Simulating Environmental Issues: New Digital Tools to Teach Biology In Silico

Abstract

Computers have been an indispensable part of human working and private life for decades, and in classrooms the use of digital devices and tools is increasing as a result of digitalization. In this study, we aimed to test the potential of computer simulations as digital tools in biology lessons to convey environmental issues. For this purpose, we conducted an online survey and evaluated 137 responses from German high-school students between 14 and 20 years of age. We asked the students about their attitude towards computer simulations as digital tools in biology lessons and tested the students’ knowledge about models and computer simulations as well as their basic knowledge about plant morphology. Additionally, we investigated the students’ self-perceived computer skills and their motivation to work with computers within information and communication technologies (ICTs). Our results show a relationship between high self-perceived computer skills and high motivation to work with computers and a high responsiveness to learning about environmental issues in silico (computer-based), even if gender differences are visible. Therefore, including computer simulations as a student-centered method can be profitable for students, increasing their understanding of environmental issues and combating their lack of botanical knowledge.

1. Introduction

1.1. Computer Simulations as Digital Tools in Biology Lessons

A computer simulation is a computer program based on a model of a natural or an artificial system that can be used in education to actively acquire knowledge through experimentation [1]. Even if simulations are a virtual environment, students can experience real scenarios and are able to interact with these scenarios to achieve learning gains. This makes integrating computer simulations into the curriculum a student-centered teaching method that allows students to be actively involved and make predictions and decisions [2]. Especially in science education, educational software like computer simulations can be beneficial for developing experimental skills that otherwise have to be developed in a laboratory, which is often very time-consuming or impossible because of the logistics and infrastructure in schools [3]. In other cases, simulations can be beneficial when processes are not even observable or measurements are not possible or affordable to carry out in school settings [4]. Computer simulations improve experimental skills, like graphic interpretation and prediction skills, and offer new teaching methods for problem-based learning. Even if they are not considered a replacement of laboratory activities, simulations can be effectively used as a supplementary tool in classrooms and in distance education. Nevertheless, it is important how the teachers incorporate these supplementary tools into the curriculum to match their learning objectives [5]. If incorporated appropriately, knowledge and skills gained from actively using tools like computer simulations are long-lasting and intuitive [1].

1.2. The WinUM 2.0 Research Project

Our educational research and developmental project WinUM 2.0 evolved from a purely research-oriented project that we aspire to transfer into the school context. In this regard, we didactically prepared the research content and developed a computer simulation to be used as a digital tool in biology lessons at the secondary-school level. To obtain conformation that this transfer will work, we conducted our survey.

WinUM is the acronym for ‘Wein im naturwissenschaftlichen Untericht–Material, Medien, Methoden’ (English: ‘Wine in Science Education—Material, Media, Methods’). The project aims at mediating the consequences of climate change in the virtual vineyard in the classroom, which is made possible by the simulation. As simulations are always based on models of natural or artificial systems, our simulation is based on the model Virtual Riesling (ViRi), developed by Schmidt et al. (2019) at Hochschule Geisenheim University (Germany). ViRi is a virtual plant model created from data from real digitalized grapevines of the German Rheingau region. This plant model shows canopy architecture in detail and aims to show relationships between canopy architecture and abiotic factors, like temperature and light, to make predictions about yield and berry quality and give suggestions for vineyard management [6]. At Hochschule Geisenheim University, ViRi is now being improved and will even be able to prospectively give advice for winemakers to avoid damage like berry sunburn that becomes more and more common due to changing conditions in vineyards because of climate change [7].

Our computer simulation is the R-shiny (R Core Team 2021) based application of ViRi and shows the dynamic performance of grapevines under the influence of temperature data originally collected from the Rheingau region. The aim of the simulation process is to show the influence of rising temperatures on grapevines as model organisms and on the vineyard as a model ecosystem in the course of global climate change. In the German school curriculum, in the context of ecology, some topics are directly linked to environmental issues, like climate change and sustainability [8]. By integrating our computer simulation about the performance of grapevines into the curricular context of ecology, students should be able to learn about abiotic influences on plant development, about the dynamics of ecosystems and about consequences of climate change noticeable in their own home region that people need to adapt to. Acquiring knowledge and changing one’s attitude based on that knowledge can lead to pro-environmental behavior [9,10]; therefore, biology classes can play a substantial role in promoting this behavior early in life.

