Time to Rethink: Educating for a Technology-Transformed World

Purpose We hope to provoke a conversation about preparing students for an uncertain future that unforeseeable technological innovations will transform in ways we cannot predict. The unprecedented disruption caused by the COVID-19 pandemic makes this an opportune time to reconsider all dimensions of education. Design/Approach/Methods We present information on how technology is transforming virtually every aspect of our lives and the threats we face from social media, climate change, and growing inequality. We then analyze the adequacy of proposals for teaching new skills, such as 21st-Century Skills, to prepare students for a world of work that is changing at warp speed. Findings Despite harbingers of a radically different future, most schools continue to operate much as they have for centuries, providing a one-size-fits-all education. Technology now enables an unprecedented degree of personalization. We can tailor learning opportunities to individual students' interests, talents, and potential with teachers serving as guides, resources, and critical friends. The Internet afford a cornucopia of learning opportunities—online courses, international experts, global collaborations, accessible databases, and libraries. Learning can occur virtually anywhere. Originality/Value The future depends on decisions we are making today about education. The value of this article is that we call for rethinking every component of education rather than considering each element independently.

Research on blended teaching reform based on computational thinking

The outbreak of COVID-19 has impacted traditional teaching methods in schools, and blended teaching in the post-pandemic has gradually become a hot topic of research in higher education. Computational thinking, as one of the core literacies to be acquired in the 21st century, can help students realize the importance of computers as well as enable them to solve specific problems more effectively when facing real-life situations. The article takes the C language programming course as an example, analyzes the problems faced in teaching in the post-pandemic, introduces the concept of computational thinking and integrates it into all aspects of blended teaching design, pays attention to students' individual differences, and proposes a blended teaching model based on computational thinking and puts it into practice. The results show that this teaching model can improve students' learning performance, exercise students' computational thinking skills, and promote blended teaching reform and students' personalized development. © 2022 IEEE.

A Universal Design for an Adaptive Context-Aware Mobile Cloud Learning Framework Using Machine Learning

Mobile learning is becoming more and more popular today. It gained popularity recently due to the COVID-19 pandemic restrictions in 2020. However, to provide learners with appropriate educational materials in such a mobile environment, the characteristics and context of the learners must be considered. Therefore, in this paper, we propose a framework for providing an adaptive context-aware learning process considering a combination of student learning models and principles of Universal Design for Learning (UDL). The proposed system consists of components capable of detecting changes in context and adapting the way the application responds and behaves. The framework uses a machine-learning algorithm to predict learners' characteristics and follow UDL principles to deliver enriched user experience and location-aware content and activities. An online survey was conducted with 20 undergraduate students. We analyzed their levels of satisfaction with the proposed m-learning system. From the analyzed data, we noticed that the average rating values are close to 4.5, which indicates that the proposed m-learning system complies with UDL principles and provides an adaptive and localized learning environment, thus enhancing the efficiency of the learning process and experiences. The study also investigated the impact of factors (i.e., noise level, physical activity, and location) on learners' concentration towards the learning process. The results show that these factors have a significant impact on the learner's concentration level. © 2023 River Publishers.

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.

Design and Development i-AVEN;GER as High-Tech Virtual Remote Teaching and Learning Platform with Experienced Based Learning and Self-regulated Learning Approaches in facilitating STEAM Education

With centuries-old lecture-based teaching methods, ingrained institutional biases, and obsolete classrooms, the slow pace of change in global education and academic institutions is lamentable. COVID-19, on the other hand, has acted as a catalyst for educational institutions worldwide to seek out novel solutions within a short or long timeframe. It is critical for countries to address the situation in such a way that the crisis fosters innovation and inclusion rather than exacerbating learning disparities. Schools are utilising distance learning programmes, educational apps, and platforms such as radio and the internet to reach students who live in remote areas. However, closing the so-called 'digital divide' - the divide between those with access to computers and the internet and those with limited or no access - is a difficult task. We propose in this paper the Interactive Accessible Virtual Education Network - Grand Educational Repository (i-AVEN$\vert$ GER), the world's first comprehensive futuristic hi-tech, comprehensive, and inclusive online structured learning platform with all types of interactivity, namely, educational app and portal. We've discussed the architecture and all of the associated functionalities thus far with recommended implementation. This proposed app and portal will be enhanced to ensure inclusivity for all people without regard for prejudice, as well as the incorporation of cutting-edge technology such as augmented reality, virtual reality, and artificial intelligence. © 2022 IEEE.

