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# Teaching sampling methods in data science professional training: A case study in France

Charlotte Derouet, Camille Doukhan, Hussein Sabra

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## To cite this version:

Charlotte Derouet, Camille Doukhan, Hussein Sabra. Teaching sampling methods in data science professional training: A case study in France. 2025. hal-05048183

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# Teaching sampling methods in data science professional training:
A case study in France

Charlotte Derouet, LISEC UR 2310, Université de Strasbourg, UL, UHA, Strasbourg, France
Camille Doukhan, LISEC UR 2310, Université de Strasbourg, UL, UHA, Strasbourg, France
Hussein Sabra, CReSTIC UR 3804, Université de Reims Champagne-Ardenne, Reims, France

## Introduction

Statistics is studied in a wide variety of courses in tertiary education (Lorah & Valdivia, 2021). In this study, we are interested in a specific programme at university for students majoring in statistics. We conduct an exploratory study to analyse and characterise teaching practices in sampling methods for future statisticians<fnref n="1" /> in France. We aim to provide insights into the teaching of statistics in the context of post-secondary professional training.

This study aims to contribute to the growing body of research on the teaching-learning of mathematics (statistics in particular) in professional training. In this context, research in mathematics education has primarily focused on the training of future engineers (Pepin et al., 2021). However, studies addressing the teaching of statistics — particularly sampling methods — and related teaching practices remain significantly underexplored.

As in all professional training, investigating teaching practices is essential in understanding both the learning that takes place and the nature of the connections (González-Martín, 2021), or gaps (Quéré, 2017) that appear between the teaching settings (didactical choices and practices) and the workplace.

By adopting an anthropological approach to the study of teachers' practices (Bosch and Gascon, 2014), we propose theoretical and methodological tools aligned with existing research in tertiary mathematics education. Recent studies have applied the Anthropological Theory of the Didactic (Chevallard, 1999) to analyse mathematics teaching practices in training programmes for non-specialist students. For instance, González-Martín and Hernandes-Gomes (2020) analysed teaching practices in engineering education and identified personal and institutional characteristics that influenced teachers' practices. More recently, Doukhan et al. (2024) used interviews and resource analysis to explore how teachers of non-specialist students approached certain didactic types of tasks identified in the literature.

In the present study, we seek to describe teaching practices using didactic praxeologies in relation to sampling methods, in the specific institutional context of tertiary professional training in France, called *Institut Universitaire de Technologie*<fnref n="2" /> (IUT). In this teaching context, driven by the competency-based approach, we have identified a key moment in teaching, called Learning and Evaluation Situations (*Situations d'Apprentissage et d'Évaluation* in French), where the articulation between: the teacher's

<footnote n="1">¹ By "statisticians", we mean statistics professionals, not statistics researchers.</footnote>

<footnote n="2">² Technological University Institute.</footnote>
choices, the needs of the professional workplace and the institutional competencies of the training is manifested (Derouet et al., 2024). We argue that the analysis of practices from the perspective of this institutional specificity, the SAE, sheds light on an important part of teachers' practices. Indeed, our general question is: how does this institutional context (professional training driven by the competency-based approach) affect the teaching practice in sampling methods?

To address our general question, we begin by presenting the institutional context (section 1). We then present our theoretical framework, based on the Anthropological Theory of the Didactic (section 2). These theoretical choices allow us to reformulate our general question (section 2.3). In section 3, we present our methodology, including the data collection method (section 3.2), which mainly involves a teacher interview and classroom observations, as well as the method of analysis (sections 3.4 and 3.5). In section 4, we provide an example of analysis that illustrates how we applied our analysis to the collected data, followed by the main results (section 5) and then our conclusion (section 6).

# 1. Institutional context

## 1.1 General presentation of the Technological University Bachelor (BUT)

The Technological University Bachelor (Bachelor Universitaire de Technologie, in French, hereafter referred to as BUT) is a three-year professional degree offered by University Institutes of Technology (IUTs), which are part of French universities. There are various BUT pathways (e.g., chemistry, communication, legal careers, etc.). They aim to provide students with both theoretical knowledge and practical skills relevant to the requirements of the professional workplace. All BUT programs follow a standardized national curriculum (MESR, 2022a), specified for each pathway, and ensure consistency across all IUTs. The aim of the BUT is to develop the students' "know-how", which will allow them to work in a professional environment (Mourlhon-Dallies, 2024).

Since 2022, the BUT has adopted a competency-based approach (in the sense of Tardif, 2006), which emphasises the development of specific professional competencies. In the rest of this paper, when we talk about competencies, we are referring to the competencies targeted in the curriculum. Competencies are "understood as 'complex knowledge of how to act' implemented in a professional context and which mobilise and combine resources acquired during the course" (MESR, 2022a). In this regard, the competency-based approach in IUT is structured around the so-called:

1) *Ressources* described as courses (content and teaching sessions - lectures and tutorials) that enable "the acquisition of fundamental knowledge and methods";

2) *Learning and Evaluation Situations* (*Situations d'Apprentissage et d'Évaluation*, SAÉ) defined as work-based scenarios in which students develop the competencies targeted by the national curriculum (MESR, 2022a).

SAÉs hold a central role in the teaching settings of the BUT; they are thought to provide opportunities to use *Ressources* in contexts closely aligned with professional realities. In the following, we will keep the French words to emphasise the specific features of this professional training.
## 1.2 The SAÉ as a central focus of the study

One of the major changes in the BUT lies in the structuring of learning around SAÉs. Unlike a simple project, a SAÉ is designed as an integrative “device”, allowing the development of competencies, in relation with the *Ressources* and the professional environments. The SAÉs are supposed to be “authentic”, even though they often occur outside actual professional environments, fostering learning and preparing students for the workplace (Bolou-Chiaravalli et al., 2022). These integrative situations are added to those encountered during work placements in order to increase the potential for professional development of the degree (Bolou-Chiaravalli et al., 2022).

On the basis of the SAÉs, the student must demonstrate the acquisition of competencies in the *portfolio approach*. This approach is “*a continuous process of self-assessment which should enable students to adopt a reflective and critical stance with regard to the competencies they have acquired or are in the process of acquiring*” throughout their training (MESR, 2022a, p. 4). The *portfolio* serves as an intermediate step between the university world and the professional world.

Our study places the SAÉ at the core of its analysis, considering it as a privileged entry point for understanding teaching practices within a competency-based approach context and in professional training (Derouet et al., 2024). The SAÉ thus acts as a critical teaching site for linking theoretical contents, competencies and practical applications.

