The Evolution of Active Learning in Response to the Pandemic: The Role of Technology.

Active learning has consistently played a significant role in education. Through interactive tasks, group projects, and a variety of engaging activities, students are encouraged to forge connections with the subject matter. However, the pandemic has necessitated that educators adapt and refine their active learning techniques to accommodate the online environment. This has resulted in stimulating innovations in the field, encompassing virtual simulations, online collaboration tools, and interactive multimedia. The COVID-19 pandemic has rapidly transformed the landscape of teaching and learning, particularly in higher education. One of the most prominent shifts has been the widespread adoption of active learning techniques, which have increased student engagement and fostered deeper learning experiences. In this chapter, we examine the evolution of active learning during the pandemic, emphasizing its advantages and challenges. Furthermore, we delve into the role of advances in artificial intelligence and their potential to enhance the effectiveness of active learning approaches. As we once focused on leveraging the opportunities of remote teaching, we must now shift our attention to harnessing the power of AI responsibly and ethically to benefit our students. Drawing from our expertise in educational innovation, we provide insights and recommendations for educators aiming to maximize the benefits of active learning in the post-pandemic era.

Implementing a challenge-based learning experience in a bioinstrumentation blended course.

BACKGROUND: Bioinstrumentation is essential to biomedical engineering (BME) undergraduate education and professional practice. Several strategies have been suggested to provide BME students with hands-on experiences throughout the curriculum, promoting their preparedness to pursue careers in industry and academia while increasing their learning and engagement. This paper describes the implementation of challenge-based learning (CBL) in an undergraduate bioinstrumentation blended course over the COVID-19 pandemic. METHODS: The CBL experience was implemented in a third-year bioinstrumentation course from the BME program at Tecnologico de Monterrey. Thirty-nine students enrolled in two sections formed fourteen teams that tackled blended learning activities, including online communication, lab experiments, and in-person CBL activities. Regarding the latter, students were challenged to design, prototype, and test a respiratory or cardiac gating device for radiotherapy. An institutional student opinion survey was used to assess the success of our CBL implementation. RESULTS: Student responses to the end-of-term survey showed that they strongly agreed that this course challenged them to learn new concepts and develop new skills. Furthermore, they rated the student-lecturer interaction very positively despite the blended format. Overall, students assessed their learning experience positively. However, implementing this CBL experience required a substantial time increase in planning, student tutoring, and constant communication between lecturers and the industry partner. CONCLUSION: This work provides an effective instance of CBL for BME education to improve students' learning experience despite decreased resource efficiency. Our claim is supported by the student's performance and the positive feedback from our industrial partner.

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process.

This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from Haematoxylum campechianum waste (ABHC). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar. The surface morphology of the adsorbent (before and after dye adsorption) was characterized by scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) and, lastly, pHpzc was also determined. Batch studies were carried out in the following intervals of pH = 4-10, temperature = 300.15-330.15 K, the dose of adsorbent = 1-10 g/L, and isotherms evaluated the adsorption process to determine the maximum adsorption capacity (Qmax, mg/g). Kinetic studies were performed starting from two different initial concentrations (25 and 50 mg/L) and at a maximum contact time of 48 h. The reusability potential of activated biochar was evaluated by adsorption-desorption cycles. The maximum adsorption capacity obtained with the Langmuir adsorption isotherm model was 114.8 mg/g at 300.15 K, pH = 5.4, and a dose of activated biochar of 1.0 g/L. This study also highlights the application of advanced machine learning techniques to optimize a chemical removal process. Leveraging a comprehensive dataset, a Gradient Boosting regression model was developed and fine-tuned using Bayesian optimization within a Python programming environment. The optimization algorithm efficiently navigated the input space to maximize the removal percentage, resulting in a predicted efficiency of approximately 90.47% under optimal conditions. These findings offer promising insights for enhancing efficiency in similar removal processes, showcasing the potential of machine learning in process optimization and environmental remediation.

Transdisciplinary experiential learning in biomedical engineering education for healthcare systems improvement.

