Saudi Cultural Missions Theses & Dissertations
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Item Restricted Uncovering Factors Driving the Adoption of Learning Analytics in Higher Education Institutions(Saudi Digital Library, 2023-09-20) Alzahrani, Asma; Gasevic, DraganIn higher education institutions (HEIs), learning analytics (LA) has been gaining popularity as a tool for understanding learning and for enhancing teaching and learning outcomes. For example, LA can provide students with insightful data on their learning outcomes. LA also allows students to track their performance in relation to their goals and check how their peers are performing. Teaching staff can have access to various sources of data for evaluating students’ learning performance and receiving up to-date information on students’ learning progress. In addition, LA allows senior managers to identify factors that improve retention or lead to abandonment. Despite the benefits of learning analytics (LA), their adoption can pose challenges. In existing research, LA has been extensively discussed, but the focus has been on its technical aspects and a growing body of literature that aims to understand the socio-technical factors (which are the factors that represent the interrelationship between humans and tools) that shape LA adoption in HEIs. However, the literature does not address the challenges (or success-enablers) and their associated factors within different LA adoption scopes, trust in LA (e.g., trust in LA tools and stakeholders), and the need for LA in unexplored regions (e.g., Saudi Arabia). We sought to fill this gap by focusing on the major factors that affect LA adoption, such as challenges, success enablers, trust, and needs. This is because the successful adoption of LA requires thoughtful consideration of all these socio-technical issues reported by main LA stakeholders. In other words, HEIs can improve the process of LA adoption by taking into account socio-technical issues, allowing them to determine issues pertaining to their adoption or use of LA. This thesis examines the perspectives of key stakeholders, including teaching staff and senior managers in HEIs, towards LA and what factors impact their adoption and use. The thesis findings identify barriers and offer strategies to curb or address the barriers using a socio-technical framework, bringing together social and technical issues to facilitate the adoption of LA in HEIs with informing strategies for the successful adoption of LA. It also contributes to the growing body of literature on LA in HEIs and will have important strategic implications for institutions seeking to adopt LA at various stages of adoption, from the early stages of interest in LA to the full adoption phase. This includes recommendations to overcome the challenges, areas that enable success, trust factors and recommendations to improve the LA adoption process, and the needs related to LA services. In the field of LA, this thesis presents a series of contributions that are represented by a set of factors that influence the adoption of LA in HEIs. First, the thesis empirically identifies the challenges of the adoption of LA and the factors associated with those challenges through the analysis of data collected from senior managers. As a result of this analysis, (i) HEIs can have a better understanding of why LA is difficult to scale LA in different scopes of LA adoption, (ii) changing patterns that may emerge in institutions that adopt LA in varying scopes are identified that provide insight into how institutions can be better prepared to deal with challenges and the ways they could be overcome in the different scopes of LA adoption: no adoption; preparation to adopt or partially adopting; and full adoption. The challenges of LA adoption can be turned into success-enablers if dealt with in the right way. Thus, the second part of this thesis scrutinises the success enablers of LA adoption from the perspective of managers in European HEIs. Aside from senior managers, teaching staff is another main stakeholder group in LA and their perspectives are equally as valuable as those of the senior managers. Therefore, the third part of this thesis covers the challenges that teaching staff may face when using LA. Specifically, the thesis focuses on trust in LA which previously received limited attention in the literature. Trust in LA was examined by analysing quantitative and qualitative data collected from teaching staff at Saudi HEIs focusing on two types of trust: trust in LA tools and trust in LA stakeholders. In addition to discovering trust factors in LA, understanding the teaching staff’s needs could be crucial to ensuring the success and widespread adoption of LA by HEIs. Accordingly, the final part of this thesis is focused on examining the needs of the teaching staff of LA from diverse cultural backgrounds. Particularly, the thesis focused on teaching staff in HEIs in the Middle East (i.e., Saudi Arabia) and compared the findings with studies conducted in other parts of the world – primarily Western and Latin American countries. Finally, the thesis concludes with a discussion of the implications of the findings of this thesis and directions for future research.24 0Item Restricted Early Identification of Dropout Students in Massive Open Online Courses(2022-12-23) Alamri, Ahmed; Cristea, AlexandraLearning analytics (LA) provides the ability to understand the patterns of students' behaviour and improve their educational outcomes. Today, the capacity to retain more data has contributed significantly to the rapid growth of the field of LA. For instance, Massive Open Online Course (MOOC) platforms offer free courses for millions of students worldwide. Therefore, students who cannot afford the expense of higher education may benefit significantly from the available knowledge in MOOCs. This opens a door for educators and academic researchers with a fascinating variety of learning behaviour data that could be used to analyse students' activities and improve their outcomes. While MOOCs platforms provide knowledge in a new and unique way, the very high number of dropouts is a significant drawback. Several variables are considered to contribute towards learner attrition or lack of interest, which may lead to disengagement or total dropout. In the past decade, many researchers have sought to explore the reasons behind learner’s attrition in MOOCs. The jury is still out on which factors are the most appropriate predictors; nevertheless, the literature agrees that early prediction is vital to allow for a timely intervention. This thesis aims to investigate the early prevention of dropout phenomenon in MOOCs by analysing the gaps in the current literature, identifying the under-researched areas, and developing continuous predictive models that can be used in real-time to identify students at risk of dropingout out of MOOCs. The current thesis explores a light-weight approach based on as little data as possible – since different MOOCs store different data on their users – and thus strive to create a truly generalisable method. Several features (e.g., registration date, students' jumping activities, and the times spent on every single task) have been proposed to predict at-risk students from an early stage. This goal was successfully achieved using different approaches such as statistical data analysis, machine learning and data visualisation. The second aim of this thesis is to employ motivational theories, mapping online student behaviour onto them, to analyse the drives and triggers promoting student engagement. This thesis further contributes by building an Engage Taxonomy of MOOC engagement tracking parameters, mapped over four engagement theories: Self-Determination Theory (SDT), Drive, Engagement Theory (ET), and Process of Engagement. The present thesis shows for the first-time metrics for measurable engagement in MOOCs, including specific measures for Autonomy, Relatedness and Competence. It also evaluates the parameters based on existing (and expanded) measures of success in MOOCs: Completion rate, Correct Answer ratio and Reply ratio.29 0