Perspectives on flexible learning towards the development of proposed quality assurance framework for HyFlex learning

Strengthening Online Education Approaches in Institutions of Higher Learning

Teaching mathematics in an EFL context at higher education; before, during and after the COVID-19 pandemic: a comparative study

Ontario’s Quality Assurance Framework (QAF) is reviewed and found not to meet all five criteria proposed for a strong quality assurance system focused on student learning. The QAF requires a statement of student learning outcomes and a method and means of assessing those outcomes, but it does not require that data on achievement of intended learning outcomes be collected, analyzed and used for continuous program improvement. By failing to be a strong framework for quality assurance, it cannot be used to demonstrate that institutions of higher education in Ontario have achieved high quality, in the “fitness-for-purpose” sense of that concept. As a result, the QAF assures only a “shooting the moon” version of quality, where having a target (the moon), aimed at carefully in a standardized way, is treated as proxy for actually hitting the target (learning outcomes).

Purpose This paper aims to investigate how global university rankings interact with quality and quality assurance in higher education along the two lines of investigation, that is, from the perspective of their relationship with the concept of quality (assurance) and the development of quality assurance policies in higher education, with particular emphasis on accreditation as the prevalent quality assurance approach. Design/methodology/approach The paper firstly conceptualises quality and quality assurance in higher education and critically examines the methodological construction of the four selected world university rankings and their references to “quality”. On this basis, it answers the two “how” questions: How is the concept of quality (assurance) in higher education perceived by world university rankings and how do they interact with quality assurance and accreditation policies in higher education? Answers are provided through the analysis of different documentary sources, such as academic literature, glossaries, international studies, institutional strategies and other documents, with particular focus on official websites of international ranking systems and individual higher education institutions, media announcements, and so on. Findings The paper argues that given their quantitative orientation, it is quite problematic to perceive world university rankings as a means of assessing or assuring the institutional quality. Like (international) accreditations, they may foster vertical differentiation of higher education systems and institutions. Because of their predominant accountability purpose, they cannot encourage improvements in the quality of higher education institutions. Practical implications Research results are beneficial to different higher education stakeholders (e.g. policymakers, institutional leadership, academics and students), as they offer them a comprehensive view on rankings’ ability to assess, assure or improve the quality in higher education. Originality/value The existing research focuses principally either on interactions of global university rankings with the concept of quality or with processes of quality assurance in higher education. The comprehensive and detailed analysis of their relationship with both concepts thus adds value to the prevailing scholarly debates.

The COVID‐19 pandemic has forced higher education institutions to implement online learning activities based on virtual platforms, allowing little time to prepare and train faculty members to familiarize students with digital technologies. While previous studies have looked at how students engaged with digital technologies in their learning activities, the characteristics of the student engagement in online learning remain underexplored. Therefore, a systematic review of the literature on student engagement in online learning in higher education is much needed. This article synthesizes the findings on student engagement in Latin American higher education institutions during the COVID‐19 pandemic. After reviewing the studies on online learning activities, this review examines student engagement from behavioural, cognitive and affective dimensions and identifies the main characteristics of student engagement from these tripartite dimensions. The implications of the findings for online learning in Latin American higher education are as follows: (a) to transform higher education, (b) to provide adequate professional training, (c) to improve Internet connectivity, (d) to ensure quality online learning in higher education and (e) to provide emotional support. These findings will provide valuable guidance for teachers, educational authorities and policy makers and help them make informed decisions to use effective strategies to support online learning in higher education institutions. Practitioner notesWhat is already known about this topic The COVID‐19 pandemic has disrupted the normal operation of higher education institutions across Latin America, impelling a shift from face‐to‐face instruction to online teaching and learning.Research on online learning in Latin American higher education has been conducted, but the findings and their implications are yet to be widely disseminated among researchers, practitioners and decision‐makers. What this paper adds Providing a systematic review of research on student engagement in online learning in Latin American higher education institutions.Analyzing the construct of student engagement in online learning from tripartite dimensions—behavioral, cognitive and affective—in the Latin American higher education context.Identifying the characteristics associated to each dimension of student engagement in online learning. Implications for practice and/or policy The need to transform the higher education system in Latin America and beyond, at two levels: to improve Internet connectivity at the technological level and to ensure the quality of online education at the pedagogical level.The urgency to offer an adequate professional training regarding the use of new technologies in online learning environments.The significance for higher education institutions to provide emotional support for students during the COVID‐10 pandemic.

