Learning to Identify Students’ Relevant and IrrelevantQuestions in a Micro-blogging Supported Classroom
Suleyman Cetintas (),
Luo Si (),
Sugato Chakravarty (),
Hans Aagard () and
Kyle Bowen ()
Additional contact information
Suleyman Cetintas: Purdue University
Luo Si: Purdue University
Hans Aagard: Purdue University
Kyle Bowen: Purdue University
No 1010, Working Papers from Purdue University, Department of Consumer Sciences
This paper proposes a novel application of text categorization for two types questions asked in a micro-blogging supported classroom, namely relevant and irrelevant questions. Empirical results and analysis show that utilizing the correlation between questions and available lecture materials in a lecture along with personalization and question text leads to significantly higher categorization accuracy than i) using personalization along with question text and ii) using question text alone.
Keywords: student in-class interaction; micro-blogging; hotseat (search for similar items in EconPapers)
JEL-codes: G21 D82 O16 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-mst and nep-sea
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Published in Lecture Notes in Computer Science, v.6095/2010
Downloads: (external link)
Our link check indicates that this URL is bad, the error code is: 500 Failed to connect to FTP server 188.8.131.52: A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:csr:wpaper:1010
Access Statistics for this paper
More papers in Working Papers from Purdue University, Department of Consumer Sciences Contact information at EDIRC.
Series data maintained by Sugato Chakravarty ().