Saudi Cultural Missions Theses & Dissertations
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Item Restricted Online Collaborative Translation in Massive Open Online Courses (MOOCs): Policy, Collaborators and Work Models(Kent State University, 2024-07-22) Aldrees, Mohammed H.; Washbourne, Richard KellyOnline participatory translation and localization spread widely with the advent of Web 2.0, and various collaborative translation practices continue to emerge in different contexts (e.g., the entertainment, technology, and software development industries). Collaborative translation also continues to evolve in online education, particularly in massive open online courses (MOOCs), most of which are delivered in English. Therefore, a range of opportunities must be provided to learners with relatively low English language proficiency. Online collaborative translation has been utilized by several prominent platforms such as Coursera, Khan Academy, and edX to increase linguistic diversity and the use of MOOCs in international development. This study explores the online collaborative translation practices evident on educational platforms, with a particular focus on the translation policies of MOOCs’ providers, the motivations driving collaborators to engage in these participatory translation initiatives, and the work models implemented by the platforms. Two MOOC providers were identified as case studies, namely Coursera and Khan Academy. This research investigates their respective translation policies, drawing on González Núñez’s (2013) systematic approach to translation policy as a complex concept that encompasses management, practice, and beliefs. Additionally, this research adopts Engeström’s (1987) activity system model to explain the technologically mediated collaborative translations involving diverse participants and tools on Coursera and Khan Academy, and to identify contradictions within and between the components of their activity system models. It also explores collaborators’ motivations through the functional approach, which identifies specific motives driving participation in collaborative translation, alongside demotivating factors. The research employs a combination of methods, including document analysis, observation, questionnaires, and follow-up interviews. The findings indicate that Coursera and Khan Academy adopt distinct translation policies that influence user practices. Coursera relies on a structured digital platform and predefined roles and tasks, while Khan Academy employs a more decentralized approach that allows flexibility and adaptation to local contexts. Moreover, collaborators are driven by a combination of intrinsic and extrinsic motivations, with intrinsic motivations, such as the desire to contribute to education accessibility, enhance native language content, and engage in personal learning and skill development, being more prevalent. The work model of online collaborative translation in this study is a dynamic and complex activity system model with opportunities for improvement and innovation in areas such as platform technologies, communication tools, and participant recruitment.64 0Item Restricted Predicting the Need for Urgent Instructor Intervention in MOOC Environments(Durham University, 2024-03-27) Alrajhi, Laila; Cristea, Alexandra I.In recent years, massive open online courses (MOOCs) have become universal knowledge resources and arguably one of the most exciting innovations in e-learning environments. MOOC platforms comprise numerous courses covering a wide range of subjects and domains. Thousands of learners around the world enrol on these online platforms to satisfy their learning needs (mostly) free of charge. However, the retention rates of MOOC courses (i.e., those who successfully complete a course of study) are low (around 10% on average); dropout rates tend to be very high (around 90%). The principal channel via which MOOC learners can communicate their difficulties with the learning content and ask for assistance from instructors is by posting in a dedicated MOOC forum. Importantly, in the case of learners who are suffering from burnout or stress, some of these posts require urgent intervention. Given the above, urgent instructor intervention regarding learner requests for assistance via posts made on MOOC forums has become an important topic for research among researchers. Timely intervention by MOOC instructors may mitigate dropout issues and make the difference between a learner dropping out or staying on a course. However, due to the typically extremely high learner-to-instructor ratio in MOOCs and the often-huge numbers of posts on forums, while truly urgent posts are rare, managing them can be very challenging –– if not sometimes impossible. Instructors can find it challenging to monitor all existing posts and identify which posts require immediate intervention to help learners, encourage retention, and reduce the current high dropout rates. The main objective of this research project, therefore, was thus to mine and analyse learners’ MOOC posts as a fundamental step towards understanding their need for instructor intervention. To achieve this, the researcher proposed and built comprehensive classification models to predict the need for instructor intervention. The ultimate goal is to help instructors by guiding them to posts, topics, and learners that require immediate interventions. Given the above research aim the researcher conducted different experiments to fill the gap in literature based on different platform datasets (the FutureLearn platform and the Stanford MOOCPosts dataset) in terms of the former, three MOOC corpora were prepared: two of them gold-standard MOOC corpora to identify urgent posts, annotated by selected experts in the field; the third is a corpus detailing learner dropout. Based in these datasets, different architectures and classification models based on traditional machine learning, and deep learning approaches were proposed. In this thesis, the task of determining the need for instructor intervention was tackled from three perspectives: (i) identifying relevant posts, (ii) identifying relevant topics, and (iii) identifying relevant learners. Posts written by learners were classified into two categories: (i) (urgent) intervention and (ii) (non-urgent) intervention. Also, learners were classified into: (i) requiring instructor intervention (at risk of dropout) and (ii) no need for instructor intervention (completer). In identifying posts, two experiments were used to contribute to this field. The first is a novel classifier based on a deep learning model that integrates novel MOOC post