Abstract
This article presents a case study of the integration of the Internet of Things (IoT) in high-school biology project-based learning. The study’s main goal was to conduct an in-depth, qualitative examination of the students’ experiences and the development of their skills over the course of this project. The following research questions were formulated: (1) What were the significant learning experiences for the high-school biology students participating in the IoT-based project? (2) How did these experiences shape students' interpersonal, intrapersonal, and cognitive skills? The research approach applied was that of an instrumental case study with multiple data collection sources (in-depth interviews, observations, one-on-one discussions with the students). The data were analyzed by applying an embedded analysis focused on students’ experiences and skills. The findings present students’ cognitive, social, and emotional experiences and how these experiences shaped their corresponding cognitive, interpersonal, and intrapersonal skills. This research contributes to the theory and practice of project-based learning in high school.
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Acknowledgements
The authors would like to thank Prof. Ilya Levin for his valuable contribution to this study. Special thanks also to the students and teachers whose collaboration was crucial to the successful implementation of this study.
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Appendices
Appendix 1. Smart greenhouse system
The smart greenhouse system comprises three subsystems: experiment control subsystem, experiment data subsystem, and experiment management subsystem. Additionally, the system includes a specific mobile application supporting the smart greenhouse system (Fig. 2).
The experiment control subsystem implements the experiment according to the predesigned algorithm. Physically, the subsystem is located within the greenhouse and comprises two units—an operating unit (OU) and a control unit (CU). The OU contains all of the physical components of the system, i.e., the sensors and the various actuators. The CU implements an automaton corresponding to the algorithm of the experiment. It receives all the necessary data regarding the structure and algorithm of the experiment from the experiment data subsystem and activates the OU according to the data. Activation of the OU can take one of two possible forms: (1) the reading of values from a sensor (s1), of temperature, air humidity, ground moisture, and radiation. The values are then transmitted to be recorded in a database of the experiment data subsystem. (2) The turning “on” or “off” of an activator Ak, such as a water-heating unit and a light bulb. After receiving an indication from the OU that the component was either turned off or on, the CU sends an update of the event to be recorded in the experiment data subsystem. The OU receives input signals from sensors, marked as S0, S1, …. Sk. Likewise, the OU has n actuators, marked as A0, A1, …, An. The OU activates one of the actuators after receiving a corresponding instruction from the CU. The experiment control subsystem is also responsible for updating the database of the experiment data subsystem according to every event that takes place in the system.
The experiment data subsystem serves to collect, follow, update, and save all of the data of the experiment. The experiment data subsystem is implemented as a specific cloud memory. It comprises four units, as follows.
Database interface: This unit is responsible for communicating with the database, and this is the only unit that has the necessary permissions to read from and write to the database. This unit examines whether the requests for reading or writing are intact and then updates, adds, or retrieves data from the database.
External interface component: This unit receives the requests from the external devices (the system control and the mobile application), ensures that they are intact, requests the relevant information from the database interface (DBI), and sends a response, according to the request it received.
Desktop application component is a unit assigned for interactions with the experiment management subsystem. This unit reviews the validity of the requests, retrieves or updates the database through the DBI, and provides a relevant response to the experiment management subsystem.
Notification component is a unit responsible for issuing updates and warnings to the experiment and participants. Updates may take the form of a daily e-mail or a phone message. This unit is activated once a day or when there is an unusual event that requires updating the users or the teacher.
The experiment management subsystem is a software program that runs on the computer in the laboratory, and its function is to feed the details of the experiment’s configuration (users, sensors, commands, etc.) and to produce graphs and reports. The subsystem is used to define the data pertaining to the laboratory, and it encompasses several major processes, as follows: (1) managing the school’s data; (2) managing the users (teachers and students); (3) managing the experiments, i.e., the algorithm of the experiment, the connections between the sensor software, and the relevant devices, and defining the experimental and control groups of the experiment; (4) overall definitions, i.e., managing the configurations of the CU, the sensors, and the actuators; and (5) producing reports, for example, reporting the values of the independent variable over a defined amount of time for a single plant, for a specific experimental group (calculating the average), or for the entire experiment.
The application for the experiment: This is a mobile software program that runs on a smartphone, and its function is to enable the participants to follow the data and update values manually. The application that was developed for this purpose monitors the system and allows for manual input. The app was installed by the science teachers and all of the science students who participated in this experiment. Through the app, users can view the indications of all of the sensors at all times. In addition, data pertaining to the experiment can be updated, for example, plant height and the number of leaves. The app was also programmed to alert the participants when certain events take place, such as activating an actuator and the amount of water that a plant received.
A structure of the smart greenhouse system is shown on Fig. 2. The structure includes all above-mentioned components of the system and communications between the components.
The smart greenhouse system
Appendix 2. An example of an experiment algorithm
An example of an experiment algorithm
Figure 3 presents the following algorithm of the experiment: When the illumination is greater than 40 (a value that represents daylight) and the soil moisture is lower than 30 (a value that represents a low degree of moisture), the rotation two will open and would irritate the plants, according to the conditional temperature (if the temperature is higher than 20 °C, irrigation will last 10 min; otherwise, irrigation will last 5 min).
The experiment algorithm shown in Fig. 3 demonstrates a profound and advanced understanding of the research design. The student must understand what the value 40 means for the illumination sensor (for the purpose of the discussion, we assume that a value over 40 signifies daylight and a value under 40 signifies either twilight, night-time, or early dawn). In other words, only when the two conditions are met, i.e., both the soil moisture of the plant is lower than 30 and the illumination is greater than 40, only then will the system perform the command. Furthermore, in this algorithm, there is also a condition for the opening of the irrigation tube: if the temperature in the greenhouse is lower than 20 °C (i.e., quite warm), then the system will open the irrigation tube for a period of 10 min. If this condition is not met, the system will open the irrigation tube for a period of 5 min. Clearly, the number of experiment algorithms that can be used referring to different values is endless.
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Tsybulsky, D., Sinai, E. IoT in Project-Based Biology Learning: Students’ Experiences and Skill Development. J Sci Educ Technol 31, 542–553 (2022). https://doi.org/10.1007/s10956-022-09972-1
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DOI: https://doi.org/10.1007/s10956-022-09972-1