Students' satisfaction, knowledge, skills and attitude towards learning (KSAs), engagement and interaction in online learning are essential indicators in ensuring that the Learning Management System (LMS) is utilised effectively and efficiently. However, most students become passive listeners and observers during online teaching and learning activities on the LMS platform, both in synchronous and asynchronous learning. Therefore, this study aimed to identify student satisfaction in terms of KSAs, engagement and interaction with a combination of synchronous and asynchronous learning in online learning platforms. A questionnaire was distributed to 163 students from a higher education institution. The results showed that student satisfaction was at a high level in KSAs; there was a significant positive relationship between KSAs, interaction and student engagement which led to student satisfaction. Therefore, a teaching design which combines synchronous and asynchronous learning methods could be applied by educators to enhance students' KSAs, interaction and engagement to help raise their satisfaction levels.

The higher education quality evaluation and assurance frameworks and their operating mechanisms of countries such as the United Kingdom, France, and the United States show that higher education systems, traditional culture, and social background all impact quality assurance operating mechanisms. A model analysis of these higher education quality assurance frameworks shows that quality assurance entities having clear and harmonious responsibilities and interest relations is a precondition to the good operation of higher education quality assurance mechanisms, and the model of internal self-discipline of the higher education institutions taking precedence and internal and external assurances integrating organically is a developing trend in higher education quality assurance.

:The higher education quality evaluation and assurance frameworks and their operating mechanisms of countries such as the United Kingdom, France, and the United States show that higher education systems, traditional culture, and social background all impact quality assurance operating mechanisms. A model analysis of these higher education quality assurance frameworks shows that quality assurance entities having clear and harmonious responsibilities and interest relations is a precondition to the good operation of higher education quality assurance mechanisms, and the model of internal self-discipline of the higher education institutions taking precedence and internal and external assurances integrating organically is a developing trend in higher education quality assurance.

For autosegmentation models, the data used to train the model (e.g., public datasets and/or vendor-collected data) and the data on which the model is deployed in the clinic are typically not the same, potentially impacting the performance of these models by a process called domain shift. Tools to routinely monitor and predict segmentation performance are needed for quality assurance. Here, we develop an approach to perform such monitoring and performance prediction for cardiac substructure segmentation. To develop a quality assurance (QA) framework for routine or continuous monitoring of domain shift and the performance of cardiac substructure autosegmentation algorithms. A benchmark dataset consisting of computed tomography (CT) images along with manual cardiac substructure delineations of 241 breast cancer radiotherapy patients were collected, including one "normal" image domain of clean images and five "abnormal" domains containing images with artifact (metal, contrast), pathology, or quality variations due to scanner protocol differences (field of view, noise, reconstruction kernel, and slice thickness). The QA framework consisted of an image domain shift detector which operated on the input CT images and a shape quality detector on the output of an autosegmentation model, and a regression model for predicting autosegmentation model performance. The image domain shift detector was composed of a trained denoising autoencoder (DAE) and two hand-engineered image quality features to detect normal versus abnormal domains in the input CT images. The shape quality detector was a variational autoencoder (VAE) trained to estimate the shape quality of the auto-segmentation results. The output from the image domain shift and shape quality detectors was used to train a regression model to predict the per-patient segmentation accuracy, measured by Dice coefficient similarity (DSC) to physician contours. Different regression techniques were investigated including linear regression, Bagging, Gaussian process regression, random forest, and gradient boost regression. Of the 241 patients, 60 were used to train the autosegmentation models, 120 for training the QA framework, and the remaining 61 for testing the QA framework. A total of 19 autosegmentation models were used to evaluate QA framework performance, including 18 convolutional neural network (CNN)-based and one transformer-based model. When tested on the benchmark dataset, all abnormal domains resulted in a significant DSC decrease relative to the normal domain for CNN models ( ), but only for some domains for the transformer model. No significant relationship was found between the performance of an autosegmentation model and scanner protocol parameters ( ) except noise ( ). CNN-based autosegmentation models demonstrated a decreased DSC ranging from 0.07 to 0.41 with added noise, while the transformer-based model was not significantly affected (ANOVA, ). For the QA framework, linear regression models with bootstrap aggregation resulted in the highest mean absolute error (MAE) of , in predicted DSC (relative to true DSC between autosegmentation and physician). MAE was lowest when combining both input (image) detectors and output (shape) detectors compared to output detectors alone. A QA framework was able to predict cardiac substructure autosegmentation model performance for clinically anticipated "abnormal" domain shifts.

