Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Investigating Factors That Influence College Students' Online Continuous Learning Intention during COVID-19 Epidemic

This study aims to explore university students' continuous intention toward online learning during COVID-19 pandemic. A total of 120 students enrolled in online learning were surveyed to collect their perception of an extended model by adding task value to the expectation-confirmation model. Structural equation modeling was employed to verify the hypotheses proposed in this study. The results indicated that task value and technology usefulness were significant predictors of students' continuous intention toward online learning. More specifically, technology usefulness had a direct impact on students' continuous intention, while students' perceived task value played an indirect role in the prediction of their continuous intention. However, the impacts of both confirmation and satisfaction were not statistically significant on students' continuous intention. The results suggest that practitioners and researchers should pay special attention to the technological usefulness of online learning environments and task value, especially task value, in order to enhance students' retention of online learning. This study would contribute to implications to better design and implement online learning. © 2023 IEEE.

Impact of e-Learning in Education Sector: A Sentiment Analysis View

The COVID-19 condition had a substantial impact on the education sector, corporate sector and even the life of individual. With this pandemic situation e-learning/distance learning has become certain in the education sector. In spite of being beneficial to students and teachers, its efficacy in the education domain depends on several factors such as handiness of ICT devices in various socio economic groups of people and accessible internet facility. To analyze the effectiveness of this new system of e learning Sentiment Analysis plays a predominant role in identifying the user's perception. This paper focus on identifying opinions of social media users i.e. Twitter on the most prevailing issue of online learning. To analyze the subjectivity and polarity of the dynamic tweets extracted from Twitter the proposed study adopts TextBlob. As Machine Learning (ML) models and techniques manifests superior accuracy and efficacy in opinion classification, the proposed solution uses, TF-IDF (Term Frequency-Inverse Document Frequency) as feature extraction technique to build and evaluate the model. This manuscript analyses the performance of Multinomial Naive Bayes Classifier, DecisionTreeClassifier, SVC and MLP Classifier with respect to performance measure as Accuracy. © 2022 IEEE.

Quizzes and Eggs: Exploring the Impact of Course Design Elements on Students' Engagement

In this paper, we explored the impact of course design elements that aim to support and sustain students' engagement during a 12-week online course. The course we analyzed targeted higher education, master-level students of Computer Science and Educational Technologies, and took place fully online during the COVID-19 pandemic. The course was facilitated by a Learning Management System (LMS), and due to the circumstances, the instructor's primary goal was to motivate students to actively participate during the course duration. To that end, the instructor implemented a course design focused on integrating elements such as interactive activities, short quizzes, hidden "easter eggs,” and real-time webinars. To study the impact of these elements on students' activity, we carried out an exploratory analysis of students' activity as recorded by the log files of the LMS and the qualitative feedback that students provided to the instructor. Our results suggest that the course design supported sustaining students' engagement. The level of students' activity varied for the learning materials and resources, but we confirmed a high usage of the quizzes over the course duration. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

Through the Lens of Students: MS Teams as a Sustainable Pedagogical Tool

The COVID-19 pandemic has highlighted the need for higher education institutions to modernize and embrace the post-digital age. This study evaluates students' perspectives of utilizing MS Teams as a means of facilitating remote learning during the pandemic. The Technology Acceptance Model (TAM) was employed as the theoretical framework to examine students' views on self-efficacy, facilitating conditions, ease of use, usefulness, and intention to use. The results showcase positive views of MS Teams, with self-efficacy rated the highest among the five constructs, followed by ease of use, facilitating conditions, intention to use, and usefulness. Additionally, no significant differences were found in students' perceptions based on gender. MS Teams has proven to be a successful platform for delivering online learning and communicating, bridging the divide of distance and time in teaching and learning. As discussions about the future of higher education in the post-pandemic world have commenced among academia and university officials, it is crucial to consider the impact of COVID-19 on student learning and provide suggestions for a more sustainable and effective post-pandemic education. © 2023 IEEE.

Nurturing Social Presence in a Blended Learning Environment

The ambition of this development study is to explore the opportunity to put the knowledge gained during the COVID-19 pandemic into practice in a blended, post-COVID, learning environment. The focus is to explore how a combination of digital and face-to-face activities may allow for fostering social presence among undergraduate students. The Social Presence model and the five elements of Affective Association, Community of Cohesion, Instructor Investment, Interaction Intensity, and Knowledge and Experience, encompass the theoretical framework of the study. The contextual setting is the first course of The Marketing Programme at Linnaeus University in Sweden, a bachelor program with a 50% Swedish intake and 50% international intake. Given the diverse background of the students in this course, challenges are typically encountered in relation to community building. Empirical data was collected during the fall of 2022 among the enrolled students using an online questionnaire. While the results from this study should be seen as preliminary, they offer an inspiring glimpse of how to nurture social presence in a blended learning environment. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Engineering in a Crisis: Observing Students' Perceptions of Macroethical Responsibilities during Pandemics and Natural Disasters

