In an era characterized by rapid changes and increasingly complex professional demands, developing complex thinking skills and digital transformation has become crucial in higher education. This study aims to analyze the relationship between academic performance and the development of these cross-sectional skills among higher education students, through the Tec21 educational model implemented at Tecnológico de Monterrey in Mexico. Using the CRISP-DM methodology for data mining, data from over 33,000 students were analyzed, highlighting the impact of academic performance in enhancing the skills necessary to face the challenges of the modern job market. In this article, we will review the research findings and their implications for shaping educational policies and pedagogical practices, aligning with sustainable development goals and emphasizing the need for an integration of academic skills with personal and professional competencies.
The Importance of Cross-Sectional Skills in Higher Education
Cross-sectional skills such as critical and analytical thinking, problem-solving, and innovation are essential elements that students in higher education need to keep pace with the challenges of modern times. In an era characterized by rapid changes in the job market, developing these skills is an urgent necessity, as it reflects the ability to think complexly and understand interrelated relationships across various fields. Data indicates that 75% of American companies struggle to find candidates who possess these skills. This underscores the necessity for educational institutions to rely on training programs that blend academic learning with practical applications that enhance these skills. By creating interactive educational environments that play a stimulating role in developing required skills, universities can enhance the employability of their graduates in the contemporary job market.
The Tec21 Educational Model and Its Role in Developing Learners
The Tec21 model implemented by Tecnológico de Monterrey is a pioneering example of how education can adapt to the needs of the 21st century. This model promotes challenge-based learning, where students are directed to tackle real-world problems and seek innovative solutions. The model is based on four main pillars, including project-based learning and self-directed learning, which contributes to motivating students to learn independently and develop their personal skills. These strategies enhance students’ abilities to think critically and solve problems creatively, making them better prepared to face challenges in practical life. Through a flexible educational approach aligned with students’ needs, Tec21 helps build future leaders who know how to navigate complex challenges in economic, social, and technological fields.
Challenges in Developing 21st Century Skills and Their Impact on Academic Performance
Despite the importance of 21st-century skills, there are significant challenges facing higher education in adopting these skills. One such challenge is the lack of a clear understanding and specific criteria for evaluating student performance. Academic pressure may lead to a decline in focus on developing personal skills in favor of traditional academic achievement, which may weaken students’ ability to adapt to job market demands. Addressing the ambiguity surrounding how to develop and assess these skills is a positive step towards enhancing students’ academic performance. Guidelines from organizations like UNESCO emphasize the importance of enhancing practical skills through educational programs, which adds real value to students’ lives. Moreover, fostering collaboration between educational and industrial institutions is a crucial step towards coordinating efforts to achieve an educational framework that combines theoretical education with practical application, enhancing students’ competitiveness and innovation in their future careers.
Education 4.0 and Its Role in Meeting Modern Industry Demands
The Fourth Industrial Revolution, known as Education 4.0, marks a turning point in how higher education is delivered. The world today faces significant transformations resulting from rapid technological advancement, making the need to restructure curricula urgent. New approaches focus on integrating modern technologies into the learning process, including artificial intelligence, the Internet of Things, and big data. This requires universities not only to teach technical skills but also to enhance social and emotional skills that contribute to developing resilience and innovation mindsets among students. Through technology-based educational strategies, students can connect how to use digital tools to improve their learning and professional performance, contributing to their readiness for future challenges in the fast-paced work environment.
Trends
Future Prospects in Skill Development in Higher Education
There is an increasing awareness of the importance of developing the necessary skills in higher education to keep up with the demands of adapting to the labor market. Studies indicate that the effective integration of transferable skills into curricula can significantly enhance students’ academic performance and provide them with a competitive advantage when entering the job market. Future trends are related to redesigning educational programs to be more interactive and include real examples and research that require collaboration among students. These trends also involve enhancing opportunities for personalized learning through the use of modern technologies such as remote learning and tailored lessons. These innovations will contribute to developing educational environments that embrace diversity and inclusion, enabling all students to learn and grow in ways that meet their individual needs and help them enhance their skills. Overcoming current gaps requires continuous efforts from educational leaders and policymakers to ensure that universities remain centers of innovation and educational excellence.
Learning Styles and Interaction between Students and Faculty
Modern learning styles involve multiple educational experiences aimed at improving student engagement and enhancing their interaction with course content. The Tec21 education model, for example, focuses on fostering collaboration among students through interdisciplinary projects, which enhances critical thinking and problem-solving. The Tec21 model calls for integrating knowledge from different fields to address real-world challenges, aligning with the needs of industries such as engineering in the age of Industry 4.0. In a previous context, studies have shown that students in these programs report improved educational experiences compared to traditional education, with surveys indicating student satisfaction with this model.