Education is the light that drives out the darkness from life and directs attention toward a child's overall growth. The procedure is tripolar. It entails communication between the instructor, the students, and the community. One important person in the country's life is the teacher. Since ancient times, the function of the teacher has been seen as crucial in forming society as well as the personalities of the students. They create societies, show the way forward for the country, and preserve the human elements of life. A commonly acknowledged psychological component of working in any field is job satisfaction. Everyman in every field works to reach his goal. Student engagement in engineering classrooms is a multifaceted construct that encompasses emotional, cognitive, and behavioral dimensions, influencing students' motivation, interest, and participation in learning activities. In engineering education, effective student engagement is essential because it helps students grasp difficult ideas more deeply, develops their critical thinking and problem-solving abilities, and gets them ready for lucrative engineering professions. However, engineering classrooms often pose unique challenges to student engagement, such as high student-to-faculty ratios, complex technical material, and limited opportunities for hands-on learning, highlighting the need for innovative instructional strategies and research-based approaches to enhance student engagement and learning outcomes in engineering education. This study uses a mixed-methods approach to examine the relationship between student engagement, instructor job satisfaction, and self-efficacy in engineering classrooms. The study will investigate how teacher self-efficacy and work satisfaction affect student engagement and pinpoint the elements that affect both student outcomes and teacher wellbeing. The results of this study will influence the creation of evidence-based tactics to improve teaching and learning outcomes in engineering education by offering insightful information to educators, administrators, and legislators.

This article presents the results of a literature review on key learning and teaching dimensions in MOOCs. 95 studies published from January 2014 to October 2016 were selected for review. Four important learning and teaching dimensions were identified, and relationships between these dimensions were presented. The key dimensions and sub-dimensions reported in this literature review are student factors (education background, country of origin, age, gender, and motivation), teaching context (motivation, challenge, and pedagogical preference), student engagement (emotional, social, behavioural, and cognitive engagement), and learning outcomes (perception, retention, and grade). The review provides evidence of a relationship between student factors and engagement and a relationship between student engagement and learning outcomes.

