Keywords

1 Introduction

The next decade is likely to witness a considerable rise in awareness of biodiversity. Climate change, continuing deforestation and large-scale fires are some of the current threats posed to the conservation of biodiversity. Efforts to educate and raise awareness to this problem in young children are an important strategy to prevent biodiversity’s further decline [21, 25]. Madeira island has a large diversity of flora and endemic species, being considered one of the biodiversity hot-spots of the Mediterranean region [3, 18]. However, its ecosystem is exposed to several threats and it needs in situ and ex situ conservation efforts.

In this context, educating the next generation about local wildlife and the importance of its’ conservation in maintaining a biodiversity equilibrium is fundamental. In particular, research has suggested that indirect experiences are important in shaping children’s perception of nature and may influence attitudes towards conservation [6]. Research also shown that in order to empower and motivate learners to engage in this topic, a learner-centre pedagogy approach is preferred [11, 13]. Thus, children prior knowledge and experiences about their local biodiversity are the starting point for stimulating the learning process.

More recently, researchers have also been studying the effectiveness of the combination of different pedagogical approaches with the use of new technologies and virtual agents. The agents can be used as teachable agents, tutors, peer learners or just as simple social characters [5, 10, 12, 23, 27]. They have been shown to be useful tools that can aid children to develop and improve language and reading skills, handwriting and in increasing children’s feelings of persistence and enjoyment during learning activities.

In the specific context of biodiversity education, virtual agents have also been shown to be useful tools in biodiversity management and conservation efforts [26], biodiversity education [4, 8], and for raising awareness about the importance of biodiversity preservation [14, 15, 22].

In this paper, we sought to extend previous findings about the effectiveness of educational virtual agents in the area of biodiversity education, by proposing and evaluating a new application involving a virtual version of the Pepper robot. Our findings show that this application was successful at providing an engaging learning experience to young children and underlines the need for future work to further explore the role of children’s individual characteristics (such as previous knowledge) in the development of similar educational applications.

2 Goals and Hypotheses

The goal of this project was to understand how children’s previous knowledge about local biodiversity had an effect in their conceptualisations of local biodiversity with the help of a Virtual Agent-driven application. More specifically, we analysed the children’s existing knowledge about biodiversity (as indicated by their school year) by measuring their ability to a) correctly identify local biodiversity elements (thorough the app) and b) create complex conceptualisations of local biodiversity (as measured by the complexity of their drawings about biodiversity scenarios, see Sect. 3.3), after interacting with a digital app.

To achieve this goal, we made an observational study with children enrolled in primary education. In this context, we devised the following hypotheses:

  • H1 - Children with some existing knowledge about local biodiversity (\(3^{rd}\) and \(4^{th}\) school years) will be able to correctly identify elements of local diversity more frequently when compared with younger children with less or no knowledge about biodiversity (\(1^{st}\) and \(2^{nd}\) school years).

  • H2 - Younger children will exhibit more misconceptions about local wildlife when compared to children with some existing knowledge.

  • H3 - Children will prioritise representations of nature-like scenarios compared with man-made representations.

  • H4 - Children will prioritise representations of local biodiversity elements when compared to non-local biodiversity elements.

3 Method

3.1 Sample

One hundred and five children enrolled in primary education participated in our experiment. Of these, 56 were male and 49 were female, (29 from \(1^{st}\) school year; 25 from \(2^{nd}\) year; 26 from \(3^{rd}\) year and 25 from \(4^{th}\) year), aged between 6 and 10 years old.

3.2 Procedure

Data collection for this experiment took place during the course of one week, at a public school during the regular daily class schedule. Informed consents were obtained from the parents prior to the beginning of the experiment. Approvals from the university Ethical Commission and the Ministry of Education were also granted before the start of the study. Participants were told that they would be interacting with a virtual robot that needed assistance in order to identify biodiversity elements that exist in Madeira island.

Students interacted with the application previously installed in the school computers (see Fig. 1) and filled out a questionnaire about their engagement with the app. The experiment took approximately 20 min.

Fig. 1.
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Children setup during the study.

