# Robotics in Primary Education: A Lexical Analysis of Teachers' Resources Across Robots

Christophe Reffay, Gabriel Parriaux, Béatrice Drot-Delange, Mehdi Khaneboubi

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Christophe Reffay, Gabriel Parriaux, Béatrice Drot-Delange, Mehdi Khaneboubi. Robotics in Primary Education: A Lexical Analysis of Teachers' Resources Across Robots. Therese Keane; Cathy Lewin; Torsten Brinda; Rosa Bottino. Towards a Collaborative Society Through Creative Learning, AICT-685, Springer Nature Switzerland, pp.209-220, 2023, IFIP World Conference on Computers in Education (WCCE), 978-3-031-43393-1. 10.1007/978-3-031-43393-1_20. hal-03798346

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# Robotics in Primary Education: A Lexical Analysis of Teachers’ Resources Across Robots

Christophe Reffay<fnref n="1" />(✉), Gabriel Parriaux<fnref n="2" />(✉), Béatrice Drot-Delange<fnref n="3" />(✉), and Mehdi Khaneboubi<fnref n="4" />

**Abstract.** Through a lexical analysis, this study examines the relationship between the terms used in various educational resources about robots. These resources were authored by novice or expert teachers in primary schools. Our hypothesis is that the computer science concepts discussed in an activity are different depending on the type of robot. The first results confirm dependence between the type of robot and the lexicon used in the resources. The corpus is explored according to three thematic sets of terms: Computer Science (CS), Pedagogy and Move with special attention for CS terms to compare the vocabulary used for sequential and event-driven programming robots.

**Keywords:** educational robotics · event-driven programming · sequential programming · teacher education

## 1 Introduction

In the recent years, we have witnessed in several countries an important movement towards the introduction of Computational Thinking (CT) skills in the curricula for compulsory education. In 2022, a publication of the European Union indicates that 15 countries have CT skills integrated in primary education and 12 countries have CT skills as a compulsory element in both primary and secondary level. 15 countries have renewed their curriculum between 2016 and 2021 to include CT skills [1].

In this context, programming is being considered a central element in the development of these new CT skills [2]. Depending on the educational goals and the material available in class, teachers design learning activities and make use

<footnote n="1">¹ University Bourgogne Franche-Comté, Besançon, France christophe.reffay@univ-fcomte.fr</footnote>

<footnote n="2">² University of Teacher Education, Lausanne, Switzerland gabriel.parriaux@hepl.ch</footnote>

<footnote n="3">³ University of Clermont Auvergne, Clermont-Ferrand, France beatrice.drot-delange@uca.fr</footnote>

<footnote n="4">⁴ CY Cergy Paris University, Paris, France mehdi.khaneboubi@u-cergy.fr</footnote>
of different Computer Programming Learning Environments. Many activities are based on the use of tangible artifacts such as robots.

In France and Switzerland, pre-assembled mobile robots (e.g. Beebot, Thymio) appear to be the most common used in class at primary level. They are also among the tools often presented to teachers in teacher education programs on CT skills. Robotics kits, including components that have to be built (e.g. LEGO®-Mindstorms™) also exist, but their use seems to be less widespread. In terms of competencies, pre-assembled robots let kids focus on the programming tasks, where robotics kits are more oriented towards the development of engineering/technology ones [3].

Pre-assembled mobile robots can be very diverse, from simple automata like Beebot or Bluebot that are being programmed with buttons and have no sensors to more advanced robots like Thymio that have sensors and can react to their external environment.

According to the classification of Computer Programming Learning Environments for primary education proposed by [4], Beebot and Bluebot robots fall in the “Logo family programming environments” category and more precisely in the “Roamers” (floor turtles) subcategory. In this category, a program is defined as a sequence of ordered instructions having a beginning and an end. Ozobot and Thymio robots are falling in another category named “Physical Computing Environments” and subcategory “Educational robotics environments.” These last two robots may use a programming language leading on event-driven programming, associating events and actions to define their behavior, i.e.: a very different way to define a program.