AI: new source of competitiveness in higher education

Purpose>This paper aims to survey the current landscape of artificial intelligence (AI) applications in higher education institutions (HEIs) and recommend future directions.Design/methodology/approach>This paper reviews the recent trends, showcases the applications and provides future directions through a review of current uses of AI in HEIs.Findings>The results of this study highlight successful applications of AI technologies in three main areas of college operation: student learning experience;student support;and enrollment management.Research limitations/implications>This review has important implications for early adopters of AI by HEIs in providing a competitive advantage. The limitation lies in the scope of the review. It is not comprehensive and does not cover other areas of college operations.Originality/value>This is the first review about AI in higher education. It is of value in building future research and serving as a framework for AI applications in HEI.

Acceptance of Pedagogical Agent (PA) enhanced eLearning communities by software engineering students in Southern Africa

Covid19 outbreak has seen eLearning becoming a viable alternative to the traditional face-to-face teaching globally. Software Engineering education has not been an exception to these changes. The use of multimedia enhanced eLearning communities is also on the increase in the teaching of software engineering. However, there is limited research on the acceptance of such technologies by African learners. Some of the multimedia being used to enhance these learning communities includes animated pedagogical agents (PAs) combining text. animation, audio, and video. Considering learner differences and aiming to achieve personalized learning, there is a need for institutions to understand how such technologies are being accepted by learners and the factors that influence the acceptance. This study focuses on the acceptance of pedagogical agent enhanced eLearning communities by Southern African learners in the teaching of Software Engineering. The aim of the study is to identify the factors that influence the acceptance of such communities. This will help eLearning designers to try and address the needs of learners in different contexts to achieve personalized learning. This study involved 137 software engineering students from South Africa and Zimbabwe who were being introduced to eLearning community enhanced with PAs. The unified theory of acceptance and use of technology2 (UTAUT2) was used in this study. The study revealed that only performance expectancy, and hedonic motivation constructs had an effect on behavioral intention to use these eLearning communities enhanced with PAs. © 2022 SEFI 2022 - 50th Annual Conference of the European Society for Engineering Education, Proceedings. All rights reserved.

Achieving Personalized Precision Education Using the Catboost Model during the COVID-19 Lockdown Period in Pakistan

With the emergence of the COVID-19 pandemic, access to physical education on campus became difficult for everyone. Therefore, students and universities have been compelled to transition from in-person to online education. During this pandemic, online education, the use of unfamiliar digital learning tools, the lack of internet access, and the communication barriers between teachers and students made precision education more difficult. Customizing models from previous studies that only consider a single course in order to make a prediction reduces the predictive power of the model because it only considers a small subset of the attributes of each possible course. Due to a lack of data for each course, overfitting often occurs. It is challenging to obtain a comprehensive understanding of the student's participation during the semester system or in a broader context. In this paper, a model that is flexible and more generalizable is developed to address these issues. This model resolves the problem of generalized models and overfitting by using a large number of responses from college and university students as a dataset that considered a broader range of attributes, regardless of course differences. CatBoost, an advanced type of gradient boosting algorithm, was used to conduct this research, and enabled the developed model to perform effectively and produce accurate results. The model achieved a 96.8% degree of accuracy. Finally, a comparison was made with other related work to demonstrate the concept, and the experimental results proved that the Catboost model is a viable, accurate predictor of students' performance. © 2023 by the authors.