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3.3 Application

A Unity application, which included a free 3D model of Pepper robotFootnote 1, was developed. The virtual robot played the role of narrator and storyteller during children’s interaction with the application. All scenes were narrated using the Unity plugin RT-Voice PROFootnote 2, since we assumed that not all children would have sufficiently good reading skills. After children finished their drawing task (more details below), the virtual robot also played the role of storyteller (not analysed in the scope of this paper) by telling each children a personalised tale involving their drawings.

Together with the narration, we also included subtitles and visual cues to enhance clarity. The visual cues were implemented through (a) the virtual robot and (b) through the use of pictures of the local biodiversity elements being mentioned throughout children’s interaction with the application.

Fig. 2.
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Drawing stages composition, (a) scenarios, (b) characters, (c) animals and plants seen in app, (d) animals that do not belong to local biodiversity and (e) drawing area.

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The visual cues in the virtual robot were implemented using the Mixamo platformFootnote 3 with its free animations. Our target animations (idle, pointing, waving, thoughtful, acknowledging, happy hand gesture, excited, nodding, disappointed, happy and angry) were used to create a more interactive, life-like agent. The pictures used were collected through a photography contest set up for local participants of all ages and from the Global Biodiversity Information FacilityFootnote 4 (GBIF) database. The photography contest was disseminated using social media during one month, and participants were asked to submit images that captured the local biodiversity of Madeira island. They had to submit original photographs of elements of wildlife, taken by themselves, and allow its use in the application. Data regarding the names of the biodiversity elements, groups and characteristics were validated by a team of specialists from Funchal Natural History Museum and biodiversity books [2, 3, 24].

The interaction with the application begins with Pepper introducing itself and requesting children’s help to learn more about the local biodiversity. Next, Pepper asks children to introduce themselves by typing their name and select their gender using the boy and girl icon available. After the introductions are finished, four different pictures of animals and plants, starting with a bird, followed by a flower, an invertebrate and finally a tree were shown to children. For each animal and plant, children were asked (a) if they know the animal or plant presented in the picture, (b) if they know the group that animal or plant belongs to and (c) if they know what are their characteristics (see [7] for more details on the characteristics for each element).

If children answer yes to the first question, they are then asked to indicate the common name of the shown animal or plant choosing from a group of four options; if they answered no, the virtual robot would tell them the plant or animals’ common name. For the second question, children were asked to choose to which group the identified animal or plant belonged to. Children had to choose from a list of eight options (Amphibians, Birds, Fish, Fungi, Invertebrates, Mammals, Plants, and Reptiles). Each one of the 8 groups was represented by an icon that the children would visually recognise, without the voice over narration of the robot. The same icon representation was used to illustrate the element characteristics. It should be noted that each element (animal or plant) has its own specific qualities. Before moving to the next stage, pictures of all four elements that children had seen up to this point, was presented to them along with their correct common names (see [7] for more details on the flow of the app).

Drawings. After completing the first stage of the experiment, children were requested to create a small story, in the form of a drawing. The request was formulated by the virtual agent Pepper, who wanted to show the drawing to its’ own friends later on. The drawing activity of the children’s was split into four stages. First children were requested to choose and drag their selection into the drawing area, see Fig. 2(e). This first element corresponded to the scenario of their story, and they could choose one from five possible options (see Fig. 2(a)), three nature-related scenarios and two with a city and road background.

Secondly, children were requested to drag one or more characters, see Fig. 2(b), (c) and (d), into their scenario. These characters could be human (little boy, little girl and three families), animals and plants (the four elements they saw during their interaction with the application) and five elements that did not belong to the local biodiversity.

After children selected all the elements, a small storytelling activityFootnote 5 was generated using that information.

Fig. 3.
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Full drawing classification on the left with light blue representing Air areas (sky and clouds); dark blue representing Water; brown representing Ground (paths and ground areas); and green Nature (trees, bushes and flowers). On the right, detail of the markers for each element, green squares surrounded by red are corners identification and the dark blue square the middle bottom point (the place where feet, paws, trunk/roots and stem might be). (Color figure online)

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Questionnaires. Finally, a short questionnaire (five questions) was presented to children after they finished interacting with the application, and completed the drawing. The survey was displayed in the application, in text and simultaneously narrated by Pepper. Children used an emoticon scale [20] to provide their answers and were informed about the faces’ meaning before answering.