BACKGROUND: The growing demand for more efficient, timely, and safer health services, together with insufficient resources, put unprecedented pressure on health systems worldwide. This challenge has motivated the application of principles and tools of operations management and lean systems to healthcare processes to maximize value while reducing waste. Consequently, there is an increasing need for professionals with the appropriate clinical experience and skills in systems and process engineering. Given their multidisciplinary education and training, biomedical engineering professionals are likely among the most suitable to assume this role. In this context, biomedical engineering education must prepare students for a transdisciplinary professional role by including concepts, methods, and tools that commonly belong to industrial engineering. This work aims to create relevant learning experiences for biomedical engineering education to expand transdisciplinary knowledge and skills in students to improve and optimize hospital and healthcare care processes. METHODS: Healthcare processes were translated into specific learning experiences using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model. This model allowed us to systematically identify the context where learning experiences were expected to occur, the new concepts and skills to be developed through these experiences, the stages of the student's learning journey, the resources required to implement the learning experiences, and the assessment and evaluation methods. The learning journey was structured around Kolb's experiential learning cycle, which considers four stages: concrete experience, reflective observation, abstract conceptualization, and active experimentation. Data on the student's learning and experience were collected through formative and summative assessments and a student opinion survey. RESULTS: The proposed learning experiences were implemented in a 16-week elective course on hospital management for last-year biomedical engineering undergraduate students. Students engaged in analyzing and redesigning healthcare operations for improvement and optimization. Namely, students observed a relevant healthcare process, identified a problem, and defined an improvement and deployment plan. These activities were carried out using tools drawn from industrial engineering, which expanded their traditional professional role. The fieldwork occurred in two large hospitals and a university medical service in Mexico. A transdisciplinary teaching team designed and implemented these learning experiences. CONCLUSIONS: This teaching-learning experience benefited students and faculty concerning public participation, transdisciplinarity, and situated learning. However, the time devoted to the proposed learning experience represented a challenge.

Measuring the Effectiveness of a Multicomponent Program to Manage Academic Stress through a Resilience to Stress Index.

In this work, we evaluate the effectiveness of a multicomponent program that includes psychoeducation in academic stress, mindfulness training, and biofeedback-assisted mindfulness, while enhancing the Resilience to Stress Index (RSI) of students through the control of autonomic recovery from psychological stress. Participants are university students enrolled in a program of excellence and are granted an academic scholarship. The dataset consists of an intentional sample of 38 undergraduate students with high academic performance, 71% (27) women, 29% (11) men, and 0% (0) non-binary, with an average age of 20 years. The group belongs to the "Leaders of Tomorrow" scholarship program from Tecnológico de Monterrey University, in Mexico. The program is structured in 16 individual sessions during an eight-week period, divided into three phases: pre-test evaluation, training program, and post-test evaluation. During the evaluation test, an assessment of the psychophysiological stress profile is performed while the participants undergo a stress test; it includes simultaneous recording of skin conductance, breathing rate, blood volume pulse, heart rate, and heart rate variability. Based on the pre-test and post-test psychophysiological variables, an RSI is computed under the assumption that changes in physiological signals due to stress can be compared against a calibration stage. The results show that approximately 66% of the participants improved their academic stress management after the multicomponent intervention program. A Welch's t-test showed a difference in mean RSI scores (t = -2.30, p = 0.025) between the pre-test and post-test phases. Our findings show that the multicomponent program promoted positive changes in the RSI and in the management of the psychophysiological responses to academic stress.

Biosorption of Pb(II) Using Natural and Treated Ardisia compressa K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm.

