Learning through cognitive, affective, and psychomotor domains: Understanding undergraduate engineering students’ perspectives in the United States

The cognitive domain

The cognitive domain has been categorized into six levels. These levels and their descriptions are provided in Table 1. The cognitive domain has been the cornerstone of education for decades, focusing on the mental processes involved in acquiring, processing, and applying knowledge (Anderson & Krathwohl, 2001). This domain is structured hierarchically, progressing from basic tasks like remembering and understanding to more complex processes such as analyzing, evaluating, and creating. In engineering education, the cognitive domain is paramount as it encompasses skills such as critical thinking, problem-solving, and the ability to apply theoretical knowledge in practical situations (Borrego et al., 2014).

Recent studies have emphasized the importance of fostering higher-order cognitive skills in engineering students to prepare them for the complexities of real-world problem-solving (Borrego & Henderson, 2014). However, research also highlights a gap between students’ and instructors’ perceptions of cognitive demands. For example, a study conducted in Indonesian universities revealed that while professors perceived their lectures to require lower cognitive effort, students reported higher cognitive demands (Fitriani et al., 2021). This disconnect underscores the need for a deeper understanding of how students perceive cognitive challenges in their education and the necessity for aligning teaching practices with these perceptions. The study by Coffman and Kittur (2024a) explored undergraduate engineering students’ understanding and perceptions of the cognitive domain of learning, as outlined in Bloom’s Taxonomy. Their findings indicate that students generally possess a strong understanding of higher-order cognitive skills such as applying and evaluating knowledge. However, the study also revealed discrepancies between students’ self-perceptions and actual abilities in these areas, suggesting a need for enhanced instructional strategies that better align with students’ perceived and real cognitive demands. Additionally, the research emphasizes the importance of fostering critical thinking and problem-solving skills in engineering education.

The affective domain

The affective domain has a total of five levels, and these levels and their descriptions are provided in Table 2. The affective domain, which encompasses emotional development, attitudes, and values, is often overlooked in higher education, particularly in technical fields like engineering (Olatunji, 2014). This domain is essential because it influences students’ motivation, engagement, and ability to persist in the face of challenges (Hoque, 2016). The affective domain is structured into five levels of increasing complexity: receiving, responding, valuing, organizing, and characterizing by value (Krathwohl et al., 1973). At the highest level, students internalize values that influence their behavior and decision-making.

Recent research underscores the significance of affective learning in promoting student success. For instance, studies show that students who develop positive emotional responses to learning are more likely to engage deeply with the material and persist through difficult tasks (Abbasi et al., 2023; Violante et al., 2020). However, there is limited research on how engineering students perceive the affective domain within their education. Olatunji (2014) notes that higher education institutions tend to treat affective learning as a secondary outcome of cognitive learning, rather than as an integral part of the learning process. This study seeks to address this gap by examining engineering students’ perceptions of affective learning and how it impacts their overall educational experience. The study by Coffman and Kittur (2024c) investigated undergraduate engineering students’ understanding and perceptions of the affective domain of learning, which focuses on emotional responses, attitudes, and values. The findings indicate that while students recognize the importance of emotional engagement and motivation in their learning, the affective domain often receives less emphasis in the classroom compared to cognitive learning. Students expressed that greater attention to emotional factors, such as valuing learning outcomes and actively participating in knowledge transfer, could improve both their motivation and overall learning experiences in engineering education.

The psychomotor domain

The psychomotor domain is classified into seven levels as presented in Table 3. The psychomotor domain, which focuses on physical skills and coordination, is particularly relevant in disciplines that require hands-on learning, such as engineering and the medical sciences (Nicholls et al., 2016). This domain progresses from basic physical tasks, such as perception and guided response, to more complex actions like adaptation and origination (Hoque, 2016). In engineering, the psychomotor domain is critical for tasks that involve manual dexterity, precision, and the use of tools and equipment.

While the cognitive domain has been extensively studied, the psychomotor domain remains underexplored, especially in engineering education (McNett, 2012). Most research on the psychomotor domain focuses on medical education, where skill acquisition is vital for tasks like surgery or nursing (Nicholls et al., 2016). In engineering, however, psychomotor skills are often relegated to laboratory settings or extracurricular activities, leaving their development less integrated into the core curriculum (Coffman & Kittur, 2024b). This lack of integration may contribute to a disconnection between students’ perceptions of their abilities in this domain and their actual skill development.

