How do we model learning at scale?:A systematic review of research on MOOCs

Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models. </jats:p

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

Analysis of the behavioral patterns of students of a MOOC on Introduction to Programming for the Arduino platform

A dificuldade para aprender programação é um assunto fortemente discutido em contextos de cursos da área da Computação, sendo diversos os fatores que atuam como barreiras. Devido a essa preocupação, discussões sobre como implementar estratégias para superar esse desafio têm sido apresentadas, desencadeando novas ações que possam auxiliar o aprendizado de programação. Diante disso, os MOOCs têm se apresentado como ferramentas com potencial para apoiar o ensino presencial e para atender demandas mais específicas de estudantes que buscam aprender programação on-line e de forma autônoma. Nesse contexto, essa pesquisa relata um estudo de caso de um MOOC introdutório sobre programação Arduino, disponibilizado em uma plataforma brasileira de cursos on-line. Técnicas de mineração de dados foram empregadas com o objetivo de encontrar os padrões comportamentais de 2031 estudantes que participaram do curso. Os resultados apontaram números de ações semelhantes entre os participantes, porém dois grupos se destacam entre os que apresentaram médias mais altas de engajamento no curso: estudantes com mais de 30 anos, com nível superior e que gostam do tema do curso; bem como estudantes mais jovens com ensino médio e que adoram o tema do curso.The difficulty to learn programming is is a topic that is strongly discussed in contexts of Computer courses, and there are several factors that act as barriers. Due to this concern, discussions on how to implement strategies to overcome this challenge have been presented, generating new actions that can help the programming learning. Thus, MOOCs have been presented as tools with the potential to support classroom teaching and to meet more specific demands of students who seek to learn programming online and autonomously. In this context, this research reports a case study of an introductory MOOC on Arduino programming, available on a Brazilian online course platform. Data mining techniques were employed aiming to find the behavioral patterns of 2031 students who participated in the course. The results showed similar numbers of actions among the participants; however two groups stand out among those who showed higher averages of engagement in the course: students over the age of 30, with higher education and who like the subject of the course; as well as younger high school students who love the course theme.Facultad de Informátic

Analytics-based approach to the study of learning networks in digital education settings

Investigating howgroups communicate, build knowledge and expertise, reach consensus or collaboratively solve complex problems, became one of the main foci of contemporary research in learning and social sciences. Emerging models of communication and empowerment of networks as a form of social organization further reshaped practice and pedagogy of online education, bringing research on learning networks into the mainstream of educational and social science research. In such conditions, massive open online courses (MOOCs) emerged as one of the promising approaches to facilitating learning in networked settings and shifting education towards more open and lifelong learning. Nevertheless, this most recent educational turn highlights the importance of understanding social and technological (i.e., material) factors as mutually interdependent, challenging the existing forms of pedagogy and practice of assessment for learning in online environments. On the other hand, the main focus of the contemporary research on networked learning is primarily oriented towards retrospective analysis of learning networks and informing design of future tasks and recommendations for learning. Although providing invaluable insights for understanding learning in networked settings, the nature of commonly applied approaches does not necessarily allow for providing means for understanding learning as it unfolds. In that sense, learning analytics, as a multidisciplinary research field, presents a complementary research strand to the contemporary research on learning networks. Providing theory-driven and analytics-based methods that would allow for comprehensive assessment of complex learning skills, learning analytics positions itself either as the end point or a part of the pedagogy of learning in networked settings. The thesis contributes to the development of learning analytics-based research in studying learning networks that emerge fromthe context of learning with MOOCs. Being rooted in the well-established evidence-centered design assessment framework, the thesis develops a conceptual analytics-based model that provides means for understanding learning networks from both individual and network levels. The proposed model provides a theory-driven conceptualization of the main constructs, along with their mutual relationships, necessary for studying learning networks. Specifically, to provide comprehensive understanding of learning networks, it is necessary to account for structure of learner interactions, discourse generated in the learning process, and dynamics of structural and discourse properties. These three elements – structure, discourse, and dynamics – should be observed as mutually dependent, taking into account learners’ personal interests, motivation, behavior, and contextual factors that determine the environment in which a specific learning network develops. The thesis also offers an operationalization of the constructs identified in the model with the aim at providing learning analytics-methods for the implementation of assessment for learning. In so doing, I offered a redefinition of the existing educational framework that defines learner engagement in order to account for specific aspects of learning networks emerging from learning with MOOCs. Finally, throughout the empirical work presented in five peer-reviewed studies, the thesis provides an evaluation of the proposed model and introduces novel learning analytics methods that provide different perspectives for understanding learning networks. The empirical work also provides significant theoretical and methodological contributions for research and practice in the context of learning networks emerging from learning with MOOCs