Artículo
Permanent URI for this collectionhttps://hdl.handle.net/11285/345284
Artículo científico o editorial en una publicación periódica académica sujeto a revisión de pares. Cumple con los índices internacionales o bases de datos de amplia cobertura, como el listado del Current Contents, ISI WEB of Knowledge (http://isiknowledge.com/) e índice de revistas mexicanas de CONACYT (www.conacyt.mx/dac/revistas). Éstos indizan y resumen los artículos de revistas seleccionadas, en todas las áreas del saber.
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- User experience measurement in design-based research on online educational platforms: contextualization of real-world environments within sustainable development goals for computational thinking(SAGE Publications, 2025-09-15) Tariq, Rasikh; Ramírez Montoya, María Soledad; Awotwe, Tabbi Wilberforce; Fernández Castro, Verónica; Martínez Reyes, Magally; Novak, AngelaThe importance of User Experience Measurement allows understanding how experiences can be optimized to meet functional and emotional needs, complemented with Design-Based Research to scale the redesign of educational platforms, four online educational platforms presented are powered by Artificial Intelligence and educational data mining to offer a tailored learning experience, combining computational thinking with Sustainable Development Goals. We present the analysis of responses of 1,573 users, which yield elements of improvement that will be implemented in the redesign. To measure User Experience of the platform, a nonexperimental quantitative analysis was carried out, using a Feedback survey based on a Likert-scale instrument, measuring perceptions: Emotional impact, Usability, and Satisfaction. The transcendence of this proposal demonstrates good practice in documenting experiences with the redesign of educational platforms that incorporate Artificial Intelligence and data mining, thereby opening a gap in Research on the quality of assessments and scaling of this approach.
- A look at sustainability through the lens of the sustainable development goals and education 5.0: A systematic review of the literature(Journal of Social Studies Education Research, 2025-03-31) Ramírez Montoya, María Soledad; Tariq, Rasikh; Rozo García, Hugo Alexander; Casillas Muñoz, Fidel; Tarman, BulentWe discuss a new construct called Industry 5.0, which has infiltrated the education sector, enabling us to explore Education 5.0. This concept is based on the use of advanced technologies that enable it to address global issues in contemporary society, many of which are related to sustainability. In line with the above, the aim of this research was to reveal the possible advances that have been made in Education 5.0 and its relationship with Sustainable Development Goals, especially sustainability, to achieve this, a systematic literature review was carried out, analyzing 92 articles from the Web of Science (WoS) and Scopus databases. The analysis was carried out through research questions that allowed the documentary corpus to be explored, organized, and segmented in order to focus on three elements How sustainability has been approached by Education 5.0, the possible challenges it faces in working toward sustainability in the immediate future, and finally, a characterization of the publications. The results take into account that: a) the management of education and its relationship with industry 5.0 seems to be the favorite topic when SDGs are addressed; b) the impact on sustainability for 5.0 technologies is diverse, ranging from facilitating intelligent resource management to refining teaching methods, raising awareness of sustainability, improving collaboration and promoting virtuality; c) SDGs is the topic less addressed in the literature in the margin of Education 5. 0; d) Most of the authors agreed that the main challenge is the widespread adoption of technologies in Education 5.0. The review concludes that the emergence of Education 5.0 introduces technological advances accentuated by a human-centered vision. However, it is urgent that institutions adopt an inquisitiveness about quality education, the achievement of SDGs, and the sustainability of education to amplify sustainable key competencies, such as creativity and human-centered thinking.
- Utilization of locally sourced waste fats for biodiesel production: Experimental characterization and environmental life cycle assessment(Elsevier, 2025-02-08) Muhammad Aqil, Khan; Nadeem Ahmed, Sheikh; Khan Zaib, Jadoon; Abubakr, Ayub; Tabbi, Wilberforce Awotwe; Rasikh, Tariq; Manuel Garcia PerezThis study presents the production, chemical characterization, and life cycle assessment (LCA) of biodiesel derived from various local waste feedstocks using experimental setup. Biodiesel was produced via transesterification in a 50-L batch reactor using oils sourced from a five-star restaurant (A), three-star café (B), rancid palm oil (C), and chicken feather oil (D). Fourier Transform Infrared Spectroscopy (FTIR) was used for identification of functional groups, while gas chromatography-mass spectrometry (GC-MS) analyzed fatty acid methyl ester (FAME) composition, revealing key variations such as Hexadecanoic acid (C16:0) and Octadecenoic acid (C18:1). Type A biodiesel exhibited the highest saturation, while Types B and C contained more unsaturated FAMEs, influencing their heating values. Conversion efficiencies were significantly influenced by the acid values of the feedstocks, with a maximum yield of 85 % achieved for a sample with an acid value of 3.5 mgKOH/g and a heating value of 35.8 MJ/kg. LCA performed using Simapro V9.5.0.2 demonstrated that biodiesels from Types A, B, C, and D reduced carbon footprints by 70 %, 64 %, 63 %, and 65 %, respectively, compared to fossil diesel. Feedstocks with lower free fatty acid (FFA) levels resulted in lower environmental impacts, while extensively reused cooking oils with higher FFA values contributed to increased carbon footprints. This study underscores the potential for scalable biodiesel production from waste resources, aligning with global and regional sustainability goals.