## 1.3 Case study: the SAÉ “Data collection and analysis by sampling or experimental design”

Our study focuses specifically on the Data Science BUT, specialising in Statistical Exploration and Modelling, which aims, according to the national curriculum, "to train professionals skilled in the collection, processing and statistical analysis of data" (MESR, 2022b, our translation). The Data Science BUT, specifically, targets the development of competencies required to address the growing demand for data processing, analysis, and modeling in various companies and public institutions. This pathway is based on four final competencies, which are: 1) Processing data for decision-making purposes; 2) Analysing data statistically; 3) Adding value to a product in a professional context; 4) Modelling data within a statistical framework. The aim of the Data Science BUT is for students to develop these competencies through the various teaching situations, in particular the SAÉs.

In this context, our focus is on the SAÉ entitled "Data collection and analysis by sampling or experimental design" (SAÉ 3.EMS.01), which is carried out during the third semester (second year). Students are already familiar with the competency-based approach used in their training. This SAÉ aims to develop the four competencies of the curriculum by placing students in a situation where they must set up a survey and implement a data collection plan to address a specific problem (MESR, 2022b). This SAÉ allows the survey methodology to be explored in greater depth. It focuses on:

- Designing and implementing data collection plans,

- Applying sampling methods (e.g., simple random sampling, stratified sampling, etc.),

- Addressing potential biases in data collection.

This SAÉ builds on content from the *Ressource* titled "Sampling methods and survey methods" (R3.EMS.10), which includes topics related to different sampling methods (simple random sampling
without replacement, stratified sampling, clustered random sampling), adjustment methods, and sources of biases. Additionally, it draws on *Ressources* and *SAÉs* from previous semesters, allowing – if applicable – continuity in the learning process. Figure 1 shows the connection between competencies, *Ressources*, and professional environments, in the *SAÉ* in question, making it a particularly relevant subject of investigation for our research. By “professional environment”, we refer to the context, the workplace, requirements and needs.

<figure><img src="image_4.png" /><figcaption>**Figure 1.** The SAÉ “Data Collection and Analysis” as an interface (Derouet et al., 2024, p. 405)</figcaption></figure>

Drawing on previous curricular analysis of the Data Science *BUT* (Derouet et al., 2024), our study examines how teachers anticipate, prepare, and implement these links. Specifically, the choices and decisions that have influence on:

- The links between the *SAÉ* and the *Ressources* (content and teaching sessions);

- The links between the *SAÉ* and the competencies to be developed;

- The links between the *SAÉ* and the professional environments.

We will also seek to understand the reasons behind the teachers' choices and actions.

## 2. Theoretical framework

To analyse teaching practices in the institutional context of Data Science *BUT*, we draw on the Anthropological Theory of the Didactic (ATD, Chevallard, 1999). According to ATD, the content taught is shaped by the institution. We define an institution as a 'total' social system that allows and requires its teachers to contribute specific ways of doing and thinking (Chevallard, 2003). Different institutions could shape the teaching of mathematics topics in different ways. This approach seems particularly well suited to our study, where the structure of the *BUT* and the presence of a national curriculum determine, at least partially, the teaching practices. Through this national curriculum, the institution 'informs' the teacher about the knowledge content to be taught and how it should be taught.
However, as Barbé et al. (2005) explain, there is still a great deal of work to be done by the teacher, who must “decode the information provided by curricular documentation in order to elaborate, in collaboration with the students, a mathematical organisation complete enough to allow the development of a quite coherent mathematical study process”.

## 2.1. Didactic praxeology

According to the ATD, teaching practices could be modelled in terms of *didactic praxeologies*. A didactic praxeology consists of four elements: for a given didactic *type of tasks*, there is one (or more) way of performing the task, which is called a *didactic technique*. A *technological discourse* can be associated with this technique, and a *theory* provides elements of justification for the technique, but this is still often implicit in the case of didactic praxeologies.

Technological discourse has two main functions: justification and explanation (Chevallard, 1999). The explanatory function is attributed to those elements of discourse that aim to describe the technique and make it understandable, while the justificatory function refers to those elements of discourse that aim to argue that the technique is actually effective in accomplishing the task. We postulate that, in the case of didactic praxeologies, the justificatory function of the technology is very difficult to identify because it is intrinsically linked to the technique itself. Indeed, Asami-Johansson (2021) notes that didactic techniques are so naturalised for teachers that it becomes difficult for them to explain the underlying reasons. In her study, this author used ATD to identify elements of teachers' discourse that explained their use of didactic techniques. Similarly, we focus here on the explanatory function of technological discourse, i.e. the reasons given by the teacher for choosing a particular didactic technique (“why I am doing that?”). Furthermore, as described by Bosch and Gascón (2001), a didactic praxeology is “spontaneous” in the sense that the techniques used are generally neither generated from nor structured by a technological discourse. From this perspective, the explanatory function of technological discourse seems to be a defining characteristic of spontaneous didactic praxeologies, which is why we will pay special attention to it in this study.

In the previous section (1.3), we outlined three teaching objectives, linked to the institutional context, which will enable us to explore teaching practices. These objectives can be modelled by didactic types of tasks: Tress “Make the link between the *SAÉ* and different *Ressources*”, Tcomp “Make the link between the *SAÉ* and competencies to be developed” and Tprof “Make the link between the *SAÉ* and students’ future professional environments”. We consider, as Bosch and Gascón (2001), that didactic praxeology is personal and comes from the individuality of the teacher. It includes the teacher's choices, but also everything related to accidents, shortcomings and contradictions. Therefore, our present study seeks to describe the teacher's personal didactic praxeologies associated with the three types of tasks: Tress, Tcomp and Tprof.

## 2.2. Scale of levels of didactic co-determinacy

ATD postulates that teaching practices in a given institution are largely determined by the constraints and possibilities at its disposal (Bosch and Gascón, 2001). The *scale of levels of didactic co-determinacy* (Chevallard, 2002) allows to characterise the origin of the conditions and constraints that influence teachers' practices in a given institutional context. This scale distinguishes between general teaching constraints, the "upper levels" (humanity <> civilisation <> society <> school <> pedagogy), and
disciplinary constraints, the "lower levels" (discipline <> domain <> sector <> theme <> question)
(Florensa et al., 2018).

In this study, we aim to highlight the levels of conditions and constraints that underlie teacher's choices. Thus, we will locate each of the identified didactic praxeologies on the scale of levels of didactic co-determination.

**2.3. Research questions**

Thus, our research question are the following:

- QR1: In relation to the three types of tasks Tress, Tcomp and Tprof, what are the personal didactic praxeologies developed by the teacher in the anticipation, preparation and implementation of the SAÉ "Data collection and analysis by sampling or experimental design"?

- QR2: What conditions and constraints affect the implementation of these praxeologies?

**3. Methods**

In order to investigate our research questions, we implemented a methodology based on an interview and classroom observations. We rely on a case study to explore the teachers' practices in terms of sampling methods in this specific professional training. In this section, we begin by presenting our case study (section 3.1), then the data collection method (section 3.2), the method of analysis for the interview (section 3.3) and the classroom sessions (section 3.4).

