1. Title Filling the Skill Gap: Text Mining Tools for Academia and Industry 2. Research question(s) The thesis aims to study the impact of technological transformation on skills and its subsequent effects on education, leveraging on Artificial Intelligence, with reference to text mining techniques. Therefore, the two following questions arise from this aim: RQ1 How to detect with text mining the skills’ needs of the technological transformation? RQ2 How to use text mining to support innovation in education for a timely and effective translation of skills needs into learning opportunities? 3. Antecedents and motivation Digitalization, recent development in Artificial Intelligence, and the Fourth Industrial Revolution already towards Industry 5.0, are producing deep transformation in various technological domains and several industries (Chiarello et al., 2018). In addition, industrial stakeholders are rethinking supply chains and business models, focusing also on the vulnerability of the environment and the effects of human activities on it, for a more sustainable mindset (Holzmann & Gregori, 2023). The initial core of the companies’ strategies concerns mainly the adoption of new technologies. By the time, they are shifting towards promoting up/re-skilling in a life-long-learning perspective (Tabrizi et al., 2019), as the development of a skilled workforce able to exploit technological potential is a catalyst for enabling the technological transformation. In this context, we still know little about the role of education.The answer of universities and education and training institutions is to update the educational offer including new programs or renewing existing ones. Sometimes educational institutions strive to adapt, although many signals indicate that digitalization will also distrust the status-quo in teaching and learning (Ramírez-Montoya et al., 2021). The transformation has been speeded up by the COVID-19 crisis, that has pushed schools, universities and learning centres through a faster digitalization of their services. Anyway, it is still uncertain how digital technologies will shape teaching and learning, and how this transformation should be managed by educational systems (Secundo et al., 2021; Alam et al., 2020a). Indeed, many factors affect innovation in higher education, either facilitating or contrasting the integration of new content, improved technologies, pedagogical methods, and other innovative practices (Lašáková et al., 2017). Therefore, the fast pace of technological transformation is in contrast with the slow change rate of education institutions, which need much time to develop and implement new or modified programs effectively. The complexity of the transformation and the uncertainty of the effects on occupations and in general labour market make even harder the updating process, as the actions of the educational system should ensure also an adequate flexibility to easily re-adapt to changes. Consequently, teachers and designers in education need support to perform faster the following activities: (1) define the learning goals, (2) design the educational contents, (3) deliver the programs and the courses, and (4) assess the learning processes. 4. Theoretical framing Design education has been widely studied, with a focus on identifying the most effective methods, tools, and techniques for teaching design courses and assessing student performance, as well as on examining various curricular structures and pedagogies used in undergraduate engineering programs, evaluating their effectiveness, and assessing the design process (Chiarello et al., 2021). For what concern the design process and the evaluation of programs’ effectiveness, many scholars rely on text mining techniques (Pejic-Bach et al., 2020; Tomy and Pardede, 2019), proving studies and analysis on the alignment between universities and labor market evolution through big data (e.g., Ashaari et al., 2021). Text mining is a sub-field of Artificial Intelligence referred to methods for the automatic information extraction from textual resources and their analysis (Tandel et al., 2019). Indeed, several documents related to the educational area are written resources, such as syllabus, programs descriptions, learning outcomes, books and learning materials, tests and exams; moreover, information regarding skills needs can be found in texts as well, such as job vacancies, organisation charts, and job descriptions.Therefore, the idea behind this thesis is to leverage on Text Mining to support educational designers and teachers in the development of learning activities to boost both academia and industry in filling the skills gap. The identification of the concepts addressed in design education, their definition, and their relations is reported in Spada et al. (2021) with a framework for supporting the use of textual data in data-driven strategies for designing courses.In addition, altough the proposed thesis do not contribute directly in the stream of literature of pedagogy, this field is taken as reference for understanging the elements addressed in the design, in relation to competence-centred pedagogical approach (focus on development of skills, real-world application, and problem-solving; (Gervais, 2016)), Constructive Alignment theory (curriculum design approach that aligns learning objectives, teaching methods and assessment techniques; (Biggs, 2012)), and Bloom's Taxonomy (framework for organizing learning objectives based on their complexity and specificity; (Krathwohl, 2002)). 