In terms of programming, there is an important difference between those two categories, one being uniquely focused on sequential programming and the other being more oriented towards event-driven programming.

## 2 Educational Robotics and Programming Paradigms

Robots are now widely used in primary education in many countries and researchers like [5] estimate their use can have a potential impact on the way science and technology is taught at all levels. Positive results can be seen in the use of educational robotics to develop an understanding of concepts related to STEM [5,6] and also, in more specific contexts, to foster 21st century skills [7].

Even if the development of social competencies using educational robotics is welcome, we should keep in mind that one of the main goal of introducing robots in classrooms is to let pupils learn basic computer science and programming concepts [8]. We are interested in understanding what programming notions and skills are taught by teachers with the different categories of robots.

The two central concepts in event-driven programming are *events* (something that occurs) and *event handlers* (a piece of program that executes as a reaction to an event). The main difference with sequential programming (where a program gets executed from a beginning to an end) is that an event-driven program waits indefinitely for events to occur and reacts to any considered events happening.
With the classic procedural approach, there is an *inversion of control* because the execution order depends on external events.

Such a different approach of programming can be challenging for young pupils to handle. First, it might be difficult to grasp the logic of functionality when it is distributed across event handlers. Then, understanding event-driven programming might require the comprehension of more advanced notions that are not seen in introduction courses like *origins* and *routing of events, device drivers, parallelism* and *concurrency, states* or *model-view-controller*. Depending on the programming environment, some parts of an event-driven program might be completely hidden from the user, making it impossible to trace code from the beginning to the end [9]. The same authors observe that students who are introduced to programming using event-driven approaches do not develop some algorithmic skills that other students would generally acquire through imperative-procedural programming languages leading on a sequential execution model.

Unfortunately, we couldn't find a research paper about the set of concepts that students should learn to master event-driven programming, or about their misconceptions of those concepts.

In the field of Educational robotics, the question of programming paradigms for robots has received little attention in the scientific literature. Among the 105 papers analyzed in their systematic review on event programming, [10] note that only two of them concern robots (Social Robot Toolkit, Thymio). We couldn't find any research articles that addresses the best practices for teachers in such a context either [10].

Based on these observations, we propose to investigate the link between the type of robots in educational robotics activities and the lexicon used to describe those activities.

## 3 Research Questions

We propose to look at pedagogical resources produced by teachers for activities in educational robotics to see if we can find any significant difference between them according to different variables. Considering teacher education as a long term development process, we compare resources authored by novices (i.e.: pre-service teachers) in our schools of education vs resources produced and published on the Internet by experienced teachers or teams.

By using lexical analysis, we capture the vocabulary used by teachers in their pedagogical resources. Then, we formulate the hypothesis that the lexicon is significantly different according to the type of robots that they are using. More precisely, we investigate if some specific terms reveal different CS notions or programming paradigms depending on the type of robots used. Finally, we can formulate our research question as follows:

RQ1 *Validation: Is the lexicon used in pedagogical resources statistically dependent on the type of robots used in educational robotics?*

RQ2 *Exploration: Which are the specific terms used in pedagogical resources for the various types of robots?*
RQ3 *Do we have lexical differences between resources authored by pre-service teachers and experts?*

# 4 Methods: Data Collection and Analysis

## 4.1 Corpus: A Collection of 120 Pedagogical Resources

The corpus contains 120 texts authored by teachers in two French-speaking countries. They describe activities in the field of educational robotics for pupils in primary schools. 59 of these texts are documents preparing or presenting pedagogical activities produced by pre-service teachers in the context of their studies in three different teacher education institutions. These 59 texts were selected from a corpus of 135 productions analyzed in a previous study on pedagogical strategies used by pre-service teachers [11]. Other 61 texts were collected from the web following a standardized query protocol and are authored by experienced teachers (teams). The resulting corpus of 120 texts is also qualified by the following criteria: the robot used in the activity the age of target pupils and the country from which the educational system depends. Table 1 presents the composition of the corpus according to the author's expertise and robots' names grouped by types (sequence-based vs. event-based).

