Teaching and Learning in the Digital Era

The following sections are included:

• Preface
• About the Editor
• Contents

This chapter discusses the impacts of digital technologies on society and on the education sector, reviews e-learning and hybrid learning, looks at learning and training in the organization, highlights the recent development of artificial intelligence (especially ChatGPT) and its impact on education, and comments on the role of the government and teacher in the education in the digital era.

Teaching and learning in the digital era refers to the use of digital technologies to support and enhance teaching and learning activities. Advancement in digital technologies has revolutionized the way teaching and learning are conducted, making education more accessible, interactive, and personalized. Digitalization of higher education systems has been considered a powerful means to promote student learning. Digital technologies can positively affect student learning in higher education and it becomes effective when teachers use it to promote student involvement in constructive and interactive as opposed to passive and traditional learning methods (Wekerle et al., 2022). Embracing technology in education is crucial for keeping up with the rapidly evolving world and preparing students for the future. This chapter provides an overview of teaching and learning in the digital era, describes the definition, types, and elements of teaching and learning in the digital era, explains the opportunities, and challenges of teaching and learning, and points out emerging patterns and provides insight into future trajectories how the use of digital technologies while providing the required support to enhance teaching and learning activities.

This chapter presents the background of ChatGPT, discusses the benefits it can provide to higher education, points out its limitations and concerns and issues, and looks at strategies and success factors for the successful implementation of ChatGPT in the higher education organizations.

The advancement of new technology in the education system has significantly impacted teaching and learning approaches in both online and face-to-face environments. The education systems are anticipating the future and adopting different modes of delivery for learning (e.g., online learning, face-to-face learning, hybrid learning) in simultaneous and asynchronous ways. Online education has acquired significant recognition recently due to the pandemic when the education industry underwent a transition from face-to-face to online mode as a spontaneous solution to prevail in this uncertainty. It is feasible to teach and learn from anywhere in the world at any time, with unlimited access to resources, which play a significant role in effective and efficient teaching and learning. This study provides an enhanced comprehension of online education. To upgrade the teaching and learning process many tools and resources are used in different circumstances.

This chapter elaborates on educational tools and how they can be used to enhance the quality of education. Given that it is of utmost importance to understand the current demands, this chapter provides an overview of the emerging trends in online education.

Educational institutions have raised paramount concerns over assessment integrity and security in the evolving landscape of the digital era and the transition to online and hybrid learning environments. This chapter starts by exploring the significance of assessment integrity and security and emphasizing their imperative role in preserving the value of academic qualification and genuine knowledge acquisition. It unfolds by presenting a detailed exploration of the multifaceted challenges in online exam settings ranging from cheating to unauthorized access to information. It then sheds light on the strategies and digital tools for addressing the challenges, and concerns and issues associated with the digital tools are also discussed.

This chapter delves into the realm of online assessments, shedding light on their advantages, prerequisites, and the critical role of student perspectives. Online peer assessments offer anonymity, flexibility, and reduced teacher workload. However, fostering positive student attitudes and providing thorough training are essential requisites. Online examinations, including platforms like Google Forms, streamline grading processes and minimize costs, albeit challenges related to Internet access and digital literacy persist. Notably, the present chapter emphasizes the pivotal influence of student attitudes, motivations, and emotions on the outcomes of online assessments. In addition, this chapter provides a scope to the online evaluation by looking into assessment types, consideration of academic integrity, alternative ways of the learners’ evaluation and different aspects of online examinations.

Higher education institutions have undergone a remarkable transformation in course delivery and assessment arrangements over the last two decades, and online learning has become more and more popular. Literature has broadly suggested that online course delivery and digital technologies will lead to greater potential for academic misconduct. This chapter starts with a brief illustration of academic integrity and online assessment arrangements, including exams. It then proceeds to explore the factors associated with academic misconduct, followed by a discussion on various measures and digital tools that can be utilized to cope with academic integrity. This chapter also provides current practices of deterring academic misconduct examples at King’s Own Institute (KOI), Australia.

