A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms
- Use the MMLA literature to present a simulated but realistic scenario that can surface the limitations of the current technical approaches involved in the orchestration of complex technical ecosystems in educational practices.
- Propose an MMLA architecture implementing SDN/NFV principles and exemplify how this architecture can solve some of the detected challenges to deploy, dismantle and reconfigure the MMLA applications in a scalable way.
- Perform several experiments to demonstrate the feasibility and performance of the proposed architecture in terms of time required to deploy and reconfigure these applications.
2. Related Work
2.1. Smart Learning Environments and Classrooms
2.2. Architectures for Smart Learning Environments and Classrooms
2.3. Remote Classrooms and Labs
2.4. SDN and NFV Applied to Different Scenarios
3. Description of Simulated Case Study
3.1. Intelligent Tutoring System in the Classroom
- Context: In this scenario, students are practicing a specific topic through the use of an intelligent tutoring system. Each student is individually interacting with the environment with the computer. In order to provide just-in-time help, instructors need to know how students are advancing in this practice and what are their mistakes or misconceptions. A usual class would be around 20 to 40 students.
- Application: When students interact with the intelligent tutoring environment, they generate events and clickstream data that can be processed to make inferences about their learning process. Based on these data, the analytics engine generates a number of indicators of students’ current skill and behavioral states. For example, it can show if a student is confused, needs help, has been idle for a number of minutes or their areas of struggle, among other pieces of information. Additionally, each computer has a webcam capturing students’ face and expression, and the analytics engine applies an affect detection Machine Learning (ML) model to infer students’ affect status. Instructors receive all these info through a dashboard in real-time and can easily move within the classroom attending students’ needs.
- Sensors and devices:
- Individual students’ devices: Students interact with the ITS by connecting to it as a web application. The ITS provides of a series of scaffolded exercises adapted to the current level of skill of each student. Students use the desktop PC available in the classroom.
- Individual students’ webcam: Each student has a front camera in their computer that is capturing a video feed of their face expression continuously. This feed is used by the analytics engine to infer the emotional state in time windows.
- Instructor device: The instructor consume the analytics via a dashboard by connecting from its device (tablet or laptop) to the visualizer provided by the architecture.
3.2. Tabletop Task Collaboration
- Context: In this case scenario, we have students interacting with a shared device known as interactive multi-touch tabletop, which can easily support face-to-face collaboration with multiple students interacting at the same time. Students carry out an activity on collaborative concept making, which is a technique where learners represent their understanding about a topic in a graphical manner by linking concepts and preposition . At the same time, students are also conversing with each other and discussing their decisions, and this voice stream is also captured through a microphone. The class is organized in groups of three students, and a usual class could have around 7 to 14 groups.
- Application: The objective is to design an application that can help teachers become more aware of the collaborative process, by making visible interactions that would otherwise be hard to quantify or notice. The application study collaboration by considering both the verbal interactions when students are talking to each other, as well as physical touches with the table-top . More specifically, it can use metrics to identify learners that are not contributing enough to the activity or are dominating it (both physical and verbal interaction), groups that can work independently or those that do not understand the task. The instructor accesses all these information though a visualization dashboard in a hand-held device.
- Sensors and devices:
- Group multi table-top: Table-top learning environments are big tactile screens that allow the collaboration of multiple users at the same time.
- Group overhead depth sensor: A Kinect sensor is used to track the position of each user automatically detecting which student did each touch.
- Group microphone array: It is located above the tabletop and captures the voice of all the group members, distinguishing the person which is speaking.
- Instructor device: The instructor consume the analytics via a dashboard by connecting from its device (tablet or laptop) to the visualizer provided by the architecture.
3.3. Programming Project-Based Learning and Instructor Indoor Positioning
- Context: Numerous programming courses have capstone projects where students need to implement an application that shows evidence of the different concepts acquired thorough the course. These courses usually have some sessions allocated for students to start developing these projects in groups while instructors move from one group to another solving doubts. Each group interacts with a shared programming environment (e.g., ) to develop the project collaboratively. The class is organized in groups of three students, and a usual class could have around 7 to 14 groups.