A Study on the Learning Early Warning Prediction Based on Homework Habits: Towards Intelligent Sustainable Evaluation for Higher Education

Teachers need a technique to efficiently understand the learning effects of their students. Early warning prediction mechanisms constitute one solution for assisting teachers in changing their teaching strategies by providing a long-term process for assessing each student's learning status. However, current methods of building models necessitate an excessive amount of data, which is not conducive to the final effect of the model, and it is difficult to collect enough information. In this paper, we use educational data mining techniques to analyze students' homework data and propose an algorithm to extract the three main features: Degree of reliability, degree of enthusiasm, and degree of procrastination. Building a predictive model based on homework habits can provide an individualized evaluation of students' sustainability processes and support teachers in adjusting their teaching strategies. This was cross-validated using multiple machine learning algorithms, of which the highest accuracy was 93.34%.

Personalized learning and teacher stress: A narrative of middle school teachers' experiences during the COVID-19 pandemic

Personalized learning allows middle school teachers to design and adjust learning pathways for their students that recognize and respond to each student's interests, past achievements, and academic and social emotional needs. Teachers act as guides and partners in students' individualized educational journeys as they orchestrate differentiated learning options and timelines for their students within this instructional framework. The magnitude of the required time and effort to implement personalized learning can contribute to teachers' fluctuating levels of stress. This narrative case study sought to unveil suburban middle school teachers' unique experiences and perceptions about their levels of instructional autonomy, stress, and job satisfaction as they engaged in all aspects of personalizing learning for students. The teachers illustrated their experiences over several months through detailed revelations about specific locations, times, and their evolving emotions related to collegial and student interactions. The element of time, a central factor of the narrative, became an unexpectedly consequential influence in this study as it was undertaken during the COVID-19 global pandemic. The narrative resulting from the intimate reflections of the teachers when personalizing learning for students during the pandemic illuminates the teachers' typically cloaked lived experiences. The teachers' remarkable stories are delicately epitomized through three emergent themes: "It's all about the students," current stress is related to COVID-19 - not personalization, and "I love what I do.". (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Intelligent and Interactive Chatbot Based on the Recommendation Mechanism to Reach Personalized Learning

With the impacts of Covid-19 epidemic, e-learning has become a popular research issue. Therefore, how to upgrade the interactivity of e-learning, and allow learners to quickly access personalized and popular learning information from huge digital materials, is very important. However, chatbots are mostly used in automation, as well as simple occasions of general standard question and answer. But to solve the different problems of e-learners in the learning process, chatbots are used to filter the blind spots of learners and to provide further relevant information, so that e-learning can improve in efficiency and interactivity. This study utilizes AI, two-stage Bayesian algorithm, and crawler technology to provide customized learning materials according to learner's current learning situation. The experimental results show that this research system can indeed correctly understand and judge the blind spots of digital learners, and effectively find the relevant e-learning and video information. The accuracy rate reaches nearly 90%.

AI: new source of competitiveness in higher education

Purpose>This paper aims to survey the current landscape of artificial intelligence (AI) applications in higher education institutions (HEIs) and recommend future directions.Design/methodology/approach>This paper reviews the recent trends, showcases the applications and provides future directions through a review of current uses of AI in HEIs.Findings>The results of this study highlight successful applications of AI technologies in three main areas of college operation: student learning experience;student support;and enrollment management.Research limitations/implications>This review has important implications for early adopters of AI by HEIs in providing a competitive advantage. The limitation lies in the scope of the review. It is not comprehensive and does not cover other areas of college operations.Originality/value>This is the first review about AI in higher education. It is of value in building future research and serving as a framework for AI applications in HEI.