This study explored the effects of solution pH, biosorbent dose, contact time, and temperature on the Pb(II) biosorption process of natural and chemically treated leaves of A. compressa K. (Raw-AC and AC-OH, respectively). The results show that the surface characteristics of Raw-AC changed following alkali treatment. FT-IR analysis showed the presence of various functional groups on the surface of the biosorbent, which were binding sites for the Pb(II) biosorption. The nonlinear pseudo-second-order kinetic model was found to be the best fitted to the experimental kinetic data. Adsorption equilibrium data at pH = 2-6, biosorbents dose from 5 to 20 mg/L, and temperature from 300.15 to 333.15 K were adjusted to the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherm models. The results show that the adsorption capacity was enhanced with the increase in the solution pH and diminished with the increase in the temperature and biosorbent dose. It was also found that AC-OH is more effective than Raw-AC in removing Pb(II) from aqueous solutions. This was also confirmed using artificial neural networks and genetic algorithms, where it was demonstrated that the improvement was around 57.7%. The nonlinear Langmuir isotherm model was the best fitted, and the maximum adsorption capacities of Raw-AC and AC-OH were 96 mg/g and 170 mg/g, respectively. The removal efficiency of Pb(II) was maintained approximately after three adsorption and desorption cycles using 0.5 M HCl as an eluent. This research delved into the impact of solution pH, biosorbent characteristics, and operational parameters on Pb(II) biosorption, offering valuable insights for engineering education by illustrating the practical application of fundamental chemical and kinetic principles to enhance the design and optimization of sustainable water treatment systems.

Key factors influencing teachers' motivation to transfer technology-enabled educational innovation.

In response to the digital transformation in education, teachers are expected to develop new competencies. Although teachers gained valuable experience in digital technology use during the COVID-19 pandemic, research and practice show that primary school teachers need to be supported and trained for the new normal of innovative, advanced use and adoption of digital technologies in educational practice. This study aims to identify the key factors that influence teachers' motivation to transfer technology-enabled educational innovation in primary education. The Learning Transfer System Inventory (LTSI) factors and the adoption factors of technology-enabled educational innovation have been conceptually mapped. The LTSI model has been empirically validated with data collected from 12.7% of Lithuanian primary school teachers. The structural equation modeling technique was utilized to analyze causal relationships of factors influencing teachers' motivation to transfer technology-enabled educational innovation. The qualitative research method was used to provide a deeper understanding of key factors that influence motivation to transfer. The conducted analysis shows that motivation to transfer is significantly influenced by all five domains of factors: perceived value, personal characteristics, social practices, organizational and technology-enabled innovation factors. Motivation to transfer innovation varies according to teachers' perceived digital technology integration skills, which underpin the importance of applying different roles and strategies based on the teachers' skills. This study provides implications for designing effective professional development for in-service teachers and creating a suitable environment in schools for the adoption of innovation in post-COVID-19 education.

Gender prediction based on University students' complex thinking competency: An analysis from machine learning approaches.

This article aims to study machine learning models to determine their performance in classifying students by gender based on their perception of complex thinking competency. Data were collected from a convenience sample of 605 students from a private university in Mexico with the eComplexity instrument. In this study, we consider the following data analyses: 1) predict students' gender based on their perception of complex thinking competency and sub-competencies from a 25 items questionnaire, 2) analyze models' performance during training and testing stages, and 3) study the models' prediction bias through a confusion matrix analysis. Our results confirm the hypothesis that the four machine learning models (Random Forest, Support Vector Machines, Multi-layer Perception, and One-Dimensional Convolutional Neural Network) can find sufficient differences in the eComplexity data to classify correctly up to 96.94% and 82.14% of the students' gender in the training and testing stage, respectively. The confusion matrix analysis revealed partiality in gender prediction among all machine learning models, even though we have applied an oversampling method to reduce the imbalance dataset. It showed that the most frequent error was to predict Male students as Female class. This paper provides empirical support for analyzing perception data through machine learning models in survey research. This work proposed a novel educational practice based on developing complex thinking competency and machine learning models to facilitate educational itineraries adapted to the training needs of each group to reduce social gaps existing due to gender.

Escape rooms technology as a way of teaching mathematics to secondary school students.

The aim of this study is to determine whether Escape Rooms can be used as an active methodological approach for the purpose of teaching mathematics. The research adapted a quantitative approach via an experimental design. Two different study groups were established, the first of which comprised the control group who were taught using conventional training methods, and the second consisted of the experimental group, who were taught using a novel approach incorporating an Escape Room activity. The participants included 80 students who were all attending secondary school within the Kingdom of Saudi Arabia. The findings revealed how the activity involving the Escape Room led to a significant enhancement in the student's motivation, achievement and autonomy. The conclusion can be drawn that using an Escape Room in mathematics teaching can enhance learning achievement, anxiety, motivation and autonomy, with students' negative attitudes regarding mathematics learning considered as an important variable, particularly with respect to autonomy and motivation. Hence, Escape Rooms can potentially be more effective than conventional methods for teaching mathematics.