One of the reasons contributing to this satisfaction is the focus on interactive learning and flexible educational environments. For instance, during the COVID-19 pandemic, assessment results reflected stability or even improvement in grades, indicating that the unique characteristics of this model helped facilitate a smooth transition to online learning. This transition illustrates the flexibility of the innovative educational system in facing challenges and reinforces the importance of having a close relationship between classroom learning and practical life experience by engaging students in tasks that reflect the professional reality they will encounter after graduation.
Developing Cross-Competencies and Their Role in Student Success
Developing cross-competencies is a negative aspect of the Tec21 model, as it offers a renewed framework that equips students with the skills necessary for academic success and a bet on their professional future. These competencies include: ethical commitment and community engagement, complex thinking, effective communication, digital transformation, self-awareness and management, innovative entrepreneurship, and social intelligence. These competencies are aimed at enhancing students’ personal and professional development alike and are considered essential in a world full of ongoing and changing challenges.
For instance, ethical commitment and community engagement focus on how to implement projects that contribute to improving the surrounding environment and enhancing the public good. This commitment enriches students’ experiences and enhances their social responsibility. On the other hand, complex thinking requires integrating different types of thinking to analyze and solve complex problems, reflecting the need for continuous learning in an era of rapid transformations.
Digital transformation is considered one of the most important competencies for equipping students to face the challenges of the modern digital economy. This element requires the use of innovative digital strategies to improve processes and products, enabling students to adapt to labor market needs. By applying these competencies within educational frameworks, students become well-prepared to face future challenges and secure sustainable professional jobs.
The Impact of Academic Performance on Skills Development
This research aims to understand the relationships between academic performance and the competencies of complex thinking and digital transformation in achieving tangible educational outcomes. Academic performance is measured by students’ average grades, highlighting the importance of coordination between curricula and teaching practices and their impact on students. Moreover, improving academic performance has a direct effect on developing life skills; higher academic achievement is linked to better opportunities for activating cross-competencies.
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The study involves a group of students enrolled in the Tec21 model, allowing them to benefit from practical and collaborative learning. The results of the study showed that students with high academic performance demonstrated better proficiency in complex thinking skills and digital transformation. This reflects the importance of the interaction between theoretical education and practical application, where modern learning environments play a vital role in supporting and developing these competencies.
By using educational and assessment data, differences in academic performance were analyzed, including details such as demographic variables. The results of this analysis may contribute to improving curricula and directing educational improvement efforts based on self-directed learning and student interaction with content that aligns with market requirements.
Conclusion of the Analysis and Future Trends in Higher Education
Higher education systems are moving towards models that embrace interactive learning and flexibility in educational approaches to meet student aspirations and address contemporary challenges. The Tec21 model is an example of innovation in higher education, combining the technical and social skills necessary for success in a digitally connected world filled with rapid changes. This clearly illustrates how higher education institutions can help students achieve a balance between academic learning and personal development.
As technological development continues, educational programs are expected to keep adapting and changing to include modern innovations such as AI-enhanced learning and data analytics. These trends will enhance the student experience and contribute directly to achieving advanced educational outcomes while building the necessary capacities to face future challenges. High-quality education that can provide real support to students will be the foundation for building empowered and sustainable civil and professional communities.
Potential Values of Student Data
The potential values of student data are one of the fundamental aspects analyzed in the dataset. The student data includes multidimensional attributes, such as gender, which is divided into the two categories “Male” and “Female”, along with various dates representing a wide range of potential values. The Python programming language, along with the Dash library, is used to analyze numerical variables. The Dash library is characterized by its ability to handle data more efficiently, allowing for the processing of large amounts of information by batch processing, which is essential when dealing with large datasets compared to the Pandas library that requires loading the entire dataset into memory. This can lead to better performance when using devices with limited resources, as memory efficiency can speed up the process of analysis and descriptive statistics.
Data Preparation
Data preparation represents a vital phase in the analysis process, where a series of transformations must be performed on the dataset to make it suitable for modeling and evaluation. It begins by splitting the dataset into two subgroups based on the targeted competencies, namely ‘digital transformation’ and ‘complex thinking’. Here, the focus is on the variable that reflects the level of competency granted to the student. The meticulous stages of data preparation are critical, as substantially empty columns are removed to maximize the usefulness of the remaining valuable information, ensuring the integrity of the rest of the dataset. Additionally, columns that contain unique identifiers or may negatively affect its quality are removed.
Modeling and Evaluation
The modeling and evaluation phase is pivotal, where a data model that aligns with the research questions is established. The number of learning cycles that enhance the efficiency of complex thinking or digital transformation is determined, for example. The subsequent data show that the focus comes more prominently in the School of Engineering and Sciences, reflecting the importance of integrating these two competencies within the curriculum. In this section, machine learning models such as random forests and XGBoost can be applied to identify the most influential variables in students’ acquisition of complex thinking and digital transformation competencies. The use of machine learning algorithms is an informed approach that provides insights related to the importance of different features in the dataset.