<table><caption>**Table 1.** Number of resources distributed in the corpus according to authors expertise and robot used</caption><thead><tr><th rowspan="2">Author</th><th colspan="4">Sequence based robots</th><th colspan="3">Event based robots</th><th>All</th></tr><tr><th>Beebot</th><th>Bluebot</th><th>Other_Seq</th><th>Sum Seq.</th><th>Ozobot</th><th>Thymio</th><th>Sum Event</th><th>Sum</th></tr></thead><tbody><tr><td>Novice</td><td>17</td><td>20</td><td>7</td><td>44</td><td>2</td><td>13</td><td>15</td><td>59</td></tr><tr><td>Expert</td><td>16</td><td>12</td><td></td><td>28</td><td>17</td><td>16</td><td>33</td><td>61</td></tr><tr><td>All</td><td>33</td><td>32</td><td>7</td><td>72</td><td>19</td><td>29</td><td>48</td><td>120</td></tr></tbody></table>

Non formatted text has been automatically extracted from original documents: text contained in presentations in various formats (.pptx, .key), in text documents (.odt, .docx) or in PDF files, and audio speech from video (excluding visual text appearing on images or video files). Some cleaning was necessary to get a syntactically correct text content.

## 4.2 Lexical Analysis

Using a textometry software TXM, we built the lexicon (12,008 different words), lemmatised according to French rules (resulting in 7,881 distinct lemmas) from the entire corpus (252,960 words). The lemmas frequencies range is [1–17, 403]. Considering the list of lemmas in decreasing order of their frequency, we found that the lemma *robot* was the first meaningful term having the highest frequency (2,250).
In Table 1, the maximum number of resources in a subset is 20 (Novice - Bluebot). This implies that a term appearing only once in each resource of this subset and in no others would have 20 occurrences. Consequently, we decided to consider only lemmas having a frequency *f* (such as 20 ≤ *f* ≤ 2250). This choice reduced the list of lemmas to be analysed from 7881 to 1104.

From these 1,104 lemmas, we manually selected the 373 ones that could be meaningfully associated to the three following themes: Pedagogy, Computer Science (CS) and Moves. Note that some lemmas (e.g. *séquence*, *programme*) may fall in more than one category (e.g. *séquence*: sequence of instructions or sequence of pedagogical activities; *programme*: computer code or national curriculum; these two words may refer either to CS or Pedagogy). In other words, our sets of lemmas are not mutually exclusive.

In the resulting extraction, we have 195 lemmas for Pedagogy (e.g. *activité*, *évaluer*, *collaborer*, *élève*, *enseignant...*; i.e.: activity, evaluate, collaborate, pupil, teacher...), 134 lemmas for CS (e.g. *algorithme*, *boucle*, *capteur*, *effecteur...*; i.e.: algorithm, loop, sensor, effector...) and 77 lemmas for Moves (e.g. *position*, *grille*, *tourner*, *gauche*, *droite*, *derrière*, *devant...*; i.e.: position, grid, turn, left, right, backward, forward...).

## 4.3 Statistical Analyses

We conducted statistics using R [12] on the lexicon following different organization of our data. To deal with categorical variables we constructed contingency tables to search for relations between robots and terms used in the resources. Chi-square test was performed. Correspondence analysis [13] plots let us represent in a two-dimension space our variables in order to interpret the kind of proximity or distance that we could find between our categories.

# 5 Results

## 5.1 Dependence Between Lexicon and Resources Classified by Robots—Words Grouped by Theme

In a first set of analyzes, we group words by theme (CS, Pedagogy and Move) and pedagogical resources by robot. Because of the very small numbers of texts for Cubetto (1), Mouse (4) and Robotdoc (2) we decided to group these 7 texts into a single category named: “Other_Seq” referring to “other sequence-based robots” (as presented in Table 1).

For each robot and corresponding resources, we counted the number of occurrences of each individual word from each theme and sum them. This gives us a contingency table with 5 categories of robots and 3 categories of words.