Systematic literature review and bibliometric analysis on virtual reality and education.

The objective of this study is to identify and analyze the scientific literature with a bibliometric analysis to find the main topics, authors, sources, most cited articles, and countries in the literature on virtual reality in education. Another aim is to understand the conceptual, intellectual, and social structure of the literature on the subject and identify the knowledge base of the use of VR in education and whether it is commonly used and integrated into teaching-learning processes. To do this, articles indexed in the Main Collections of the Web of Science, Scopus and Lens were analyzed for the period 2010 to 2021. The research results are presented in two parts: the first is a quantitative analysis that provides an overview of virtual reality (VR) technology used in the educational field, with tables, graphs, and maps, highlighting the main performance indicators for the production of articles and their citation. The results obtained found a total of 718 articles of which the following were analyzed 273 published articles. The second stage consisted of an inductive type of analysis that found six major groups in the cited articles, which are instruction and learning using VR, VR learning environments, use of VR in different fields of knowledge, learning processes using VR applications or games, learning processes employing simulation, and topics published during the Covid-19 pandemic. Another important aspect to mention is that VR is used in many different areas of education, but until the beginning of the pandemic the use of this so-called "disruptive process" came mainly from students, Institutions were reluctant and slow to accept and include VR in the teaching-learning processes.

Impact of digital technologies upon teaching and learning in higher education in Latin America: an outlook on the reach, barriers, and bottlenecks.

Digital technology and literacy can heighten the transformation of teaching and learning in higher education institutions (HEIs). This study uncovers the extent to which digital technologies have been used to advance the teaching and learning process in HEIs, and the barriers and bottlenecks to why it may not have been effectively implemented across the HEIs. The study used nine selected countries in Latin America (LATAM) based on the main focus of the educators, commercial, and financial investors; to show the level of impact/implications of computer technologies on the teaching and learning processes. We applied a two-step (mixed) methodology (through a quantitative and qualitative lens) for the research investigation, using data collected from survey we administered to faculty members in HEIs across the different countries in LATAM. In turn, we implemented a Text Mining technique (sentiment and emotional valence analysis) to analyze opinions (textual data) given by the participants to help determine challenges and obstacles to using the digital technologies for teaching and learning in the region. Quantitatively, we applied a Kruskal-Wallis H-test to analyze the collected multiple choice and ranked items in the questionnaire in order to identify prominent factors that consummately influence the reach, barriers, and bottlenecks, and where the differences may lie across the different LATAM countries. The results show that the users upheld the emphasis on lack of training, infrastructures and resources, access to internet and digital platforms, as the main challenges to the teaching-learning process. The study also empirically discussed and shed light on critical factors the HEIs, particularly in LATAM, should resolve and adopt in support of the decision-making strategies, operational policies and governance, financial investments, and policymaking, at a time when "digital technologies" have become an inevitable and indispensable part of education and learning.

Use of Robotic Platforms as a Tool to Support STEM and Physical Education in Developed Countries: A Descriptive Analysis.

The lack of interest of children at school is one of the biggest problems that Mexican education faces. Two important factors causing this lack of interest are the predominant methodology used in Mexican schools and the technology as a barrier for attention. The methodology that institutions have followed has become an issue because of its very traditional approach, with the professor giving all the theoretical material to the students while they listen and memorize the contents, and, if we add the issue of the growing access to technological devices for students, children carrying a phone are more likely to be distracted. This study aims to integrate technology through assistive robots as a beneficial tool for educators, in order to improve the attention span of students by making the learning process in multiple areas of the Mexican curriculum more dynamic, therefore obtaining better results. To prove this, four different approaches were implemented; three in elementary schools and one in higher education: the LEGO® robotic kit and the NAO robot for STEM (science, technology, engineering, and mathematics) teaching, the NAO robot for physical education (PE), and the PhantomX Hexapod, respectively. Each of these technological approaches was applied by considering both control and experimental groups, in order to compare the data and provide conclusions. Finally, this study proves that the attention span is indeed improved as a result of implementing robotic platforms during the teaching process, allowing the children to become more motivated during their PE class and become more proactive and retain more information during their STEM classes.

