The Impact of Disadvantage on Higher Education Engagement during Different Delivery Modes: A Pre- versus Peri-Pandemic Comparison of Learning Analytics Data

The pandemic forced many education providers to pivot rapidly their models of education to increased online provision, raising concerns that this may accentuate effects of digital poverty on education. Digital footprints created by learning analytics systems contain a wealth of information about student engagement. Combining these data with student demographics can provide significant insights into the behaviours of different groups. Here we present a comparison of students' data from disadvantaged versus non-disadvantaged backgrounds on four different engagement measures. Our results showed some indications of effects of disadvantage on student engagement in a UK university, but with differential effects for asynchronously versus synchronously delivered digital material. Pre-pandemic, students from disadvantaged backgrounds attended more live teaching, watched more pre-recorded lectures, and checked out more library books than students from non-disadvantaged backgrounds. Peri-pandemic, where teaching was almost entirely online, these differences either disappeared (attendance and library book checkouts), or even reversed such that disadvantaged students viewed significantly fewer pre-recorded lectures. These findings have important implications for future research on student engagement and for institutions wishing to provide equitable opportunities to their students, both peri- and post-pandemic.

Flipped online approach with learning analytics for supporting higher education students' learning. Course feedback results

Using learning analytics and dispositional learning analytics in teaching is difficult. Examples of their use are required for higher educational institutions and teachers. In this paper, we present a flipped learning approach in online settings (due to COVID-19) with particular emphasis on learning analytics and dispositional learning analytics. For this, an understanding of flipped approaches (i.e., flipped classroom and flipped learning) as well as the role of technology in the teaching context is required and presented. The role of technology includes (1) a digital learning system, (2) a conferencing system, (3) the collection and use of learning analytics and dispositional learning analytics, and (4) content-specific technology. Additionally, our aim is to present students' course feedback results from quantitative research methods course practices (2020, 2021) for preservice teachers (i.e., students;N = 70). The content is highly challenging for these students, causing fear, frustration, anxiety, and boredom. Generally, the results for pedagogy were positive, but the results of students' learning perceptions were lower. Based on the approach and results, discussion with new insights is provided. © 2022 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)

Who's Cheating? Mining Patterns of Collusion from Text and Events in Online Exams

As the COVID-19 pandemic motivated a shift to virtual teaching, exams have increasingly moved online too. Detecting cheating through collusion is not easy when tech-savvy students take online exams at home and on their own devices. Such online at-home exams may tempt students to collude and share materials and answers. However, online exams' digital output also enables computer-aided detection of collusion patterns. This paper presents two simple data-driven techniques to analyze exam event logs and essay-form answers. Based on examples from exams in social sciences, we show that such analyses can reveal patterns of student collusion. We suggest using these patterns to quantify the degree of collusion. Finally, we summarize a set of lessons learned about designing and analyzing online exams.

Intelligent Prediction-Intervention approach to Support Students' Success in Web-based Learning Environments: A Case Study in Higher Education

Due to the COVID-19 pandemic, the demand for distance learning has significantly increased in higher education institutions. This type of learning is usually supported by Web-based learning systems such as Massive Open Online Courses (Coursera, edX, etc.) and Learning Management Systems (Moodle, Blackboard-Learn, etc.). However, in this remote context, students often lack feedback and support from educational staff, especially when they face difficulties or challenges. For that reason, this work presents a Prediction-Intervention approach that (a) predicts students who present difficulties during an online learning course, based on two main learning indicators, namely engagement and performance rates, and (b) offers immediate support to students, tailored to the problem they are facing. To predict students' issues, our approach considers ten machine learning algorithms of different types (standalone, ensemble, and deep learning) which are compared to determine the best performing ones. It has been experimented with a dataset collected from the Blackboard-Learn platform utilized in an engineering school called ESIEE-IT in France during 2021-2022 academic year, showing thus quite promising results. © 2022 IEEE.

