Ryan Shaun Joazeiro de Baker, Tiffany Barnes, Joseph E. Beck
Proceedings of the First International Conference on Educational Data Mining
EDM, 2008.

@proceedings{EDM-2008,
	editor        = "Ryan Shaun Joazeiro de Baker and Tiffany Barnes and Joseph E. Beck",
	publisher     = "{www.educationaldatamining.org}",
	title         = "{Proceedings of the First International Conference on Educational Data Mining}",
	year          = 2008,
}

Contents (31 items)

EDM-2008-RomeroVEH #algorithm #data mining #mining #student

Data Mining Algorithms to Classify Students (CR, SV, PGE, CH), pp. 8–17.

EDM-2008-Antunes

Acquiring Background Knowledge for Intelligent Tutoring Systems (CA), pp. 18–27.

EDM-2008-MostowZ #behaviour #comparison #tutorial

Analytic Comparison of Three Methods to Evaluate Tutorial Behaviors (JM, XZ), pp. 28–37.

EDM-2008-BakerC #behaviour #performance #student

Labeling Student Behavior Faster and More Precisely with Text Replays (RSJdB, AMJBdC), pp. 38–47.

EDM-2008-DesmaraisVG #adaptation #design #framework #naive bayes

Adaptive Test Design with a Naive Bayes Framework (MCD, AV, MG), pp. 48–56.

EDM-2008-MerceronY #education #metric

Interestingness Measures for Associations Rules in Educational Data (AM, KY), pp. 57–66.

EDM-2008-BakerCA #modelling #set

Improving Contextual Models of Guessing and Slipping with a Trucated Training Set (RSJdB, ATC, VA), pp. 67–76.

EDM-2008-PavlikCWK #performance #using

Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor (PIP, HC, LW, KRK), pp. 77–86.

EDM-2008-Mavrikis #data-driven #interactive #modelling #student

Data-driven modelling of students’ interactions in an ILE (MM), pp. 87–96.

EDM-2008-HubscherP #data mining #mining

Integrating Knowledge Gained From Data Mining With Pedagogical Knowledge (RH, SP), pp. 97–106.

EDM-2008-FengBHK #predict #question

Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standarized Test? (MF, JEB, NTH, KRK), pp. 107–116.

EDM-2008-ShihKS

A Response Time Model For Bottom-Out Hints as Worked Examples (BS, KRK, RS), pp. 117–126.

EDM-2008-JeongB #behaviour #mining #modelling #student

Mining Student Behavior Models in Learning-by-Teaching Environments (HJ, GB), pp. 127–136.

EDM-2008-LynchAPA #classification #graph #programming #search-based

Argument graph classification with Genetic Programming and C4.5 (CL, KDA, NP, VA), pp. 137–146.

EDM-2008-PardosHRB #analysis #composition #multi

The Composition Effect: Conjuntive or Compensatory? An Analysis of Multi-Skill Math Questions in ITS (ZAP, NTH, CR, JEB), pp. 147–156.

EDM-2008-KoedingerCSL #analysis #fine-grained #repository #tool support

An Open Repository and analysis tools for fine-grained, longitudinal learner data (KRK, KC, AS, BL), pp. 157–166.

EDM-2008-AllevatoTEP #automation #mining #testing

Mining Data from an Automated Grading and Testing System by Adding Rich Reporting Capabilities (AA, MT, SHE, MAPQ), pp. 167–176.

EDM-2008-VenturaRH #dataset #education #evaluation #framework #metric

Analyzing Rule Evaluation Measures with Educational Datasets: A Framework to Help the Teacher (SV, CR, CH), pp. 177–181.

EDM-2008-RomeroGFV #education #mining #visualisation

Mining and Visualizing Visited Trails in Web-Based Educational Systems (CR, SGS, MF, SV), pp. 182–186.

EDM-2008-PechenizkiyCVB #assessment #case study #mining #student

Mining the Student Assessment Data: Lessons Drawn from a Small Scale Case Study (MP, TC, EV, PDB), pp. 187–191.

EDM-2008-Cho #classification #physics

Machine Classification of Peer Comments in Physics (KC), pp. 192–196.

EDM-2008-BarnesSLC #case study #logic #proving #student #using

A pilot study on logic proof tutoring using hints generated from historical student data (TB, JCS, LL, MJC), pp. 197–201.

EDM-2008-AbbasS #database #mining #relational #towards

Towards Argument Mining from Relational DataBase (SA, HS), pp. 202–209.

EDM-2008-AyersND #clustering #set #student

Skill Set Profile Clustering Based on Weighted Student Responses (EA, RN, ND), pp. 210–217.

EDM-2008-FengHBK #predict #question #student

Can we predict which groups of questions students will learn from? (MF, NTH, JEB, KRK), pp. 218–225.

EDM-2008-HershkovitzN #motivation

Developing a Log-based Motivation Measuring Tool (AH, RN), pp. 226–233.

EDM-2008-ZhangMDTVC #mining

Mining Free-form Spoken Responses to Tutor Prompts (XZ, JM, ND, CT, JV, ATC), pp. 234–241.

EDM-2008-ShapiroPG

Computational Infrastructures for School Improvement: A Way to Move Forward (RBS, HP, LMG), pp. 242–249.

EDM-2008-Heiner #analysis #student

A Preliminary Analysis of the Logged Questions that Students Ask in Introductory Computer Science (CH), pp. 250–257.

EDM-2008-ChiJVH #effectiveness #tutorial

Reinforcement Learning-based Feature Seleciton For Developing Pedagogically Effective Tutorial Dialogue Tactics (MC, PWJ, KV, MH), pp. 258–265.

EDM-2008-MathewsM #concept #question #student

Do Students Who See More Concepts in an ITS Learn More? (MM, TM), pp. 266–273.