Advanced Innovation Center for Future Education, Beijing Normal University, Beijing 100875, China.
Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 138632, Singapore.
Sensors (Basel). 2017 Jun 14;17(6):1382. doi: 10.3390/s17061382.
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).
我们提倡并引入了 LEARNSense，这是一个使用商品可穿戴设备进行学习分析的框架，用于捕获学习者的身体动作，并相应地推断学习者的情境信息（例如，学生在课堂上的活动和参与状态）。我们的工作受到以下观察结果的启发：（a）细微的、特定于个体的学习者动作对于理解学习者及其情境信息至关重要；（b）最新可穿戴设备（例如腕戴式和眼戴式设备）上可用的传感器数据可以有效地识别学习者动作，并有助于推断学习者情境信息；（c）市场上广泛可用的商品可穿戴设备可以提供一种无干扰的解决方案。根据上述观察结果，并在提出的框架下，我们设计并实现了一个基于传感器的学习者情境收集器，运行在可穿戴设备上。最新的数据挖掘和传感器数据处理技术被用于检测不同类型的学习者动作和情境信息。此外，我们通过提供一个新颖的示例用例详细介绍了所有上述工作：它成功地提供了对学生动作的准确检测，并推断了学生在课堂上的参与状态。专门设计的学习者情境收集器已经在商品腕戴式设备上实现。基于收集和推断的学习者信息，系统服务引入了新颖的干预和激励反馈。最后，通过真实世界的实验、调查和访谈进行了全面评估，证明了所提出的框架和这个用例的有效性和影响。学生动作分类任务的 F1 分数达到 0.9，系统能够有效地区分定义的三个学习者状态。最后，调查结果显示学习者对我们系统的使用感到满意（平均得分为 3.7，标准差为 0.55）。