5. Empirical research design and preliminary findings The research design of the thesis rely on the method of DMAIC (acronym for Define, Measure, Analyze, Improve and Control), a data-driven method for improving, and optimizing processes and designs, related to the tool used to drive Six Sigma projects (Dahlgaard & Mi Dahlgaard‐Park, 2006). For each step of the method the related goals, activities, and outcomes are following described.D Define the impact of technologies on skills, linking emerging technologies andcompetences. The outcome regards method to detect skills related to emerging technologies, leveraging both on automatic text analysis and expert knowledge.M Measure skills demand and offer, providing quantitative evidence of the contentsaddressed in educational programs and skills required in labour market. The outcome is a text mining tool to extract skills from documents related to education (e.g., degree programs description) and labour market (e.g., job vacancies).A Analyse skills demand and offer, comparing information extracted from relevantresources of the two areas. The outcome regards quantitative method and indicators to measure and compare the relevance of skills related to emerging technologies.I Improve educational activities, developing learning path addressing skills gaps. The outcome is a text mining tool to link skills based on their relations ineducational materials and scientific documents, leveraging on graph analysis.C Control the effectiveness of design process. This part is still under developmentin the thesis. Therefore, the results include methods and tools for analyzing the impact of technological transformation on skills, list of skills related to emerging technologies, and their relationship. Those elements can provide support in designing education and training to address technology transformation and filling the gap between academia and industry.The preliminary findings of the thesis show that there is a need for universities to be more exhaustive and clearer while describing the courses and their learning content. Tipically, companies describe very specifically their skills needs. Thus universities mention less skill in their learning outcome, and usually cover broader concepts related to the knowledge acquired in the program. Secondly, universities and industry need to align on a shared language to express student competencies and company needs. Indeed, the terminology used to describe academic programs and job requirements varies in abstraction. Universities tend to use more generic terms and some educators may only explicitly mention certain competencies while implying others.The analysis lead to the identification of the need of a strong technical background, to exploit the potential of the technologies, together with professional expertise, to properly perform the functional activities in companies. However, universities universities tend to be more theoretical, focusing on understanding the underlying principles of digital technologies, on how they can be used to advance knowledge in a particular field, and on how they work or can be further developed. On the contrary, companies typically focus on the practical application of the technologies in specific industries and organisations them to improve their processes and performance. Finally, the findings highlight the need for greater emphasis not only technical skills but also soft skills to enable workers to drive technological transformation in companies. The data reveals that soft skills are more highly valued in the job market compared to what is covered in degree programs.
Filling the Skill Gap: Text Mining Tools for Academia and Industry
Irene Spada
2023-01-01
Abstract
1. Title Filling the Skill Gap: Text Mining Tools for Academia and Industry 2. Research question(s) The thesis aims to study the impact of technological transformation on skills and its subsequent effects on education, leveraging on Artificial Intelligence, with reference to text mining techniques. Therefore, the two following questions arise from this aim: RQ1 How to detect with text mining the skills’ needs of the technological transformation? RQ2 How to use text mining to support innovation in education for a timely and effective translation of skills needs into learning opportunities? 3. Antecedents and motivation Digitalization, recent development in Artificial Intelligence, and the Fourth Industrial Revolution already towards Industry 5.0, are producing deep transformation in various technological domains and several industries (Chiarello et al., 2018). In addition, industrial stakeholders are rethinking supply chains and business models, focusing also on the vulnerability of the environment and the effects of human activities on it, for a more sustainable mindset (Holzmann & Gregori, 2023). The initial core of the companies’ strategies concerns mainly the adoption of new technologies. By the time, they are shifting towards promoting up/re-skilling in a life-long-learning perspective (Tabrizi et al., 2019), as the development of a skilled workforce able to exploit technological potential is a catalyst for enabling the technological transformation. In this context, we still know little about the role of education.The answer of universities and education and training institutions is to update the educational offer including new programs or renewing existing ones. Sometimes educational institutions strive to adapt, although many signals indicate that digitalization will also distrust the status-quo in teaching and learning (Ramírez-Montoya et al., 2021). The transformation has been speeded up by the COVID-19 crisis, that has pushed schools, universities and learning centres through a faster digitalization of their services. Anyway, it is still uncertain how digital technologies will shape teaching and learning, and how this transformation should be managed by educational systems (Secundo et al., 2021; Alam et al., 2020a). Indeed, many factors affect innovation in higher education, either facilitating or contrasting the integration of new content, improved technologies, pedagogical methods, and other innovative practices (Lašáková et al., 2017). Therefore, the fast pace of technological transformation is in contrast with the slow change rate of education institutions, which need much time to develop and implement new or modified programs effectively. The complexity of the transformation and the uncertainty of the effects on occupations and in general labour market make even harder the updating process, as the actions of the educational system should ensure also an adequate flexibility to easily re-adapt to changes. Consequently, teachers and designers in education need support to perform faster the following activities: (1) define the learning goals, (2) design the educational contents, (3) deliver the programs and the courses, and (4) assess the learning processes. 