Towards teaching analytics: a contextual model for analysis of students' evaluation of teaching through text mining and machine learning classification.

Recent trends in educational technology have led to emergence of methods such as teaching analytics (TA) in understanding and management of the teaching-learning processes. Didactically, teaching analytics is one of the promising and emerging methods within the Education domain that have proved to be useful, towards scholastic ways to make use of substantial pieces of evidence drawn from educational data to improve the teaching-learning processes and quality of performance. For this purpose, this study proposed an educational process and data mining plus machine learning (EPDM + ML) model applied to contextually analyze the teachers' performances and recommendations based on data derived from students' evaluation of teaching (SET). The EPDM + ML model was designed and implemented based on amalgamation of the Text mining and Machine learning technologies that builds on the descriptive decision theory, which studies the rationality behind decisions the learners are disposed to make based on the textual data quantification and statistical analysis. To this effect, the study determines pedagogical factors that influences the students' recommendations for their teachers, what role the sentiment and emotions expressed by the students in the SET play in the way they evaluate the teachers by taking into account the gender of the teachers. This includes how to automatically predict what a student's recommendation for the teachers may be based on information about the students' gender, average sentiment, and emotional valence they have shown in the SET. Practically, we applied the Text mining technique to extract the different sentiments and emotions (intensities of the comments) expressed by the students in the SET, and then utilized the quantified data (average sentiment and emotional valence) to conduct an analysis of covariance and Kruskal Wallis Test to determine the influential factors, as well as, how the students' recommendation for the teachers differ by considering the gender constructs, respectively. While a large proportion of the comments that we analyzed (n = 85,378) was classified to be neutral and predominantly interpreted to be positive in nature considering the sentiments (76.4%), and emotional valence (88.2%) expressed by the students. The results of our analysis shows that for the students' comments which contain some kind of positive or negative sentiment (23.6%) and emotional valence (11.8%); that females students recommended the teachers taking into account the sentiments (p = .000). While the males appear to be slightly borderline in terms of emotions (p = .056) and sentiment (p = .077). Also, the EPDM + ML model showed to be a good predictor and efficient method in determining what the students' recommendation scores for the teachers would be, going by the high and acceptable values of the precision (1.00), recall (1.00), specificity (1.00), accuracy (1.00), F1-score (1.00) and zero error-rate (0.00) which we validated using the k-fold cross-validation method, with 63.6% of optimal k-values observed. In theory, we note that not only does the proposed method (EPDM + ML) proves to be useful towards effective analysis of SET and its implications within the educational domain. But can be utilized to determine prominent factors that influences the students' evaluation and recommendation of the teachers, as well as helps provide solutions to the ever-increasingly need to advance and support the teaching-learning processes and/or students' learning experiences in a rapidly changing educational environment or ecosystem.

Do sentiments of professors feedback change after migrating from in-person to online modalities? Pre- and during COVID-19 experience.

The COVID-19 pandemic forced higher education institutions to alter how they offer classes at an unprecedented pace. Due to ambiguities and lockdown restrictions, the transition phase negatively impacted students' and professors emotions. As a result, lecturers had to cope with unfamiliar online class teaching responsibilities and develop new teaching dynamics. This work aims to analyze one of the most adversely affected procedures of teaching, the written feedback provided to students. This research strives to explore whether the professors' feedback style altered from face-to-face education to online education on digital platforms during the COVID-19 restrictions. This exploratory-design study uses a mixed methodology to explain the subject on hand based on data collected from 117 undergraduate students. Sentiment lexicographers are utilized to address and identify the emotions expressed in the texts. Trust was the most frequent emotion expressed in face-to-face and online courses. It is also observed that the sentiments of joy and sadness changed significantly among online and face-to-face groups based on the professors' feedback style and approach. Finally, the study reveals that the joy words and the sadness words associated with the learning process are the most commonly utilized sentiments. This study suggests that when the courses transitioned from face-to-face to online learning, the professors' feedback changed to a more positive feeling that expressed appreciation for the students' work, encouraging them to strive for their complete academic development, and usher them into a better learning environment.