1.3. Promoting ICT Skills in School

Working with computers as collaborative and supportive tools in the classroom holds some important advantages for students and teachers. For instance, using technology in school is a major part of the novel learning culture [11] which aims to provide authentic and interactive content that is motivating and engages students in the learning process [12]. Also, computer-based learning environments are especially effective when learning about complex and challenging topics [13], as students are required to work self-regulatorily and have to make personal decisions to achieve learning goals [14]. In addition, the acquisition of digital competencies in school is crucial for nearly any future job, in which these skills are essential [12]. The skills and competencies to work with digital tools are generally described as ICT competencies. ICTs (information and communication technologies), largely defined as a ‘diverse set of technological tools and resources used to communicate, and to create, disseminate, store, and manage information’ [15], play a considerable role in economic, social and educational change in developed countries with high technological standards. The governments of each country are responsible for appropriate educational programs [16], and schools are expected to design learning environments for students to acquire digital competencies to use ICT tools as part of their educational outcomes [17].

Digital competencies depending on gender are still debated. International studies about computer skills show mixed results concerning gender differences. There is no broad consensus in the literature to be found stating that either boys or girls have higher digital competencies [18]. In the International Computer and Information Literacy Study (ICLS) 2013, female students in class 8 showed significantly higher scores in computer skills than male students [19], as well as in ICLS 2018 [20]. Those tests, however, were mainly text-based and relied a lot on reading skills that are almost always higher in females. In general, the use of digital devices outside school is very common and very frequent for students, and only slight differences in the extent of technology use between males and females are perceptible [19].

1.4. Research Questions

We conducted an online survey to find out if students will be able to deal efficiently with the content and especially the method of our teaching project. For this goal, we needed to find out what level of knowledge and what ICT skills students already have. Our research questions were the following:

• What do students know about models and computer simulations?
• What are the levels of students’ computer skills and what is their motivation to work with computers?
• How do students perceive working with technical tools to learn about current environmental issues like climate change?

As we intend to implement our teaching unit in the context of ecology to provide knowledge about grapevine growth and the vineyard as an ecosystem, we posed one additional research question:

4.

How much do students already know about grapevine morphology?

2. Materials and Methods

Matching the digital content of our survey, we chose an online format. Our online survey was accessible via a link or a QR code for easy distribution and access with computers, tablets or smartphones. We contacted teachers from our region via email and asked them to let their students participate. Our study was permitted by the ADD (Aufsichts- und Diensleistungsdirektion), and all participants were informed that the study was voluntary, anonymous and ungraded. We were able to obtain a study sample of 137 students, aged between 14 and 20 years, with 57 male, 77 female and 3 diverse. The survey took about 10 min for students to complete and was conducted either in school or as part of homework.

The survey started with formal information on age, gender and personal technical equipment. Afterwards, three major topics were covered: (1) ICT competencies (knowledge about models and computer simulations, computer skills and motivation to work with computers), (2) students’ attitudes towards using digital tools to learn about environmental issues in biology lessons and (3) students’ basic knowledge about the grapevine.

We assessed knowledge about models and simulations with closed questions giving two contradictory answer possibilities. Students were asked to tick only one of the answer possibilities (see

Table 1. Knowledge about models and computer simulations was evaluated with six items. For each item, students had to choose between two contradictory options, one of them being them being the correct choice (marked in green).

Item with Two Options
A model is the application of a system using mathematical equations. A model is the description of a system using mathematical equations.
A model is the highly simplified representation of a real system. A model is the complex representation of a real system.
A model is the exact representation of a real system. A model is the approximate representation of a real system.
A simulation is the description of a model using mathematical equations. A simulation is application of a model using mathematical equations.
A simulation shows a dynamic process. A simulation shows a fixed value.
The result of a simulation varies with each execution. The result is exactly the same for each execution.