Analysis
Data and Forecasting
The importance of data analysis lies in the ability to estimate the impact of various variables, particularly the final grade (final_grade), on students’ competencies. The analysis shows that students’ final grades are considered one of the most influential variables in acquiring essential competencies. By analyzing the results, grades can be categorized, and it can be shown how these grades affect the likelihood of acquiring competence. The data estimation model demonstrates the increasing relationship between academic level and success in acquiring required competencies, indicating positive trends associated with achieving remarkable outcomes.
Linking Data to Discussions
Linking the results derived from the analysis to documented discussions helps enhance understanding and effectively communicate findings to the academic community. From the model’s conclusions, it becomes clear that academic performance, as evidenced by final grades, plays a significant role in achieving specified competencies. This connection between data and theories aids academics and researchers in promoting discussions related to digital transformation and complex thinking, thus finding effective educational solutions. Utilizing evidence derived from analysis for better understanding contributes to formulating strategic visions for educational systems.
The Importance of Academic Performance in Acquiring Competencies
Many view academic performance as one of the primary indicators of students’ success in developing various competencies. Research has shown that good academic performance is closely associated with the opportunity to acquire the necessary skills and competencies in the modern work environment. For example, data indicates that students who achieve high grades are more likely to develop complex thinking competencies and digital transformation skills, reflecting the impact of academic performance on personal and professional growth.
The significance of academic performance goes beyond merely achieving high grades. It often reflects – in many cases – the effort, dedication, and achievement that students invest in their learning. For instance, in the Tec21 education model, a distinction is made between academic success and the acquisition of competencies. A student may pass the course with a good average but may not acquire the required skills. Thus, it can be said that education in the competency-based model requires students to engage in effective learning strategies that enable them to achieve academic success and acquire skills simultaneously.
The relationship between a student’s academic performance and the acquisition of competencies is one aspect that requires further analysis and study. Although high grades are associated with achieving competencies, we must be cautious in interpreting this relationship, as obtaining high grades does not necessarily guarantee the acquisition of necessary skills. Some students may receive passing grades in courses but may fail to gain a deep understanding of the prescribed competencies.
The biggest challenge is how to design effective educational curricula that incorporate both academic performance and the competencies acquired. Educational programs at universities and higher institutes should aim to achieve a balance between academic effort and skill acquisition, enhancing students’ ability to successfully interact in the job market.
The Competency-Based Model in Education
The competency-based education represents a radical shift in how skills and knowledge are acquired. In this type of education, responsibility is placed on students for their learning, as they are given the opportunity to acquire skills through a range of courses instead of being committed to specific courses only. This model includes evaluating students’ performance based on the broader concept of competencies and specialties needed in the market.
Research shows that the competency-based model transcends traditional education methods, focusing on developing skills that are deemed essential in the technological era. Specifically, complex thinking and digital transformation skills are among the core competencies. For example, students who possess critical thinking skills are capable of analyzing and solving problems in innovative ways, which enhances their capabilities in a rapidly changing world. This transformation also requires teachers to employ new teaching strategies that stimulate critical thinking and promote students’ self-development.
The competency-based model also benefits performance evaluation. Instead of focusing solely on certifications and grades, students are assessed based on specific skills acquired, encouraging them to delve deeper and invest in learning accurately. This is evident through examples showing how some competency-based educational programs provide students with opportunities to enhance their skills and knowledge through practical and experiential projects, thereby improving students’ readiness for future jobs.
Competency-based learning represents an opportunity to enhance self-learning and motivation, as it encourages students to reduce reliance on teachers and direct supervision. Students should be given the flexibility and freedom to choose courses and areas in which they wish to develop their skills, thus fostering their passion for learning and providing a supportive educational environment.
Challenges Facing Academic Achievement and Competency Acquisition
Despite the significant gains mentioned in the examples, the competency-based model is not without challenges. These challenges include the diversity in student performance and varying backgrounds. Additionally, many factors negatively affect academic performance, such as teaching quality, instructional tools used, and students’ socio-economic conditions. For instance, significant disparities in the quality of education provided by one teacher compared to another can lead to varying academic outcomes. More effective teachers can contribute to better outcomes for students, while those exposed to lower-quality educational environments may struggle.
Furthermore, individual circumstances of students must be considered. Many students may face difficulties outside the realm of education itself, such as economic or health burdens, which may hinder their performance in classes. For example, students experiencing mental stress due to personal issues may find it difficult to concentrate, negatively impacting their final grades and their ability to acquire desired competencies.