Aim/Purpose: The purpose of this study is to review and categorize current trends in student engagement and performance prediction using machine learning techniques during online learning in higher education. The goal is to gain a better understanding of student engagement prediction research that is important for current educational planning and development. However, implementing machine learning approaches in student engagement studies is still very limited. Background: The rise of online learning during and after COVID-19 has created new difficulties for students’ engagement and academic achievements. Lecturers’ manual monitoring and supporting of students are inadequate online, leading to disengagement and performance challenges that may be very difficult to notice. Machine learning has great potential to provide an accurate prognosis of students’ engagement and outcomes to make early interventions possible. Nevertheless, the current knowledge deficit is in the systematic presentation of trends and insights concerning the utilization of these approaches in higher education online learning, especially with a focus on student engagement research. This research fills a crucial void by explaining and analyzing current trends in machine learning-based prediction models to enhance the quality and efficiency of an online learning system. Methodology: This research examines the existing literature on the application of machine learning, which allows computers to learn from data and improve their performance for early identification of student engagement and academic performance in higher education during online learning. The PICOC protocol was implemented to guide the search process and define the relevant keywords aligned with the research questions. Based on the PRISMA framework, a structured approach is adopted to identify and select studies to screen and extract the relevant papers from the database. Meta-analysis was adopted in data analysis whereby studies are combined and evaluated to provide insights into machine learning techniques’ effectiveness in student engagement and academic performance research. Contribution: This paper aims to present the current trends in predicting student engagement and academic achievement by applying machine learning approaches with a focus on their relevance in the context of online learning. It defines challenges that emerge with an interpretation of the extent of student engagement, which include the absence of consensus on levels of student engagement that hampers the use of explainable artificial intelligence – approaches that make training of machine learning models more logical, understandable and easily interpretable by lecturers. The finding points to the fact that through the prediction models, lecturers are enabled to recognize disengaged students early and foster their needs towards learning, providing direction toward more customized and effective online learning. Findings: A total of 96 primary studies have been identified and included in this systematic review. It is important to highlight the relevance of classification machine learning methods that are implemented in 88.60% of papers, while clustering methods are only employed in 15.19% of studies. Furthermore, the review shows that most research focuses on student performance prediction (82.28%) compared to student engagement level prediction (12.66%). Besides, student engagement datasets are used in 92.14% of studies, emphasizing student engagement’s popularity in educational prediction research. Moreover, classification machine learning methods are more prevalent in educational prediction research. In contrast, classification methods for student engagement research are still limited due to challenges in constructing consistent engagement levels. Recommendations for Practitioners: Lecturers need to occasionally assess student engagement levels during online learning to identify students who are left out and take immediate planning and action to encourage the student to engage during online learning. The syllabus designer should observe the students’ engagement level during online learning to plan the course content that can attract and engage the students. Students’ engagement during online learning can ensure their academic success and prevent them from dropping out. Recommendation for Researchers: Researchers should focus on defining the consensus on differentiating student engagement levels and implementing more explainable AI to enhance the interpretability and transparency of student engagement level predictive models. Researchers should enhance educational predictive models’ explainability, transparency, and accuracy by addressing issues brought about by feature selection, resampling techniques, and hyperparameter tuning. Impact on Society: The study highlights the growing importance of understanding student engagement through digital footprints, which can support personalized learning experiences and provide better educational outcomes. The efficient predictive models on student engagement can improve the effectiveness of higher education systems, benefiting students and institutions. Future Research: The challenges of current computational methods need to be overcome, including the need for more consistent approaches in differentiating engagement levels and enhancing the explainability and accuracy of educational predictive models through better feature selection, resampling techniques, and hyperparameter tuning.

Since the early 90s higher education institutions (HEIs) have been globally experiencing a shift from ‘traditional’ to ‘outcome-based education (OBE)’ approach, which allows learners to actively construct knowledge and enjoy learning with peers and teachers. However, the concept of OBE and student engagement is recently discussed in Bangladesh and needs to be studied well, especially, in terms of understanding the influence of various socio-demographic factors. This study examined the influence of student socio-demographic factors on student engagement and the interrelation between student engagement, student satisfaction, and teaching practices in undergraduate engineering education at the largest private university in Bangladesh. An online student survey was conducted in June 2021. Student engagement consisted of 5 domains: academic, cognitive, affective, social engagement with peers and social engagement with teachers. Independent sample t-test on 348 students showed that the 5 domains of student engagement vary by certain socio-demographic factors of students. For example, students aged 25 years and up scored lower than students aged 18 to 24 years in academic engagement (t=-2.2, p=0.026<0.05) and social engagement with teachers (t=-2.4, p=0.015<0.05). Similarly, students from English medium higher secondary education scored lower, in comparison to Bengali medium students in cognitive engagement (t=-3.1, p=0.002<0.05) and affective engagement (t=-3.4, p=0.001<0.05). Pearson correlation tests evidenced that student engagement is significantly correlated to elements of OBE: student satisfaction (r=0.68, p=0.000<0.01) and teaching practices (r=0.65, p=0.000<0.01). The path to OBE should be facilitated by improvement in all domains of engagement. Additionally, HEIs should address the varying needs of the student cohorts who are vulnerable to low student engagement.