**3.1. A case study: profile of the teacher and local context**

We are reviewing the case of a teacher called Carine (pseudonym). She is a statistics teacher and biostatistics researcher. She has been teaching statistics for 13 years in the Data science training of IUT of the same university. She has been teaching the sampling methods course for four years, before the 2021 reform. In the context of this new curriculum, she is in charge of the 20-hours *Ressource* "Sampling methods and survey methods", which she allocates to lectures and tutorials, complemented by ten hours of practical work (local choice). She is also in charge of the implementation of the associated SAÉ "Data collection and analysis by sampling or experimental design" (section 1.3).

The SAÉ which she has set up is an autonomous work for the students (60 hours over 5 weeks). The teacher conducts an introductory session to present the investigation to be undertaken. There are also two intermediate sessions for the teacher to review progress with the students, followed by a defense session at the end of the SAÉ.

In the following, when we use the term "principal *Ressource*", we will be referring to lectures and tutorials (according to the meaning in the curriculum), otherwise we will be referring to practical work or other *Ressources* of the program.

**3.2. Method of collecting data**

In this study, we seek to describe Carine's didactic praxeologies in relation to the three types of task: Tress, Tcomp and Tprof, previously identified (cf. section 2). In order to access Carine's declared practices, we first designed an interview protocol for semi-structured interviews to be conducted prior
to the teaching of the *Ressource* "Sampling methods and survey methods" and SAÉ "Data collection and analysis by sampling or experimental design". To supplement and deepen the analyses derived from the interview, we collected additional data through classroom observations to better understand certain aspects of Carine's practices.

### 3.2.1. Interview protocol

The interview protocol consists of five different parts. In order to explore the teaching context and Carine's practices in this context, we tried to cover different didactic aspects through the interviews. The five parts of the interview are as follows:

- Part 1: questions on profile, background and teaching experiences;

- Part 2: questions on the *Ressource* "Sampling methods and survey methods" in order to explore the institutional dimensions and organisational aspects that shape the content taught and influence teaching practices;

- Part 3: questions about the needs and potential difficulties of students. We seek to examine the link between her teaching choices and the students' future professional practices;

- Part 4: questions about the use of resources, whether they are designed, offered to students or used by the teacher;

- Part 5: specific questions on the preparation and implementation of the SAÉ "Data collection and analysis by sampling or experimental design", the main focus of the study.

The fifth part of the interview is our entry point for having insights on the role of SAÉ in Carine's teaching, and in particular the links she makes between SAÉ and the three identified components (see figure 1). To this end, we have already carried out an initial analysis of this part of the interview in Derouet et al. (2024), which we refine in this article. In our aim to describe Carine's didactic praxeologies, the questions formulated in the fifth part aim to identify the "how" (the techniques) and the "why" (the technologies, with their explanatory function) in relation to the three types of tasks that interest us here. The explanatory function of the technologies corresponds to the question "why am I doing (this) like that?". The questions in the interview protocol relating to each type of tasks are therefore aimed at identifying the techniques and their rationale.

For Tress, we asked the following questions:

- How do you consider the [principal] *Ressource* in relation to the SAÉ?

- Do you make connections between the SAÉ and the [principal] *Ressource*? Can you give an example?

- Do you make links with the lessons and tutorials of other courses?

These questions lead us to distinguish two subtypes of tasks associated with Tress: "Make the link between the SAÉ and the principal *Ressource* and the practical sessions" (Tress1, first and second questions) and "Make the link between the SAÉ and other *Ressources* or SAÉs" (Tress2, third question). The first question gives us access to explanatory technological discourse, the next two provide access to techniques.

For Tcomp, we asked the following questions:

- What links do you make between the SAÉ and the competencies?

- What are your objectives in terms of students' learning and skills development from the SAÉ? The first question provides information about the technique, while the second question identifies the technologies that explain the choices made.

Finally, for Tprof, we asked additional questions to identify the ways in which the student's future professional environment does or does not influence the teacher's decisions or choices.
The other parts of the interview, even though they do not contain questions directly focused on the SAÉ, still make it possible to address the praxeologies we are trying to describe. It seems to us that these indirect questions can provide additional information about the techniques (or even technologies) related to the task types: Tress, Tcomp and Tprof.

Pre-teaching interview with Carine took place by a 48 minutes video-recorded-conference at the end of August 2023, just before the start of the academic year. Two researchers (out of the three authors) were present, but only one conducted the interview. The interview was transcribed for analysis.

### 3.2.2. Classroom sessions observation

In order to access effective teaching practices, we filmed the principal *Ressource* and SAÉ sessions. The practical work sessions were filmed to a lesser extent because of the autonomous nature of the work and the few interactions between the teacher and the students during these sessions. Other sessions were not filmed because they were either exam sessions or because there was a technical problem. In the end, we recorded nine of two hour-sessions of lessons and tutorials, one session of practical work and three sessions of the SAÉ. The camera was placed at the back of the classroom to make the blackboard visible, the teacher and a part of the students. Sometimes, one of the researchers from the larger DidaStat<sub>Expl</sub> project was present, but in most cases the teacher set up the equipment herself. Moreover, we collected all the documents that were either projected or distributed to the students during the sessions, along with those made available by the teacher through an online platform. The chronology of the recorded sessions is detailed in figure 2.

<figure><img src="image_5.png" /><figcaption>Figure 2. Chronology and data collection</figcaption></figure>

### 3.2.3. Articulation of the methodology

The analysis of the interview will enable us to identify the techniques and technologies associated to Carine's declared practices concerning the three types of tasks: Tress, Tcomp and Tprof. These techniques and technologies are explained by the teacher in the interview. They may concern choices made in anticipation, preparation or implementation of her teaching, and provide information on her declared practices.
Furthermore, analysing the classroom observations and the various documents provided to students by the teacher will enable us to deepen our analysis of the teacher's statements in terms of her actual teaching practices. It will also help illustrate praxeologies by identifying the techniques and choices (technologies) made explicit to the students. We claim that the techniques and choices shared with students will enhance their ability to make links between the SAÉ and the other components: principal *Ressource* and other *Ressources* or SAÉs, competencies and professional environment.

**3.3. Data analysis: analysis of the pre-teaching interview**

From the interview transcript, we selected for our analysis the extracts relating to the types of tasks identified as Tress (subdivided into Tress1 and Tress2), Tcomp and Tprof. These extracts correspond either 1) to the teacher's answers to the questions of part 5 (see the detailed protocol in section 3.2.1, or 2) to elements of the teacher's answers to other questions. The questions in the interview protocol relating to each type of tasks made it possible to identify the techniques that were used. The technologies that justify these techniques are sometimes determined in the answers where the techniques are identified. Otherwise, the technologies can be identified from the answers to the other related questions.

For each type of tasks, we then reconstruct the teacher's personal didactic praxeologies. Specifically, for each technique mentioned by the teacher, we looked in her discourse for possible explanations or justifications for the use of these techniques. We consider that these "I'm doing this because ..." explanations are an integral part of the praxeology linked to the type of tasks and we interpret them as elements of technology. Moreover, in view of the way they are formulated, it is generally obvious that the techniques used enable the associated type of tasks to be performed. In other words, we consider that the justificatory function of the technology is too naturalised and implicit to be caught here.