Moreover, complexities in measuring academic performance and competency acquisition make matters more complicated. Academic achievement may also be influenced by the assessment methods employed by teachers, whether through written tests, practical projects, or oral evaluations. Therefore, there may be a need for multiple strategies to better assess students’ performance in a way that reflects their actual skills.
From here, it becomes essential to implement strategies that support students and help them overcome these challenges. It is also crucial to incorporate individual factors into the design of curricula and educational programs, to ensure the needs of all students are met, and thus guide them towards achieving academic success and personal development.
Challenges Facing Digital Transformation in Higher Education
Digital transformation in higher education is considered a vital issue that attracts the attention of scholars and researchers alike. This transformation requires a radical change in traditional teaching and learning methods. However, this process faces several challenges, the most prominent of which is resistance to change from certain educational institutions and faculty members. This resistance may stem from fear of technology or from a loss of control over the learning process. Additionally, there is a divide in vision regarding how to integrate technology into curricula, with some preferring to focus on traditional learning while others advocate for the application of modern digital methods.
For example, studies have shown that many faculty members lack the necessary skills to effectively use digital tools in their classrooms. This necessitates comprehensive training programs to support teachers in using technology effectively. Furthermore, there are challenges related to technological infrastructure. In many institutions, there is insufficient availability of technological resources such as fast computers and appropriate software. This can lead to a reluctance among students to participate in the digital learning process.
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It is also worth noting that there are discrepancies in access to technology among students, as these discrepancies can affect the level of knowledge and skills achieved. Social justice standards ensure that all students must have equal access to technological resources. Therefore, it becomes essential for educational institutions to address this gap and provide the necessary support for students who may face difficulties.
Opportunities for Innovation in Modern Education Models
The digital transformation is accompanied by significant opportunities for innovation in education models. Many educational institutions are shifting towards adopting new educational models, such as project-based learning, where students engage in real projects that reflect the challenges faced by their community. This type of learning not only enhances knowledge but also reflects their critical thinking and problem-solving abilities.
On the other hand, challenge-based education encourages students to think independently and positively develop their skills. For example, many engineering students have designed sustainable projects as part of their curriculum, giving them the opportunity to apply theoretical knowledge in practical practices. This type of learning requires greater interaction and collaboration among students, enhancing teamwork spirit and establishing an inclusive educational environment.
Moreover, technology allows students access to a wide range of educational resources, including recorded lectures, digital libraries, and online open courses. These platforms are not only available around the clock but also offer a variety of learning styles that meet the diverse needs of students. Through these options, students can learn at their own pace, which enhances the potential for greater success in their studies.
Impact of COVID-19 on Digital Education
The COVID-19 pandemic has accelerated the digital transformation in higher education, forcing educational institutions to quickly move their classrooms to online platforms. By utilizing technologies such as video conferencing and online lectures, institutions were able to continue providing education even under social distancing conditions.
However, this rapid shift also revealed that many institutions were not fully prepared for the challenges of remote education. Generally, the experience was not equal among different educational bodies, with some institutions performing well while others faced significant difficulties.
For instance, successful distance learning experiences were found in institutions that had already begun investing in technology and ongoing training for faculty members, compared to institutions that relied on traditional methods. There is a discussion about the importance of providing the necessary technical support for both teachers and students to successfully navigate this phase.
Additionally, our experience with distance learning emphasized the importance of in-person education and its necessity in developing social skills and personal interaction. Therefore, educational institutions should seek hybrid approaches that combine traditional and digital education to ensure that all educational aspects for students are met in a balanced manner.
Towards Continuous Education and Flexibility
Education systems around the world are increasingly shifting towards the concept of continuous education as a means to meet the changing needs of the job market. Digital transformation also requires sustainable skills, which means individuals need to continue learning and developing new skills throughout their lives. This sustainability in learning is a fundamental part of the concept of Education 4.0, which focuses on flexibility and adaptation to the demands of the modern world.
With the proliferation of on-the-job training and self-learning, it is essential for individuals in society to have the ability to acquire new skills in line with rapid technological advancements. This innovation in educational patterns creates immense opportunities for learners of any age, but it requires a clear vision from educational bodies to instill these values in current systems.
In addition, the integration of technology in learning environments continues to evolve, enabling institutions to better engage students and foster an environment that propagates excellence and inclusivity.
For example, many freelance companies have started to offer specialized training programs in collaboration with universities, giving students a unique ability to obtain education related to the job market. These collaborative partnerships should be taken into consideration when designing educational curricula that meet the actual needs of individuals and the labor market.
Enhancing a culture of continuous education is an important step towards achieving success in the future. This requires all stakeholders — governments, educational institutions, and companies — to work together to ensure the creation of an interactive and encouraging learning environment that makes the learning process enjoyable and effective, thus enhancing the competitiveness of individuals and the economy as a whole.
