Technology teachers can use a variety of methods to approach the design or analysis of technical systems. Most of these methods have their origins in functional analysis from the world of industry. Faced with the problem of matching training to jobs in the workplace, technology teachers are introducing these same functional analysis tools into the teaching-learning of mechanical design at higher technological education establishments ([4]). The integration of functional analysis tools into the teaching of industrial science and technology has transformed the teaching of mechanical design. Indeed, the use of artifacts such as functional analysis tools in teaching-learning activities presupposes the consideration and modification of instrumental geneses, teaching practices and conceptualizations of these practices ([14]). Therefore, the mechanical design teacher must consider how to use these tools in teaching-learning situations. What are the factors that promote the effectiveness of students’ mechanical design learning process when functional analysis tools are used? What difficulties do analysis tools pose for students in the mechanical design learning process? This finding justifies the study carried out as part of this research to contribute to improving the quality of mechanical design learning at higher technological education institutions. This article explores the quantitative aspects of the pedagogical uses of functional analysis tools in a didactic context at higher technological education establishments.

Functional analysis has become an essential part of the product design process. Indeed, the adoption of functional analysis tools in the teaching and learning of mechanical design in industrial science and technology has qualitatively improved the product design process. As a result, they are being increasingly used in product design activities. On the other hand, students are often confronted with problems linked to the pedagogical exploitation of analysis tools and their use and/or appropriation. In other words, it is essential to examine the appropriation of functional analysis tools by students in the mechanical design learning process. What factors make it easier for students to learn mechanical design using functional analysis tools? What difficulties do analysis tools pose for students in the mechanical design learning process? How can we help students use analysis tools in the learning process?

The design of technical systems is an intellectual activity in which a number of provisions are devised to change an existing situation into a preferred one ([17]). It is the result of complex processes that consist of providing materiality to an object that does not yet exist and that exists only in the minds of those who design it ([8]). Contextualizing it, it consists of providing a set of proposals for describing a product (shape, size, and means of production) that globally meets a set of specifications. Mechanical design can be divided into four categories: routine design, redesign, innovative design, and creative design. All these categories share the following three stages: problem definition to determine requirements; a conceptual definition to set up the functional structure and physical principles; and a detailed definition to offer a complete description of the design ([11]). Functional analysis is used systematically at every stage. Functional analysis provides both a technical and pedagogical method as part of a rational approach to building knowledge and know-how. It is an approach that uses different tools to characterize a product in the form of diagrams (Horned Beast, APTE, FAST). It consists of finding, ordering, characterizing, prioritizing and/or enhancing a product’s functions. It is useful in the design or redesign phases of a product.

To fully understand the phenomenon in question, to fully understand the phenomenon we are interested in, this research articulates three theoretical concepts. First, the instrumental approach developed by [15] is based on the importance of instrumental mediation. Next, Engeström’s model of the “basic structure of an activity” takes into account the different interactions with functional analysis tools in a didactic context. Then, the use of cognitive tools (functional analysis tools) is used to develop knowledge.

On a general level, this research contributes to improving the quality of the mechanical design learning process in higher education institutions. More specifically, it aims to identify factors likely to improve the effectiveness of the mechanical design learning process.

Figure 2. Dimensions of the evaluation of functional analysis tools.

Bartlett’s sphericity test verifies whether the observed correlation matrix differs significantly from an identity matrix in which all correlation coefficients are zero.

Table 5.Bartlett’s test of sphericity.

The significant Bartlett sphericity test presented in Table 5 (p < 0.001) confirms that the correlations between the variables are not zero, indicating that the correlation matrix is well suited to exploratory factor analysis (EFA).

In parallel, the Kaiser-Meyer-Olkin (KMO) measure is used to assess sample suitability for exploratory factor analysis (EFA). Exploratory factor analysis (EFA) is considered appropriate if KMO values are greater than 0.5. As shown in Table 6, the KMO values of the selected observed variables are greater than 0.6, suggesting good sampling adequacy.

Table 6.KMO sampling dequacy test.

Following the verification of the assumption of sphericity and the adequacy of Kaiser-Meyer-Olkin sampling, exploratory factor analysis (EFA) allowed us to obtain four factors, as presented in Table 7. The latter presents the factorial contributions and shows how the 16 items of learning effectiveness apply to each of the four selected factors.