Automation Pyramid as Constructor for a Complete Digital Twin, Case Study: A Didactic Manufacturing System.

Nowadays, the concept of Industry 4.0 aims to improve factories' competitiveness. Usually, manufacturing production is guided by standards to segment and distribute its processes and implementations. However, industry 4.0 requires innovative proposals for disruptive technologies that engage the entire production process in factories, not just a partial improvement. One of these disruptive technologies is the Digital Twin (DT). This advanced virtual model runs in real-time and can predict, detect, and classify normal and abnormal operating conditions in factory processes. The Automation Pyramid (AP) is a conceptual element that enables the efficient distribution and connection of different actuators in enterprises, from the shop floor to the decision-making levels. When a DT is deployed into a manufacturing system, generally, the DT focuses on the low-level that is named field level, which includes the physical devices such as controllers, sensors, and so on. Thus, the partial automation based on the DT is accomplished, and the information between all manufacturing stages could be decremented. Hence, to achieve a complete improvement of the manufacturing system, all the automation pyramid levels must be included in the DT concept. An artificial intelligent management system could create an interconnection between them that can manage the information. As a result, this paper proposed a complete DT structure covering all automation pyramid stages using Artificial Intelligence (AI) to model each stage of the AP based on the Digital Twin concept. This work proposes a virtual model for each level of the traditional AP and the interactions among them to flow and control information efficiently. Therefore, the proposed model is a valuable tool in improving all levels of an industrial process. In addition, It is presented a case study where the DT concept for modular workstations underpins the development of technologies within the framework of the Automation Pyramid model is implemented into a didactic manufacturing system.

Excel Methods to Design and Validate in Microelectronics (Complementary Metal-Oxide-Semiconductor, CMOS) for Biomedical Instrumentation Application.

CMOS microelectronics design has evolved tremendously during the last two decades. The evolution of CMOS devices to short channel designs where the feature size is below 1000 nm brings a great deal of uncertainty in the way the microelectronics design cycle is completed. After the conceptual idea, developing a thinking model to understand the operation of the device requires a good "ballpark" evaluation of transistor sizes, decision making, and assumptions to fulfill the specifications. This design process has iterations to meet specifications that exceed in number of the available degrees of freedom to maneuver the design. Once the thinking model is developed, the simulation validation follows to test if the design has a good possibility of delivering a successful prototype. If the simulation provides a good match between specifications and results, then the layout is developed. This paper shows a useful open science strategy, using the Excel software, to develop CMOS microelectronics hand calculations to verify a design, before performing the computer simulation and layout of CMOS analog integrated circuits. The full methodology is described to develop designs of passive components, as well as CMOS amplifiers. The methods are used in teaching CMOS microelectronics to students of electronic engineering with industrial partner participation. This paper describes an exhaustive example of a low-voltage operational transconductance amplifier (OTA) design which is used to design an instrumentation amplifier. Finally, a test is performed using this instrumentation amplifier to implement a front-end signal conditioning device for CMOS-MEMS biomedical applications.

Time to move from past to future by making present creative: A Scoping review.

Creativity is one of the tools in the toolkit of modern day learners. Scientific progress cannot be accomplished without developing creative capacities of individuals. The Muslim countries, despite their historical achievement in different creative and innovative discoveries and positive encouragement by Islam towards creativity, have paradoxically gone down from their position. Creativity is one of the priorities of modern age due to its role in the progress of humanity and future. The education system is spoon-feeding our learners and killing creativity. The main aim of the current scoping review is to summarise and disseminate research findings pertaining to the evidence on the definition of the concept of creativity, its importance in education, role of teachers and learners in making creativity a part of their educational endeavours, and their implications. Moreover, it provides a broad and detailed outline of the topic and exploring more general questions rather than providing answer to limited questions. It will help to determine how important it is to foster creativity as educators.

Using a digital whiteboard for student engagement in distance education.