Few studies have examined students’ perceptions of psychomotor learning in engineering. The limited research that does exist suggests that students often undervalue their psychomotor skills in relation to their cognitive abilities, despite the importance of both in professional engineering practice (Abbasi et al., 2023). This study aims to fill this gap by exploring how engineering students perceive their development in the psychomotor domain, particularly in relation to the practical demands of their field.

Perceptions of learning domains in engineering education

While research on the cognitive, affective, and psychomotor domains is extensive, there is a noticeable lack of studies that explore how engineering students perceive their own learning across these domains. Most existing studies focus on specific aspects of learning, such as the effectiveness of instructional methods or the development of particular skills, without considering students’ subjective experiences (Gottipati & Shankararaman, 2018). However, understanding students’ perceptions is critical for developing teaching strategies that address their needs holistically.

The closest related study to this research was conducted by Vezzani, Vettori, and Pinto (2018), who explored how environmental factors, such as socio-economic status and race, influence students’ views on learning. While this study offered valuable insights, it did not focus on engineering students, or the specific domains of learning outlined in Bloom’s Taxonomy. This research seeks to build on that work by providing a comprehensive analysis of how engineering students perceive their learning across all three domains, with the goal of informing more effective and integrated educational practices.

Given the complexity of engineering education, understanding how students perceive their learning in the cognitive, affective, and psychomotor domains is essential for creating more effective instructional strategies. By exploring these perceptions, this study aims to contribute to the development of a more holistic approach to engineering education, one that equally prioritizes intellectual, emotional, and physical skill development. This research will provide valuable insights into how educators can better align their teaching practices with students’ experiences, ultimately enhancing learning outcomes in engineering programs.

Recent empirical evidence from engineering education

Development of the survey instrument

The survey instrument was developed during the spring of 2024 and comprises three scales measuring the cognitive, affective, and psychomotor domains of learning. This study was approved by the Institutional Review Board (IRB #17058; approval date: March 27, 2024), and participating students provided informed consent. Refer to Table 4 for the definitions and example items for each scale. These scales, along with their respective items, were constructed based on insights from existing literature (Hoque, 2016). Participants were asked to evaluate their perceptions using a 5-point Likert-type scale, where they rated their confidence in their abilities on each item. The survey consisted of 18 questions, distributed across the three learning domains. The Likert scale ranged from (5) strongly agree, (4) agree, (3) neither agree nor disagree, (2) disagree, to (1) strongly disagree. Additionally, the survey included a section gathering demographic information from the participants.

Evidence of content and face validity

To ensure content and face validity, feedback was gathered from experts in survey design as well as potential participants. Three experts evaluated the survey instrument, offering insights on the relevance and appropriateness of the items in relation to the scales and overall study. Additionally, three potential participants reviewed the survey to provide feedback on the clarity and wording of the questions. Revisions were made to the survey instrument based on the feedback received from both the experts and participants.

Exploratory factor analysis (EFA)

Participants

A total of 170 participants completed the survey instrument. The participants’ demographic information is shown in Table 5. Following data cleaning and pre-processing, 115 responses were retained for analysis. These 115 responses fit in the recommended items per respondent: 5 to 10 (Hair et al., 2010). Responses that were excluded were either largely incomplete or had identical answers selected for every question. Missing data were addressed using the group mean substitution method. The sample consisted of over 65 percent male participants. In terms of race and ethnicity, participants identified as White (56.52%), Asian (21.74%), Black or Hispanic/Latinx (17.4%), African American (14.8%), American Indian or Alaska Native (9.57%), and Native Hawaiian or Other Pacific Islander (1.74%). Please note that the total percentage of race and ethnicity is greater than 100% as some participants identified they belong to multiple race and ethnicity. Graduate students made up 75 percent of the sample, and participants represented ten different engineering majors across various academic standings.

EFA

According to Seltman (2013), each survey item must exhibit absolute skewness and kurtosis values below 3.0 to be considered within acceptable limits for representing a normal distribution (Table 6). Several aspects that students rated highly based on average response scores (greater than 4 out of 5) include: “Apply” (apply knowledge in new situations, item #3, mean = 4.03), “Receive” (receive knowledge, item #7, mean = 4.36), “Value” (find value in learning, item #9, mean = 4.24), “Characterize” (control learning outcomes through behavior, item #11, mean = 4.1), “Perception” (use sensory cues to guide motor activity, item #12, mean = 4.02), “Set” (act mentally and physically when required, item #13, mean = 4.27), “Guided Response” (learn through imitation and practice, item #14, mean = 4.34), and “Complex Overt Response (Expert)” (skillfully perform patterns of action, item #16, mean = 4.16).