This chapter examines the benefits and challenges of creating engaging lessons in an online learning environment. Suggestions for increasing student engagement online are provided. The importance of gaining attention, demonstrating relevance, and providing state changes during a lesson are highlighted. The reader is encouraged to adopt and trial any suggested techniques which fit with their particular teaching style. As this list cannot be exhaustive, the reader is also encouraged to use these as a prompt to further investigate and reflect on the myriad ways to engage students in their online classes.

In this technological era, programming skill has become highly valuable across IT and non-IT sectors. Teaching programming can be challenging, requiring a deep understanding of the subject and effective communication skills. Traditional pedagogy, focused on lectures, textbooks, and written assessments, tends to emphasize content over student-centered learning, resulting in limited practical application and knowledge retention. This chapter explores the potential of “Game-Based Learning” (GBL) and gamification as an alternative pedagogy to improve learner’s engagement, problem-solving skills, and learning skills. This chapter provides insights on GBL and its application in programming education, which enables instructors to make better decisions in their teaching practices for programming courses and improve students’ learning experiences.

The survival and economic equilibrium of Higher Education institutions are heavily contingent on student retention. Predictive models for retention play a crucial role in identifying students at risk of attrition during the early phases, thereby aiding the financial sustainability of these educational establishments. In recent times, scholars have employed a range of data sources, including robust ones like the Learning Management System (LMS), which are meticulously examined and incorporated into these models by researchers to mitigate the perils of attrition.

Student engagement is an important factor toward academic success, and machine learning techniques are valuable tools for predicting and improving it. This chapter explores some applications of machine learning to predict student engagement by using different data sources, particularly in the context of online education. The chapter discusses different machine learning algorithms, including, logistic regression, decision trees, support vector machines, Naïve Bayes, deep learning, K-nearest neighbors, and random forests. It also dives into the importance of student engagement, the challenges involved, and the utilization of learning analytics in the educational process. The findings highlight the potential of machine learning in identifying at-risk students and optimizing educational outcomes in online learning environments.

While Learning Analytics has been widely used to improve learning experiences such as course content, activities, and assessments, it plays a significant role in providing data-driven insight into the efficacy of a program. This chapter sheds light on how big data can be used in curriculum development to ensure that the skills and competencies students learn at educational institutions align with those required in the current and future job market. By exploring three case studies in which big data has been utilized to revise and update curricula in different fields, this chapter suggests that big data allows curriculum designers to make data-driven decisions which leads to a higher rate of employability and satisfaction among students. This chapter also discusses the limitations and challenges of using big data in education.

Presently, Information and Communication Technology (ICT) education holds substantial importance within higher education institutions worldwide. This research compiles and examines 51 ICT programs and 1,251 associated ICT courses from 11 universities in China’s Shanxi province. The micro-level analysis aims to disclose insights about the macro-level scenario, delving into the course design of ICT education in China and potentially on a global scale. This case study discovers that the most sought-after ICT programs primarily center around Information and Computing Science, as well as Computer Science and Technology. Mathematic and Programming-related courses lay the foundation for ICT programs, while Programming is the most frequently offered course in ICT programs at Shanxi universities. Through the data analysis, a suggestion is made to include courses in the context of Cloud Computing and Cybersecurity for the ICT programs offered by Shanxi universities.

Technology-enabled feedback encompasses many significant features including AI. It also allows students greater flexibility in providing feedback, and the university/institute enables them to make data-driven decisions as the collected data are free from bias and data analyses are accurate. There are different technology-centric feedback collections such as Open edX Insight, speech-to-text, Kahoot, Dialogflow, and so on. These technology-centric feedback collections mean collecting more realistic data from the students. Both qualitative and quantitative data could be analyzed by applying technology-centric data analyses tools. AMOS, LABLEAU, SAS, SPSS, Stata, Statista, XLSTAT, and ROOT are a few of the quantitative data analyses software, and Atlas, Airtable, Coda, Condens, MaxQDA, Notion, and NVivo are a few of the qualitative data analyses software. To make the students’ feedback and survey more successful we recommend sharing survey methods/survey questions with other educational institutions, conducting broader surveys, broad topics to ask questions on, technology-based analysis of qualitative feedback comments, and including skills development-focused questions.

The following section is included:

• Index