dimensions such as numerical data in addition to textual data; this represents a novel contribution to the literature as all available models at the time of writing were based on text-only. The results demonstrate that the combined, multidimensional features model proposed in this project is more effective than the text-only model. The second contribution relates to creating various simple and hybrid deep learning models by applying plug & play techniques with different types of inputs (word-based or word-character-based) and different ways of representing target input words as vector representations of a particular word. According to the experimental findings, employing Bidirectional Encoder Representations from Transformers (BERT) for word embedding rather than word2vec as the former is more effective at the intervention task than the latter across all models. Interestingly, adding word-character inputs with BERT does not improve performance as it does for word2vec. Additionally, on the task of identifying topics, this is the first time in the literature that specific language terms to identify the need for urgent intervention in MOOCs were obtained. This was achieved by analysing learner MOOC posts using latent Dirichlet allocation (LDA) and offers a visualisation tool for instructors or learners that may assist them and improve instructor intervention. In addition, this thesis contributes to the literature by creating mechanisms for identifying MOOC learners who may need instructor intervention in a new context, i.e., by using their historical online forum posts as a multi-input approach for other deep learning architectures and Transformer models. The findings demonstrate that using the Transformer model is more effective at identifying MOOC learners who require instructor intervention. Next, the thesis sought to expand its methodology to identify posts that relate to learner behaviour, which is also a novel contribution, by proposing a novel priority model to identify the urgency of intervention building based on learner histories. This model can classify learners into three groups: low risk, mid risk, and high risk. The results show that the completion rates of high-risk learners are very low, which confirms the importance of this model. Next, as MOOC data in terms of urgent posts tend to be highly unbalanced, the thesis contributes by examining various data balancing methods to spot situations in which MOOC posts urgently require instructor assistance. This included developing learner and instructor models to assist instructors to respond to urgent MOOCs posts. The results show that models with undersampling can predict the most urgent cases; 3x augmentation + undersampling usually attains the best performance. Finally, for the first time, this thesis contributes to the literature by applying text classification explainability (eXplainable Artificial Intelligence (XAI)) to an instructor intervention model, demonstrating how using a reliable predictor in combination with XAI and colour-coded visualisation could be utilised to assist instructors in deciding when posts require urgent intervention, as well as supporting annotators to create high-quality, gold-standard datasets to determine posts cases where urgent intervention is required.13 0Item Restricted Predicting Paid Certification in Massive Open Online Courses(Durham University, 2024-02-08) Alshehri, Mohammad Abdullah; Cristea, AlexandraMassive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as “the Year of the MOOCs”, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5–13% and 1–3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learner’s activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learners’ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learners’ discussion forums to predict paid certification after annotating learners’ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learners’ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7.16 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 0Item Restricted Arabic Massive Open Online Courses and Teachers in Saudi Arabia: Technology, Space, Gift and Entrepreneurship(2023) Alsayegh, Nada; Ross, Jen; Knox, JeremyThis thesis critically examines Arabic Massive Open Online Courses (MOOCs) and the MOOC teacher experience by studying MOOCs in the context of Saudi Arabia from a posthumanist and sociomaterial perspective. It considers the materials, digital technologies and social context as active components in forming MOOCs and developing teachers’ identities and practices. Examination of Arabic MOOC platforms and online courses was conducted through visual analysis, interviews with MOOC teachers, and theoretical work on sociomateriality and spatial theory. Through this analysis, these MOOCs were shown to be deeply implicated in Islamic culture and in educational and policy context of Saudi Arabia. The findings highlight the spatial implications of Arabic MOOCs in a cultural context and show new forms of teaching, spaces, and concepts. Specifically, the MOOC project in Saudi Arabia appeared actively engaged in producing new meanings of gift-giving and entrepreneurship. It reframed giving and knowledge-sharing practices in Islamic culture, including zakat of knowledge and waqf, and reconceptualised entrepreneurship in a digital educational context through the formation of entrepreneurial teachers. These different practices and the identities they produced were overlapping and unpredictable and confirmed the dynamic role of materials and digital technology in forming MOOC spaces, in addition to the entanglements of materials and social dimensions in MOOC teaching and MOOC teacher identity formation. These findings add empirical evidence to theoretical claims that MOOCs are not only a technological medium for online education, but also spatially and relationally produced and enacted. This thesis contributes new knowledge in three main areas. First, it challenges the assumption that MOOCs are ‘universal’ or ‘global’ by shedding light on the Arabic MOOCs and presents an alternative evidence-based perspective from an under-represented cultural context. Secondly, it offers a critical examination of MOOCs by adopting a relational approach and considering the material and digital technology in studying teachers’ experiences in Saudi Arabia. Finally, it shows how Arabic MOOCs actively engage in shaping cultural and entrepreneurial spaces in Saudi Arabia. This thesis makes an original contribution to scholarship in digital education, MOOCs and open education, online teaching, sociomaterial and spatial studies and education in the Gulf and Arab region.18 0