Creation of software platform for distance use of lab equipment and data in Virumaa College at

Tallinn University of Technology

Oleg Shvets*, Kristina Murtazin **, Gunnar Piho*** Tallinn University of Technology, Information Systems Group,

Tallinn, Estonia *oleg.shvets@taltech.ee

**kristina.murtazin@taltech.ee ***gunnar.piho@taltech.ee

Abstract - Modern e-learning technologies provide various opportunities for students and teachers. E-learning in engineering education has some specific facets, for instance, in classes where a task or experiment is associated with some specific equipment. Therefore, in engineering education, there are some domain-specific issues to solve: (a) the time of usage of equipment in laboratories is not always efficiently disposed, (b) data of experiments and practical work disappear or are saved on different devices and in different laboratories, (c) other educational institutions are not able to acquire or use special equipment or appliances, (d) main part of equipment can be inaccessible for those students who receive remote education, and (e) it is difficult to organise effective feedback between student and teacher. In the training platforms, it is also important to consider the need of the student to acquire practical skills in using laboratory equipment. The presented work proposes a prototype software platform that integrates the equipment, located in different laboratories of Virumaa College at Tallinn University of Technology, and enables students to use the equipment remotely.

Keywords - e-learning, remote labs, remote students, personalised feedback, knowledge, competencies, IoT.

I. INTRODUCTION Today, classes of electronics for students in technical

specialisations and engineering, almost always need practical knowledge to use equipment or instruments in some laboratory or at home. Therefore, the laboratory instruments and equipment should be accessible for the students, preferably also remotely. Achieving this is sometimes challenging, especially when the number of students is increasing. In technical training, a combination of real-world laboratory practice with realistic virtualisation, that includes visualisation and interactive online intervention, helps students in the development of practical skills with a deep understanding of basic scientific and engineering concepts [1]. The Internet of Things (IoT) is here a promising tool to organise a practical work of students in engineering education.

IoT is a convenient way to use a variety of equipment. Its application is possible in various fields of science and technology. The relative simplicity of use and deployment provide unlimited possibilities in the application of IoT

systems. Besides, IoT is a form of the Internet with the ability to collect, analyse and diffuse data, which further are processed to obtain information [2]. Quite often, e-learning is limited only by using virtual classes, video lectures and animations, online lessons, and distributing educational materials. However, IoT can change this practice.

Applying IoT in e-learning can probably help to transform education [3]. All the features of IoT systems make IoT quite universal for use in the learning processes, also in the various courses of higher technical education. Modern laboratories of engineering education institutions normally combine an amount of quite expensive devices that often duplicate each other. In those laboratories, one can meet a situation where laboratory equipment is idle because students work with them only during hours in the classroom. Virumaa College at Tallinn University of Technology is not an exception. It would be much more efficient to use some of these devices, stands and modules in this idle time by other students in the remote access mode. Modern software together with IoT provides this opportunity [16].

The goal of our work is to create a learning environment. This environment integrates the equipment, educational materials, data, cloud storage with output data for analysis and visualisation, as well as storage of student competence and the possibility of personalised feedback between student and teacher.

Following are the stages we followed so fare when developing such an environment:

• A concept development that included an analysis of relevant published papers over the past seven years.

• A prototype creation based on the developed concept, available equipment and needed data.

• Implementation of the prototype for obtaining laboratory data and for visualising these data.

• Identification of needs for future research and development directions.

In creating a prototype for a learning environment, the requirement is that student can select devices from different laboratories. The data, obtained from experiments with

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these devices, are saved and can be visualised and analysed remotely. This aspect is especially important for those students who use laboratory equipment remotely. We use Wonderware (https://www.wonderware.com/industrial-information-management/online-insight) Online Insight for connecting the devices. Usually, this software is used in enterprises to automate industrial processes, but in our case, we use it to organise our processes in an educational laboratory.

II. METHODOLOGY

A. Literature Review Using IoT systems in engineering education is a rapidly

developing and changing field. Therefore, it is important to have an idea of what has been published in this area recently. A state-of-the-art literature review is a good way to understand this. To conduct the literature review, we formulated the following research questions (RQ) [17], [18]:

• RQ1 - What forms of integration of IoT and e-learning are in use now?

• RQ2 - What are the main components of educational software platforms that are using IoT?

IEEE Explore Digital Library was chosen as the main indexed database for finding relevant papers. IEEE Explore was chosen because especially the papers from electronic engineering domain are relevant. For the search of published papers, the simple query ((("All Metadata":IoT) AND "All Metadata": e-learning)) was selected. Only articles from 2012 up to 2019 we included in the selection. Based on this selection, we obtained 59 publications.

The next step was the analysis of the selected publications in order to find answers for the RQ1 and RQ2.

Related to the RQ1, the following features of IoT application in the learning processes were identified:

• IoT systems are affordable tools for organising of practical experiments [4], [11].

• IoT devices are used as a tools of monitoring and data collection, where these data are later used in learning environments for analysis [5].

• IoT devices and applications for these devices can be used for detecting and personalising the learning experiences [6].

• The IoT technology enables emulate physical situation, sensing, and analysis and visualisation of the environment [7].

• IoT-based learning systems can increase students motivation to complete learning courses [8] and generally increase the effectiveness of learning [9].

• Smart classes and classrooms created based on IoT systems, contribute to creating effective feedback between teacher and student in real-time [10].