The Negative Impacts of the COVID-19 Pandemic on Youth
The COVID-19 pandemic has brought about radical changes in various aspects of life, especially in the fields of work and employment. The years following the onset of the pandemic have witnessed a significant increase in unemployment rates among youth, rising to 23% in the age group of 18 to 24 years. This percentage is a major concern, as the pandemic was expected to affect not only job stability but also salaries. According to statistics, the wages of many young people who managed to stay employed declined, which exacerbated their feelings of uncertainty and fears about their professional future. Estimates suggest that about 40% of young people are living in a state of doubt regarding their future, while 14% of them fear what the future may hold.
These challenges are manifested in the difficulties companies face in attracting suitable talent, as three-quarters of American companies express suffering in finding individuals with the necessary skills. Among the missing skills, the top ranks ranged from critical thinking, problem-solving, innovation, and creativity. This reflects the urgent need for educational institutions to develop curricula that include skills such as critical thinking and the ability to adapt to complex global changes.
The current situation reflects the importance of developing soft skills among students, to ensure their alignment with the future job market’s needs. The ability to deal with complexity and uncertainty has become one of the most sought-after skills by companies, and educational institutions must play an active role in this regard by implementing educational strategies focused on developing these skills and encouraging students to engage in critical thinking and creativity.
Higher Education Strategies for Developing 21st Century Essential Skills
Today, educational institutions are looking to adopt new strategies that meet the needs of the labor market. These strategies come in the context of enhancing the skills known as 21st-century skills, which include the intellectual and social capacities necessary to adapt to the rapid transformations in the world of work. There is an urgent need to develop educational curricula that enhance the development of technological, social, and emotional skills in real time.
Literature points to the importance of integrating complex thinking skills and digital transformation into educational curricula, as these skills represent the pathway towards quality and inclusive education. It requires the development of critical and systemic thinking abilities, in addition to creative and scientific thinking, which helps students face complex challenges eloquently and efficiently.
By creating learning environments that encourage the development of these skills, it becomes easier for students to acquire the necessary awareness of the realities of the labor market, which enhances their academic performance and thus their readiness to enter the job market. Higher education institutions should set clear standards for teaching and assessing these skills, to ensure that students benefit from them effectively and apply them in future professional contexts.
Sustainable Learning and Encouraging Innovation in Academia
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Under the changing circumstances imposed by the pandemic, there is a growing need to adopt the concept of sustainable learning that enhances innovation in educational institutions. The success of this concept relies on the ability to use technology to promote learning and meet the individual needs of each student. Research shows that integrating technology into education not only helps to develop technical skills but also enhances critical thinking and active learning.
Innovations in the educational curriculum, such as project-based learning, are effective means to enhance students’ skills and develop their analytical thinking. Universities and research centers must collaborate with industries and companies to promote innovation and provide practical experiences for students that enable them to apply what they have learned in real-world contexts. The transformations brought about by technology make it essential for higher education to adopt more flexible and interactive teaching methods.
Exchange experiences with companies can be part of the educational process, where students can work on real projects that require them to use their skills in problem-solving and apply their academic knowledge. Through these experiences, students can also understand the connection between education and practical life, making them more prepared to face future challenges.
Future Challenges and How to Address Them through Education
The pressures resulting from economic and social transformations continue to shape the future of education and the labor market. It will not be easy for educational institutions to face these challenges, but they can play a pivotal role in promoting the values and skills required for the future. All educational strategies must revolve around developing and enhancing critical thinking, problem-solving, and innovation, as these skills are essential in a complex world that requires informed and rapid decision-making.
It is essential for universities and educational institutions to adopt an effective teaching model that focuses on developing soft skills and preparing students to take responsibility in their future professions. Supporting programs that focus on teamwork, problem-solving, and critical thinking can be real enablers for students to face the challenges of the changing market.
Educational policymakers must work collectively with all stakeholders in the private and public sectors to improve the educational system and ensure its relevance to the needs of society. The future requires comprehensive sustainable education that combines rationality and innovation to tackle the challenges of the new era. Transitioning to a model of continuous learning will enable students to acquire the necessary skills to interact effectively and responsibly with future transformations.
The Importance of Digital Transformation in Higher Education
Digital transformation in higher education is considered one of the most important contemporary phenomena affecting educational systems around the world. This includes the use of digital technologies to create new learning environments that support access to information and facilitate interaction between students, teachers, and the community. In an era characterized by rapid technological changes, digital transformation is a key tool for improving the educational process, as it allows students and faculty to utilize a wider array of educational resources.
Technological tools such as the Internet of Things (IoT), cloud computing, virtual reality, and 3D holograms contribute to changing the way educational content is delivered and viewed. For example, virtual reality technologies are used to provide immersive educational experiences, enhancing students’ ability to understand academic material better.