In order to understand the origin of the teacher's choices, we applied ourselves to place each of Carine's personal didactic praxeologies on the scale of levels of didactic co-determinacy. To do this, we rely on the explanatory function of technological discourse. Indeed, it generally helps to determine what level of constraint the *raison d'être* of the technique used lies.

**3.4. Data analysis: analysis of class observations**

**3.4.1. First level of analysis: global analysis**

In order to analyse the actual practices, and more specifically the implementation and, above all, the explanation to the students of the techniques and/or technologies identified during the analysis of the pre-teaching interview, we first carried out an initial analysis of all the videos available to us.

In this initial global analysis, we consider the sessions corresponding to the principal *Ressource*, the practical work and the sessions relating to the SAÉ (except for the defense session). We reconstructed *a posteriori* the synopsis of the different sessions, identifying different episodes within each session. We focused on episodes where the teacher mentioned the forthcoming SAÉ, analysing her statements during the principal *Ressource* and practical work sessions. Within these episodes, we further identified instances where the teacher makes links with the competencies, *Ressources* (or other SAÉs) or the professional environment, specifying which links were made.
Once these episodes had been identified, we noticed that the SAÉ “Data collection and analysis by sampling or experimental design” was only really mentioned, and several times, in session 9 of the principal *Ressource*. For the most part, elements of the teacher’s discourse related to the SAÉ establish links either between the principal *Ressource* and the SAÉ, or between the SAÉ and other components. It should be noted that the students did not know in concrete terms the nature of the statistical sample survey that they were to conduct during this session. The content of the SAÉ, and in particular the description of the survey to be conducted, will be explained later during the first SAÉ session (see Figure 2). In this session, the teacher outlined the framework of the SAÉ and, in particular, made the links between the SAÉ and the different components.

This first level of analysis allows us to identify the 9th session of the principal *Ressource* and the first session of the SAÉ as relevant for the next step. Indeed, the analysis of both these sessions will enable us to highlight the techniques and technologies that were explained to the students and then put them into perspective with what Carine stated in the interview.

### 3.4.2. Second level of analysis: local analysis

For these two sessions, we transcribed the episodes in which the teacher referred to the three types of tasks. In the selected episodes, we linked each excerpt of her discourse to one of the praxeologies identified in the interview analysis. We also complemented our analyses with the analysis of the collected resources. For example, when she presents competencies through her slideshow, her speech closely mirrors the content of the document provided to the students. This correspondence enables us to analyse the alignment between the slideshow, the in-class discourse, and the statements from the interview.

## 4. Examples of analysis

The examples detailed here illustrate the implementation of our analysis tools and the way in which we situate didactic praxeologies on the scale of levels of didactic co-determinacy.

### 4.1. A first example related to Tprof

The first example concerns the following question asked during the interview (part 3 of the interview, see section 3.2.1): “Does the consideration of your students’ future professions influence your teaching choices?” By asking this question, we are trying to find out whether the teacher uses techniques to achieve Tprof. We also hope to gather information explaining her choices (technology).

In response to this question, the teacher explained:

> “[In our BUT], we have a long tradition of surveys and polls, which is why we decided to keep this course in its current format. Other BUTs don’t necessarily do it this way. Here, we have collaborations that already existed. The [Data Science] department is renowned for its ability to propose surveys and survey-related projects. So that’s important, and we’ve also decided to keep it for that reason.”

The analysis of this excerpt allows us to identify a first technique associated with Tprof, which is to propose the practical implementation of a survey<fnref n="3" /> (tprof_2). Through this excerpt, we also identify an

<footnote n="3">³In some *BUT*, for the same SAÉ, the students do quality control.</footnote>
explanation for this technique. The reason given here (other reasons were mentioned later in the interview) relates to the local context of the training: historically, this training has long been oriented towards polls and surveys (øprof_2_1). The technological discourse gives us information about the positioning of the constraints that influence the way of doing. Following the definition by Florensa et al. (2018), who analysed the pedagogical challenges faced by university teachers in their practice, we consider the level of School which encompasses the conditions and constraints specific to the educational institution in question. In our case, School can refer to the BUT in general (at national level), but also to the local context of training. This is the case for the excerpt above. We therefore make a distinction between School1 (national level) and School2 (local level). Thus, the didactic praxeology [Tprof, tprof_2, øprof_2_1] is located at the School2 level on the scale of levels of didactic co-determinacy.

Later in the interview, the teacher said:

> "It's also a way of showing them that there's teamwork behind it and that everyone has to be rigorous to get results that can be analysed. The first aim is really to construct the survey and to see the constraints that this entails. And [the second aim is], depending on the problem, they have to ask themselves the right questions to apply the right methods to select their sample."

Analysis of this excerpt allows us to identify two additional explanations for the practical implementation of a survey (tprof_2). The first one is to confront students with the construction of a survey and the constraints involved, such as those related to data collection (who and how to question?) and the importance of rigour in data entry, linked to teamwork (øprof_2_2). The second explanation relates to the questions that she wants the students to ask themselves in order to make their choices of the method relevant in relation to the survey problem (øprof_2_3). For these two praxeologies [Tprof, tprof_2, øprof_2_2] and [Tprof, tprof_2, øprof_2_3], we identify the constraints linked to the teacher's choices, at the level of Domain, i.e., in our case, the domain associated with sampling methods. These excerpts show that the teacher wanted the students to be confronted with all the stages of the survey process in order to understand all the constraints.

From our analyses of the class sessions, we extracted the following episode from the first session of the SAÉ:

> "We made this choice in [this Data Science BUT], to put you into the experience of constructing a survey and to put you in the place of a surveyor, which is an important role when you're a statistician [...] In your professional work, you may have to deal with data from surveys, and so it's really important to understand how this is done".

This excerpt reveals a fourth technology, relating to the implementation of the technique tprof_2. This technology relates to the fact that, in their future profession, students might need to process data derived from surveys (øprof_2_4). We consider this technology to be anchored in the Society level on the scale of levels of didactic co-determinacy. Indeed, the conditions and constraints presented in this excerpt are not related to the institution, nor to the content of statistics, but to the practical professional applications of statisticians. This is in line with the definition of Florensa et al. (2018), for whom the Society level presents dimensions that do not depend on the specific institution considered, and which can be extended to any type of teaching and to other levels of education.