The four factors revealed are composed of 16 items. The first factor related to the educational use of functional analysis tools includes items linked to the structuring of the contents of the horned-beast diagram (struct_cont_ horn_beast_diag), the pieuvre diagram (struct_cont_ pieuvre_diag), the formulation of the main functions (Formul_ main_funct) and constraints (Formul_constr_funct). However, we note a significant contribution from the item relating to the identification of the main function (identif_main_funct), a criterion of the utility factor.

The second factor relates to the usability of functional analysis tools by students. We note a strong contribution of items related to the comprehensibility of the main function (compreh_main_funct), the readability of the pieuvre diagram (readab_pieuvre_diag), the coherence between the main function and constrained functions (coher_main_constr_funct) and the clarity of the functional specifications (clar_funct_specif).

Table 7.Contributions of factors.

The third factor highlights student satisfaction. Satisfaction is significantly influenced by the perception of the ease of learning of the pieuvre diagram (Easy_learn_pieuvre_diag), the perception of the ease of learning of the horned-beast diagram (Easy_learn_horn_beast_diag), and the ease of memorization of the FAST diagram (Easy_memo_fast_diag).

Finally, the last factor relating to the usefulness of functional analysis tools brings together items linked to the identification of the main function (identif_main_funct), technical functions (identif_techn_funct) and functional specifications (identif_funct_specif).

Furthermore, Table 8 shows correlations between factors greater than 0.3, allowing the use of oblique rotation (oblimin).

Table 8.Interfactor correlations.

We can also see from Table 9 that the proportion of the overall data variance attributable to the four factors is 66.8%. The variance of factor (1) is 24.7. That of factors (2) to (3) is approximately 16% each. For the last factor (4), the variance represents 9%.

Table 9.Statistical summaries of factors.

An analysis of internal consistency reliability made it possible to determine to what extent the retained items were related to each other. Furthermore, reliability provides a general index of the internal consistency of the scale as a whole (Table 10). The table shows that Cronbach’s alpha for the learning effectiveness scale is 0.87. This value indicates that the homogeneity and internal consistency of the items are satisfactory for understanding the effectiveness of learning.

Table 10.Internal consistency of the measurement scale.

The heatmap (Figure 3) made it possible to visualize the correlation matrix between the selected items. This highlights the strong correlations between certain items.

Figure 3. Correlation heatmap.

This study aimed to explore the factors that promote the effectiveness of students’ learning in mechanical design through the use of functional analysis tools. In light of the literature on the effectiveness of learning and the empirical data available to us, we have developed a theoretical context allowing us to understand the framework in which students’ learning of mechanical design using functional analysis tools would be considered successful, at least in part. This context is articulated around the hypothesis of student performance in interaction with functional analysis tools under the influence of four factors: the profile of the student, the ergonomics of the functional analysis tools, the educational use of the functional analysis tools and, finally, the interaction of the student with the functional analysis tools.

Overall, the effectiveness of learning depends on a set of factors, including the pedagogy of functional analysis tools, their usefulness, their usability, their acceptability and their interaction with the student.

However, it is the structuring of the contents of the diagrams (horned beast, pieuvre), the formulation of the functions (main, constraints), the identification of the functions (main, technical) and their understandability, the readability of the pieuvre diagram, the coherence between the main function and the constrained functions, the perception of the ease of learning of the diagrams (pieuvre, horned beast) and the ease of memorization of the FAST diagram, which constitute the most important factors for the explanation of the effectiveness of student learning. Indeed, the structuring of the horned beast and pieuvre diagrams must be clear for a better understanding of the need and the interactions.

The main function makes it possible to meet the main needs of the mechanical system, while the technical functions are the means implemented to achieve these functions.

Primary functions are those that define the usefulness of the mechanical system, whereas constrained functions are the requirements that the mechanical system must meet. The wording must be precise and concise for effective functional analysis.

The ease of learning of the octopus and horned-beast diagrams, as well as the ease of memorization of the FAST diagram, constitute key factors for the effectiveness of students’ learning. The easy learning of these diagrams improves the understanding and memorization of concepts and therefore the learning efficiency of students.

This exploratory study in technological education draws on the practices declared by students to examine the problem of learning mechanical design. It thus enriches existing research in this area. In other words, the study explores how students perceive and use functional analysis tools while learning mechanical design, thus contributing to a better understanding of the factors that promote the perceived effectiveness of their learning.