Additionally, digital transformation has improved the flow of information through remote learning courses and open access to educational resources. Students can now learn from anywhere, facilitating access to knowledge and enriching their educational experience, especially during crises like the COVID-19 pandemic, where remote learning was the only way to ensure the continuity of the educational process.
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universities to adopt the concept of digital transformation comprehensively, as it can enhance academic performance and social interaction among members of the academic community. Digital transformation also helps to meet future needs, as the demand for digital skills in the labor market continues to increase.
Tec21 Educational Model
The Tec21 model, implemented by Tecnológico de Monterrey, represents a transformative educational initiative aimed at aligning education with the demands of the 21st century. This model is characterized by four main pillars: challenge-based learning, flexibility in learning methods, inspiring teachers, and an engaging university experience.
The Tec21 model places students at the heart of the educational process, encouraging them to tackle real-world problems and develop innovative solutions that meet community needs. This model contrasts significantly with traditional educational methods, making it attractive to students, as studies have shown a positive response from students compared to traditional approaches.
Thanks to its structure that supports interactive learning, the Tec21 model has proven its ability to adapt to the challenges faced by education during the Covid-19 pandemic. Student evaluations showed stability or even improvement in grades, indicating that the model’s characteristics contributed to the successful transition to remote learning.
Additionally, this model focuses on developing competencies across seven areas, which include ethical commitment, complex thinking, communication, digital transformation, self-management, innovative entrepreneurship, and social intelligence. These competencies aim to prepare students for professional success and personal growth, making them responsible citizens capable of managing future challenges.
Comprehensive Competencies and Their Impact on Academic Performance
Comprehensive competencies are a fundamental part of the Tec21 model, aiming to enhance the personal and professional development of students. Competencies such as complex thinking and digital transformation are urgent in the current context, as higher education encourages producing graduates capable of providing innovative solutions to the contemporary world’s challenges.
The competency centered around complex thinking contributes to enhancing students’ ability to integrate different types of thinking to analyze and solve problems. This type of thinking is crucial given the increasing challenges in complex and changing work environments, where adapting to diverse conditions and critical thinking in deriving solutions are integral parts of success.
On the other hand, digital transformation enhances the ability to use technological tools innovatively, contributing to improved productivity and creativity. This competency gains particular importance considering the rapid shift toward a digital economy, which requires students to be able to use technology wisely and quickly.
When studying the relationship between these competencies and academic performance, interesting results will emerge. The development of these competencies may improve students’ academic outcomes, as some research has shown that students with high skills in complex thinking and digital transformation often achieve better academic results than their peers who lack these skills.
This trend in higher education is not just a response to technological changes; it is part of a comprehensive strategy for developing the future workforce, capable of flexible learning and effectively interacting with various challenges. Focusing on developing competencies such as digital transformation and complex thinking not only enhances individual skills but also supports broader trends in social and economic development.
Research Methodology and Ethics
When considering the evaluation of the impact of complex thinking and digital transformation competencies on academic performance, a precise scientific methodology is adopted, represented by the application of the CRISP-DM (Cross-Industry Standard Process for Data Mining) model. This model is the ideal framework for conducting studies based on data analysis, as it includes stages such as understanding business, preparing data, modeling, evaluation, and deployment.
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These stages ensure that each step of the analysis is closely connected to the defined objectives, enhancing research outcomes and maximizing its benefits in decision-making. The first phase -Understanding the Business- involves studying the relationships between competencies and the specified academic criteria.
Ethics also emerges as a vital element in this research, as strict standards were adhered to in order to ensure the confidentiality of students’ information. The data was anonymized and stored securely, reflecting a commitment to the highest ethical standards and the nature of sustainable research.
When reviewing the results generated by the study, it is essential to clarify the relationship between students’ academic performance and the relevant competencies. The methodological steps and ethical considerations reflect the seriousness and professionalism with which this research was conducted, enhancing its importance in creating a positive impact on higher education outcomes.
Understanding Data
The data understanding phase is crucial in any study that relies on data analysis, as it aims to provide detailed analysis of each column in the dataset to determine its potential correlation with the research. This process includes a comprehensive evaluation of all possible values for each column, helping to provide a clear understanding of the information contained. At this stage, a data dictionary was utilized to obtain a general description of the content present in each column. It was confirmed that all columns were present as documented, with no missing elements, and that the data matched the description. In cases where the description did not match the data, efforts were made to understand the nature of the available data.
A total of 45 columns were identified in the dataset, which included 26 categorical columns and 19 numerical columns. Among the numerical columns, there were 14 with decimal values and 5 with integer values, comprising a total of 583,073 rows. A detailed analysis was conducted for each categorical column to determine the possible values it could take. It was noted that some columns contained a limited number of potential values, such as the gender column which includes only “Male” and “Female,” while other columns, such as dates, contained a wide range of possible values.
