Oliver Ferschke
Carnegie Mellon University
World Wide WebArtificial intelligenceEducational technologyNatural language processingInformation retrievalData scienceSelf-regulated learningEducational data miningLearning analyticsComputer-supported collaborative learningCollaborative writingQuality (business)Experiential learningComputer scienceProcess (engineering)Pipeline (software)MultimediaKnowledge managementAnalyticsSocial learningDiscourse analysisLearning sciences
27Publications
11H-index
360Citations
Publications 26
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#1Carolyn Penstein Rosé (CMU: Carnegie Mellon University)H-Index: 54
#2Iris Howley (CMU: Carnegie Mellon University)H-Index: 15
Last. Oliver Ferschke (CMU: Carnegie Mellon University)H-Index: 11
view all 5 authors...
This chapter reports on our efforts to develop automated assessment of collaborative processes, in order to support effective participation in learning-relevant discussion. This chapter presents resources that can be offered to this assessment community by machine learning and computational linguistics. The goal is to raise awareness of opportunities for productive synergy between research communities. In particular, we present a three-part pipeline for expediting automated assessment of collabo...
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#1Ryan S. Baker (Columbia University)H-Index: 10
Problem difficulty estimates play important roles in a wide variety of educational systems, including determining the sequence of problems presented to students and the interpretation of the resulting responses. The accuracy of these metrics are therefore important, as they can determine the relevance of an educational experience. For systems that record large quantities of raw data, these observations can be used to test the predictive accuracy of an existing difficulty metric. In this paper, w...
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#1Carolyn Penstein Rosé (CMU: Carnegie Mellon University)H-Index: 54
#2Dragan Gaesevic (Edin.: University of Edinburgh)H-Index: 1
Last. Simon Yang (NTU: Nanyang Technological University)H-Index: 2
view all 15 authors...
#1Yohan Jo (CMU: Carnegie Mellon University)H-Index: 8
#2Gaurav Singh Tomar (CMU: Carnegie Mellon University)H-Index: 10
Last. Dragan Gasevic (Edin.: University of Edinburgh)H-Index: 67
view all 5 authors...
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 ...
Apr 25, 2016 in LAK (Learning Analytics and Knowledge)
#1Yohan Jo (CMU: Carnegie Mellon University)H-Index: 8
#2Gaurav Singh Tomar (CMU: Carnegie Mellon University)H-Index: 10
Last. Dragan Gasevic (Edin.: University of Edinburgh)H-Index: 67
view all 5 authors...
An important research problem in learning analytics is to expedite the cycle of data leading to the analysis of student progress and the improvement of student support. For this goal in the context of social learning, we propose a pipeline that includes data infrastructure, learning analytics, and intervention, along with computational models for individual components. Next, we describe an example of applying this pipeline to real data in a case study, whose goal is to investigate the positive e...
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#1Carolyn Penstein Rosé (CMU: Carnegie Mellon University)H-Index: 54
#2Oliver Ferschke (CMU: Carnegie Mellon University)H-Index: 11
This article offers a vision for technology supported collaborative and discussion-based learning at scale. It begins with historical work in the area of tutorial dialogue systems. It traces the history of that area of the field of Artificial Intelligence in Education as it has made an impact on the field of Computer-Supported Collaborative Learning through the creation of forms of dynamic support for collaborative learning, and makes an argument for the importance of advances in the field of La...
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Jan 1, 2016 in EDM (Educational Data Mining)
#2Gaurav Singh Tomar (CMU: Carnegie Mellon University)H-Index: 10
Last. Dragan Gasevic (Edin.: University of Edinburgh)H-Index: 67
view all 5 authors...
Jul 1, 2015 in CSCL (Computer Supported Collaborative Learning)
#1Oliver Ferschke (CMU: Carnegie Mellon University)H-Index: 11
#2Iris Howley (CMU: Carnegie Mellon University)H-Index: 15
Last. Carolyn Penstein Rosé (CMU: Carnegie Mellon University)H-Index: 54
view all 6 authors...
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Jun 21, 2015 in AIED (Artificial Intelligence in Education)
#1Iris Howley (CMU: Carnegie Mellon University)H-Index: 15
#2Gaurav Singh Tomar (CMU: Carnegie Mellon University)H-Index: 10
Last. Carolyn Penstein Rosé (CMU: Carnegie Mellon University)H-Index: 54
view all 5 authors...
Through the lens of Expectancy Value Theory, we examine the effect of help giver badges, information about helper expertise, and up- and downvoting on help seeking in a MOOC discussion forum. Results show that badges alleviated the negative impact on help seeking introduced by up- and downvoting.
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