). Next, we measured perceived and not actual computer skills (indirect measurement) via self-evaluation. Self-perceived computer skills were evaluated with a questionnaire within the survey that included 10 items (see

Table 2. Survey items that evaluated self-perceived computer skills. For each item, students had to choose one option of a 4-point Likert scale (‘strongly agree’, ‘agree’, ‘disagree’, ‘strongly disagree’). Translation of the original questions that were posed in the German language.

Please Tick for Each Item if You ‘Strongly Agree’, ‘Agree’, ‘Disagree’ or ‘Strongly Disagree’
I can write an essay or letter using word processing (e.g., Word).
I can draw with a graphics program.
I can enter data into a program (e.g., Excel) and use its calculation function.
I can create a presentation (e.g., with PowerPoint).
I can send and receive e-mails with an e-mail program (e.g., Outlook).
I can install software and apps on my computer.
I can set up hardware (e.g., printers or scanners) with my computer.
I can find and eliminate a virus or trojan on my computer.
I can create a PDF document from a Word file.
I can download files from the Internet and retrieve them from the download folder.

). Motivation to work with computers was evaluated with a questionnaire that included 16 items (see

Table 3. Survey items that evaluated motivation to work with computers. For each of the 16 items, students had to choose one option of a 4-point Likert scale (‘strongly agree’, ‘agree’, ‘disagree’, ‘strongly disagree’). Translation of the original questions that were posed in the German language.

Please Tick for Each Item if You ‘Strongly Agree’, ‘Agree’, ‘Disagree’ or ‘Strongly Disagree’
I really enjoy working with a computer.
I find that time flies faster than usual when dealing with a computer.
I enjoy working with a computer because I have better skills in this field than in others.
The computer draws me so much that I can forget everything around me.
By using programs, I can do a lot of things faster and better than by hand.
By using a computer, I can work quicker and more time efficient.
With the computer I can do school tasks better (e.g., homework, prepare papers).
By using a computer, I can learn better and faster.
I start working on the computer without problems.
I work on tasks on the computer until the end, even when I could be doing other interesting things.
I can concentrate while studying on the computer, even if I have many other things distracting me.
On the computer, I manage to motivate myself to work on the tasks, even if I have little interest in them.
I make a plan to finish my task with a computer.
When researching on the computer, I remember information from a textbook and from class.
I organize my tasks that need to be done with a computer.
I look for and use appropriate instructions when I need to accomplish a task on a computer that is unfamiliar to me

). Answers for both questionnaires were assessed by a 4-point Likert scale (‘strongly agree’, ‘agree’, ‘disagree’, ‘strongly disagree’), where we rated the highest level with the value 4 and the lowest level with the value 1 (see Supplementary Materials S1 for questionnaires in German).

To gain an insight into students’ attitudes, we first asked them about their opinion about using computers in biology lessons, giving various answer possibilities. After that, we posed four statements: (1) climate change is often discussed in school lessons, (2) working with technical tools in biology lessons is important, (3) computer simulations about plant growth under high temperatures are useful, and (4) biology lessons should be based on current scientific research. The participants were asked to rate their responses according to their opinion on a 7-point Likert scale ranging from ‘strongly agree’ to ‘strongly disagree’. Lastly, we assessed the botanical knowledge and identification skills of students in a performance test by presenting a detailed picture of the grapevine (Vitis vinifera) showing morphological features (see Figure 1).

3. Results

The students’ knowledge about models and computer simulations turned out to be high (M = 0.83, SD = +/−0.16). Correct answers were rated with 1 and incorrect answers were rated with 0. Most of the students (>81%) answered five of the six questions correctly (see Figure 2). In particular, the fact that a model is a highly simplified and approximate representation of a real system was common knowledge among the students (95%). Only when asked if the result of a simulation varies with each execution, the percentage of correct answers decreased to 66%.

In addition to students’ theoretical knowledge about models and in silico simulations, we tested their skills and motivation to work with computers. We evaluated self-perceived computer skills and motivation to work with computers with a 4-point Likert scale (value 1 representing low and value 4 representing high skills and motivation). The results show that the students had high self-perceived computer skills (N = 128, M = 3.3, SD = +/−0.46). The students had also high motivation to work with computers (N = 128, M = 2.8, SD = +/−0.56).