Children were questioned if they liked Pepper; if they liked the application used; if they enjoyed creating the drawing; if they liked the story created with their drawing and if they understood Pepper’s speech throughout the app.

4 Data Analysis

4.1 Misconceptions Classification

From a pilot study in which we asked children to depict a story including the animals and plants they saw in the application (same as the ones in this study), we found that children often exhibited several misconceptions about the local wildlife. These misconceptions were evident through their depiction of these elements (e.g., placing ground-based elements, like snails, flying in the air).

In the context of this study, children’ drawings were analysed based on the position of the biodiversity elements in the scenario they chose. For each scenario, five areas were created according to the background present (Air, Water, Ground, Nature and Buildings; see Fig. 3 left). The classification of the position of each element was calculated based on the location of the bottom middle point of each element (see Fig. 3 right). By using the bottom middle point, usually correspondent to the position of the feet, paws or base of each biodiversity element, we can guarantee that each item can only be identified as overlapping with one background scenario area. By comparing this information to the correct information about each biodiversity element’s usual locations, we can classify whether each element was placed in the correct location. For example, regarding the snail example mentioned above, we know that snails can not fly; so when a child places the snail in the air, we considered that a misconception regarding the location of this animal. This process was applied for all elements present in the drawing and the information recorded.

4.2 Categorisation of the Level of Knowledge

We computed the knowledge level of all children based on their answers for the four elements seen on the app, see Eq. 3. We had into consideration their knowledge about the names of the elements (EK) and the group they belonged to (GK), the number of characteristics selected (TCS), see Eq. 1, and the number of features they selected for each biodiversity element: food(CF), location(CL), physical(CPF), need for life(CNL), and all features (CAll), see Eq. 2. Plus we also consider the maximum number of features each element seen could have and that for each four elements they had to identify three things (name, group and features).

$$\begin{aligned} ElementsInfo = \sum _{1}^{4} \left[ EK(i) + GK(i) + TCS(i) \right] \end{aligned}$$
(1)
$$\begin{aligned} Feature = \sum _{1}^{4} \left[ CF(i) + CL(i) + CPF(i) + CNL(i) + CAll(i) \right] \end{aligned}$$
(2)
$$\begin{aligned} KnowledgeLevel = \frac{ElementsInfo + Features}{\sum _{1}^{4}\left[ MaxFeatures(i) \right] + 12} \end{aligned}$$
(3)

A Cluster Analyses was applied to Knowledge level variable (Eq. 3) using a Hierarchical cluster with a single solution of three groups. We used the Interval - Square Euclidean Distance for the cluster measure and the between-groups linkage for the cluster method. Our results grouped our data into the following categories of Knowledge: Low with values between \([.20 - .31]\); Medium with values between \([.33 - .54]\) and High with values between \([.55 - .85]\).

4.3 Data Analysis Strategy

The data was analysed using SPSS software (v. 26). H1 and H2 were tested using one-way ANOVA, whereas H3 was tested by performing a \(\chi ^2\) test of independence and H4 was tested using one-way MANOVA. Post-hoc analysis were conducted using the Tukey test. Regarding the assumptions for the tests conducted, we found that the distribution of the level knowledge according to the school year did not conform to the normal distribution (\(p<.001\)); however, given that both the skewness and kurtosis values were contained between \(-1\) and 1 (.13 and \(-.47\), respectively), in which conditions has been shown that the F test remains robust (thus controlling for Type 1 Error within the bounds of Bradley’s criterion) [17]; and considering that a sufficient sample size tends to minimise this disparity [9], we opted to conduct the comparisons using parametric methods. In addition, although we will use the conventional alpha value of .05, we also calculated a Dunn-Sidak’s adjustment to the p-value in order to correct for multiple comparisons and provide a more conservative standard for interpretation of results, which yielded an ideal alpha value of approximately 0.