As a result, Pearson's chi-square test shows that we can reject the null hypothesis ($\chi^2(8) = 1365.8, p < 0.01$). There is a significant dependence between the frequency of the words grouped by theme and the type of robot. But they are only weakly associated (Cramer's V of 0.1105).
<figure><img src="image_3.png" /><figcaption>Fig. 1. Correspondence Analysis: Robots - Themes</figcaption></figure>

Figure 1 spreads the different themes and robots on a factorial plan for correspondence analysis (CA).

On Fig. 1, the first axis (Dim 1) is strongly discriminant (almost 99%). This first factor of the CA seems to separate mostly between CS and Move categories of words. Pedagogy, being near the origin, doesn't discriminate according to this first factor. This first factor also serves to discriminate between event-based robots (Thymio and Ozobot Bit), which are closer to CS, and sequence-based robots. The latter (Beebot and Other_Seq) are closer to Move. Bluebot does not seem to discriminate according to this factor.

This first analysis shows us that there is a dependence—even weak—between words classified by theme and pedagogical resources classified by robots. An association exists between CS lexicon and resources produced on event-based robots on one side, and between Move lexicon and resources produced on sequence-based robots on the other side.

## 5.2 Dependence Between Lexicon and Resources Classified by Robots/Level of Expertise of Authors—Words Grouped by Theme

In order to observe how the level of expertise influences these positions, we conduct a second set of analyzes splitting each robot category from Fig. 1 to identify those authored by novices and those by experts in Fig. 2. We obtain a contingency table with 9 categories mixing robots and level of expertise and 3 categories of words.

Pearson's chi-square test shows that we can reject the null hypothesis ($\chi^2(16) = 1761.8, p < 0.01$). There is a significant dependence between the frequency of the words grouped by theme and the type of robot when we take into account the level of expertise. But they are only weakly associated (Cramer's V of 0.1255).

Again, on the CA presented in Fig. 2, the first axis is strongly discriminant (83%). As in our previous analysis, the first factor of the CA seems to separate mostly between CS and Move categories of words. If we look at the pedagogical resources grouped by robot and by level of expertise, we see the separation between resources produced by experts for event-based robots (Thymio and Ozobot)—associated to CS—and resources produced for sequence-based robots—associated to Move—without real distinction in the level of expertise.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Correspondence Analysis: Authors - Robots - Themes</figcaption></figure>

This second analysis shows us that there is a dependence—even weak—
between words classified by theme and pedagogical resources classified by robots
and by level of expertise of their authors. An association exists between CS lex-
icon and resources produced by experts on event-based robots on one side, and
between Move lexicon and resources produced on sequence-based robots on the
other side.

5.3 Dependence Between CS Lexicon and Resources Classified by Robots—Words Taken Individually

Having considered until now our lexicon as an aggregation of words grouped by
themes, we analyze now our data considering each word of the lexicon separately.

In this third set of analysis, we first extract from the lexicon all the
words belonging to the CS theme, drop the others, and keep all pedagogical
resources. Then we grouped resources by robot. For each robot and correspond-
ing resources, we counted and summed the number of occurrences of each indi-
vidual word from the CS theme. Other sequential robots (Cubetto, Robotdoc
and Mouse) with few corresponding resources had to be abandoned as they had
a lot of zero values. For the same reason, words appearing in too few resources
were removed. This gives us a resulting contingency table with 94 words from
the CS lexicon and four robots (Beebot, Bluebot, Thymio and Ozobot Bit).

In this case, Pearson's chi-square test shows that we can reject the null
hypothesis (χ²(279) = 5600.6, p < 0.01). There is a significant dependence
between the frequency of the words of the vocabulary in the theme "CS" and
the type of robot on which pedagogical resources are based. The strength of the
association is moderate (Cramer's V of 0.3312).

Correspondence analysis offers a representation where the first two factors
cover 80% of variance (49.38 and 30.99 respectively). To facilitate the interpre-
tation, we display a bi-plot of the 30 most contributive variables in Fig. 3.
<figure><img src="image_5.png" /><figcaption>Fig. 3. Correspondence Analysis with CS lexicon: most contributive words</figcaption></figure>

According to the first axis, there is an opposition between Thymio/Ozobot and the couple Beebot/Bluebot. On the second axis, we observe an opposition between Ozobot Bit (always used in following lines mode) and the three other robots.