The other techniques associated with Tprof will be presented in the results (section 5).
## 4.2. A second example related to Tress2

Florensa et al. (2018) point out that the level of Pedagogy corresponds to the conditions and constraints common to the different subjects or disciplines that can be taught. This level includes teachers' resources and teachers' actions, such as assessment, lesson format or types of activities, and everything that falls under a pedagogical contract. We identify the following praxeology related to this level in question. For “Make the link between the SAÉ and other Ressources or SAÉs” (Tress2), the teacher suggests a situation that links two SAÉs: the SAÉ that is the subject of this study (section 1.3), and a second related to Data Compliance and Regulation (related to the GDPR<fnref n="4" /> rules) (tress2_1). She explains that this way of doing things is practical not only for her and the other teacher, but also for the students, as it allows for a shared assessment process:

> *“We worked like this on the SAÉ because it's more convenient for everyone and it's a real case study, so we didn't have to do another SAÉ for the students”* (øress2_1_1).

We identify this choice as being subject to the teacher's pedagogical resources and strategies, and thus at the level of Pedagogy.

Another technology is identified in the following interview excerpt:

> *“the students have a course called Data Compliance and Regulation, which covers topics in relation with the GDPR. So, what we decided to do is merge these two SAÉs a bit. In the SAÉ I'm involved in, at some point, they collect data, which leads them to think about all the legal issues related to this topic”*.

This explanation refers to the level of Domain (sampling methods). Indeed, in the sample survey that will be carried out in the SAÉ, students will be asked to collect data on individuals, which will in any case force them to ask themselves questions related to the legal aspect (øress2_1_2).

In class, during the introduction to the SAÉ, Carine explains to the students how the two SAÉs will be linked (tress2_1). Using a document provided to the students, she explains the two objectives of the students' work in their survey: each objective is directly linked to one of the SAÉs. She said that she would do the two SAÉs together, telling them that "in practice" the SAÉ "Data collection and analysis by sampling or experimental design" was the "support" for the Data Compliance and Regulation SAÉ. She pointed out that both teachers would be present during the evaluation of the SAÉ which takes the form of a defense. She only makes the second technology (øress2_1_2) explicit to the students by specifying: "you will have anonymised data, so that means you have to warn people".

# 5. Results

This section presents the different didactic praxeologies identified as a result of our analyses. We start by the type of tasks Tress (section 5.1), and then we continue with Tcomp (section 5.2), followed by Tprof (section 5.3).

## 5.1. Make the link between the SAÉ and the different Ressources (Tress)

Two subtypes of tasks (Tress1 and Tress2) are associated with the didactic type of tasks “Make the link between the SAÉ and different Ressources” (Tress) (see section 3.2.1). The description of praxeologies

<footnote n="4">⁴ General Data Protection Regulation.</footnote>
associated to these two subtypes of tasks allows us to emphasise elements of response to our research question in the case of the didactic praxeology associated with Tress.

### 5.1.1. Tress1: make the link between the SAÉ and the principal Ressource and the practical sessions

Table 1 shows the praxeologies and the associated level of didactic co-determinacy identified in the interview and/or classroom observations for the subtype of tasks Tress1.

<table><caption>Table 1: Techniques and technologies associated to the subtype of tasks Tress1 and the associated level of didactic co-determinacy</caption><thead><tr><td>Technique</td><td>Technology</td><td>Level of didactic co-determinacy</td></tr></thead><tbody><tr><td>tress1_1: Design a SAÉ that allows the theoretical concepts encountered on the principal Ressource to be applied</td><td rowspan="2">øress1_1: Ensure that the students have fully understood what is presented and seen in the principal Ressource</td><td>School1</td></tr><tr><td>tress1_1bis: Adapt the SAÉ to the content actually encountered on the principal Ressource</td><td>School1</td></tr><tr><td rowspan="2">tress1_2: Adapt the methods and concepts presented in the principal Ressource according to what will be needed to study the SAÉ</td><td>øress1_2_1: The principal Ressource is the support for the SAÉ</td><td>School1</td></tr><tr><td>øress1_2_2: The curriculum is very dense, so choices have to be made about what to teach</td><td>School1</td></tr><tr><td>tress1_3: Adapt the practical work to complete what could not be illustrated in the SAÉ</td><td>øress1_3: The SAÉ, as a situation that is intended to be a real survey, does not allow all the concepts encountered in the principal Ressource to be tackled</td><td>Pedagogy</td></tr><tr><td>tress1_4: Make the links between the SAÉ and the principal Ressource explicit to the students</td><td>x</td><td>x</td></tr></tbody></table>

The identified techniques and elements of technology highlight that the principal *Ressource* provides theoretical elements while the *SAÉ* enables practical applications, thus creating an interaction between the *SAÉ* and the *Ressource*. These choices are based on institutional constraints (School1 level), but Carine is also aware that not all the theoretical elements worked on the principal *Ressource* can be covered in the *SAÉ*, so she has made the pedagogical choice to complement the *SAÉ* with practical work (tress1_3).

Carine did not explicitly mention the technique tress1_4 in her interview. It is identified in class observations. In the 9th session, she made several explicit references to the links that the students would need to establish between the principal *Ressource* content and the *SAÉ* that they would dislike performing. For example, when the students are looking for a short exercise to propose a sampling plan using the quota method on the basis of a contingency table, Carine says:
"I'm telling you this because in the SAÉ that you're going to carry out, you're going to have contingency table of this kind, which means that you're bound to have multiple criteria. It will be up to you to decide whether it is worth using a quota method. And whether you have sufficient and necessary information to use a quota method."

This technique (tress1_4) is not supported by a technological discourse based on the data available to us. For this reason, we cannot classify the praxeology associated with this technique on the scale of didactic co-determinacy.

### 5.1.2. Tress2: Make the link between the SAÉ and other Resources or SAÉs

Table 2 shows the praxeologies and the associated level of didactic co-determinacy identified in the interview and/or classroom observations for the subtype of tasks Tress2.

<table><caption>Table 2: Techniques and technologies associated to the subtype of tasks Tress2 and the associated level of didactic co-determinacy</caption><thead><tr><th>Technique</th><th>Technology</th><th>Level of didactic co-determinacy</th></tr></thead><tbody><tr><td rowspan="2">tress2_1: Propose a situation that networks two SAÉs: one, the SAÉ object of this study, and the other linked to Data Compliance and Regulation (GDPR)</td><td>øress2_1_1: This way of working is convenient for her and the other teacher, and for students, as it allows the evaluation to be shared</td><td>Pedagogy</td></tr><tr><td>øress2_1_2: Thinking about legal constraints when collecting data on individuals is an integral part of the survey practice</td><td>Domain</td></tr><tr><td rowspan="2">tress2_2: Make the contents from other Resources or SAÉs (from the first year or from the current semester) on which the SAÉ is based explicit to the students.</td><td>øress2_2_1: For students to make the links between the content of the different Resources: for example, to understand that the statistical tools used in the first year form an essential basis for understanding sampling methods and therefore for this SAÉ</td><td>Discipline</td></tr><tr><td>øress2_2_2: The SAÉ relies on several Resources and SAÉ</td><td>School1</td></tr></tbody></table>

The praxeologies associated with technique tress2_1 are detailed in section 4.2. Furthermore, the technique tress2_2 is taken from the interview in which Carine says:

"The SAÉ is based on the SAÉs they had in their first year, so that's at the beginning when we present the SAÉ. We tell them, well, there are certain concepts, you had them in the courses in your first year."

