesi, Péter; Lukowicz, Paul; Klein, Pascal; Heisel, Carina; Gröber, Sebastian; Kühn, Jochen; Sick, Bernhard

doi:10.1145/2968219.2971383

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…In the biosensor context, different approaches are investigated to attach existing commercial level sensors to learners measuring their body's signals and determining how they feel, while participating in paper equivalent exercises [25]. The identified publications in this context exclusively use students as participant type but could be applied to any kind of learner as well.…”

Section: Overview Of Search Resultsmentioning

confidence: 99%

Learning Analytics and Privacy—Respecting Privacy in Digital Learning Scenarios

Priedigkeit¹,

Weich²,

Schiering³

2021

IFIP Advances in Information and Communication Technology

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With the rise of digital systems in learning scenarios in recent years as learning management systems, massive open online courses, serious games, and the use of sensors and IoT devices huge amounts of personal data are generated. In the context of learning analytics, this data is used to individualize contents and exercises, predict success or dropout. Based on a meta analysis it is investigated to which extent the privacy of learners is respected. Our research found that, although surveys have shown that privacy is a concern for learners and critical to adopt to establish trust in learning analytic solutions, privacy issues are very rarely addressed in actual learning analytic setups.

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Section: Overview Of Search Resultsmentioning

confidence: 99%

Learning Analytics and Privacy—Respecting Privacy in Digital Learning Scenarios

Priedigkeit¹,

Weich²,

Schiering³

2021

IFIP Advances in Information and Communication Technology

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“…As part of these efforts, sensor data [9,10], online learning data [11] and teaching data [9] have been widely used to tackle a variety of problems and challenges for the learning analytics, including learner modelling [12], collaborative learning [13], teaching analytics [14] and privacy issues [15]. For example, Fidalgo et al [16] utilize student online data to improve teamwork assessment; Guia et al [17] use wearable device data to assist language learning for young children.…”

Section: Related Workmentioning

confidence: 99%

“…Taking advantage of the vast amounts of data generated from heterogeneous sources in the educational space, learning analytics [ 1 ] has become a fast-growing field in recent years, which mainly focuses on understanding and analysis of data generated during the learning process [ 8 ]. As part of these efforts, sensor data [ 9 , 10 ], online learning data [ 11 ] and teaching data [ 9 ] have been widely used to tackle a variety of problems and challenges for the learning analytics, including learner modelling [ 12 ], collaborative learning [ 13 ], teaching analytics [ 14 ] and privacy issues [ 15 ]. For example, Fidalgo et al [ 16 ] utilize student online data to improve teamwork assessment; Guia et al [ 17 ] use wearable device data to assist language learning for young children.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Learning Analytics Using Commodity Wearable Devices

Zhang

et al. 2017

Sensors

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We advocate for and introduce LEARNSense, a framework for learning analytics using commodity wearable devices to capture learner’s physical actions and accordingly infer learner context (e.g., student activities and engagement status in class). Our work is motivated by the observations that: (a) the fine-grained individual-specific learner actions are crucial to understand learners and their context information; (b) sensor data available on the latest wearable devices (e.g., wrist-worn and eye wear devices) can effectively recognize learner actions and help to infer learner context information; (c) the commodity wearable devices that are widely available on the market can provide a hassle-free and non-intrusive solution. Following the above observations and under the proposed framework, we design and implement a sensor-based learner context collector running on the wearable devices. The latest data mining and sensor data processing techniques are employed to detect different types of learner actions and context information. Furthermore, we detail all of the above efforts by offering a novel and exemplary use case: it successfully provides the accurate detection of student actions and infers the student engagement states in class. The specifically designed learner context collector has been implemented on the commodity wrist-worn device. Based on the collected and inferred learner information, the novel intervention and incentivizing feedback are introduced into the system service. Finally, a comprehensive evaluation with the real-world experiments, surveys and interviews demonstrates the effectiveness and impact of the proposed framework and this use case. The F1 score for the student action classification tasks achieve 0.9, and the system can effectively differentiate the defined three learner states. Finally, the survey results show that the learners are satisfied with the use of our system (mean score of 3.7 with a standard deviation of 0.55).

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Learning Analytics Through Internet of Things for Better Learning

Bakla

2021

The Internet of Things for Education

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Any problems? a wearable sensor-based platform for representational learning-analytics.

Cited by 7 publications

References 20 publications

Learning Analytics and Privacy—Respecting Privacy in Digital Learning Scenarios

Learning Analytics and Privacy—Respecting Privacy in Digital Learning Scenarios

A Framework for Learning Analytics Using Commodity Wearable Devices

Learning Analytics Through Internet of Things for Better Learning

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