The data processing was conducted using Python with the Dask library, which contributed to the efficient analysis of numerical variables. This library provided descriptive statistics analysis, including the count of non-empty values, mean, standard deviation, minimum, maximum, and quartiles. Dask was preferred over the Pandas library due to the large volume of data, as Dask helps to optimize memory efficiency by processing data in chunks, reducing memory consumption, which facilitates analysis using less powerful computers.
Data Preparation
The data preparation phase plays a critical role in the analysis process, as the dataset undergoes a series of transformations to be ready for subsequent modeling and evaluation. Initially, the dataset was divided into two subgroups, each related to one competency of interest in the research: “Digital Transformation” and “Complex Thinking,” based on the column that describes the competencies. Special priority was given to a variable indicating the level assigned to students in this competency.
To assist in subsequent analysis, the terms “Observed” and “Unobserved” were numerically encoded, with 1 and 0 assigned to them, respectively. Data preparation involved a thorough evaluation of the empty values in each dataset. During this analysis, several columns were identified with a high proportion of empty values, which were not related to the research objectives and compromised the integrity of the remaining dataset.
Given the impossibility of filling these values due to their volume and lack of relevance to our goals, it was decided to exclude those columns to maintain the quality of the other relevant variables. Subsequently, columns containing unique identifiers such as “student.id”, columns containing detailed descriptions such as “subject.longName”, and columns that represented uniqueness in all their values such as “group.isLIFE” and “group.isAcademicSupport” were removed. Finally, the earned level column was separated from the rest of the data to prepare it for use in predictions.
Modeling
Data and Evaluation
The modeling and evaluation stage is a central point in the research, where a data model was created that fits both the research questions. Regarding the first research question, the number of courses that enhance complex thinking skills or digital transformation was identified. These courses were grouped according to the academic specialty in which they are taught. The results showed that there is a greater integration of the two responsibilities in the School of Engineering and Sciences, indicating the importance of those competencies in the engineering field.
The modeling and evaluation process related to the second research question was conducted through the application of machine learning models. The practices demonstrated the use of techniques such as Random Forest and XGBoost to identify the variables with the most significant impact on students’ acquisition of complex thinking and digital transformation competencies. The results indicated that the final grade variable for students was the most influential in acquiring the relevant competencies, as it had an importance nearly double that of any other variable, with models such as RF and XGBoost showing reliable results.
However, it is worth noting that the dataset suffered from significant imbalance, with more than 90% of the records indicating the acquisition of competencies, which may pave the way for model bias. Despite this, the models demonstrated good performance in accuracy, percentage value, and various scores, reflecting the strong predictive ability of the variables despite that challenge. The probabilities associated with the final grade as a key player in competencies acquisition were also analyzed, and the results showed a positive correlation between high grades and the achievement of academic goals.
Grade Classification and Its Impact on Competency Acquisition
The existing data provides an in-depth analysis of the extent to which student grades influence their ability to acquire competencies such as complex thinking and digital transformation. The grading range was divided into four categories, revealing a steady increase in the chances of acquiring these competencies as grades rise. Students who scored between 0 and 69 had a 46.63% chance of acquiring complex thinking and 44.80% for digital transformation. Meanwhile, in the second category from 70 to 79, the percentages jumped to 83.20% and 89.67% respectively. Finally, students who received grades between 90 and 100 had a chance of acquiring complex thinking and digital transformation reaching 98.64% and 98.88%, indicating a strong correlation between academic performance and competency acquisition.
The data provide evidence that high academic performance is positively linked to students’ achievement of essential skills. In educational institutions, the final grades can serve as a marker for assessing a student’s ability to tackle complex challenges and adopt modern digital technologies. This highlights the significance of academic performance in higher education and suggests that it may have long-lasting effects on students’ futures in the workforce.
Statistical Analysis and Its Role in Confirming Results
Advanced statistical analysis models, such as Random Forest and XGBoost, were employed to analyze the data and thus enhance the results derived. These models showed that final grades were the most influential factor in acquiring competencies. This finding supports the hypothesis that academic performance is a prominent indicator for acquiring essential competencies.
Graphs, such as the box plots in Figure 2, help visualize the distribution of final grades among students who acquired competencies and those who did not. Clearly, students with higher grades are more likely to develop the relevant competencies. This information illustrates how data-driven educational practices can enhance the overall understanding of the factors influencing student outcomes, forming a strong basis for evaluating the quality of education across various academic fields.