Open, Distance, and Digital Education (ODDE) has potential to help educational institutions address the various challenges which usually result in the disruptions of the learning process. This system of education is flexible, agile, and resilient enough to adjust to the different contexts and also enables the academic institution to respond to some expectations like making available lifelong learning opportunities to all types of learners. There is, however, a lingering perception that ODDE is of lower quality compared to the conventional mode of education despite results of research showing otherwise which can prevent the realization of the full potential of this system of instruction.Through an intensive review of literature, this chapter looked at how quality in ODDE was and is being articulated with respect to curricular programs and courses and how they are evaluated for quality with the goal of determining if there are gaps which need to be addressed to help dispel that perception of lower quality. Eleven Quality Assurance (QA) Frameworks developed by various organizations from different parts of the world during the last 20 years (2000–2019) were evaluated for a more focused review process. Results showed that there is a general agreement as to what constitutes quality in this system of education. For the methodologies for program and course evaluation, some improvements and innovations can be done as informed by the QA Frameworks and tapping on what information can the technology provide as in the case of learning analytics which served as basis for the recommendations made.

Student engagement is emerging as a key focus in higher education, as engagement is increasingly understood as a prerequisite for effective learning. This paper reports on the development of an Engagement Framework that provides a practical understanding of student (and staff) engagement which can be applied to any discipline, year level or course. The Engagement Framework proposes five non- hierarchical elements: personal engagement, academic engagement, intellectual engagement, social engagement, and professional engagement. As well as describing these elements, the paper also explores the theoretical foundations of the Engagement Framework, including a recognition of the importance of conation as one of three faculties of the mind alongside cognition and affect. By adopting this Framework, the Faculty aims to enhance unit design and development, teaching practice, and student support practices. This paper describes the development of an Engagement Framework that is designed to be used as an underpinning tool to support a range of initiatives to enhance both staff and student engagement within a Faculty of Education in a regional Australian university. Many students engage actively in their own learning and are passionate about and committed to their studies. This is particularly the case in pre-service teacher education where the goal of becoming a teacher acts as a motivator for many students. This is not to say that all students who are engaged are all engaged equally, or remain fully engaged across the course of their studies. The Engagement Framework described in this paper was designed as a means of breaking apart the concept of engagement in order to more explicitly explore the question of 'how' students engage. A range of courses is offered within the Faculty - including a number of 4-year Bachelor of Education courses (Early Childhood, Primary, and Secondary Specialisations) and 2-year Master of Teaching courses (Primary and Secondary). The majority of courses are offered online as well as on-campus. Over half the student population studies in the wholly online mode and many of these students live in other Australian states or in other countries. The wholly online courses have no residential component and it is possible that students will not physically meet lecturers, tutors, professional staff or their peers during their course of study. Online students interact through the University's learning management system (currently Blackboard) and use a range of other technologies to communicate and develop learning communities. The online nature of much of the teaching and learning presents other challenges for students and staff - being engaged in a virtual space is different from being engaged as an on-campus student, where the support of peers and staff can appear more 'real'. The Engagement Framework described below can be applied to both modes of study, and can be used by staff, students and Faculty leadership to determine various aspects of engagement and how these might be enhanced.