When the ethical responsibilities of engineers are discussed in classrooms, the focus is usually on microethics, which concentrates on individual decision-making, rather than macroethics, that addresses broad societal concerns. Pandemics (e.g., COVID-19) and natural disasters (e.g., hurricanes, derechos) have presented unique opportunities to observe engineering macroethical responsibilities, because unjust social, economic, and environmental systems have been brought to the forefront amidst the responses (e.g., inequitable transportation access). In this paper, we consider pandemics and natural disasters through the lens of engineering macroethics, aiming to understand students' perceptions about the macroethical responsibilities of engineers. In the fall of 2020, we deployed a survey to undergraduate engineering students at two universities (n=424). Students were asked to discuss what they perceived to be the role of engineering professionals in response to the global COVID-19 pandemic and natural disasters. We used a qualitative content analysis to explore the macroethical responsibilities mentioned in students' responses. Many of these responses include considerations of infrastructure resilience, resource distribution, and community equity. Logit models were used to identify which sociodemographic factors were associated with responses that included macroethical responsibilities, revealing engineering major (specifically, civil engineering), employment status, gender identity, and family size, among others as significant factors. The implications from this study include recommendations on curricular content, and identifying which student sociodemographic groups would especially benefit from macroethical content in coursework. © 2023 American Society of Civil Engineers.

A Predictive Model for Assessing Satisfaction with Online Learning for Higher Education Students During and After COVID-19 Using Data Mining and Machine Learning Techniques: A Case of Jordanian Institutions

Higher education institutions confronted an escalating unexpected pressure to rapidly transform throughout and after the COVID-19 pandemic, by replacing most of the traditional teaching practices with online-based education. Such transformation required institutions to frequently strive for qualities that meet conceptual requirements of traditional education due to its agility and flexibility. The challenge of such electronic learning styles remains in their potential of bringing out many challenges, along with the advantages it has brought to the educational systems and students alike. This research came to shed the light on several factors presented as a predictive model and proposed to contribute to the success or failure in terms of students' satisfaction with online learning. The study took the kingdom of Jordan as a case example country experiencing online education while and after the covid -19 intensive implementation. The study used a dataset collected from a sample of over "300” students using online questionnaires. The questionnaire included "25” attributes mined into the Knime analytics platform. The data was rigorously learned and evaluated by both the "Decision Tree” and "Naive Bayes” algorithms. Subsequently, results revealed that the decision tree classifier outperformed the naïve bayes in the prediction of student satisfaction, additionally, the existence of the sense of community while learning electronically among other reasons had the most contribution to the satisfaction. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

E-learning Challenges for Lecturers in Indonesia Higher Education Institutions

Since the Covid-19 outbreak in March 2020, e-learning has become a necessity. There may have been some uncertainty on how to perform e-learning properly because many educational institutions may not have expected the rapid change in learning style. This is especially true when it comes to offering an e-learning management system (LMS). This study aims to examine the affordances of higher education institutions in Indonesia in conducting the e-learning during the pandemic. To understand more about the adoption of e-learning in their individual institutions, we conducted an online survey towards 100 university lecturers from several cities. The results revealed that 79% of the participants used a specially built LMS in their universities, while the rest still used commercially built LMS like Moodle. Then, 82% of the participants prefer blended learning model which combined face-To-face and e-learning models, and 46% of them wanted to have a fifty-fifty division between face to face and e-learning. As for the challenges, no interaction with students was deemed as the most disturbing challenge for the lecturers. The results imply that e-learning will continue to be implemented in Indonesia, regardless of the condition of the pandemic. Thus, universities should provide an e-LMS that can cater all the e-learning needs, while lecturers should also equip themselves with pedagogical as well as technological skills to face the e-learning challenges. © 2023 IEEE.

Design Experience of Laboratory Practices in COVID Times

The development of laboratory practices is necessary for training mechatronics engineering students because they must learn in scenarios that allow checking the theories reviewed in class and implementing their solutions to real-world challenges posed in a course. Unexpectedly, the COVID pandemic caused a rethinking of how to develop the laboratory as a form of teaching, looking for online alternatives using simulation platforms, portable instruments, and 3D printing to design prototypes. This work presents the experience of two online laboratory practice activities in two mechatronics engineering subjects, which allowed students to complement their training without the risk of contagion, develop the planned competencies, and acquire skills in this form of teaching. © 2023 IEEE.