Bartlett’s test of sphericity confirmed that the data were suitable for factor analysis (p < 0.001), and the KMO measure of sampling adequacy (KMO = 0.79) supported the extraction of factors to explain meaningful variance (McCoach et al., 2013). Additionally, Kaiser’s criterion, parallel analysis, and the scree plot suggested that the data contained three, three, and two factors, respectively. Based on these results, three factors were chosen for further analysis, aligning with the initial hypothesis. Due to the high correlations between the factors (p > 0.33), the Promax rotation method was employed (McCoach et al., 2013).

None of the items cross-loaded, meaning no items had factor loadings greater than 0.3 on more than one factor (Fitriani et al., 2021; Kittur, 2023; Sullivan et al., 2003). Six items, which had factor loadings below 0.4, were excluded from the final analysis (Seltman, 2013). These items were: “Remember” (recall data/information, item #1), “Analyze” (break down information for relationships, item #4), “Receive” (receive knowledge, item #7), “Value” (find value in learning, item #9), “Perception” (use sensory cues for motor activity, item #12), and “Guided Response” (learn through imitation, item #14).

The EFA resulted in three distinct factors: the cognitive domain of learning, the affective domain of learning, and the psychomotor domain of learning. The factor loadings of the final factor structure are shown in Table 7. Factor loadings for the first factor (F1) ranged between 0.52 and 0.83, for the second factor (F2) from 0.57 to 0.75, and for the third factor (F3) from 0.43 to 0.54. The internal consistency reliability, measured by Cronbach’s alpha, ranged from 0.63 to 0.73 across the three factors, indicating good reliability.

Cognitive domain: Promoting higher-order thinking skills

The cognitive domain has traditionally dominated engineering education, focusing on problem-solving, reasoning, and knowledge application (Anderson & Krathwohl, 2001). Recent studies emphasize the importance of developing higher-order thinking skills such as evaluation and creation, which are crucial for real-world engineering challenges (Fitriani et al., 2021).

Affective domain: Enhancing emotional engagement and motivation

The affective domain, involving emotions, attitudes, and values, has often been overlooked in engineering education (Lozzi, 1989; Olatunji, 2014). However, recent research highlights the importance of emotional engagement for student success, particularly in fostering motivation and resilience (Abbasi et al., 2023). Developing students’ emotional intelligence can also enhance their ability to collaborate, empathize, and persist in challenging tasks.

Psychomotor domain: Strengthening hands-on skill development

Despite the centrality of psychomotor skills to many engineering tasks, this domain is often underrepresented in the curriculum, particularly in theoretical courses (Nicholls et al., 2016). Psychomotor learning involves the development of physical skills and coordination, which are essential in lab work, prototyping, and field engineering tasks.

Integrated learning experiences: A holistic approach

The integration of cognitive, affective, and psychomotor domains can significantly enhance the effectiveness of engineering education. Research by Hussain et al. (2016) supports the view that holistic educational strategies lead to better learning outcomes. For instance, combining lab-based work (psychomotor) with reflective assessments (affective) and problem-solving tasks (cognitive) can provide students with a more comprehensive learning experience. Integrated learning approaches, such as capstone projects, should be central to the curriculum, requiring students to demonstrate skills across all three domains. This holistic approach not only prepares students to tackle technical problems but also equips them with the emotional intelligence and physical dexterity required in professional practice.

To better prepare engineering students for the complexities of the field, curriculum designers must adopt a holistic approach that integrates cognitive, affective, and psychomotor learning. The evidence from recent research highlights the importance of this multidimensional approach for fostering well-rounded graduates who are intellectually capable, emotionally resilient, and physically skilled. By reimagining the structure of engineering programs to address all three domains, educators can create more engaging, relevant, and effective learning experiences, ensuring that future engineers are equipped to meet the demands of both industry and society.

Implications for teaching practices

To translate these findings into actionable strategies for educators, the following practices are recommended:

Directions for future research

This study also opens several avenues for future investigation: