On the validity of peer grading and a cloud teaching assistant system

Vogelsang, Tim; Ruppertz, Lara

doi:10.1145/2723576.2723633

Cited by 18 publications

(10 citation statements)

References 17 publications

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“…The main conference papers of LAK '15 still looked at privacy as a search term (Sekiya, Matsuda, & Yamaguchi, 2015), a course subject (Vogelsang & Ruppertz, 2015), or an abstract concern (Scheffel, Drachsler, & Specht, 2015), which could limit access to data (Wang, Heffernan, & Heffernan, 2015;Drachsler et al, 2015b), or one that must "be addressed given the larger scale of the tools usage compared with pilot studies" when "testing the tool in-the-wild" (Martinez-Maldonado et al, 2015, p. 6). …”

Section: Related Workmentioning

confidence: 99%

Privacy-driven Design of Learning Analytics Applications – Exploring the Design Space of Solutions for Data Sharing and Interoperability

Hoel¹,

Chen

2016

Learning Analytics

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ABSTRACT:Studies have shown that issues of privacy, control of data, and trust are essential to implementation of learning analytics systems. If these issues are not addressed appropriately, systems will tend to collapse due to a legitimacy crisis, or they will not be implemented in the first place due to resistance from learners, their parents, or their teachers. This paper asks what it means to give priority to privacy in terms of data exchange and application design and offers a conceptual tool, a Learning Analytics Design Space model, to ease the requirement solicitation and design for new learning analytics solutions. The paper argues the case for privacy-driven design as an essential part of learning analytics systems development. A simple model defining a solution as the intersection of an approach, a barrier, and a concern is extended with a process focusing on design justifications to allow for an incremental development of solutions. This research is exploratory in nature, and further validation is needed to prove the usefulness of the Learning Analytics Design Space model.

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Section: Related Workmentioning

confidence: 99%

Privacy-driven Design of Learning Analytics Applications – Exploring the Design Space of Solutions for Data Sharing and Interoperability

Hoel¹,

Chen

2016

Learning Analytics

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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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“…Along the same lines assessment is considered as a process of measuring a person's knowledge, comprehension and skills [4]. The types of assessment, in the MOOC context, that are mostly discussed in the existing literature are auto-assessment, peer-assessment and self-assessment [5,6,7,8] and therefore are the types under investigation in this study. The voice and the standpoint of learners about the different assessment types in MOOCs are not prominent in the current literature and further research is required.…”

Section: Overviewmentioning

confidence: 99%

Investigating Learners’ Views of Assessment Types in Massive Open Online Courses (MOOCs)

Papathoma

Blake

Clow

et al. 2015

Design for Teaching and Learning in a Networked World

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On the validity of peer grading and a cloud teaching assistant system

Cited by 18 publications

References 17 publications

Privacy-driven Design of Learning Analytics Applications – Exploring the Design Space of Solutions for Data Sharing and Interoperability

Privacy-driven Design of Learning Analytics Applications – Exploring the Design Space of Solutions for Data Sharing and Interoperability

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

Investigating Learners’ Views of Assessment Types in Massive Open Online Courses (MOOCs)

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