Learning analytics to support teachers in the challenge of overcoming the learning gaps in k-12 students

The emergency remote teaching caused by the covid-19 pandemic has potentiated the learning gaps of several students in Brazilian education, especially in the K-12 settings. Amidst the many challenges imposed by the pandemic, the adoption of digital tools in the school context has provided the generation of educational data, which can be collected and analyzed in order to provide evidence-based decision making, taking into account all the stakeholders in the teaching and learning process. Such decisions can provide for the personalization of learning, which aims to provide the student with educational resources that promote the building of weakened skills caused by learning gaps. The present thesis plan aims to present the work plan for the development of a Learning Analytics Dashboard tool for teachers in a basic education school in order to support data-driven pedagogical decision-making and to enable personalized monitoring of learning. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Personalized and Adaptive Context-Aware Mobile Learning: Review, challenges and future directions.

Due to the outbreak of COVID 19, digital learning has become the most efficient learning and teaching technique adopted across the world. The pervasiveness of Personalized and Adaptive Context-Aware Mobile Learning (PACAML) technologies is improving the academic performances of learners by providing an efficient learning platform that supports social interactivity, context sensitivity, connectivity, and individuality in a ubiquitous manner. Several studies have demonstrated the efficacy of PACAML in a modern and innovative educational environment. Based on the recent studies and development of mobile learning technologies, there is clearly a gap in the research that provides a comprehensive body of knowledge on PACAML. In this paper, a review has been conducted on the existing PACAML, analyzing the recent research and development progress using Kitchenham et al. (2009) for systematic reviews. The review was conducted on 25 papers which were selected using the PRISMA technique to put forward the quality criteria that are based on the research aims, objectives and knowledge relevant to the study of PACAML. The results identified the contextual information used in the PACAML studies, the infrastructural requirements of PACAML, the application of PACAML in functional educational settings and the major methodological approaches applied in the studies of PACAML. Finally, the paper presents challenges and future directions that will be of interest to researchers in the educational technologies in the context of PACAML.

Developing Thai Undergraduate Online Digital Citizenship Skills (DCS) Under the New Normal

The study aims to develop a model and assess the suitability of a digital citizenship skills (DCS) model for online teaching and learning for Thai undergraduate students. In-depth interviews and content analysis from seven Thai academic experts in 2020 were used for the analysis. The experts' questionnaires suitability was analyzed using propriety standards, utility standards, feasibility standards, and accuracy standards as outlined by the Joint Committee on Standards for Educational Evaluation (JCSEE). After that, descriptive statistics including the mean and standard deviation (SD) were used to assess the results from the five-level agreement scale used. The final DCS model consisted offive components. These included the learner (L), the instructors (I), the Internet, intranet, and extranet network components (I), the platform componentfor online teaching, and information and communication technology (ICT) enabled devices (P), and personalized learning (P) (LIIPP Model). Overall model suitability according to the experts ' input was judged to be at a very high level (mean = 4.61, SD = .51).

Video Playback Speed Influence on Learning Effect From the Perspective of Personalized Adaptive Learning: A Study Based on Cognitive Load Theory.

Following the COVID-19 pandemic, online learning has become a new mode of learning that students must adapt to. However, the mechanisms by which students receive and grasp knowledge in the online learning mode remain unknown. Cognitive load theory (CLT) offers instructions to students considering the knowledge of human cognition. Therefore, this study considers the CLT to explore the internal mechanism of learning under the online mode in an experimental study. We recruited 76 undergraduates and randomly assigned them to four groups in which they will watch videos at four different kinds of speed (1.0× or 1.25× or 1.5× or 2× speed). The study observed and analyzed how video playback speed affected students' learning and cognitive load to obtain the following results: (1) Video playback speed significantly influenced the students' learning effect. The best effect was observed at the speed of 1.25× and 1.5×. (2) The speed that affected the learning effect best differed according to the students' learning abilities. High-level group students performed best at the speed of 1.5×, whereas low-level group students performed best at the speed of 1.25×. (3) The 1.5× speed showed significant differences in the learning effect by students' majors. This indicates that the cognitive load of liberal arts students increased greatly at this speed. (4) A change in playback speed has a significant impact on the cognitive load. Accelerated playback speed increases the cognitive load of students. The highest learning effect is observed under medium cognitive load.

