Abstract:The Pittsburgh Science of Learning Center's DataShop is an open data repository and set of associated visualization and analysis tools. DataShop has data from thousands of students deriving from interactions with on-line course materials and intelligent tutoring systems. The data is fine-grained, with student actions recorded roughly every 20 seconds, and it is longitudinal, spanning semester or yearlong courses. Currently over 188 datasets are stored including over 42 million student actions and over 150,000 … Show more
“…PSLC Datashop: It is a free multifunctional web application which offers a secure place to store & access research data and it supports various kinds of research [73]. DataShop has functionalities allowing a focus on learner-tutor interaction data with a learning curves & error reports provide summary and low/high level views of learner performance (e.g.…”
In this decade, the use of learning management systems (LMS) does not cease to increase, becoming one of the most popular approaches adopted and widely used in the learning process. Learners' online activities generate a huge amount of unused data that is wasted because traditional learning analyses are not able to process them. In this regard, a large collection of applications/tools have emerged to conduct research in educational data mining (EDM) and / or learning analysis (LA). This study looks into the recent applications/tools of Big Data technologies in education and presents some of the most widely used, accessible, and powerful tools in this field of research. The majority of these tools are for researchers with the purpose of conducting research on educational data mining and learning analysis.
“…PSLC Datashop: It is a free multifunctional web application which offers a secure place to store & access research data and it supports various kinds of research [73]. DataShop has functionalities allowing a focus on learner-tutor interaction data with a learning curves & error reports provide summary and low/high level views of learner performance (e.g.…”
In this decade, the use of learning management systems (LMS) does not cease to increase, becoming one of the most popular approaches adopted and widely used in the learning process. Learners' online activities generate a huge amount of unused data that is wasted because traditional learning analyses are not able to process them. In this regard, a large collection of applications/tools have emerged to conduct research in educational data mining (EDM) and / or learning analysis (LA). This study looks into the recent applications/tools of Big Data technologies in education and presents some of the most widely used, accessible, and powerful tools in this field of research. The majority of these tools are for researchers with the purpose of conducting research on educational data mining and learning analysis.
“…For the Intelligent Tutoring System (ITS) field, the PSLC DataShop presented in (Stamper et al, 2010) provides a data repository including datasets and a set of associated visualisation and analysis tools. These data can be uploaded as well-formed XML documents that conform to the Tutor_message schema.…”
Section: What Is New In This Arena Since 2006?mentioning
In order to make replication possible for interaction analysis in online learning, the French project named Mulce (2007Mulce ( -2010 and its team worked on requirements for research data to be shareable. We defined a learning and teaching corpus (LETEC) as a package containing the data issued from an online course, the contextual information and metadata, necessary to make these data visible, shareable and reusable. These human, technical and ethical requirements are presented in this paper. We briefly present the structure of a corpus and the repository we developed to share these corpora. Related works are also described and we show how conditions evolved between 2006 and 2011. This leads us to report on how the Mulce project was faced with four particular challenges and to suggest acceptable solutions for computer scientists and researchers in the humanities: both concerned by data sharing in the Technology Enhanced Learning community.
“…In the context of TEL, there are already well-known initiatives pushing the concept of open data. One of these initiatives is DataShop (Stamper et al, 2010), which aims at providing a central repository of research data and a set of tools for its analysis. Another example of the relevance of open data in TEL is the EATEL Special Interest Group dataTEL (Drachsler et al, 2010).…”
In this paper, we make the case for an open science in technology enhanced learning (TEL). Open science means opening up the research process by making all of its outcomes, and the way in which these outcomes were achieved, publicly available on the World Wide Web. In our vision, the adoption of open science instruments provides a set of solid and sustainable ways to connect the disjoint communities in TEL. Furthermore, we envision that researchers in TEL would be able to reproduce the results from any paper using the instruments of open science. Therefore, we introduce the concept of open methodology, which stands for sharing the methodological details of the evaluation provided, and the tools used for data collection and analysis. We discuss the potential benefits, but also the issues of an open science, and conclude with a set of recommendations for implementing open science in TEL.
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