Competency-Based Education Model and Its Impact on Higher Education
Reflects
The competency-based educational model establishes an independent relationship between grades and acquired competencies. In the educational model, there are two factors for evaluation: the final grade and the acquisition of competencies related to each subject. These factors are independent, meaning that a student can pass a subject without achieving the required competencies. It is noteworthy that competencies are not confined to a single subject but are distributed across a range of academic disciplines.
This system allows students the opportunity to acquire the required competencies throughout their studies, which helps ensure achieving good academic performance while maintaining opportunities for skill development. This comprehensive vision contributes to the development of higher education by focusing on the balance between academic performance and the acquisition of essential skills required in the job market.
Challenges and Future Considerations in Higher Education
While findings indicate a strong relationship between academic performance and competency acquisition, these relationships may be more complex than they appear. External factors such as education quality, assessment methods, and individual student circumstances should be taken into account, as they can significantly affect academic performance and consequently the acquisition of competencies. These circumstances include variations in teaching levels and evaluation standards, as well as personal considerations like students’ socioeconomic status and health.
Understanding these complexities requires a comprehensive approach that considers various factors. Educational institutions must continue to explore how these factors influence competency development and enhance educational strategies that promote academic success while emphasizing the importance of essential skills required in professional life. This can include innovation in teaching methods and assessment techniques, as well as fostering a positive understanding of the importance of diversity and meeting the needs of all students.
Future Trends to Ensure Academic Performance and Competency Acquisition
The results of this study reveal the significant relationship between academic performance and competency acquisition in higher education. These findings highlight the necessity of designing educational interventions that focus on academic performance while simultaneously promoting the holistic development of competencies important in the contemporary job market. Educational programs should strive to equip students with the necessary tools not only for academic success but also for making valuable contributions in their fields.
Furthermore, the trend towards integrating experiential learning and applying knowledge in real-world scenarios can have positive effects on competency acquisition. It is crucial for educational institutions to continue assessing the impact of educational programs and systems on developing essential skills, considering data and evidence that support these transformations. This discussion summarizes the importance of adopting educational models that consider labor market aspirations and achieve a balance between academic performance and the acquisition of skills vital for student success.
Researching Alternative Methods to Control Academic Deviations
Researching new methods to control academic deviations is a topic of particular importance. Many researchers aim to understand the factors affecting students’ academic performance and how they can be influenced effectively. Among these innovative methods, in-depth analysis of the root causes of deviations serves as a driving force. These causes can involve psychological, social, and cultural aspects that affect student learning. For example, social and economic pressures can lead to variations in academic performance, necessitating flexible adaptation strategies.
Moreover, mediating and moderating factors that affect the relationship between academic performance and competency acquisition should be explored. This requires the use of advanced research methods that include experimental cases and qualitative and quantitative data analysis. These methods can help clarify how changes, such as different teaching methods, lead to different academic outcomes and how these methods can be adapted to better meet students’ needs.
This research benefits from…
The studies also highlight the importance of learning environments. Different learning environments, such as self-directed learning, project-based learning, or distance learning, can significantly affect the development of interrelated competencies. This calls for research into how these environments can be more inclusive and accommodating for all students, especially those facing unique challenges.
The Impact of Teaching Methods and Learning Environments on Interrelated Competencies
Teaching methods and learning environments are critical elements in determining the efficiency of higher education. Both the teacher and educational technologies play an important role in how students absorb information and develop necessary skills. For instance, project-based education can enhance critical thinking and problem-solving skills more than traditional lecture-based instruction.
Research also indicates that digital learning environments have provided new opportunities to enhance these competencies. Online education and virtual classes have helped students develop self-learning skills, research techniques, and digital communication. However, it is important to consider that the digital learning experience is not available to everyone, requiring institutions to identify strategies to provide equal opportunities for students from diverse backgrounds.
Many studies show that using multi-sensory learning methods, such as combining visual, auditory, and interactive elements, contributes to enhancing students’ understanding of educational material. Therefore, it is crucial for educational institutions to develop training programs that focus on equipping teachers with the necessary strategies to effectively implement these methods.
Ethical Challenges and Data Accessibility in Scientific Research
Ethical issues are an integral part of research involving humans. Conducting research requires informed consent from those involved, and some national laws mandate the presence of such consent. However, in some cases, like the study referenced, supervisors may allow the recording of information without the need for written approvals if the studies comply with specific guidelines. This could have significant implications for how academic research behaves in the future.
Furthermore, transparency in data accessibility represents another challenge. It has been established that study data is available for academic purposes only, which restricts the potential use of this data in other areas such as industry or commerce. Therefore, requiring researchers to establish a framework to ensure that their data can be used in a way that maintains ethical guidelines, while still contributing to broader research goals, is crucial.
Modern technologies offer new solutions to achieve a balance between ethical security and open data access. This can profoundly impact how research is conducted and how data is used in multiple contexts, promoting innovation and development.
Source link: https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2024.1426183/full
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