The COVID-19 pandemic transformed educational processes across different educational levels. As institutions and faculty members effort focused on guaranteeing academic continuity, the challenge was how to translate the learning methodologies applied in the classroom to virtual settings. A digital whiteboard was integrated to synchronous class sessions to complement the educational experience. During these sessions, students and teachers interacted to co-construct ideas and socialize learning. The objective of this study was to assess the impact of introducing a digital whiteboard in student engagement. The quantitative approach integrated student's perception through an online survey with 12 items. The results show that the students enjoyed the dynamic(4.56), students believe that the incorporation of digital whiteboard contributed to understanding abstract concepts(4.83), and perceived the resource contributed for class engagement(4.72). The design of educational projects that incorporate these resources translate to active learning dynamics which foster student engagement.

Technology-mediated teaching and learning process: A conceptual study of educators' response amidst the Covid-19 pandemic.

The COVID-19 pandemic has disrupted many areas of the human and organizational ventures worldwide. This includes new innovative technologies and strategies being developed by educators to foster the rapid learning-recovery and reinstatement of the stakeholders (e.g., teachers and students). Indeed, the main challenge for educators has been on what appropriate steps should be taken to prevent learning loss for the students; ranging from how to provide efficient learning tools/curriculum that ensures continuity of learning, to provision of methods that incorporate coping mechanisms and acceleration of education in general. For several higher educational institutions (HEIs), technology-mediated education has become an integral part of the modern teaching/learning instruction amidst the Covid-19 pandemic, when digital technologies have consequently become an inevitable and indispensable part of learning. To this effect, this study defines a hybrid educational model (HyFlex + Tec) used to enable virtual and in-person education in the HEIs. Practically, the study utilized data usage report from Massive Open Online Courses (MOOCs) and Emotions and Experience Survey questionnaire in a higher education setting for its experiments. To this end, we applied an Exponential Linear trend model and Forecasting method to determine overall progress and statistics for the learners during the Covid-19 pandemic, and subsequently performed a Text Mining and Univariate Analysis of Variance (ANOVA) to determine effects and significant differences that the teaching-learning experiences for the teachers and students have on their energy (learning motivation) levels. From the results, we note that the hybrid learning model supports continuity of education/learning for teachers and students during the Covid-19 pandemic. The study also discusses its innovative importance for future monitoring (tracking) of learning experiences and emotional well-being for the stakeholders in leu (aftermath) of the Covid-19 pandemic.

Effect of Surgical Expertise on Biomechanical Properties of Sutures After Abdominal Wall Closure.

BACKGROUND: Despite preventive methods and careful surgical technique, surgical site infection and incisional hernias are of main concern after the closure of surgical incisions and keep haunting abdominal wall wound healing. The aim of this study is to find how surgical expertise level modifies biomechanical properties of sutures commonly used in abdominal wall fascial closure (polypropylene, polyglactin 910, polydioxanone). MATERIALS AND METHODS: Surgery residents with different experience levels performed abdominal wall fascial closure in swine models with the previously mentioned suture materials. A standardized technique was used. Sutures were removed, and a tensile stress test was performed on the removed sutures. A total of 81 abdominal fascial closures were achieved. Time, extension, maximum tensile force (Ftmax), and maximum stress were measured and analyzed. RESULTS: The results of the polydioxanone stress test present a trend in three variables: extension, tensile force, and stress. The trend shows higher medians in the expert group and lower medians in the novice group. While using polypropylene sutures, medians in the expert group are the highest; however, a trend is not observed. Polyglactin 910 sutures have nonspecific behavior among the different experience groups and variables. Polypropylene is the material with the lowest Ftmax tested and fails at 42.64 (IQR 40.98-44.89) N. Regarding the elastic properties of the material, polyglactin demonstrates the least extension of all sutures tested, with a 14 (IQR 13.33-14.83) mm extension. This study demonstrates that polydioxanone has a superior Ftmax compared with polypropylene and has a superior extension at failure properties compared with polyglactin, confirming that polydioxanone could be the suture of choice used for abdominal wall fascial closure. CONCLUSIONS: Study results do not show statistically significant differences regarding the impact of the experience level of different general surgery residents in the biomechanical properties of sutures used in abdominal wall fascial closure.