In the interview, she also explained that the content of the principal Resource (SAÉ support) on sampling methods comes upstream of data collection and analysis and that "so it's good to make them understand that everything I'm going to teach them in this course, they've already seen in courses
somewhere [...] to analyse data. And in fact, I'm not going to use it for that, I'm going to use it to help them understand how to select their data" (øress2_2_1). We consider these elements of discourse as explaining the *raison d'être* of technique tress2_2, i.e. the technology øress2_2_1. Moreover, during the 9th session, she emphasized the usefulness of the statistical tests they were studying in another *Ressource*: she took some time (about 5 minutes) to go back over the statistical tests that would be useful for the SAÉ. Making explicit to the students the links between the SAÉ and other *Ressources* or SAÉs is important for Carine, so that students understand that the statistical tools used in the first year are an essential basis for sampling methods and therefore this SAÉ, and in other current *Ressources*, tools are important to the SAÉ. She wants students to make links between domains within statistics (Discipline level).

Moreover, from an institutional point of view, and in terms of the curriculum recommendations (MESR, 2022b), the SAÉ is relied on several *Ressources* and SAÉ. During the first session of the SAÉ, Carine says:

> "I'll say it again, it's really important: in this SAÉ, you're normally going to have to look up concepts and things you've already done (or missed) in the first year as well as in the second year statistics courses, since they're in fact based on the [...] four competencies of the BUT. "

According to us, this rational discourse (øress2_2_2), which explains the technique tress2_2, refers to the School1 level of the didactic co-determination scale.

## 5.2. Make the link between the SAÉ and competencies to be developed (Tcomp)

Table 3 shows the praxeologies and the associated level of didactic co-determinacy identified in the interview and/or classroom observations for the type of tasks Tcomp.

<table><caption>Table 3: Techniques and technologies associated to the type of tasks Tcomp and the associated level of didactic co-determination</caption><thead><tr><td>Technique</td><td>Technology</td><td>Level of didactic co-determination</td></tr></thead><tbody><tr><td>tcomp_1: Make the competencies targeted by the SAÉ explicit to the students</td><td>øcomp_1: Respond to an institutional obligation</td><td>School1</td></tr><tr><td>tcomp_2: Get students to complete a grid of competencies related to the SAÉ, in which they have to position themselves for each competency (not acquired, in the process of acquiring, acquired) by providing evidence</td><td>øcomp_2: Help students build their portfolio<sup>5</sup></td><td>School1</td></tr></tbody></table>

The praxeologies associated with the type of tasks Tcomp are based on institutional constraints (School1) prescribed by the national curriculum: competency-based approach and the production of a *portfolio* by the students. To illustrate, in the interview, Carine says:

<footnote n="5">⁵ The *portfolio*, within the context of the Data Science BUT, allows students to self-assess and self-position along the semesters, to document their progress and ability concerning the four competencies.</footnote>
"we have to list the competencies and they have to project the SAÉ onto these competencies.
And in particular the skills they need to [...] master by the end of the SAÉ"

and

"In the new curriculum, we talk about portfolios, which means that students are supposed to list the competencies they have acquired in relation to the professional situations they have encountered. So [...] they list them in something called a portfolio and so we help them to build their portfolio".

### 5.3. Make the link between the SAÉ and students' future professional environments (Tprof)

Table 4 shows the praxeologies and the associated level of didactic co-determinacy identified in the interview and/or classroom observations for the type of tasks Tprof.
<table><thead><tr><th>Technique</th><th>Technology</th><th>Level of didactic co-determinacy</th></tr></thead><tbody><tr><td rowspan="3">tprof_1: Propose a situation that is as concrete/authentic as possible</td><td>øprof_1_1: By definition, the SAÉ is a work-based scenario</td><td>School1</td></tr><tr><td>øprof_1_2: Trying to get the students to project themselves into a professional context</td><td>School1</td></tr><tr><td>øprof_1_3: Students need practical experience to be operational in the field at the end of their training</td><td>Society</td></tr><tr><td rowspan="4">tprof_2: Propose the practical implementation of a survey</td><td>øprof_2_1: The idea to implement a survey in practice is part of an historical continuity in this course at the IUT, which has long been oriented on surveys</td><td>School2</td></tr><tr><td>øprof_2_2: Confront the students with the construction of a survey and the constraints involved, such as those related to data collection and the importance of rigour in data entry, linked to teamwork</td><td>Domain</td></tr><tr><td>øprof_2_3: To ensure that, depending on the survey problem, students ask themselves the right questions in order to apply the relevant methods to select their sample</td><td>Domain</td></tr><tr><td>øprof_2_4: In their future professions, students may need to process data from surveys</td><td>Society</td></tr></tbody></table>
<table><caption>Table 4: Techniques and technologies associated to the type of tasks Tprof and the associated level of didactic co-determinacy</caption><tr><td rowspan="3">τprof_3: Impose a face-to-face approach to data collection</td><td>øprof_3_1: To help students understand the biases associated with data collection and the quality of the data</td><td>Domain</td></tr><tr><td>øprof_3_2: It is a local and a collective choice (associated to øprof_2_1)</td><td>School2</td></tr><tr><td>øprof_3_3: The role of the surveyor is important for a statistician to understand</td><td>Society</td></tr><tr><td>τprof_4: Ask external clients to propose and guide the SAÉ survey</td><td>øprof_4: Allow students to be confronted with a real study with a real problem, that it is a real challenge</td><td>Domain</td></tr></table>

The praxeologies associated with Tprof are rich and varied. Conditions and constraints that have an impact on the teacher's choices are found all along the scale of levels of didactic co-determinacy. In first, Carine makes sure that the SAÉ should be as concrete as possible (τprof_1). Because of institutional constraints (School1 level), she goes further: she believes that it is essential if students are to be operational in the workplace by the end of their BUT (Society level).

The identification of the praxeologies related to τprof_2 is detailed in section 4.1. The technique τprof_3 (“impose a face-to-face approach to data collection”) is closely linked to “Propose the practical implementation of a survey” (τprof_2). Indeed, these two techniques are often approached together by the teacher (see section 4.1). Carine wants to put the students in the position of surveyor (face-to-face) so that they realise the importance of this phase (to limit bias, øprof_3_1). During the 9th session, she says:

> "This is the most important thing, because if you miss the mark, if you don't do it properly, no matter how good your quota method, you'll never get it right. This is what will ensure that you find the right people, interview them and get your survey responses."

We associated this excerpt with the Domain level.