Structured Cabling Development in 3D Virtual Learning Environment en OpenSim

Virtual Learning Environments in 3 Dimensions can be considered a valuable tool in Education, especially in this context of Virtual Education due to the COVID-19 Pandemic, which is why after determining some problems related to this type of platform. That is why the present investigation has been guided under the Design Science Research Methodology, where the investigation has to go through the 6 phases iteratively: Identification of the problem, Design, Development, Demonstration, Evaluation, and Communication of the results., that is why the present platform developed in OpenSim has been used for the construction of the present virtual world. The results have been evaluated using the Technologies Acceptance Model methodology. Finally, it can be determined that the results indicate that this type of platform can be considered by students as a useful tool and also that there is no greater resistance to its use due to its ease of use. © 2023, Ismail Saritas. All rights reserved.

Multi-stakeholder Perspective on the Gap Between Existing Realities and New Requirements for Online and Blended Learning: An Exploratory Study

Online and blended learning are teaching modalities that have become very popular and widespread all over the world. Applying these modalities requires specific knowledge as well as an appropriate technological infrastructure. The COVID-19 pandemic caused an important online migration in most educational institutions. In this regard, existing literature covers issues such as the impact, the challenges, the tools, the problems, etc. What could also be interesting is to understand students', teachers' and administrative staff's perspectives about how blended and online learning were developed and how it is going to be applied in the future. With this in mind, the ILEDA project team has carried out an exploratory study, which takes into account these three collectives in four different European universities. From the study, it is possible to see that the institutions and their lecturers and staff were probably not prepared for the online migration and the possibilities they had were quite different from students' expectations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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.

A Web-Based Public Health Intervention for Addressing Vaccine Misinformation: Analysis of Learner Engagement and Shift in Hesitancy to Vaccinate.

Web-based public health interventions can be a useful tool for disseminating evidence-based information to the public. However, completion rates are traditionally low, and misinformation often travels at a faster pace than evidence-based sources. This study describes the design of a web-based public health intervention to address COVID-19 vaccine hesitancy. A quasi-experimental approach was used in which a validated instrument, the Adult Vaccine Hesitancy Survey, was given to learners both pre and post intervention to observe any change in attitude towards vaccination. Our pilot observed a small positive shift in vaccine hesitancy and experienced higher than average completion rates. By integrating motivational learning design into public health interventions we increase the likelihood that learners finish the entire intervention, creating greater chance for positive behavior change.

Who's Cheating? Mining Patterns of Collusion from Text and Events in Online Exams

As the COVID-19 pandemic motivated a shift to virtual teaching, exams have increasingly moved online too. Detecting cheating through collusion is not easy when tech-savvy students take online exams at home and on their own devices. Such online at-home exams may tempt students to collude and share materials and answers. However, online exams' digital output also enables computer-aided detection of collusion patterns. This paper presents two simple data-driven techniques to analyze exam event logs and essay-form answers. Based on examples from exams in social sciences, we show that such analyses can reveal patterns of student collusion. We suggest using these patterns to quantify the degree of collusion. Finally, we summarize a set of lessons learned about designing and analyzing online exams. Copyright: © 2021 The Author(s).

Machine Learning-Based Hybrid Ensemble Model Achieving Precision Education for Online Education Amid the Lockdown Period of COVID-19 Pandemic in Pakistan

