Aggregating social and usage datasets for learning analytics

Niemann, Katja; Wolpers, Martin; Stoitsis, Giannis; Chinis, Georgios; Manouselis, Nikos

doi:10.1145/2460296.2460345

Cited by 15 publications

(6 citation statements)

References 2 publications

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“…These data are more varied and usable: work is being done in terms of interoperability of data coming from several learning environments (Walker, 2012;Niemann et al, 2013).…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Understand, develop and enhance the learning process with big data

Sedkaoui

Khelfaoui

2019

IDD

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Purpose With the advent of the internet and communication technology, the penetration of e-learning has increased. The digital data being created by the educational and research institutions is also on the ascent. The growing interest in recent years toward big data, educational data mining and learning analytics has motivated the development of new analytical ways and approaches and advancements in learning settings. The need for using big data to handle, analyze this large amount of data is prime. This trend has started attracting the interest of educational institutions which have an important role in the development skills process and the preparation of a new generation of learners. “A real revolution for education,” it is based on this kind of terms that many articles have paid attention to big data for learning. How can analytics techniques and tools be so efficient and become a great prospect for the learning process? Big data analytics, when applied into teaching and learning processes, might help to improvise as well as to develop new paradigms. In this perspective, this paper aims to investigate the most promising applications and issues of big data for the design of the next-generation of massive e-learning. Specifically, it addresses the analytical tools and approaches for enhancing the future of e-learning, pitfalls arising from the usage of large data sets. Globally, this paper focuses on the possible application of big data techniques on learning developments, to show the power of analytics and why integrating big data is so important for the learning context. Design/methodology/approach Big data has in the recent years been an area of interest among innovative sectors and has become a major priority for many industries, and learning sector cannot escape to this deluge. This paper focuses on the different methods of big data able to be used in learning context to understand the benefits it can bring both to teaching and learning process, and identify its possible impact on the future of this sector in general. This paper investigates the connection between big data and the learning context. This connection can be illustrated by identifying the several main analytics approaches, methods and tools for improving the learning process. This can be clearer by the examination of the different ways and solutions that contribute to making a learning process more agile and dynamic. The methods that were used in this research are mainly of a descriptive and analytical nature, to establish how big data and analytics methods develop the learning process, and understand their contributions and impacts in addressing learning issues. To this end, authors have collected and reviewed existing literature related to big data in education and the technology application in the learning context. Authors then have done the same process with dynamic and operational examples of big data for learning. In this context, the authors noticed that there are jigsaw bits that contained important knowledge on the different parts of the research area. The process concludes by outlining the role and benefit of the related actors and highlighting the several directions relating to the development and implementation of an efficient learning process based on big data analytics. Findings Big data analytics, its techniques, tools and algorithms are important to improve the learning context. The findings in this paper suggest that the incorporation of an approach based on big data is of crucial importance. This approach can improve the learning process, for this, its implementation must be correctly aligned with educational strategies and learning needs. Research limitations/implications This research represents a reference to better understanding the influence and the role of big data in educational dynamic. In addition, it leads to improve existing literature about big data for learning. The limitations of the paper are given by its nature derived from a theoretical perspective, and the discussed ideas can be empirically validated by identifying how big data helps in addressing learning issues. Originality/value Over the time, the process that leads to the acquisition of the knowledge uses and receives more technological tools and components; this approach has contributed to the development of information communication and the interactive learning context. Technology applications continue to expand the boundaries of education into an “anytime/anywhere” experience. This technology and its wide use in the learning system produce a vast amount of different kinds of data. These data are still rarely exploited by educational practitioners. Its successful exploitation conducts educational actors to achieve their full potential in a complex and uncertain environment. The general motivation for this research is assisting higher educational institutions to better understand the impact of the big data as a success factor to develop their learning process and achieve their educational strategy and goals. This study contributes to better understand how big data analytics solutions are turned into operational actions and will be particularly valuable to improve learning in educational institutions.