Related to the RQ2, it was important to determine which modules generally exist in software and hardware platforms where IoT systems and their derivatives are integrated into the learning environments. The main components of such platforms include the following ( [12], [13], [14], [15]) :

• A connectivity and normalisation component that guarantees accurate data transfer and interaction with all devices.

• A device management component that ensures the connection and uninterrupted operation of all the connected IoT components.

• A scalable database component that allows the storage of data received from the IoT components.

• A processing and action management component that allows, for instance, the event triggers and other smart actions based on specific data received from the sensors.

• An analytics component for clustering, analysis and deep machine learning right up to the predictive analytics.

• A visualisation component for a convenient interface to represent analytics results that allow building various graphs and diagrams. It is also necessary to provide the ability to display the data received from the IoT in a real-time.

• Some additional components, which, for instance, allows IoT developers to create and test prototypes of learning environments using test data.

• External interfaces to offer the possibility to transfer and exchange data between third-party systems.

Based on the conducted literature review, one can conclude that the integration of IoT and e-learning can improve the learning quality. It also indicates what components should be considered when creating prototypes of such learning platforms. As a minimum, such prototype should contain parts as (a) device management, (b) database, (c) processing and action management, (d) analytics, and (e) external interfaces.

B. Connection of devices It should be noted that in the educational systems

integrating IoT and e-learning, both the conventional and industrial controllers are in use. Before deciding, what is relevant for our college, we went through and analysed some important constraints.

In various laboratories of Virumaa College at Tallinn University of Technology, we have different types of equipment with various types of controllers. Some examples of these types of equipment are illustrated in Figures 1, 2 and 3. Furthermore, in the future, the set of devices can change.

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Figure 1: One of the controllers from the college laboratories.

Figure 2: Example of the mount from the laboratory of automated technology with its programmable industrial controller.

Figure 3: Prototype model of «Smart House» which uses a controller and some data for experiments.

Besides, we plan to use this learning environment in various classes of the college. In some of these classes, the students learn the principles of programming of controllers, including IoT systems. In other classes, the students undergo practical training at enterprises where they study the work of different industrial controllers.

After considering these various constraints, our choice was to use a universal industrial SCADA system from Wonderware. Wonderware (www.wonderware.com) HMI/SCADA software provides the reliable connection of

devices based on controllers, and Wonderware Historian can be used as a data source.

III. RESULTS The main result of the work done was the creation of a

prototype software platform where we used the knowledge, that we obtained from the conducted literature review. Figure 4 illustrates the proposed schema by showing the interaction of all components of the developed system.

Figure 4:The conceptual schema of the proposed learning environment

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The sensors module includes sensors that are installed in various laboratories of the college and measure such data as temperature, humidity, movement, pressure, etc. This data is collected by various controllers that are connected directly or through the PLC module to the Wonderware Server. Wonderware Server includes its own database. To ensure that this database works properly, the SQL module was installed.

Data, received from various sensors and other devices can be presented to students for analysis in various forms, for example, in the form of tables and graphs. It is possible to analyse the data obtained both from the local network of the college and remotely. For remote analysis, visualisation tools in the cloud storage are connected to Wonderware Online InSight. Online InSight uses MS Azure cloud. The system can also be used to visualise data (e.g. temperature and humidity) that are collected from different sensors. Data visualisation is exemplified in Figure 5.

Because the Moodle learning environment is an official learning environment for all the learning courses in TalTech, hence our software platform for distance use of lab equipment and data is also connected with Moodle. All is straight forward. In the Moodle learning environment, the official learning course is created. Students are registering to this course and gain access to the training materials, class assignments and schedules. Performing the assignments, students must make the analysis of schedules and draw conclusions. Graphs are the objects that visualize data coming in from various sensors in online mode. To date, related to the software platform for distance use of lab equipment and data, the Moodle is used only as an environment into where the data in the form of graphs are sent from the Wonderware Online Inside cloud storage. We used the Frames toolkit and placed graphics as HTML objects on one of the pages of the course in Moodle. This is illustrated in Figure 5.

IV. CONCLUSIONS Based on the results of our work, we can conclude that

the concept of a learning environment system was developed. This system combines the equipment, educational materials, data, and cloud storage for later analysis and visualisation of data. In order to develop the concept, the systematic literature review was conducted, and the needs of the learning processes of Virumaa College at Tallinn Technical University were considered. Especially we focussed in supporting the practical laboratory experiments. Visualised data can be transferred to the Moodle learning environment in the form of frames. Currently, during the educational process of remote students, some of the attached devices and Moodle environment for work with educational materials and for registration of students are used.

It should be noted that the main purpose of creating of an above-described software environment is to use it in the educational process. Students will have the opportunity not only to study the operation of equipment located in different laboratories of the educational institution, but also to analyse the results of this work. The proposed learning platform allows not only to transfer learning content, but also to connect many devices, including IoT, to unified laboratory experiments system. Data received from devices can be preserved both on the college server and in the provided cloud storage. Data can be visualised and analysed. It is possible to organise remote access for students and teachers to the created e-learning platform.

We can single out the following general directions of future research and development in improving the prototype environment: (a) attachment of more equipment to the project, (b) creation of own cloud-storage to collect experiment data, (c) creation of own web-interface for more flexible control and experiment data analysis, (d) analysis of feedback and knowledge obtained by students

Figure 5: Visualised of data from sensors measuring temperature and humidity

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