Android Based Smart Appointment System (SAS) for Booking and Interacting with Teacher for Counselling

An appointment system is going to be popular nowadays. The necessity of these types of systems is increasing day by day specially in education sector. Worldwide COVID-19 pandemic provoke the demand of these types of application. In this research paper, an Android-based appointment is built for booking an appointment and communicating with the teacher. To use this system both student and teacher have to an android device with connection of the internet. A single android application will be used for both types of users. Students can get the information of all teachers and book an appointment with teachers and teachers can accept or decline this appointment. Java programming language is used for this system and Google's Firebase is used for the database. In addition, the modern coding Architecture pattern MVVM (Model- View-View Model) followed to build this system. Hopefully, this system saves valuable time and makes the teacher-student interaction journey easier. © 2023 IEEE.

Comparison of Predictive Machine Learning Models to Predict the Level of Adaptability of Students in Online Education

With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), K-Nearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as accuracy, specificity, sensitivity, F1 count and precision, have been used to evaluate the performance of each model. The results have shown that all five models can provide optimal results in terms of prediction. For example, the RF and XGB models presented the best performance with an accuracy rate of 92%, outperforming the other models. In consequence, it is suggested to use these two models RF and XGB for prediction of students' adaptability level in online education due to their higher prediction efficiency. Also, KNN, SVM and LR models, achieved a performance of 85%, 76%, 67%, respectively. In conclusion, the results show that the RF and XGB models have a clear advantage in achieving higher prediction accuracy. These results are in line with other similar works that used ML techniques to predict adaptability levels. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Impacts of the COVID-19 Pandemic on Sub-Saharan African Third-Year Students Studying Computer Science and Information Technology

Low vaccination rates, inferior-quality vaccines, limited testing, and a lack of funding forced many institutions in Sub-Saharan Africa into online-learning-only environments for two years during the COVID-19 pandemic. Instructors scrambled to put classes online. Only in 2022 did some face-to-face classes resume. Unforeseeable and unprecedented circumstances forced university personnel to function with reduced budgets and without regard for the return to in-person classes. We taught, studied, and analyzed a cohort of third-year Sub-Saharan African students who spent their first two years of studies online. We describe the struggles they faced and what can be done to make up for their shortcomings and missed opportunities. We quantify the shortcomings through focus groups, an analysis of what parts of an accredited program would have fallen short, interviews, and through anecdotal evidence. Our findings can help those who suffered a similar fate. These observations can be applied to non-STEM disciplines. © 2023 IEEE.

Augmented, Virtual and Immersive Reality as Learning Support in TEC21 Educational Model

Virtual, augmented, and immersive reality opens a world of possibilities in education by allowing students to recreate authentic situations, such as operating machinery, assembling a product, or training tool handling, to mention a few. In the TEC21 educational model, the core is the challenge: A project with a real-world challenge assigned by the training partner results in students offering solution proposals.The trigger that accelerated the development of virtual, augmented, and immersive reality activities in distance learning was COVID-19 confinement. During this, these technologies recreated the laboratory and its facilities' learning through augmented reality (AR) and virtual reality (VR) experiences.Using these technologies in the classroom allows students to achieve a great learning experience and develop skills for postgraduate studies and professional futures.Furthermore, now that we have returned to our physical facilities and laboratories, we can accelerate the learning obtained at the training partners' facilities, recreating processes and machinery through these immersive technologies and a hybrid experience for our students.The present research shows the activity learning design process and the statistical treatment of the data to provide continuous feedback during the activity;we examine the three transcendental variables in the educational process: The learning (academic rigor), the development of competencies, and the involvement or immersion of the students in the classroom. © 2023 IEEE.

Social Media Acceptance and e-Learning Post-Covid-19: New factors determine the extension of TAM

The key objective of our study involves devising a conceptual model for estimation of social media acceptance by students for effectively accomplishing their educational and academic goals. Factors e.g., perceived social capital, social influence, and perceived mobility that associated with student acceptance of social media were investigated, and integrated into the TAM model using the PLS-SEM. Data were collected through online survey (461 students) at UAE universities. The findings revealed that mentioned factors positively affected students' intention to use social media during their learning process. Respondents' behavioral intention were also linked to both the core and external constructs of the TAM. Important practical insights on technology acceptance in education were provided. © 2022 IEEE.

Practical Analyses on the Flipped Classroom Approach to Management Accounting Education

The outbreak of COVID-19 in 2020 has greatly changed teaching methods. With the normalization of the pandemic, teaching has gradually entered the era of a pandemic. Online teaching greatly limits the scope of management accounting courses and requires experimental operation and interaction between students and teachers. Therefore, mixed teaching has become a breakthrough in the development of management accounting teaching. This study mainly studies accounting students who adopt the mixed teaching method of the flipped classroom. Flipped classroom teaching mode can stimulate students' learning autonomy, adjust the traditional classroom teaching activities based on teachers' teaching into an interactive and exploratory new classroom, and play a positive role in the teaching development of management accounting courses. When designing a new paradigm of management accounting teaching based on the flipped classroom, by sorting out the five basic functions of management accounting prediction, decision-making, planning, control and evaluation, knowledge point learning, ability training, and independent exploration awareness run through three different periods before, during and after class. At the end of the semester, qualitative and quantitative analysis will be conducted in the form of questionnaires and interviews. The survey found that a large number of students recognized this teaching mode, and most students believed that this teaching mode improved their learning efficiency and ability to understand and master knowledge. The survey also found that the effect of rehearsal before class determines the learning level of the class to a certain extent. This means that we must focus on optimizing the rehearsal content and selecting more effective tools to ensure that students watch the rehearsal. © 2023 IEEE.