4. Theoretical framing Design education has been widely studied, with a focus on identifying the most effective methods, tools, and techniques for teaching design courses and assessing student performance, as well as on examining various curricular structures and pedagogies used in undergraduate engineering programs, evaluating their effectiveness, and assessing the design process (Chiarello et al., 2021). For what concern the design process and the evaluation of programs’ effectiveness, many scholars rely on text mining techniques (Pejic-Bach et al., 2020; Tomy and Pardede, 2019), proving studies and analysis on the alignment between universities and labor market evolution through big data (e.g., Ashaari et al., 2021). Text mining is a sub-field of Artificial Intelligence referred to methods for the automatic information extraction from textual resources and their analysis (Tandel et al., 2019). Indeed, several documents related to the educational area are written resources, such as syllabus, programs descriptions, learning outcomes, books and learning materials, tests and exams; moreover, information regarding skills needs can be found in texts as well, such as job vacancies, organisation charts, and job descriptions.Therefore, the idea behind this thesis is to leverage on Text Mining to support educational designers and teachers in the development of learning activities to boost both academia and industry in filling the skills gap. The identification of the concepts addressed in design education, their definition, and their relations is reported in Spada et al. (2021) with a framework for supporting the use of textual data in data-driven strategies for designing courses.In addition, altough the proposed thesis do not contribute directly in the stream of literature of pedagogy, this field is taken as reference for understanging the elements addressed in the design, in relation to competence-centred pedagogical approach (focus on development of skills, real-world application, and problem-solving; (Gervais, 2016)), Constructive Alignment theory (curriculum design approach that aligns learning objectives, teaching methods and assessment techniques; (Biggs, 2012)), and Bloom's Taxonomy (framework for organizing learning objectives based on their complexity and specificity; (Krathwohl, 2002)). 5. Empirical research design and preliminary findings The research design of the thesis rely on the method of DMAIC (acronym for Define, Measure, Analyze, Improve and Control), a data-driven method for improving, and optimizing processes and designs, related to the tool used to drive Six Sigma projects (Dahlgaard & Mi Dahlgaard‐Park, 2006). For each step of the method the related goals, activities, and outcomes are following described.D Define the impact of technologies on skills, linking emerging technologies andcompetences. The outcome regards method to detect skills related to emerging technologies, leveraging both on automatic text analysis and expert knowledge.M Measure skills demand and offer, providing quantitative evidence of the contentsaddressed in educational programs and skills required in labour market. The outcome is a text mining tool to extract skills from documents related to education (e.g., degree programs description) and labour market (e.g., job vacancies).A Analyse skills demand and offer, comparing information extracted from relevantresources of the two areas. The outcome regards quantitative method and indicators to measure and compare the relevance of skills related to emerging technologies.I Improve educational activities, developing learning path addressing skills gaps. The outcome is a text mining tool to link skills based on their relations ineducational materials and scientific documents, leveraging on graph analysis.C Control the effectiveness of design process. This part is still under developmentin the thesis. Therefore, the results include methods and tools for analyzing the impact of technological transformation on skills, list of skills related to emerging technologies, and their relationship. Those elements can provide support in designing education and training to address technology transformation and filling the gap between academia and industry.The preliminary findings of the thesis show that there is a need for universities to be more exhaustive and clearer while describing the courses and their learning content. Tipically, companies describe very specifically their skills needs. Thus universities mention less skill in their learning outcome, and usually cover broader concepts related to the knowledge acquired in the program. Secondly, universities and industry need to align on a shared language to express student competencies and company needs. Indeed, the terminology used to describe academic programs and job requirements varies in abstraction. Universities tend to use more generic terms and some educators may only explicitly mention certain competencies while implying others.The analysis lead to the identification of the need of a strong technical background, to exploit the potential of the technologies, together with professional expertise, to properly perform the functional activities in companies. However, universities universities tend to be more theoretical, focusing on understanding the underlying principles of digital technologies, on how they can be used to advance knowledge in a particular field, and on how they work or can be further developed. On the contrary, companies typically focus on the practical application of the technologies in specific industries and organisations them to improve their processes and performance. Finally, the findings highlight the need for greater emphasis not only technical skills but also soft skills to enable workers to drive technological transformation in companies. The data reveals that soft skills are more highly valued in the job market compared to what is covered in degree programs.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
