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<p class="3">The increasing costs of higher education (HE), growing numbers of flexible anytime, anywhere learners, and the prevalence of technology as a means to up-skill in a competitive job market, have brought to light a rising concern faced by graduate students and potential graduate employers.  Specifically, there is a mismatch of useful skills obtained by students through HE institutions which is evident upon graduation.  Faced with this dilemma, “graduate students,” or more specifically newly graduated students, with a with bachelor’s degree, and a growing number of employers are turning to Massive Open Online Courses, or MOOCs, as a complimentary mechanism through which this skills gap may be bridged. </p><p class="3">It is found in the literature that MOOCs are often discussed within the capacity of their development, their retention rates, institutional policies regarding their implementation, and other such related areas.  Examinations into their broader uses, benefits, and potential pitfalls have been limited to date.  Therefore, this paper aims to analyse the literature highlighting the use of MOOCs as a means to reduce the mismatch in graduate skills.  As such, this literature analysis reviews the following relevant areas: higher education and graduate skills gap, today’s graduates and employability, and MOOCs and graduate skills.  Through analysing the literature in these areas, this paper identifies gaps in the existing literature.                  </p>

The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of direct supervision by instructors during final examinations poses a significant risk of academic misconduct. In this paper, we propose a new approach to detecting potential cases of cheating on the final exam using machine learning techniques. We treat the issue of identifying the potential cases of cheating as an outlier detection problem. We use students’ continuous assessment results to identify abnormal scores on the final exam. However, unlike a standard outlier detection task in machine learning, the student assessment data requires us to consider its sequential nature. We address this issue by applying recurrent neural networks together with anomaly detection algorithms. Numerical experiments on a range of datasets show that the proposed method achieves a remarkably high level of accuracy in detecting cases of cheating on the exam. We believe that the proposed method would be an effective tool for academics and administrators interested in preserving the academic integrity of course assessments.

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The COVID-19 pandemic forced many higher education institutions to suddenly pause in-person teaching and learning in favor of Emergency Remote Teaching and Learning (ERTL). Strict social distancing measures required institutions to offer courses, programs, and services without any direct contact between students, faculty, and staff; higher education created a contactless teaching and learning environment. This exploratory study analyses various applications of ERTL through a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The results from the review of the literature are presented through a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis for students, faculty, and the institutions.

The immediacy of the COVID-19 pandemic highlighted the sheer importance of internal and external communication with stakeholders. Universities had to rapidly grasp an unfolding and fast-changing crisis, gauge their level of preparedness, review decision and implementation processes, devise strategies, and adapt communication approaches. This exploratory study conducts a literature review in order to identify relevant studies that address how higher education institutions communicated to their stakeholders during the COVID-19 pandemic. The review of the literature revealed that although many higher education institutions had disaster recovery plans in place, few were well-equipped for a disruption of global proportions. Using a grounded theory approach, five important themes emerged from the relevant studies.

PurposeThe purpose of the study is twofold: to offer a theoretical model that illuminates families' motivation to invest in private tutoring and to consider the implications of such investments in the context of South Korea. Given that parents invest in private tutoring for their child if the perceived expected benefits, at the time of enrollment, are greater than the direct and indirect costs of such tutoring, the study explores how private tutoring may affect educational inequities and possibly lead to inferior social outcomes.Design/methodology/approachA theoretical model based on the human capital approach was developed. Three questions based on stylized facts were addressed: (1) Why would a household send a child to private tutoring? (2) Why do different households invest in different amounts of private tutoring? (3) Why may a household over-invest in private tutoring?FindingsThe findings of this study indicate that the demand for private tutoring services decreases with the costs of private tutoring, while increasing as levels of academic readiness and aptitude, levels of household education, levels of current wealth and expected returns to private tutoring increase. These findings imply that private tutoring may exacerbate social inequities and cause an inferior social outcome, but that a government can influence the demand for tutoring through taxation.Research limitations/implicationsThis study did not address the non-pecuniary benefits that may be derived from private tutoring. The most important limitation and potential source of weakness of the study is that the model is theoretical. These results therefore need to be interpreted with caution.Practical implicationsThe study indicates the need for private households, as well as government officials, to carefully consider the costs and benefits of private tutoring in South Korea. Although the study focuses on South Korea, the findings may apply to other countries in which private tutoring offerings are prominent.Social implicationsThe educational choices that families make for their children have important financial and social implications in all countries, but especially in South Korea. The important implication is that private tutoring will tend to aggravate educational and social inequality.Originality/valueThe existing body of research on private tutoring investment in South Korea suggests that the phenomenon is ubiquitous, growing and spreading to other countries. Furthermore, the motivation behind households' decisions to invest in private tutoring for their children is not always addressed in the published literature. Also, far too little attention has been paid to the economic impact private tutoring has on households and children, as well as society in general.

Academic research in the past decade has indicated that using data and analyzing learning in curriculum design decisions can lead to improved student performance and student success. As learning in many instances has evolved into the flexible format online, anywhere at any time, learning analytics could potentially provide impactful insights into student engagement in massive open online courses (MOOCs). These may contribute to early identification of “at risk” participants and provide MOOC facilitators, educators, and learning designers with insights on how to provide effective interventions to ensure participants meet the course learning outcomes and encourage retention and completion of a MOOC. This chapter uses the essential human biology MOOC within the Australian AdelaideX initiative to implement learning analytics to investigate and compare demographics of participants, patterns of navigation including participation and engagement for passers and non-passers in two iterations of the MOOC, one instructor-led, and second self-paced.