Regarding the technical equipment in school lessons, we found that most students (75%) have access to tablets as digital devices in their school and that 37% have access to computers. We evaluated the students’ opinions about using computers in biology lessons by giving the percentage of students for each of the five answer possibilities. We found that a major part of the students (77%) was convinced of the usefulness of computers in biology lessons and especially of the usefulness of computers in the field of ecology (58%), even if computers are rather rarely used in biology lessons (see Figure 3).

Evaluating the answers to the four statements about the content and the methods that are part of our teaching project, we found that environmental issues like climate change are not often discussed in school (see Figure 4a). Even if working with digital tools in biology lessons was important but not very important to students (see Figure 4b), most of them were convinced that computer simulations about plant growth under high temperatures especially could be a useful tool (80% of the students strongly agreed, agreed or somewhat agreed), leaving out the neutrals, with only 9% of the students not agreeing with this last statement (see Figure 4c).

Additionally, 84% of the students thought that biology lessons should be based on current scientific research. Leaving out the neutrals, only 7% did not agree with this statement (see Figure 4d).

Lastly, we assessed basic knowledge about the grapevine by first asking the students to identify it and then match the five morphological structures with the correct botanical terms. The percentages of correct answers in total as well as separated for both genders are shown in

Table 4. Knowledge about the grapevine and identification skills. The table shows the botanical term for each morphological feature of the grapevine, marked A–E in the picture. Also shown is the percentage of correct answers.

Task	Morphological Feature	Correct Answers (%)
A	Shoot	16
B	Stem	93
C	Tendril	35
D	Internode	56
E	Berry	21
Name	Grapevine	36

. The grapevine was correctly identified by less than half of the students. With reference to plant parts, almost all students could label the stem correctly (93%). Mixed results were shown in identifying the internode (56%), and few students could name the tendril, shoot and berry. Often, the shoot was confused with the tendril (45%) and the leaf (38%). The berry was frequently labelled as a ‘bunch of grapes’ (75%) and only correctly identified by 21% as a berry.

4. Discussion

This study was conducted in the larger context of our educational research and developmental project that consists of several teaching units, one of them using a computer simulation as a digital tool. To find out what level of knowledge and skills students already have regarding computer simulations as digital tools, we conducted the survey with our target group for whom this teaching unit is intended. Generally speaking, we found that students already have a lot of the knowledge and the skills they need to use computer simulations efficiently in biology lessons, but some areas still need some classroom work. To clarify what we discovered and how these findings can help to integrate computer simulations as digital tools efficiently into biology lessons, we will now discuss our research questions.

• What do students know about models and computer simulations?

Models are an integral and important part of science education in schools because they not only provide information but also active learning opportunities [21]. In biology lessons, models are often used throughout all grades. They serve as visual objects, facilitate the understanding process and make it possible to learn about the originals, particularly when these are not easily accessible in the school context [22]. Students’ knowledge about models can be assumed, and our results show that students know exactly what distinguishes a model from the original and that a model is a highly simplified and approximate representation of an original and not an exact and complex representation.

However, regarding our results for the three items testing knowledge about simulations, the situation seems to be more problematic. Most of the students knew that a simulation is the application of a model (86%), but especially when asked if the result of a simulation varies with each execution (66%) or is exactly the same with each execution (34%), a higher level of uncertainty became visible. This might be due to the fact that models are frequently used in education but simulations are not. There is also little contemporary research to be found on implementing simulations in biology lessons. Working cooperatively on a simulation themselves should be beneficial for students for understanding how simulations work. Moreover, they will understand how their results vary from one execution to another if the simulation is based on a semi-stochastic model.

2.

What are the levels of students’ computer skills and what is their motivation to work with computers?

Integrating digital tools in education helps students to develop the skills required for their individual life as part of society and for their work life as well. Girls and boys equally engage with technology, are equally digitally skilled and own as many digital devices [23], but girls especially seem to be passively consuming digital media rather than creating digital technology. In addition, they do not aspire to a career in computer programming very often [24]. As a result, ICT disciplines are still associated with men in our society [25,26]. In this context, the percentage of females working in the field of ICT has shown a downward trend in Europe, the USA and Australia [25]. These findings align with our results, that you can find in the Supplementary Material S2, which indicate that the male participants scored higher compared to the female participants both in their own perception of their skills and in their personal motivation to work with computers. As being digitally skilled is equally important to both genders, integrating digital devices and digital tools into education is absolutely crucial to ensuring that both genders are engaged and comfortable with using digital tools to perform experiments and learn from them.