- Computational thinking in STEM education: current state-of-the-art and future research directions(Frontiers, 2025-01-08) Rasikh, Tariq; Aponte Babines, Bertha María; Ramírez, Jesús; Icaza Longoria, Inés Álvarez; Naseer, Fawad; Todor GanchevThe knowledge society exists mainly due to advancing technology and the exponential development of professionals’ capabilities. Digital transformation and new technologies generate complex environments demanding high-level skills. This work analyzes the current state of pedagogical approaches with a special focus on project-based learning that develops computational thinking in STEM students. A Systematic Literature Review examined the current state of pedagogical approaches along with project-based learning aimed at enhancing computational thinking within the context of higher education. Results allowed us to infer that (a) computational thinking promotes sustainable development through STEM education and novel teaching practices; (b) it is a fundamental skill for the problem-solving processes that evolve with technological progress; (c) its development is a global concern, not limited to a country’s development level; and (d) its introduction at an early stage provides opportunities for the advancement of vulnerable groups. Outlining, this study conducts a Systematic Literature Review (SLR) using PRISMA 2020 guidelines to analyze pedagogical approaches including project-based learning for enhancing computational thinking in STEM higher education, identifying global research trends, common strategies, and areas for improvement, while proposing a framework to align computational thinking skills with emerging technological challenges and promote sustainable educational practices. This study presents relevant results on the construction of state-of-the-art computational thinking and education; it is valuable for curricular design underpinning disciplinary and interdisciplinary approaches.
- Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society(PLOS ONE, 2024-12-19) Muhammad, Tayyab Zamir; Fida, Ullah; Rasikh, Tariq; Waqas, Haider Bangyal; Muhammad, Arif; Alexander, Gelbukh; Fredrick Romanus IshengomaInformal education via social media plays a crucial role in modern learning, offering selfdirected and community-driven opportunities to gain knowledge, skills, and attitudes beyond traditional educational settings. These platforms provide access to a broad range of learning materials, such as tutorials, blogs, forums, and interactive content, making education more accessible and tailored to individual interests and needs. However, challenges like information overload and the spread of misinformation highlight the importance of digital literacy in ensuring users can critically evaluate the credibility of information. Consequently, the significance of sentiment analysis has grown in contemporary times due to the widespread utilization of social media platforms as a means for individuals to articulate their viewpoints. Twitter (now X) is well recognized as a prominent social media platform that is predominantly utilized for microblogging. Individuals commonly engage in expressing their viewpoints regarding contemporary events, hence presenting a significant difficulty for scholars to categorize the sentiment associated with such expressions effectively. This research study introduces a highly effective technique for detecting misinformation related to the COVID-19 pandemic. The spread of fake news during the COVID-19 pandemic has created significant challenges for public health and safety because misinformation about the virus, its transmission, and treatments has led to confusion and distrust among the public. This research study introduce highly effective techniques for detecting misinformation related to the COVID-19 pandemic. The methodology of this work includes gathering a dataset comprising fabricated news articles sourced from a corpus and subjected to the natural language processing (NLP) cycle. After applying some filters, a total of five machine learning classifiers and three deep learning classifiers were employed to forecast the sentiment of news articles, distinguishing between those that are authentic and those that are fabricated. This research employs machine learning classifiers, namely Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Trees, and Random Forest, to analyze and compare the obtained results. This research employs Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) as deep learning classifiers, and afterwards compares the obtained results. The results indicate that the BiGRU deep learning classifier demonstrates high accuracy and efficiency, with the following indicators: accuracy of 0.91, precision of 0.90, recall of 0.93, and F1-score of 0.92. For the same algorithm, the true negatives, and true positives came out to be 555 and 580, respectively, whereas, the false negatives and false positives came out to be 81, and 68, respectively. In conclusion, this research highlights the effectiveness of the BiGRU deep learning classifier in detecting misinformation related to COVID-19, emphasizing its significance for fostering media literacy and resilience against fake news in contemporary society. The implications of this research are significant for higher education and lifelong learners as it highlights the potential for using advanced machine learning to help educators and institutions in the process of combating the spread of misinformation and promoting critical thinking skills among students. By applying these methods to analyze and classify news articles, educators can develop more effective tools and curricula for teaching media literacy and information validation, equipping students with the skills needed to discern between authentic and fabricated information in the context of the COVID-19 pandemic and beyond. The implications of this research extrapolate to the creation of a society that is resistant to the spread of fake news through social media platforms.