Concerning τprof_4, we rely on the excerpt which explains the rationale for using this technique, drawn from the practice of statistical surveys. This is why we have placed it at the Domain level. However, we could also consider it at a more general level (like School1). Indeed, the principle of the SAÉ allows students to be confronted with a real study based on a real problem, which seems to be transversal to any discipline and, therefore, transferable to other levels.

## 5.4. Conditions and constraints associated with the teacher's choice

After presenting the different praxeologies associated with the three types of tasks (Tress, Tcomp and Tprof), In this section, we propose a summary of the conditions and constraints underpinning teachers' choices, based on the different levels of the co-determinacy scale at which these praxeologies are
situated. To do this, we start by summarising in Table 5 the levels at which the determinants of teaching practices are found, based on the analyses carried out above.

<table><caption>Table 5: Summary of the levels of the co-determinacy scale, showing the conditions and constraints influencing the teacher's choices for each praxeology related to (Tress, Tcomp, Tprof).</caption><thead><tr><th>Level</th><th>Tress</th><th>Tcomp</th><th>Tprof</th></tr></thead><tbody><tr><td>Society</td><td></td><td></td><td>τprof_1 (øprof_1_3)<br>τprof_2 (øprof_2_4)<br>τprof_3 (øprof_3_3)</td></tr><tr><td>School1</td><td>tress1_1/tress1_1bis<br>(øress1_1)<br>tress1_2 (øress1_2_1;<br>øress1_2_2)<br>tress2_2 (øress2_2_2)</td><td>τcomp_1 (øcomp_1)<br>τcomp_2 (øcomp_2)</td><td>τprof_1 (øprof_1_1;<br>øprof_1_2)</td></tr><tr><td>School2</td><td></td><td></td><td>τprof_2 (øprof_2_1)<br>τprof_3 (øprof_3_2)</td></tr><tr><td>Pedagogy</td><td>tress1_3 (øress1_3)<br>tress2_1 (øress2_1_1)</td><td></td><td></td></tr><tr><td>Discipline</td><td>tress2_2 (øress2_2_1)</td><td></td><td></td></tr><tr><td>Domain</td><td>tress2_1 (øress2_1_2)</td><td></td><td>τprof_2 (øprof_2_2;<br>øprof_2_3)<br>τprof_3 (øprof_3_1)<br>τprof_4 (øprof_4)</td></tr></tbody></table>

The three types of tasks selected in our study (Tress, Tcomp, Tprof) stem from an analysis of the national curriculum (see section 1). It is therefore not surprising to find a number of praxeologies at School1 level. We can also see that all the techniques developed by the teacher for Tcomp are governed by determinants at this level, whereas this is not the case for the other two types of tasks. In fact, it is quite natural that only the School1 level should appear for this type of tasks since the four competencies in the training, imposed by the national curriculum, are at the core of competency-based approach.

As far as Tress and Tprof are concerned, we can see that the conditions and constraints underpinning the different praxeologies range from the Domain level to the Society level. Through our analyses, we are able to see that while the link between the SAÉ and the *Ressouces* is structured by the national curriculum (School1 level), pedagogical and subject-related reasons (Domain et and Discipline levels) also guide the teacher's choices. Regarding Tprof, the implementation of the SAÉ “Data collection and analysis by sampling or experimental design” requires the teacher to take into account the professional environment (Society level). In this type of professional training, she wants the students
to be operational in the workplace. However, her choices are also guided by reasons specific to the field of sampling methods (Domain level), as well as by local conditions (School2 level).

Finally, the pedagogical choices made by Carine reflect the possibilities offered by the framing of the curriculum: the number of hours allocated, the possibility of combining two SAEs, etc. Thus, the praxeologies found at Pedagogy level seem to represent the room for manoeuvre left to the teacher by the design of the curriculum.

# 6. Conclusion

By investigating the underexplored domain of teaching practices in tertiary professional training, this article constitutes a contribution providing new insights into the interplay between institutional constraints and individual teaching choices and actions. This study brings theoretical and methodological developments by situating didactic praxeologies within the scale of levels of co-determinacy. Our findings extend existing research; for instance, Gueudet et al. (2022) classify didactic praxeologies of mathematics teachers to non-specialists students, using a simplified version of the co-determination scale. Florensa et al. (2018) highlight that teachers' questions concerning their difficulties are mainly located at the upper levels, without really focusing on disciplinary content. They interpret this phenomenon as the result of "pedagogical generalism" (Gascón & Bosch, 2007), which reflects a vision of teaching and learning as independent of disciplinary content. Our results introduce nuances to these findings; in the context of professional training, such as the Data Science *BUT*, we found a large link between teaching practices and the future professional environment of the students. Furthermore, we locate the determinants of teaching practices both on the upper scale (such as Society level) and on the lower scale (Discipline and Domain levels). Thus, we consider that the presence of an institutional framework based on the competency-based approach, coupled with the students' need for their future professional workplace, seems to be a determinant of her practices (choices and actions). Our findings highlight the dynamic relationship that exists between institutional constraints and expectations and the competencies that the teacher aims to develop in their students. In what follows, we explore these findings in relation to our research questions.

## 6.1 Findings regarding our research questions

**Our first research question QR1** is: "In relation to the three types of tasks Tress, Tcomp and Tprof, what are the personal didactic praxeologies developed by the teacher in the anticipation, preparation and implementation of the SAÉ "Data collection and analysis by sampling or experimental design"?" We identified key elements for each of the three types of tasks. For **Tress**, the techniques reflect a dynamic form of interaction that follows a two-way adaptation process to ensure the link between the **Ressources** and the proposed SAÉ. The content is continuously adapted and updated based on the SAÉ, while the SAÉ is presented to the students in relation to the principal **Ressource**. The application of content of the principal **Ressource** forms the core of the teacher's choices. With this in mind, the teacher complements the SAÉ with practical works. Moreover, the teacher highlights the necessity to establish links across various **Ressources** and SAÉs, ensuring a coherent learning experience. For **Tcomp**, similar to Tress, the teacher emphasises on the importance of making explicit the competencies involved. However, in this case, it is strongly justified by the recommendation of the curriculum, which explicitly highlights the fact that each SAÉ aims to develop one or more of the competencies targeted in the training, and that students have to reflect on their competency
development (*portfolio*). The teacher's choices aim to ensure that students understand and develop the necessary competencies in line with the *Ressources* and future professional expectations. The praxeologies associated with **Tprof** appear to be varied and rich. This diversity is largely driven by the teacher's intention to create a situation that closely reflects professional environments practice. In this context, the techniques identified rely on an authentic situation (as described by the teacher), guiding students to make decisions about which statistical methods to use, and by confronting them with the bias caused in particular by data collection (importance of the surveyor role). These techniques aim to prepare students for the complexities of their future professional environments.