Institutions of higher learning have made persistent efforts to provide students with a high-quality education. Educational data mining (EDM) enables academic institutions to gain insight into student data in order to extract information for making predictions. COVID-19 represents the most catastrophic pandemic in human history. As a result of the global pandemic, all educational systems were shifted to online learning (OL). Due to issues with accessing the internet, disinterest, and a lack of available tools, online education has proven challenging for many students. Acquiring accurate education has emerged as a major goal for the future of this popular medium of education. Therefore, the focus of this research was to identifying attributes that could help in students' performance prediction through a generalizable model achieving precision education in online education. The dataset used in this research was compiled from a survey taken primarily during the academic year of COVID-19, which was taken from the perspective of Pakistani university students. Five machine learning (ML) regressors were used in order to train the model, and its results were then analyzed. Comparatively, SVM has outperformed the other methods, yielding 87.5% accuracy, which was the highest of all the models tested. After that, an efficient hybrid ensemble model of machine learning was used to predict student performance using NB, KNN, SVM, decision tree, and logical regression during the COVID-19 period, yielding outclass results. Finally, the accuracy obtained through the hybrid ensemble model was obtained as 98.6%, which demonstrated that the hybrid ensemble learning model has performed better than any other model for predicting the performance of students.

Digital Teaching and Learning in Higher Education: Developing and Disseminating Skills for Blended Learning

This book explores the challenges and opportunities faced by universities as they move to digital education. The COVID-19 pandemic as well as students' increasing levels of comfort with digital technology has accelerated the digitalization of learning and teaching, even among teachers who are less confident. The editor and contributors ask how successful digital teaching materials can be developed, what are the unique benefits of this type of teaching and how it can be linked with industry and society so as to better aid the development of student learning. The book maintains that the digital educator should be able to orchestrate diversity in the supply of digital teaching materials and project-based learning to meet the needs of students and prepare them for their future careers. Leonid Chechurin is Professor for Industrial Engineering and Management Unit of School of Engineering Science or Lappeenranta-Lahti University of Technology, FINLAND. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Replies to commentaries on IDC Theory in practice

We thank the seven scholars who have provided commentaries to our paper on the Interest-Driven Creator (IDC) experimental school in Taiwan. We provide replies to their commentaries in the interest of continuing the productive discussions that we hope to see in the further pursuance and refinement of the IDC theories. We also use the opportunity here to provide replies to the commentaries written by Dillenbourg et al. (2019) and Roschelle and Burke (2019), specifically written for the original IDC paper (Chan et al., 2018). © The Author(s). 2023.

An Empirical Investigation into Students' Actual Use of MOOCs in Saudi Arabia Higher Education

Massive Open Online Courses, or MOOCs, are a type of educational innovation where enrollment in the courses given is free and available online. The MOOCs course selection is extensive and may accommodate hundreds or thousands of students at once. The current study, however, aims to look into how the academic self-efficacy of real MOOC users affects learning engagement and perseverance in higher education in Saudi Arabia. This study added the Technology Acceptance Model (TAM) to social cognitive theory. Therefore, the primary goal is to create a new model by examining the variables that affect the perceived utility and perceived service quality, as well as the students' general perceptions of MOOCs that are really used. Therefore, this research used a quantitative approach and distributed the questionnaire online through a Google Form. It collected data from 276 King Saud University students and used it to test the hypothesized correlations using structural equation modeling (SEM-PLS). The study's findings showed that perceptions of perceived benefits and service quality consistently had a significant influence on social interaction, influence, networks of support, and social identity. A further finding was that reported utility and perceived service quality have always been significantly influenced by academic self-efficacy in actual MOOC use. Because of this, learning engagement and perseverance in Saudi Arabian higher education are significantly impacted by the academic self-efficacy of real MOOC users. According to the findings, MOOC programs generally have a positive influence on the kingdom's higher education system. As a result, it is almost certain that this research model will assist university decision-makers in determining whether or not MOOC usage is prevalent at Saudi educational institutions.

