A review on massive e-learning (MOOC) design, delivery and assessment

MOOCs or Massive Online Open Courses based on Open Educational Resources (OER) might be one of the most versatile ways to offer access to quality education, especially for those residing in far or disadvantaged areas. This article analyzes the state of the art on MOOCs, exploring open research questions and setting interesting topics and goals for further research. Finally, it proposes a framework that includes the use of software agents with the aim to improve and personalize management, delivery, efficiency and evaluation of massive online courses on an individual level basis.Peer ReviewedPostprint (author's final draft

Personalisation in MOOCs: a critical literature review

The advent and rise of Massive Open Online Courses (MOOCs) have brought many issues to the area of educational technology. Researchers in the field have been addressing these issues such as pedagogical quality of MOOCs, high attrition rates, and sustainability of MOOCs. However, MOOCs personalisation has not been subject of the wide discussions around MOOCs. This paper presents a critical literature survey and analysis of the available literature on personalisation in MOOCs to identify the needs, the current states and efforts to personalise learning in MOOCs. The findings illustrate that there is a growing attention to personalisation to improve learners’ individual learning experiences in MOOCs. In order to implement personalised services, personalised learning path, personalised assessment and feedback, personalised forum thread and recommendation service for related learning materials or learning tasks are commonly applied

Delving into instructor‐led feedback interventions informed by learning analytics in massive open online courses

Producción CientíficaBackground:Providing feedback in massive open online courses (MOOCs) is chal-lenging due to the massiveness and heterogeneity of learners' population. Learninganalytics (LA) solutions aim at scaling up feedback interventions and supportinginstructors in this endeavour.Paper Objectives:This paper focuses on instructor-led feedback mediated by LAtools in MOOCs. Our goal is to answer how, to what extent data-driven feedback isprovided to learners, and what its impact is.Methods:We conducted a systematic literature review on the state-of-the-art LA-informed instructor-led feedback in MOOCs. From a pool of 227 publications, weselected 38 articles that address the topic of LA-informed feedback in MOOCs medi-ated by instructors. We applied etic content analysis to the collected data.Results and Conclusions:The results revealed a lack of empirical studies exploring LA todeliver feedback, and limited attention on pedagogy to inform feedback practices. Our find-ings suggest the need for systematization and evaluation of feedback. Additionally, there isa need for conceptual tools to guide instructors' in the design of LA-based feedback.Takeaways:We point out the need for systematization and evaluation of feedback. Weenvision that this research can support the design of LA-based feedback, thus contribut-ing to bridge the gap between pedagogy and data-driven practice in MOOCs.Consejo de Investigación de Estonia (PSG286)Ministerio de Ciencia e Innovación - Fondo Europeo de Desarrollo Regional y la Agencia Nacional de Investigación (grant PID2020-112584RB-C32) and (grant TIN2017-85179-C3-2-R)Junta de Castilla y León - Fondo Social Europeo y el Consejo Regional de Educación (grant E-47-2018-0108488

Setting an Agenda for Urban AI Adaptivity in Urban Planning and Architecture E-learning

The rapid spread of technology and learning systems have altered the viewpoint about the lack of E-learning to the human element. The intersection of AI and education is highlighted by many technologists and researchers showing the diverse possibilities and challenges of using AI in education. However, little research addresses the potential of using AI to create an adaptive e-learning experience that brings a fully personalized experience to e-learners in architecture and urban educational fields. Building on that, we postulate that adaptive AI learning could be useful for urban online teaching and urban development Massive Open Online Courses (MOOCs), specifically as urban planners need to explore different scenarios of future city making. Therefore, the aim is to explore how educators from the architecture and urban field E-Learning stakeholders perceive AI in the creation of urban Moocs as well as other online teaching activities, as well as address the ways in which adaptive learning can be created in urban e-learning MOOCs using AI. In an attempt to answer the question, what is the current perception of educators about AI adaptivity in e-learning?To achieve this, first, we review the literature available on the topic to provide a comprehensive and inclusive look at adaptive AI learning, its potential, and its challenges. This overview informed and guided the formulation of the survey questions. Then we conducted a survey on educators in Architecture and urban fields from universities in Egypt. The unfamiliarity of the participants with AI provides us with deeper insights into perceptions of educators\u27 AI adaptivity in online learning and MOOCs. The study develops a framework for adaptive e-learning using AI in an attempt to create more interactive and personalized e-learning experiences that can be used in different fields and for different types of learners