- Application: In this scenario, there are two main applications. The first one is to provide analytics regarding how the collaboration is working out and how the project is advancing. This can include information regarding areas of struggle based on the code written and code compilations , but also regarding the level of contribution to the project of each member, analysis of the conversation and engagement levels obtained through the analysis of the physiological signals to measure activation and engagement levels. The second one is an automatic control of how much time the instructor has spent helping each one of the groups through indoor positioning; this way, the instructor can balance the help that each group receives. The instructor can consult all this information through a dashboard in order to provide just-in-time and personalized support to each group.
- Sensors and devices:
- Individual students’ devices: Students interact with the collaborative programmings environment by connecting to it through a web application.
- Individual Empatica E4 wristband: Each student wears an E4 empatica wristband that captures the heart rate, a three-axis activity through an accelerometer, and the electrodermal activity of their skin.
- Group microphone array: It is located above each one of the groups’ tables, distinguishing the person which is speaking.
- Group positioning sensor: It is located in each one of the groups’ tables to detect the center position of each group.
- Instructor positioning badge: It is carried by the instructor when moving around the class. It implements Pozyx (https://www.pozyx.io/) technology which is an ultra wide band solution that provides accurate positioning and motion information with sub-meter accuracy (10 cm).
- Instructor device: The instructor consumes the analytics via a dashboard by connecting from its device (tablet or laptop) to the visualizer provided by the architecture.
3.4. Requirements of the Previous Scenarios
- External Data Sources. This level contains different external databases and tools such as data from the Academic Records, Learning Management System (LMS) or Massive Open Online Courses (MOOC) that can feed our architecture with relevant students’ data.
- Learning Analytics Platform. It hosts the components focused on analysing data provided by external sources and generated during the realization of learning activities.
- MEC System Level Management. This level is focused on (1) processing requests from instructors to reconfigure heterogeneous classroom devices in real-time and on-demand, (2) making decision and orchestrating them to configure learning applications running on top of classroom devices, and (3) sensing classroom devices to detect misconfigurations or problems.
- MEC Host. Heterogeneous classroom devices, also known as MEC Hosts, such as electronic blackboards, tablets, personal computers, servers, or Raspberry Pi that need to be reconfigured according to the current learning course or subject.
- MEC Host Level Management. Level hosting the different managers able to control the life-cycle of the Virtualization infrastructure, MEC Platform, and MEC Apps running on the MEC Hosts.
- Network Level. This level contains the network infrastructure enabling the communication of MEC Hosts and the rest of the levels making up the architecture.
4.1. Learning Analytics Platform
4.2. MEC System Level Management
4.3. MEC Host Level
4.4. Network Level
4.5. Solutions Provided by our Architecture to the Previous Requirements
5. Experimentation Results
5.1. Testing Environment
5.2. Docker Container with High-Intensive Computing Application
5.3. Docker Container with Medium Computing Application
5.4. Docker Container with a High-Data Consuming Application
7. Conclusions and Future Directions
Conflicts of Interest
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Huertas Celdrán, A.; Ruipérez-Valiente, J.A.; García Clemente, F.J.; Rodríguez-Triana, M.J.; Shankar, S.K.; Martínez Pérez, G. A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms. Sensors 2020, 20, 2923. https://doi.org/10.3390/s20102923
Huertas Celdrán A, Ruipérez-Valiente JA, García Clemente FJ, Rodríguez-Triana MJ, Shankar SK, Martínez Pérez G. A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms. Sensors. 2020; 20(10):2923. https://doi.org/10.3390/s20102923Chicago/Turabian Style
Huertas Celdrán, Alberto, José A. Ruipérez-Valiente, Félix J. García Clemente, María Jesús Rodríguez-Triana, Shashi Kant Shankar, and Gregorio Martínez Pérez. 2020. "A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms" Sensors 20, no. 10: 2923. https://doi.org/10.3390/s20102923