Sustainable Social Development through the Use of Artificial Intelligence and Data Science in Education during the COVID Emergency: A Systematic Review Using PRISMA

In this paper, we aimed to investigate how sustainable development can be involved in educational contexts that use new trends in technology such as Artificial Intelligence (AI) and Data Science (DS). To achieve this goal, we conducted a documentary Systematic Review using PRISMA research to find and analyze applications of sustainable development in these aforementioned contexts. In the results, we explain how some applications of both AI and DS, including Big Data and Learning Analytics among others, offer alternatives to substantially improve the educational process by allowing either education to be personalized, learning to be predicted, or even possible school dropouts to be predicted. We found that the COVID emergency sped up the introduction of these technologies in educational environments. Nevertheless, the inclusion of new technologies to develop better processes in education is still in progress and will continue to grow. In conclusion, we identified and analyzed some of the main applications found in the literature regarding new computing technologies. AI and DS have introduced new learning and teaching methods to solve different context problems that promote sustainable development while making educational environments more dynamic. On the other hand, we observed that the divide in many countries will continue due to their economic and technological situations. We describe some of the challenges that the incorporation of these technologies will bring in the near future.

Learning Analytics Overview: Academic Approach and Machine Learning Possibilities

In the Covid-19 era, traditional lecture-based teaching has been undergoing changes in learning design, learners' engagement, and technology integration. Online learning has become an integral part of education around the globe due to its flexibility in learning with respect to place and time. These online courses are available to larger audiences and enable students to have more freedom over the study process. However, freedom also means that instructors have less control to keep students making progress on the course. The flexibility of online courses is encouraging the students to enrol with a few clicks but most of these students are dropping out due to losing interest in the course contents within a few weeks. On the contrary, online learning produces large amounts of data that can be used to follow the learning process and give useful insights for both teachers and students. Learning analytics algorithms utilize these data to identify the factors and parameters that can explain learners' dropout rate, learning performance, as well as suggest possible actions to intrigue learners' active engagement. To conduct further research on the parameters affecting learners' participation in an online course, it is essential to find out the previous research works, best practices, and research trends of learning analytics. In the scope of this work, the authors formulated a search query to generate a pool of most relevant papers from the Scopus database and, hence, identified seven clusters which illustrate the landscape of the learning analytics domain. The authors also employed the Latent Dirichlet allocation (LDA) topic modeling algorithm which is a form of text data mining and statistical machine learning approach to compare the similarities with the clusters generated as a part of literature review. The authors further analyzed the papers in each cluster to identify the parameters which are significant to build a predictive model on learners' dropout rate. This work not only provides a baseline to conduct further research to find out the parameters affecting learners' retention rate but also introduces a systematic methodology to validate the findings of the literature review with a data-driven algorithmic approach. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Leading Edge or Bleeding Edge: Designing a Framework for the Adoption of AI Technology in an Educational Organization

Adopting Artificial Intelligent Technology in an Educational Organization is often problematic due to many internal and external environmental reasons, and often fails to attain the desired goals. This study aims to design a framework for adopting AI technology in the education sector. Most of the research focuses on the acceptance of a particular technology and ignores the study of what else is needed for a technology acceptance. The framework in this study provides a step-by-step process of the Technological Transformation of an organization never designed before. We recommend that before making any technological changes in an organization, generally and in the educational organization particularly, the processes must be followed for the successful and meaningful adoption of AI technology.

Predictive Model for Clustering Learning Outcomes Affected by COVID-19 Using Ensemble Learning Techniques

The influence of COVID-19 has caused a sudden change in learning patterns. Therefore, this research studied the learning achievement modified by online learning patterns affected by COVID-19 at Rajabhat Maha Sarakham University. This research has three objectives. The first objective is to study the cluster of learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. The second objective is to develop a predictive model using machine learning and data mining technique for clustering learning outcomes affected by COVID-19. The third objective is to evaluate the predictive model for clustering learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. Data collection comprised 139 students from two courses selected by purposive sampling from the Faculty of Information Technology at the Rajabhat Maha Sarakham University during the academic year 2020-2021. Research tools include student educational information, machine learning model development, and data mining-based model performance testing. The research findings revealed the strengths of using educational data mining techniques for developing student relationships, which can effectively manage quality teaching and learning in online patterns. The model developed in the research has a high level of accuracy. Accordingly, the application of machine learning technology obviously supports and promotes learner quality development. © 2023 The Author(s). Open Access - This article is under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