4.4 Results

  • H1 - Children’s Previous Knowledge. We found a main effect of children’s level of knowledge in their ability to correctly identify biodiversity elements (\(F(3,94) = 5.489, p = .002\)). In particular, we observed that children from \(4^{th}\) (\( .54 \pm .12, p= .001\)) and \(3^{rd}\) (\( .49 \pm .15, p=.043\)) years were able to identify more elements correctly than children from the \(1^{st}\) year (\( .39 \pm .13\)). We found no difference between the \(1^{st}\) and \(2^{nd}\) years (\(p = .063\)); \(2^{nd}\) and \(3^{rd}\) \((p=1)\); \(2^{nd}\) and \(4^{th}\) \((p=.47)\) and \(3^{rd}\), and \(4^{th}\) \((p=.52)\) school years.

  • H2 - Children misconceptions on drawings. We found a main effect of children’s previous knowledge on the number of misconceptions represented by children in the drawing task (\(F(3,101) = 8.151, p = .0\)). In particular, the number of misconceptions was significantly lower for \(1^{st}\) (\( 1.69 \pm 1.75, p= .007\)), \(2^{nd}\) (\( .88 \pm .73, p= .0\)) and \(3^{rd}\) (\( 1.46 \pm 1.45, p= .002\)) years when compared to \(4^{th}\) year (\( 3.44 \pm 3.07, p= .007\)). No differences were observed between the \(1^{st}\) and \(2^{nd}\) (\(p = .421\)); \(1^{st}\) and \(3^{rd}\) (p=.972); and \(2^{nd}\) and \(3^{rd}\) (p=.706) school years.

  • H3 - Children scenario preferences. We found a relation between children’s previous level of knowledge and their choice of scenario for the story (\(\chi ^2\) (3 \(N=105\)) \(= 7.85; p=.049\)). Overall, children demonstrated a preference towards nature-based scenarios (\(n=17,22,22,18\) for \(1^{st}\) to \(4^{th}\) years respectively), when compared to the human-made ones (\(n= 12,3,4,7\) for \(1^{st}\) to \(4^{th}\) years, respectively).

  • H4 - Children selection preferences regarding animals. Our results revealed that there was a main effect of children’s previous level of knowledge in their choices related to the inclusion of wildlife elements in their drawings (\(F(6,200)=6.31, p = 0\); Wilk’s \(\varLambda = .71\), partial \(\eta ^{2} = .16\)); but only regarding their inclusion of non-local biodiversity elements (\(F(3,101) = 12.82; p = 0\); partial \(\eta ^{2} = .28\)) and not on the selection of local biodiversity elements (\(F(3,57) = .63; p =.6\); partial \(\eta ^{2} = .01\)).

The mean scores for non-biodiversity elements were statistically significantly different between \(1^{st}\) and \(2^{nd}\) school years (\(p<.001\)), \(1^{st}\) and \(3^{rd}\) school years (\(p<.001\)) and \(1^{st}\) and \(4^{th}\) school years (\(p<.001\)), but not between \(2^{nd}\) and \(3^{rd}\) school years (\(p=.95\)), \(2^{nd}\) and \(4^{th}\) school years (\(p=.91\)) and \(3^{rd}\) and \(4^{th}\) school years (\(p=.63\)). Regarding the mean scores for the local biodiversity elements, those did not present statistically significantly differences among each school year (\(p >.05\) for all cases).

Our data reveals that most of children choices regarding the total number of non-biodiversity elements varied across a total of 1 and 2 elements. On the \(1^{st}\) year, majority of children picked a total of 5 elements \((n=20)\) from non-biodiversity options. Children on the \(2^{nd}\) year preferred a total of 1, 2 and 3 elements as their main choices \((n=19)\). Plus, the \(3^{rd}\) year students elected a total of 1, 2 and 5 elements \((n=21)\) on their drawings. Finally, the majority of children on \(4^{th}\) school year \((n=20)\) selected a total of 1, 2 and 3 elements from non-biodiversity options into their drawings.

