The Wit-Learning Methodology as a Means for Research Skills Acquisition: A Longitudinal Assessment

The Wit-Learning Methodology as a Means for Research Skills Acquisition: A Longitudinal Assessment

Luis Ibarra (Tecnologico de Monterrey, Mexico), Arturo Soriano (Tecnologico de Estudios Superiores de Ixtapaluca, Mexico), Pedro Ponce (Tecnologico de Monterrey, Mexico) and Arturo Molina (Tecnologico de Monterrey, Mexico)
Copyright: © 2020 |Pages: 27
DOI: 10.4018/978-1-5225-7793-5.ch010
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The present blistering modernization, epitomized by artificial intelligence, demands engineering graduates to address holistic, fast-changing, and even unknown problems. Thus, skills development, such as self-learning, is gaining importance against the traditional educational approach. To this regard, the wit-learning methodology was designed to stimulate the research skills during a course, supported on the similitude of the research endeavor with the desired engineers' profile. It stands out from other proposed alternatives targeting the students' capabilities that do not focus on research skills or misses their quantitative evaluation. In this chapter, the wit-learning methodology is presented together with its assessment regarding research skills acquisition, using direct and indirect measurements. Notably, it was verified to help improve the research skills through three criteria: the improvement of the students' research reports, the increased students' agreement to researchers while evaluating research reports, and their understanding of the research goals and processes.
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The rapid technological development and the accentuated availability of IT commodities shape an uncertain future. The versatility of artificial intelligence (AI) not only finds application in everyday services such as interaction in social networks and chatbots, it directly impacts the value chains of corporations and organizations and, in the end, is redefining the value of work of many people. In this way, the interaction of the AI in the individual life of a worker exhibits, in the words of Abeles (2016), even a threat to his identity and his sense of well-being. One might think that professional practice would be exempt from such a risk; however, applications of AI are present in areas related to high-level thinking such as learning, emotion evaluation, and disease detection (Wright & Schultz, 2018; Williams, 2019). That locates AI, according to the interpretation of Abeles (2016), as a competitive entity similar to a community capable of filling job vacancies traditionally occupied by the original population.

This panorama imposes new considerations on a sophisticated space of work opportunities per se and has motivated research regarding the incorporation of AI into professional practice. The consulted literature agrees that, although the AI is already modifying businesses, it has not conclusively affected the current work environment (Morikawa, 2017; Fountaine et al., 2019); however, a definitive growth is expected. In this sense, the current concerns about professions lie in their foreseen relevance and in the type of preparation necessary to face the future. In this regard, Wright & Schultz (2018) present a framework to identify the activities that most likely and promptly face an AI labor disruption. In that work, three axes with antipodal linguistic scales are identified, namely: (1) social or non-social, (2) skilled or unskilled and (3) non-routine or routine based. A routine based non-social activity that does not demand skill will, according to Wright & Schultz (2018), be more easily and promptly undertaken by AI.

Interestingly, the activities that are further away from such “absorption” are activities such as scientific practice and surgical care. It is foreseen that mid-level jobs will disappear, giving rise to specialized or low-level activities, focusing the latter on services (Wright & Schultz, 2018). The same authors point out that skilled jobs could even benefit from the exhaustive automation provided by the AI, polarizing society in terms of economic compensation. A similar situation occurs between big, global organizations with investment capacity against small companies (Morikawa, 2017). This gap impacts society from an ethical point of view, not abandoned by current research. Full discussions can be found in (Wright & Schultz, 2018) and the references therein, regarding the holistic meaning of work for the individual. Similarly, higher education is urged to aim at educating high-skilled professionals (Williams, 2019), now required not to be technically proficient but also competent toward the modernization mentioned above, capable to face such a holistic and changing environment proactively and being prepared for future, unpredicted problems.

As asserted by Chan et al. (2017), accreditation bodies, employers, engineering students, and teachers have recently placed their attention in generic skills which are defined by the same authors as “skills, abilities and attributes which are fundamental in helping students to acquire, construct, and apply knowledge” and are “generic to different learning situations.” This implies a change in the way engineering programs are designed, taught, and evaluated, and points directly to skills acquisition rather than knowledge transmission. Such point of view is familiar to many researchers who state that the graduates require analyzing and synthesizing capabilities, be critical thinkers, and autonomously find solutions for the daily problems (Brew & Mantai, 2017; Huet, 2018; Williams, 2019). Similarly, Clements & Joswick (2018) state that “twenty-first-century skills are focused on creativity, ingenuity, critical thinking, and the like,” affirming that persevering at problem-solving, producing extended chains of reasoning, and communicating effectively orally and in writing are decidedly expected.

Key Terms in this Chapter

Perceptual Change: A modification in the means and processes through which a person interprets his/her experiences, and the consequent meaning obtained from them.

Cognitive Change: A modification in the means and processes through which a person understands, acquires knowledge, or build abstract structures from experience.

Meta-Cognitive Knowledge: Refers to the understanding of the cognitive process and the usage of such knowledge toward some purposeful use.

Accreditation Bodies: Institutions devoted to the evaluation, ranking, and certification of higher-education programs and the institutions that offer them.

Procedural Knowledge: The ability of identifying the processes followed to attain an objective.

Peer Review: An evaluation process of contributed material where the participating authors perform the revision among each other.

Epistemology: In a broad sense, the theory of knowledge. Deals with what knowledge is, how is it acquired, and how is it validated.

Capabilities: Set of holistic abilities, skills, aptitudes, and attitudes that determine what a person can do and to what extent.

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