The Effectiveness of Using H5P for Undergraduate Students in the Asynchronous Distance Learning Environment

As the COVID-19 pandemic caused many schools to go online, asynchronous distant learning has become popular. One of the main challenges of asynchronous distance learning is keeping students engaged and motivated, as they do not have the same engagement with their peers and teachers as in traditional face-to-face learning environments. HTML 5 package (H5P) is an interactive learning tool that has the potential to fill this need due to its numerous immediate interactive features, such as interactive videos, pop quizzes, and games during media playback. This study investigates the effectiveness of using H5P and Moodle in asynchronous distance learning environments for undergraduate students. The data collection methods included pre-and post-surveys for Moodle and H5P and the questions related to the student perspectives towards H5P features. The technology acceptance model (TAM) is employed to find student satisfaction. The results of this study suggest that both the H5P and Moodle could be valuable tools for making E-learning more effective. The interactive and engaging nature of H5P can provide students with a more enjoyable and effective learning experience, helping to keep them motivated and engaged throughout their studies.

Videoconferencias interactivas en educación superior: una propuesta de mejora para el aprendizaje y la participación

En educación superior se han implementado sesiones híbridas que pueden permanecer más allá de los tiempos de emergencia docente provocados por la pandemia de COVID-19. Esta modalidad mixta presenta varias incertidumbres entre docentes y estudiantes. Especialmente en el primer caso surgen cuestiones sobre cómo diseñar las secuencias didácticas, qué plataformas utilizar, entre muchas otras. Esta investigación tiene como objetivo estudiar las percepciones de docentes y estudiantes con relación a las videoconferencias sincrónicas interactivas, llevadas a cabo por 5 docentes de 3 asignaturas diferentes de la Facultad de Educación de la Universidad de Barcelona. Las herramientas utilizadas son Bb Collaborate para gestionar la videoconferencia y Nearpod para presentar los contenidos y actividades que favorezcan la interacción entre estudiantes y con los contenidos. Se ha realizado un análisis cualitativo de la percepción de los docentes sobre el diseño y desarrollo de las sesiones mediante autoinforme y de una muestra de 82 estudiantes a partir de las analíticas de aprendizaje y de un formulario en línea con preguntas abiertas. Los resultados indican que esta metodología es adecuada con la forma de aprender de los estudiantes, aumentando su participación. Los docentes no aprecian grandes diferencias respecto a la preparación de la secuencia didáctica de clases expositivas, aunque el nivel de competencia digital docente sea un factor clave. Estudiantes y docentes valoran positivamente la información de las analíticas de aprendizaje de la plataforma. Estos resultados positivos pueden alentar a los docentes a usar este tipo de enfoques pedagógicos y herramientas basadas en la interactividad.Alternate :In Higher Education, hybrid sessions have been implemented in higher education that may remain in place beyond the times of teaching emergency caused by the COVID-19 pandemic. This mixed modality presents several uncertainties among teachers and students. Especially in the first case, questions arise about how to design the lesson sequencing, which platforms to use, among many others. This research aims to study the perceptions of teachers and students about interactive synchronous videoconferences, carried out by 5 coordinating teachers of 3 different subjects of the Faculty of Education of the University of Barcelona. The tools used are Bb Collaborate to manage the videoconference and Nearpod to present the learning content and the interactive activities that favor the interaction among students and with the contents. A qualitative analysis of the teachers' perception of the design and development of the sessions has been carried out through self-reporting and a sample of 82 students from the learning analytics and an online form with open questions. The results indicate that this methodology is adequate with the students' way of learning, increasing their participation. The results indicate that the teachers do not appreciate significant differences with respect to the preparation of the sequencing lesson plan of expository classes, although the level of teachers' digital competence is a key factor. Students and teachers value positively the information in the learning analytics of the platform. These positive results may encourage teachers to use this type of pedagogical approaches and tools based on interactivity.