A Systematic Mapping on the Learning Analytics Field and Its Analysis in the Massive Open Online Courses Context

Moissa, Barbara; Gasparini, Isabela; Kemczinski, Avanilde

doi:10.4018/ijdet.2015070101

Cited by 28 publications

(21 citation statements)

References 19 publications

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“…Learning analytics over the years faces challenging demands and difficulties when applied in MOOC contexts [5], but as Knox [6] mentioned, learning analytics has a strong potential for discovery when it is applied to MOOC datasets. There were few research studies that combine learning analytics practices in MOOCs [7,8,9,10].…”

Section: Related Studymentioning

confidence: 99%

Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources

Onah

Pang

Sinclair

et al. 2019

Advances in Intelligent Systems and Computing

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Nowadays, the digital learning environment has revolutionized the vision of distance learning course delivery and drastically transformed the online educational system. The emergence of MOOCs (Massive Open Online courses) has exposed web technology used in education in a more advanced revolution ushering a new generation of learning environments. The digital learning environment is expected to augment the real world conventional education setting. The educational pedagogy are tailored with the standard practice which has been noticed to increase student success in MOOCs and provide a revolutionary way of self-regulated learning. However, there are still unresolved questions relating to the understanding of learning analytics data and how this could be implemented in educational contexts to support individual learning. One of the major issue in MOOCs is the consistent high dropout rate which over time has seen courses recorded less than 20% completion rate. This paper explores learning analytics from different perspectives in a MOOC context. Firstly, we review existing literature relating to learning analytics in MOOCs, bringing together findings and analyses from several courses. We explore meta-analysis of the basic factors that correlate to learning analytics and the significant in improving education. Secondly, using themes emerging from the previous study, we propose a preliminary model consisting of four factors of learning analytics. Finally, we provide a framework of learning analytics based on the following dimensions: descriptive, diagnostic, predictive and prescriptive, suggesting how the factors could be applied in a MOOC context. Our exploratory framework indicates the need for engaging learners and providing the understanding of how to support and help participants at risk of dropping out of the course.

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Section: Related Studymentioning

confidence: 99%

Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources

Onah

Pang

Sinclair

et al. 2019

Advances in Intelligent Systems and Computing

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show abstract

“…In achieving this goal, researchers, educators, and instructional designers will be able to capture the multifaceted nature of such alternative learning experiences, as well as the patterns and the interrelationships that occur between the trainees and the learning context. Despite the growing number of studies in educational technology and the evidence gained by combining different domains [7,12], the investigation of the potential of AR combined with LA is still lacking. Similarly, no tools have been identified that can contribute toward the evaluation of such practices.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, [10] introduced tools that could support instructional designers to develop learning scenarios and orchestrate teaching strategies for multilayered activities. Others [11,12] focused on the improvement of the services that massive online open courses (MOOCs) offer. Some researchers [13] have introduced review tools and analysis protocols for students' and teachers' interactions to support the process of reflection on the applied teaching strategies.…”

Section: Introductionmentioning

confidence: 99%

A Learning Analytics Conceptual Framework for Augmented Reality-Supported Educational Case Studies

Kazanidis

Πέλλας

Christopoulos

2021

MTI

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The deployment of augmented reality (AR) has attracted educators’ interest and introduced new opportunities in education. Additionally, the advancement of artificial intelligence has enabled educational researchers to apply innovative methods and techniques for the monitoring and evaluation of the teaching and learning process. The so-called learning analytics (LA) discipline emerged with the promise to revolutionize traditional instructional practices by introducing systematic and multidimensional ways to improve the effectiveness of the instructional process. However, the implementation of LA methods is usually associated with web-based platforms, which offer direct access to learners’ data with minimal effort or adjustments. On the other hand, the complex nature of immersive technologies and the diverse instructional approaches which are utilized in different scientific domains have limited the opportunities for research and development in this direction. Within these research contexts, we present a conceptual framework that describes the elements of an LA process tailored to the information that can be gathered from the use of educational applications, and further provide an indicative case study for AR-supported educational interventions. The current work contributes by elucidating and concretizing the design elements of AR-supported applications and provides researchers and designers with guidelines on how to apply instructional strategies in (augmented) real-world projects.

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“…Interventions (devising precise actions to "shape the learning environment") (p. 5) However, it is easy to be caught up in a particular taxonomy, the specifics of a project, or one article's perspective. Data can derive from different sources or technologies, like Learning Management Systems (LMS); Digital Learning Environments (DLE); MOOCs (Moissa, Gasparini, & Kemczinski, 2015); and social networks, web tools, or student information systems (SIS); as well as from surveys, course evaluations, or self-reporting. Data can be about students interacting with other students, mentors, teachers, learning materials, and even learning environments (location, temporal data; Avella et al, 2016).…”

Section: A Simplified Storymentioning

confidence: 99%

“…6). An abundance of overview and introductory articles (Avella et al, 2016;Clow, 2013;Sclater, Peasgood, & Mullan, 2016;Ferguson et al, 2016;Haythornthwaite, de Laat, & Dawson, 2013;Pardo, Dawson, Gašević, & Steigler-Peters, 2016;Sundorph & Mosseri-Marlio, 2016;Swan, 2012) and literature reviews (Ferguson, 2012b;Moissa, Gasparini, & Kemczinski, 2015;Sclater, Peasgood, & Mullan, 2016;Sergis & Sampson, 2017) attempt to get a handle on this exponentially growing field, its key concepts, potentials, and limitations, as well as on strategies for its implementation and adoption.…”

Section: Introductionmentioning

confidence: 99%

What Constitutes an ‘Actionable Insight’ in Learning Analytics?

Jørnø

Gynther

2018

Learning Analytics

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The possibilities of Learning Analytics as a tool for empowering teachers and educators have created a steep interest in how to provide so-called actionable insights. However, the literature offers little in the way of defining or discussing what the term "actionable insight" means. This selective literature review provides a look into the use of the term in current literature. The review points to a dominant perspective in the literature that assumes the perspective of a rational actor, where actionable insights are treated as insights mined from data and subsequently acted upon. It also finds evidence of other perspectives and discusses the need for clarification of the term in order to establish a more precise and fruitful use of the term. Notes for Practice• At the time of this review, we only found a single source in the literature that discusses the definition of "actionable insights." All other sources task the reader to infer the meaning of the concept from its use.• This paper provides a selective overview of learning analytics literature that sheds light on the use of the concept and its equivalents.• We identify a widely used perspective taken in learning analytics, which we dub "data-informed decision-making." This perspective is characterized by an insistence of the perspective of a rational actor and the use of learning analytics for the institutional goal of increasing retention.• We contend that "actionable insights" should be interpreted as data that allows a corrective procedure, or feedback loop, to be established for a set of actions.• We argue that the field of learning analytics would benefit from greater attention to the role of perspective and action capabilities in determining what "actionable insights" are.• The implications of these findings are that the perspective of data-informed decision-making is challenged, and with it, the idea that the presence of data alone provides the basis for determining insights. Instead, it charges any learning analytics researcher to map out the workflow of actions, the end goals of the actors involved, and the relevant couplings between them.

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A Systematic Mapping on the Learning Analytics Field and Its Analysis in the Massive Open Online Courses Context

Cited by 28 publications

References 19 publications

Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources

Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources

A Learning Analytics Conceptual Framework for Augmented Reality-Supported Educational Case Studies

What Constitutes an ‘Actionable Insight’ in Learning Analytics?

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