Teaching Machines: The History of Personalized Learning Boston: MIT Press, 2021. 328 pp

Larry Cuban has spent four decades laying the foundation of the field, starting with the landmark Teachers and Machines (1986), then continuing with Oversold and Underused (2001), and, most recently, Inside the Black Box of Classroom Practice (2013).1 In the last few years, the field has expanded in new directions with Morgan Ames's The Charisma Machine (a ethnographic investigation of the One Laptop per Child project), Victoria Cain's new Schools and Screens: A Watchful History (an archival investigation of arguments for and against technology adoption), and my own Failure to Disrupt (an effort to carry Teachers and Machines from the 1980s to the present day).2 As learners and educators across the world rethink their relationship to digital learning in the course of the pandemic, these new entries provide a guide for understanding why the dreams of edtech reforms are so often dashed on the shoals of actual schools. In Teachers and Machines, Cuban frames the history of education technology around the adaptation of new consumer media to classroom applications, tracing a line from radio to filmstrips to television to personal computers. [...]defenses have been mounted many times in the past seventy years in response to teachers’ warnings that computers were coming not to aid but to replace them. [...]Watters's writing in the last decade, this connection between the behaviorist advocates of mechanical teaching machines and influences on the development of online learning had largely been forgotten. According to the master narrative of behaviorism, Noam Chomsky authored a ferocious review of Skinner's 1957 book Verbal Behavior that purged behaviorism from the academy and paved the way for cognitivism, situated learning, and other modern pedagogical philosophies to take over the field. [...]as the story goes, when teaching machines died in the 1950s, cognitivism was better prepared to inform the development of computer-assisted instruction that emerged in its wake.

Application of Volleyball Teaching Through Distance Education Utilizing a Personalized System of Instruction

Countries worldwide have imposed restrictions on social life and education to decrease the spread of the COVID-19 pandemic. Numerous interventions have been made in every country to ensure continued learning by utilizing distance education (DE) tools. Infrastructure and institutions have made arrangements to curtail the spread of infection while simultaneously minimizing learning loss, ensuring continuity of learning. However, physical education lessons have had the option to be conducted online the last few decades;the number of states allowing online physical education (OLPE) has grown rapidly since 2010. DE arrangements and studies on conducting physical education courses through online education suggest that a personalized system of instruction (PSI) can be determined. Further, adaptations of PSI may increase the effectiveness of DE. Using a volleyball course as example, this article elaborates on the availability of physical education and sports courses through DE by supporting PSI with several technologies.

Adaptive Instructional System for Complex Equipment Trainings in the Post-covid Era: Breaking the Ice of Time-Consuming Tasks

Complex equipment trainings frequently rely on in-person trainings to describe instrument parts and personalize explanations based on training objectives, prior knowledge, and cognitive abilities of trainees. In this study, we assess the main challenges of adapting intelligent instructional system by training centers with limited human and technological resources. We found that the preparation of the training material is the most time-consuming task. During the COVID-19 pandemic, many training centers were forced to remotely conduct their trainings, thus generating a massive amount of digital training content. Here, we explore this unique opportunity to recycle this training material to design an adaptive instructional system (AIS) for bioimaging training. In this paper, we discuss the functional features of AIS that facilitate autonomous training for trainees and instructors: progress bar, notification system, built-in teleconferencing, and chatting tools. To address a high level of customization of in-person trainings, we designed AIS trainings as module-based instructions that can be easily tailored to accommodate the objectives and needs of the trainees. We also demonstrate that modular design of the training material database accelerates allocation and preparation of training content for similar types of equipment. We set up a framework for implementing a recommendation system that would accommodate the training material to the trainee’s experience. Our study shows that over the short or medium term, the potential of AIS solution for equipment trainings significantly outweighs the most time-consuming tasks like preparation of the training material. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.