We observe a so-called “Guttman effect” with points drawing a kind of parabolic shape. It means that there is a link between variables. We can interpret it by saying that the vocabulary is discriminant.

If we look at the vocabulary, the first axis separates words related to the machine and to the technical dimension (*comportement*, *allumer*, *éteindre*, *action*, *objet*...; behavior, switch on/off, action, object...)—associated with Thymio—from words related to programming (*instruction*, *symbol*, *order*, *séquence*, *coder*...)—associated with Beebot and Bluebot.

## 5.4 Dependence Between CS Lexicon and Novices' Resources
Classified by Robots—Words Taken Individually

In a fourth set of analyses, we conduct the same analysis, now considering only resources authored by novices. Having only two resources on Ozobot authored by novices, this robot is not considered here. Taking into account only a sub part of the corpus, the set of CS terms also differs.

In the case of novices, we obtain a contingency table with 54 words from the CS lexicon and three robots (Beebot, Bluebot and Thymio). Pearson's chi-square test shows that we can reject the null hypothesis ($\chi^2(106) = 448.37, p < 0.01$). For novices, there is a significant dependence between the frequency of the words of the vocabulary in the theme “CS” and the type of robot on which pedagogical resources are based. The strength of the association is still moderate (Cramer's
V of 0.2955), but less than in the previous analysis: when we did not separate resources by level of expertise.

Correspondence analysis offers a representation where the first two factors cover 100% of variance (57.70 and 42.29 respectively). To facilitate the interpretation, we display a biplot of the 30 most contributive variables in the case of novices in Fig. 4.

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Correspondence Analysis with CS lexicon (novices): most contributive words</figcaption></figure>

For the first axis, the most discriminant words—those with the highest contribution to the orientation of the axis 1 (contrib) and those best represented by axis 1 ($cos^2$)—are code, carte, appuyer on the left-hand side (i.e.: code, card, push—contrib=12.60, 11.62 and 8.61; $cos^2$ = 0.850, 0.994 and 0.999 respectively) and robot (i.e.: robot—contrib=7.75, $cos^2$ = 0.657) on the right-hand side. On this dimension, we observe an opposition between the Beebot and the other two robots. For the second axis, the most discriminant words are automate, programming, bouton, machine (automaton, programming, button, machine—contrib=10.80, 7.18, 6.57 and 5.70; $cos^2$ = 0.837, 0.618, 0.584 and 0.692 respectively) on the top and si (if—contrib=9.66, $cos^2$ = 0.756) on the bottom. This second dimension shows an opposition between Thymio and Bluebot. If we compare Fig. 3 with Fig. 4, we can see that the very similar robots Beebot and Bluebot are plotted near each other on Fig. 3 (all authors) but very far away on Fig. 4 (novices only).

If we look at the vocabulary, it's not so easy to interpret. But we can find:

- near Beebot (on the left-hand side): vocabulary typically used for the first manipulation of such a toy with young pupils: appuyer, carte, touche, code, objet (i.e.: push, card, button, code, object)
- near Bluebot (in the upper right quadrant): the terms rather used one step further in programming: informatique, algorithme, programmation, exécuter, notion, automate (i.e.: CS, algorithm, programming, to execute, notion, automaton)

- and finally near Thymio (in the lower right quadrant): the words describing the behavior of a reactive object in its environment: matériel, robot, si, comportement, environnement (i.e.: material, robot, if, behavior, environment).