Setting an Agenda for Urban AI Adaptivity in Urban Planning and Architecture E-learning

The rapid spread of technology and learning systems have altered the viewpoint about the lack of E-learning to the human element. The intersection of AI and education is highlighted by many technologists and researchers showing the diverse possibilities and challenges of using AI in education. However, little research addresses the potential of using AI to create an adaptive e-learning experience that brings a fully personalized experience to e-learners in architecture and urban educational fields. Building on that, we postulate that adaptive AI learning could be useful for urban online teaching and urban development Massive Open Online Courses (MOOCs), specifically as urban planners need to explore different scenarios of future city making. Therefore, the aim is to explore how educators from the architecture and urban field E-Learning stakeholders perceive AI in the creation of urban Moocs as well as other online teaching activities, as well as address the ways in which adaptive learning can be created in urban e-learning MOOCs using AI. In an attempt to answer the question, what is the current perception of educators about AI adaptivity in e-learning?To achieve this, first, we review the literature available on the topic to provide a comprehensive and inclusive look at adaptive AI learning, its potential, and its challenges. This overview informed and guided the formulation of the survey questions. Then we conducted a survey on educators in Architecture and urban fields from universities in Egypt. The unfamiliarity of the participants with AI provides us with deeper insights into perceptions of educators\u27 AI adaptivity in online learning and MOOCs. The study develops a framework for adaptive e-learning using AI in an attempt to create more interactive and personalized e-learning experiences that can be used in different fields and for different types of learners

Software agents in large scale open e-learning: a critical component for the future of Massive Online Courses (MOOCs)

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.MOOCs or massive open online courses are a recent trend in online education. They combine online resources with social tools and have unique challenges due to the large number of simultaneous participants. This paper analyzes some of the challenges in the areas of MOOC design, delivery and assessment. Then the authors present an approach using software agents to overcome some of the challenges that have been identified, as well as optimize efficiency, reduce costs, and ensure the pedagogical effectiveness and educational quality of large scale online learning courses. This paper is a first step towards research in the usage of software agents in massive online courses that we hope will shed more light on potential real life applications.Peer ReviewedPostprint (author's final draft

Chapter 35 Digital Learning for Developing Asian Countries

Education – that is, the development of knowledge, skills, and values – is an important means by which to empower individuals in a society. As both a means towards and an outcome of gaining the capabilities necessary to participate in and contribute to society, education is an essential enabler in many social aspects, such as economic growth, poverty reduction, public health, and sustainable development, especially in today’s knowledge society. At the same time, however, education can still be a social institution that reflects and reproduces the social, cultural, and economic disadvantages that prevail in the rest of society (Bourdieu & Passeron, 1990). For example, students who are discriminated against socio-culturally or who are economically poor are more likely to receive an education that is characterized by inadequate infrastructure, few qualified teachers and encouraging peers, and outmoded pedagogical practices, which often results in a lower quality of life

Who are the top contributors in a MOOC? Relating participants' performance and contributions

The role of social tools in massive open online courses (MOOCs) is essential as they connect participants. Of all the participants in an MOOC, top contributors are the ones who more actively contribute via social tools. This article analyses and reports empirical data from five different social tools pertaining to an actual MOOC to characterize top contributors and provide some insights aimed at facilitating their early detection. The results of this analysis show that top contributors have better final scores than the rest. In addition, there is a moderate positive correlation between participants' overall performance (measured in terms of final scores) and the number of posts submitted to the five social tools. This article also studies the effect of participants' gender and scores as factors that can be used for the early detection of top contributors. The analysis shows that gender is not a good predictor and that taking the scores of the first assessment activities of each type (test and peer assessment in the case study) results in a prediction that is not substantially improved by adding subsequent activities. Finally, better predictions based on scores are obtained for aggregate contributions in the five social tools than for individual contributions in each social tool.This work has been partially funded by the Madrid Regional Government eMadrid Excellence Network (S2013/ICE-2715), the Spanish Ministry of Economy and Competitiveness Project RESET (TIN2014-53199-C3-1-R) and the European Erasmus+ projects MOOC-Maker (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP) and SHEILA (562080-EPP-1-2015-BE-EPPKA3-PI-FORWARD).Publicad