Comparing the Use of Virtual Models vs. Fieldwork in Developing Geomatics Skills in Undergraduate Engineering Education during the COVID-19 Pandemic

Undergraduate students enrolled in Civil Engineering, Architecture, and Urban Planning (CAU) must develop competencies in Geomatics and Topography (G&T) as part of their learning process. During this time, theoretical concepts are traditionally taught with field practice using specialized tools such as a theodolite, laser level, and total station. Due to the environmental restrictions of the COVID-19 pandemic, traditional field practice (TFP) was suspended, preventing access to equipment and study areas. The use of Information and Communication Technologies (ICT), such as Building Information Modeling (BIM) and Virtual Reality (VR), have been explored in the last decade for educational purposes. This paper studies the benefits of using these tools for developing G&T skills. This research aimed to assess students' learning outcomes using a traditional G&T teaching method and a new methodology based on Virtual Field Practice (VFP) for CAU students. The methodology provides a virtual study area for the CAU student by integrating point clouds derived from photogrammetry and terrestrial laser scanning. It also assesses their learning results and compares them against a control group using a validated instrument. Findings suggest continuing with fieldwork for a greater understanding and correct application of G&T concepts by students, and using virtual models as an efficient way to complement the acquisition of spatial information in the teaching-learning process. Until the publication of this article, we found no evidence in the literature at the undergraduate level of applying exercises like those proposed. © 2023 IEEE.

Measuring College Students' Satisfaction with the Teaching Effectiveness of Online English Courses Based on the IPA Model

The English learning ability and academic performance of pre-service teachers affect the future professional development of preschool and primary education teachers. The English course has been transferred to online due to COVID-19. Whether the practicability of e-learning is consistent with students' expectations primarily affect teaching effectiveness. A paired-sample t-test on the importance and satisfaction of online English learning effectiveness of pre-service teachers from freshmen to juniors at a private university revealed no significant difference in the overall importance and satisfaction. Then the coordinated system is constructed according to the Importance -Performance Analysis (IPA) to identify the critical indicators for improving the teaching effect of online courses. The results imply that network stability and teachers' timely responses to students' questions should be concentrated. In addition, students are pretty satisfied with the e-learning platform, teaching quality and management, which should be further maintained. The suggestions for improving the effectiveness of online English teaching in private universities are proposed accordingly. © 2023 IEEE.

Exploring the Factors Affecting Learners' Adoption Intention of MOOC in Higher Education during the COVID-19 Pandemic

The COVID-19 epidemic has had a huge impact on all education systems throughout the world. The lockdown policy caused higher education to find alternative teaching solutions to serve students' needs. Consequently, MOOCs have become a fascinating solution for several universities. However, the evidence from existing research still needs to be better understood by significant factors to support learners during COVID-19. This study examined the factors influencing students' adoption intention of MOOCs in developing countries during the coronavirus outbreak. This research reports the online survey of 1,384 university students enrolled in Thai MOOC as the primary part of the curriculum. The extended UTAUT2 was proposed and analyzed using a structural equation model to improve the comprehension of students' adoption intention on Thai MOOC. The result found Performance Expectancy, Hedonistic Motivation, Habit, and Local Language Support significantly influence MOOCs adoption intentions. While Habit was found to be the most significant to students' adoption intention, only gender had a moderating effect on the relationship between Habit and Adoption intention. The overall proposed model explained 84% of the variance in MOOC adoption intention of university students in Thailand during the COVID-19 pandemic. © 2023 IEEE.

Factors to improve online education: A study on the impact of COVID-19 on Delhi students

Due to the COVID-19 outbreak, people worldwide had to self-quarantine in their homes, resulting in the youth having to continue their education online. The lockdown and the effects of the pandemic impacted students' mental health, exhibiting frustration, stress, and depression. The latter is not ideal for a healthy learning environment, as it involves many coping mechanisms. This study analyzed a database compiling the habits of 1182 individuals in different age groups at various educational institutes in the Delhi-National Capital Region (NCR), India. It identifies factors leading to proposing recommendations to improve students' online education experiences worldwide and facilitate their learning while caring for their mental health. A CRISP-DM methodology was followed to build a model capable of predicting students' satisfaction ratings for online classes by analyzing the students' demographic information and daily habits. © 2023 IEEE.