However, these are the terms we were able to interpret and that we expected to find in the neighbourhood of each of these robots. But other words have positions that are difficult to explain. For example, the word séquence appears near Thymio and far away from Beebot and Bluebot. These two robots being considered as sequence-based ones we expected that this word would be placed near to them and not near to the event-based robot: Thymio. An in-depth read of the 47 occurrences of the word séquence in resources authored by novices reveals that 44 of them refer to a pedagogical sequence and only 3 concern a sequence of actions. Another word, objet (i.e.: object) is placed near Beebot on Fig. 4. When checking the 18 occurrences of objet in the novices’ resources, we verified that all of them designate a physical object to be manipulated or detected by the robot. It never refers to object oriented programming.

## 5.5 Dependence Between CS Lexicon and Experts' Resources
Classified by Robots—Words Taken Individually

In the case of experts, we obtain a contingency table with 83 words from the CS lexicon and 4 robots (Beebot, Bluebot, Thymio and Ozobot).

The first observation is that CS lexicon is narrower for novices (54 words) than for experts (83 words).

Pearson's chi-square test shows that we can reject the null hypothesis ($\chi^2(246) = 4635.4, p < 0.01$). For experts, there is a significant dependence between the frequency of the words of the vocabulary in the theme "CS" and the type of robot on which pedagogical resources are based. The strength of the association is moderate and a little bit greater than for novices or than in the case when we did not separate resources (Cramer's V of 0.3426).

The correspondence analysis for resources from experts draws the robots nearly in the same position as in the previous analysis which did not take into account the level of expertise and presented on Fig. 3.

# 6 Conclusion

Our exploratory research compared the lexicon of pedagogical resources authored by novices and experienced teachers. A focus has been made on Computer Science terms to analyse differences across the robots used in educational robotics: sequence-based versus event-based. The statistical analysis shows a significant dependence between vocabulary and type of robots used. This dependence is even
greater for experts who tend to use more CS terms than novices. The discrimi-
nant terms oppose technical object description (machine aspects) for Thymio to
more general programming concepts for Beebot and Bluebot.

Coming back to our research questions, we were asking whether the lexicon
used in pedagogical resources was statistically dependent on the type of robots
used in educational robotics (RQ1). All our analyzes have shown that there was
a significant dependence between the lexicon used inside pedagogical resources
and the type of robots it was focusing on. We could find in the pedagogical
resources that we analyzed three lexical fields that were different for each group
of robots. We can answer positively to our first question.

Then we were asking what were the specific terms used in pedagogical
resources for the various types of robots (RQ2). We have seen that there were
differences according to the level of expertise, with experts differentiating more
clearly between a vocabulary around programming when using sequence-based
robots and a vocabulary around machine when using event-based robots. Such
a difference of vocabulary has not been observed so clearly with novices.

Finally, we wanted to know if there were lexical differences between resources authored by pre-service teachers and experts (RQ3). We have seen that CS vocabulary is less developed in resources produced by novices. We have also observed that the distribution of words according to the factors of the Correspondence analysis were not the same for novices than for experts, with a distinction between basic manipulation of a robot and more general programming vocabulary for novices where experts were opposing programming to machine. We can also answer positively to our third question.

We are aware of some limits for these results. In data collection, the words
appearing on images or video files were not captured and then are not considered
in the statistical analyses. The nature of the productions authored by novices and
experts is different. Novices produced shorter preparation document to conduct
an activity with their pupils. Experts documents were three times longer (as a
mean) and more explicit because the goal was to share with peer teachers. This
can partly explain the unbalanced use of CS terms.

Our conclusions can not easily be generalized, as our analyses might be spe-
cific to the context of the three institutions of teacher education where they were
conducted. The cultural aspect can also play a role and other countries could
have very different situations.

Based on this study, one can see that event-based robots are the one used in
primary school to discover machine aspects of technical objects and sequence-
based programming robots are rather used to initiate pupils to sequential pro-
gramming. The authors of this article, involved in the field of teacher education
on computational thinking, note that different types of robots are presented dur-
ing teacher training sessions as equivalent alternatives. This might represent a
difficulty for teachers who want to teach programming concepts to their pupils.
This could be more explicitly explained to pre-service teachers in their teacher
education sessions. Another possible road in teacher education could be to teach
the distinction between the two notional models: sequential or event-driven, changing the locus of control from the programmer to the environment.

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