User Subjective Evaluations. The overall feedback provided by children through the questionnaire was very positive. \(91.4\%\) of the children reported liking Pepper and enjoyed the interaction a lot. The remaining children either reported merely liking Pepper \((8.6\%)\) and the application \((7.6\%)\) or not liking the application \((1\%)\). When questioned about the drawing activity, the majority \((88.6\%)\) reported enjoying the activity a lot, whereas the remaining children reported medium \((9.5\%)\) or neutral levels of enjoyment \((1.9\%)\). Regarding their comprehension of Pepper speech, three children reported having some issues, with these children reporting poor levels of comprehension. However, the majority \((9.5\%\) (\(n=10\)), \(14.3\%\) (\(n=15\)) and \(73\%\) (\(n=77\))) reported not having issues understanding Pepper’s speech (neutral, good and very good understanding respectively).

5 Discussion

In this study, we sought to characterise children’s knowledge and illustrations of biodiversity elements during a task related to biodiversity identification. Plus, from the four hypotheses, we proposed to analyse, we partially validated H1, validated H3 and H4, but did not validate H2.

We hypothesised that children level of previous knowledge would have an impact on children ability to identify elements of local biodiversity correctly. In particular, older children would get more elements than younger ones. Our analysis revealed some differences among children of the \(4^{th}\) and \(1^{st}\)school year and \(3^{rd}\) and \(1^{st}\), but not among \(2^{nd}\) and the remaining years.

Our expectations regarding misplacement elements on the drawings revealed to be opposite of what we anticipated. We did not foresee that children with a higher level of knowledge would place more elements in wrong areas. Could this fact be related to children’s organisation of elements according to their beliefs and understanding about theme (biodiversity environment) [1]? Alternatively, this phenomenon can be related to our scenarios being displayed at black and white and not fully coloured. There is the possibility that the lack of colour lead to children failure to recognise certain areas. For instance, in the scenario with a fence and a small stream, children might have though of the stream area as being a trail delimitation. Further work needs to be done in order to asses if this issue is related with children idealisations; the scenarios we presented (black and white vs colourful) or if children were just disengaged in the drawing activity.

Children preferences regarding scenarios were congruent with our H3. Most children chose scenarios related to nature instead of scenarios that included human-made structures. Despite that, the first-year students almost matched the number of choices between the two types of scenarios (nature - 17 and human-made - 12) when compared to the remaining school years. This could indicate that younger children are more used to see animals and plants in their surroundings (city areas) than in their natural habitat. Concerning the other school years, the difference between the two types of scenarios was much higher, 17 to 22 children choose nature-related options and 3 to 7 children human-made ones (among school years).

Our results suggested that children choices regarding animals that do not belong to local biodiversity were aligned to our expectations (H4). Children preferences for non-biodiversity elements ranged among 1 and 2 elements for children of the \(2^{nd}\), \(3^{rd}\) and \(4^{th}\) years. While the majority of children on the \(1^{st}\) year (n = 20 out of 29) actually demonstrated preferences for selecting all five elements (bear, tiger, panda, toucan and lion) that were out of local biodiversity. This outcome is not surprising; since most of young children at this stage (age of 6 to 7) still have an immature reasoning [19].

6 Conclusions and Future Work

Our main aim with this project is research on how virtual agents (and social robots) can promote young children’s engagement with biodiversity issues and facilitate learning about this fascinating topic. Furthermore, we believe that learning about biodiversity needs grounding in local knowledge, situations, environment and stories. The findings here reported provide us information that such localisation is of paramount importance, and that individual differences must be taken into account. To further our efforts to achieve this goal, in the future we intend to use the data collected using this virtual agent and robot, to develop an educational adaptive system [16]. Such adaptive learning system will use an intelligent agent and in particular a physical robot (Pepper) to deliver customised information, tailored to the user’s abilities, cognitive skills, and knowledge. We believe that our results so far will help us in the future create and train a model capable of adjusting to children individual needs. At the same time that we take advantage of the growing amount of evidence in favour of the benefits and potentialities that Human-computer interaction and Human-Robot interaction can have in the education of children [4, 5, 14, 27].