LearnLab's DataShop: A Data Repository and Analytics Tool Set for Cognitive Science

Koedinger, Kenneth R.; Stamper, John C.; Leber, Brett; Skogsholm, Alida

doi:10.1111/tops.12035

Cited by 22 publications

(18 citation statements)

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“…Koedinger et al. () show how the choice between different cognitive models of mathematical reasoning can be adjudicated with the help of large amounts of data, courtesy of the Datashop repository of educational technology data (Koedinger, Stamper, Leber, & Skogsholm, ). The particular data they studied concerned the patterns of successes and errors that students make across a suite of related mathematical problems.…”

Section: Nods Experiments and Modelsmentioning

confidence: 99%

Discovering Psychological Principles by Mining Naturally Occurring Data Sets

Goldstone

Lupyan

2016

Topics in Cognitive Science

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The very expertise with which psychologists wield their tools for achieving laboratory control may have had the unwelcome effect of blinding psychologists to the possibilities of discovering principles of behavior without conducting experiments. When creatively interrogated, a diverse range of large, real-world data sets provides powerful diagnostic tools for revealing principles of human judgment, perception, categorization, decision-making, language use, inference, problem solving, and representation. Examples of these data sets include patterns of website links, dictionaries, logs of group interactions, collections of images and image tags, text corpora, history of financial transactions, trends in twitter tag usage and propagation, patents, consumer product sales, performance in high-stakes sporting events, dialect maps, and scientific citations. The goal of this issue is to present some exemplary case studies of mining naturally existing data sets to reveal important principles and phenomena in cognitive science, and to discuss some of the underlying issues involved with conducting traditional experiments, analyses of naturally occurring data, computational modeling, and the synthesis of all three methods.

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Section: Nods Experiments and Modelsmentioning

confidence: 99%

Discovering Psychological Principles by Mining Naturally Occurring Data Sets

Goldstone

Lupyan

2016

Topics in Cognitive Science

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“…For this reason, there has been an increasing emphasis, at least in the United States, on producing large-scale public datasets. One such effort is the Pittsburgh Sciences of Learning Center’s DataShop (Koedinger et al, 2013). This is an online repository of student data and analytical tools that have been collected from existing tutoring systems or online courses.…”

Section: Benefits and Risksmentioning

confidence: 99%

Who prophets from big data in education? New insights and new challenges

Lynch

2017

Theory and Research in Education

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Big Data can radically transform education by enabling personalized learning, deep student modeling, and true longitudinal studies that compare changes across classrooms, regions, and years. With these promises, however, come risks to individual privacy and educational validity, along with deep policy and ethical issues. Education is largely a public service targeted primarily at minors. Participation is compulsory in most advanced societies, and in many ways, it is seen as a fundamental right. Academic success is necessary for advancement, but students often have little individual say in the process. For these reasons, it poses unique policy challenges that do not arise in other domains. Big data requires deep and constant monitoring of students, classes, and instructors. Who consents to such monitoring, and how will student or instructor privacy be preserved? Data also has immense commercial value. Who owns it? And who is permitted to profit from its use? In this article, I will discuss some of these unique issues, and I will outline some potential approaches that may be taken to address them.

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“…DataShop [9] is another repository of learning data that focuses on the interaction between students and educational software. DataShop offers a set of tools for analyzing these datasets and building cognitive models that allow researchers to explore the relationships between students' skills, concepts or misconceptions, and their trajectory in learning environments.…”

Section: Data Infrastructuresmentioning

confidence: 99%

Expediting Support for Social Learning with Behavior Modeling

Jo¹,

Tomar²,

Ferschke³

et al. 2016

Preprint

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An important research problem for Educational Data Mining is to expedite the cycle of data leading to the analysis of student learning processes and the improvement of support for those processes. For this goal in the context of social interaction in learning, we propose a three-part pipeline that includes data infrastructure, learning process analysis with behavior modeling, and intervention for support. We also describe an application of the pipeline to data from a social learning platform to investigate appropriate goal-setting behavior as a qualification of role models. Students following appropriate goal setters persisted longer in the course, showed increased engagement in hands-on course activities, and were more likely to review previously covered materials as they continued through the course. To foster this beneficial social interaction among students, we propose a social recommender system and show potential for assisting students in interacting with qualified goal setters as role models. We discuss how this generalizable pipeline can be adapted for other support needs in online learning settings.

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LearnLab's DataShop: A Data Repository and Analytics Tool Set for Cognitive Science

Cited by 22 publications

References 1 publication

Discovering Psychological Principles by Mining Naturally Occurring Data Sets

Discovering Psychological Principles by Mining Naturally Occurring Data Sets

Who prophets from big data in education? New insights and new challenges

Expediting Support for Social Learning with Behavior Modeling

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