**Regarding our second research question (QR2)** "What conditions and constraints affect the implementation of these praxeologies?", several aspects emerge from our analysis. Our results show how the institutional context of *BUT* (School1 level) constraints and influences the teacher's practices in relation to the three types of tasks identified (Tress, Tcomp, Tprof). However, although certain didactic praxeologies are inscribed in this institutional framework, some of them find their *raison d'être* at other levels of the co-determination scale. Concerning **Tress**, we have shown that determinants of teaching practices located at the discipline level (statistics) and at the domain level (sampling methods) are shaping the links that the teacher establishes between the *SAÉ* and *Ressources* (courses content). In the case of **Tprof**, the determinants of her practices are to be found at the level of the domain (sampling technique), the discipline (statistics), the local study context (local choices) and also at the level of society (considerations regarding the professional integration of her students). The teacher develops rich praxeologies for this type of task and her choices are underpinned by disciplinary, societal (professional) and institutional reasons. These results lead us to consider that for the teacher, linking the *SAÉ* to students' future professional environment, is very coherent in her practice because it is the only type of tasks for which she gives the most explicit reasons and explanations. On the other hand, Tcomp seems more artificial for the teacher, who finds it difficult to move beyond the School1 level to explain and justify the rationale behind the techniques used.

## 6.2 Scope of the research and transferability of results

Even though we only considered a single case study, we would like to emphasise the potential transferability of Carine's personal praxeologies to other contexts. This study makes a significant contribution by highlighting three fundamental types of tasks that are intrinsically linked to the institutional framework of the *BUT* curriculum, but which could be questioned in professional training in general. This could lead us to consider that the personal techniques identified in this study are transferable to other training programs, depending on the position of the praxeologies on the co-determination scale. For instance, praxeologies identified at the School1 level could be found in teachers' practices in professional training with competency-based approach, such as *BUT* in Data Science or other pathways. Those related to the discipline (statistics) or domain could be applied in other statistics training programs, or more specifically in other courses on sampling methods. Finally, as our analyses have shown, the techniques associated with Tprof almost all relate to the Society level. Thus, we consider that the associated techniques have a much more general purpose than that of training: to be operational in the workplace at the end of training, to know about related occupations in order to be more competent in one's field, etc. We can therefore imagine that teacher's choices will be found among other teachers in other professional training than the *BUT* program, in statistics and beyond.
## 6.3 Limitations of this research and future prospects

The theoretical choices made in this study, as well as their applications, seems to be relevant for our context of study. They have enabled us to characterise the teacher's practices, providing a detailed understanding of the way the teacher anticipates, prepares and implements the SAÉ. The analysis based on locating the didactic praxeologies to the levels of didactic co-determinacy has also allowed us to distinguish elements coming from the teacher's personal choices, and those determined by the institutional conditions and constraints (all considered as determinants of her practices). This dual perspective emphasizes the relevance of our theoretical framework in characterising the complexity of teaching practices in professional training contexts. However, for certain praxeologies, it could be interesting to complement the analysis by considering the teacher's personal relationship with the knowledge at stake, especially in the fields of statistics and sampling methods. Doing so would allow for a more nuanced characterization of the identified praxeologies and a deeper understanding of the teacher's choices. For instance, it could be particularly significant for Tprof, where tensions between theoretical knowledge, practical applications, and the professional requirements are most pronounced. Integrating elements of analysis related to the personal relationship with the statistics could help in exploring how these tensions "navigate" in the teacher's practices within the institutional constraints at various levels.

From a methodological point of view, despite our study being a case study, the methodological tools we suggested for collecting data and for analysis enabled us to explore their potential. We introduced tools that not only allow us to identify praxeologies through the interviews but also integrate these findings with classroom observations. This method appears relevant in highlighting how a teacher's discourse in class can go beyond capturing techniques to also uncover associated technologies. While this approach has shown to be useful, further refinement is needed to fully develop its potential. Indeed, our study can be seen as a snapshot of the teacher's practices at a given moment. In this sense, we do not claim that the identified praxeologies cover all the diversity and complexity of real and actual teacher's practices. A second interview could allow us to confront the teacher with the techniques and technologies identified, but also to complete this initial analysis with questions oriented towards the levels for which we have not identified any praxeology.

The study highlights the crucial place of the SAÉ as key moments to understand teachers' practices. The SAÉ offered a lens to explore how the implementation of work-based scenario into Data Science BUT provides settings for practical applications of statistical knowledge. However, it also presents significant challenges for the teacher. While the curriculum presents strong constraints, it simultaneously somehow appears as a support for the implementation of the SAÉ; by aligning the situation proposed with curriculum recommendations and future professional environments of her students, the teacher seems to ensure meaningful alignment in her practices. This study highlights the need for further research aimed at reducing the gap between professional practices (statisticians in professional environments) and what is really taught in statistics courses. Investigating how to mind this gap is an area for future work, which we intend to pursue. Such research could provide findings on how professional training can prepare students for the needs and demands of their future professional careers.
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**Abstract**

This article examines teaching practices in statistics within the professional training of statisticians in France, in *Institut Universitaire de Technologie (IUT)*, where the national curriculum follows a competency-based approach. Through a case study and using the Anthropological Theory of the Didactic, we analyse the practices of a teacher in sampling methods during the second year. Our study focuses on three didactic types of tasks that emerged from a curricular analysis, all linked to an authentic Learning and Evaluation Situation (called *SAÉ*) related to data collection and analysis by sampling. One type of tasks concerns the course (to be implemented and designed), another relates to the competencies outlined in the curriculum, and the third addresses students' future professional environments. Drawing on an interview and classroom observations, we model teaching practices as didactic praxeologies and position them within the scale of levels of didactic co-determinacy. Our findings show how institutional constraints (e.g., national curriculum), disciplinary contents and professional considerations (e.g., preparing students for statistical work) can shape personal didactic techniques and teaching choices. The study also highlights that the determinants of teacher's practices span the entire scale of co-determination, from the Domain level to the Society level. These results could be enlightening for teachers' practices in other professional training contexts.

**Keywords**

anthropological theory of the didactic, professional training, statistics, teaching practices, tertiary level

**Statements and Declarations**

This work was financially supported by the *GIS Education et formation* (Education and training) of Grand-Est (France) and the GDR CNRS n°2076 DEMIPS (which is now a focus of the HiDiM CNRS thematic network).

The authors have no relevant financial or non-financial interests to disclose.

**Acknowledgement**

This paper is a result of a research program funded by the *GIS Education et formation* (Education and training) of Grand-Est (France) and the GDR CNRS n°2076 DEMIPS (which is now a focus of the HiDiM CNRS thematic network). We would like to thank Manon Branchereau for all the video analysis work she did during her placement.

**Disclosures and declarations**

This study, as part of the DidaStatExpl project "Teaching and learning statistics in professional training: an exploratory study of the practices of teachers in the Data Science BUT" has been accredited by the University of Strasbourg's Research Ethics Committee (Unistra/CER/2023-31).

**Author contributions**

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors. The first draft of the manuscript was written by all authors and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Charlotte Derouet and Camille Doukhan supervised the project and obtained the funding.