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“…These data are more varied and usable: work is being done in terms of interoperability of data coming from several learning environments (Walker, 2012;Niemann et al, 2013).…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Understand, develop and enhance the learning process with big data

Sedkaoui

Khelfaoui

2019

IDD

View full text Add to dashboard Cite

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“…It is a Representational state transfer (REST) web service based on JavaScript Object Notation (JSON) for its data format. xAPI is a specification that produced an application program interface (API), which captures the learning data in the stream and stores them in a repository in the form of "statement" objects using the following structure: <actor> <verb> <object>, with <result>, in <context> xAPI is selected from the reviewed and compared specifications xAPI, Activ-ityStrea.ms [16], IMS 1 Caliper [17], CAM 2 (which is an early approach to capturing and storing attention metadata for single users), NSDL paradata 3 (defined to capture aggregated usage data about a resource which is designated by audience, subject or education level) and Organic.Edunet format [18] (to store social data provision activities and designed in an extendable way, so other user activities) for communication between heterogeneous learning environments in a major contribution that was published [19].…”

Section: Next Generation Of Scorm: Xapi and Cmi5 Specificationsmentioning

confidence: 99%

A Semantic Web Solution for Enhancing The Interoperability of E-learning Systems by Using Next Generation of SCORM Specifications

Bakhouyi¹,

Dehbi²,

Banane³

et al. 2019

Int. J. Emerg. Technol. Learn.

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E-learning is a very dynamic field, constantly growing, which refers to an educa-tional content or learning experiences provided or made by means of digital tech-nologies. The development of this area has a direct impact on the quality of edu-cation and cost reduction. E-learning is now dominated by learning management systems such as Moo-dle, to improve the interoperability of these systems; the use of data / resources is a major requirement, this need to push the e-Learning community to seek new approaches to improve this interoperability of systems. On the other hand, se-mantic Web technologies offers standards like RDF allows to describe Web re-sources with their metadata. We propose in this work an approach based on Se-mantic Web technology to improve the interoperability of e-Learning systems like Moodle.

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“…Some writers use the terms AfL and FA synonymously (Glasson, 2008;Wiliam, 2007), while others differentiate between the terms FA and AfL (Chappuis & Chappuis, 2008;Stiggins, 2005). We observe that FA has been taken up in university settings as an individualistic process where there is a focus of the learner's metacognitive engagement with classroom material (Niedwiecki, 2013). We are in accord with Klenowski (2009), who reports a second generation definition of AfL, which is embedded in the "everyday practice by students, teachers and peers that seeks, reflects upon and responds to information from dialogue, demonstration and observation in ways that enhance ongoing learning" (Klenowski, 2009;p.2).…”

Section: The Relationship Between Afl and Famentioning

confidence: 68%

e-Assessment for learning and performativity in higher education: A case for existential learning

Charteris¹,

Quinn²,

Parkes³

et al. 2016

AJET

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<p class="Normal1">This paper provides a critical and contextualised exploration of assessment for learning (AfL) as an important area of scholarship in higher education, particularly in online learning environments. Although AfL can speak to a range of education discourses, the specific focus here is on the performativity and experiential learning discourses around individual and collective notions of AfL in online settings (e-AfL). We argue that e-AfL practices that emphasise performativity and are used primarily for technicist purposes impoverish their potential to promote learning. We explore the existential notion that e-AfL can transcend formulaic and procedural interpretations of formative assessment in higher education. Rich, divergent approaches to e-AfL can support students in higher education courses to develop their funds of identity, thereby enhancing learner reflexivity and agency.</p>

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Aggregating social and usage datasets for learning analytics

Cited by 15 publications

References 2 publications

Understand, develop and enhance the learning process with big data

Understand, develop and enhance the learning process with big data

A Semantic Web Solution for Enhancing The Interoperability of E-learning Systems by Using Next Generation of SCORM Specifications

e-Assessment for learning and performativity in higher education: A case for existential learning

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