3.

How do students perceive working with technical tools to learn about current environmental issues like climate change?

Students are interested in working with technical tools in biology lessons but are especially interested in biology lessons that are based on current scientific research (84%). They are convinced that computer simulations showing plant growth under elevated temperatures can be useful (81%). It was noticeable that when asked about the usefulness of computer simulations on the subject of plant growth under high temperatures, the percentage of students who did not agree with these statements was very low (≤9%). This holds true for current scientific research as a basis of biology lessons, too.

This is also to be seen in the context of nature conservation and pro-environmental behavior outside of school. A personal understanding of nature conservation as well as the possibility and willingness to become actively involved in environmental issues depends on one’s knowledge as well as on one’s attitude towards nature. To combine biological education in schools with environmental issues, a variety of teaching methods, including problem-based methods as well as experimental and in silico methods, are seen to be useful [27]. Especially problem-based learning through teamwork, critical evaluation of the literature as well as defining one’s own learning goals based on a given scenario are seen as very effective in science learning [28].

Computer simulations are an effective tool for science education to combine problem-based learning through teamwork with digital tools. Hereby, simulations allow engagement in realistic experiments and results to be gained from experiments that would have been too costly and timely to carry out live in a school context [29]. Here, we only place computer simulations in the context of ecology, but previous studies show that they can successfully be used to also work on topics like evolutionary processes [30] and meiosis [29].

4.

How much do students already know about grapevine morphology?

As our research was conducted in the Rheingau wine region and many schools we cooperate with are located in the Rheinhessen wine region, which is the largest wine region in Germany, vineyards are a common sight. Nevertheless, knowledge about the grapevine cannot be taken for granted and must be worked on in advance even in the wine regions. This is not very surprising, as the botanical knowledge and identification skills of students are generally low. Studies from different countries point towards a lack of identification skills for wild flowers [31], herbs and trees [32] in students, even if all common names of the plants in question were accepted in the conducted surveys. Botanical knowledge can, for instance, be acquired at home through childhood experience [33] or during field trips [34]. As botanical knowledge is often acquired out of school or in early childhood, colloquial terms are used frequently by students, which was also shown in our survey. For example, the German translation for a single berry of the grapevine is ‘Weinbeere’, and a bunch of grapes translates as ‘Traube’. To name a single berry of the grapevine ‘Traube’ instead of ‘Weinbeere’ is to use one of these colloquial terms that is not botanically correct but very common, as was also shown in a previous survey, where students were asked to draw a Traube and almost all students drew a Weinbeere [34]. In the same course, the identification of the grapevine as a ‘grape’ or a ‘grape plant’ is also due to misconceptions and colloquial language. Therefore, correct technical terms and basic knowledge should be conveyed in lessons in advance to get the most out of working with a computer simulation that is based on the model of a virtual grapevine. Teachers should start the teaching unit about models and computer simulations by first conveying knowledge about the grapevine and the vineyard as a model ecosystem. When fully prepared, students will be able to use the computer simulation correctly, put the obtained data in the right perspective and learn from the results.

5. Conclusions

Based on our results, we are convinced that conveying environmental issues with research-based in silico simulations will be beneficial both for students’ computer skills and for their botanical knowledge and thus their environmental attitudes. Students are very positive towards this teaching method. We assessed their computer skills only in the context of our simulation and may state that students already have sufficient computer skills to operate the simulation and that they would like to learn more about biology based scientific research that is provided in our simulation. It also seems appropriate to explicitly give more prominence to the topic of climate change in the curriculum and to teach with problem-based teaching methods to get students actively involved. As schools become more and more technically equipped, even if not fully, in silico simulations will have a greater chance in the future of being integrated into education. More specifically, we have shown that climate change is not very often discussed in school. Thus, bringing computer simulations and teaching materials that are both based on ongoing scientific research into the classroom seems to be appropriate for teaching biology in times in which ecological issues are relevant both on a local and a global scale.